A Solution Quality Assessment Method for Swarm Intelligence Optimization Algorithms
Wang, Gai-Ge; Zou, Kuansheng; Zhang, Jianhua
2014-01-01
Nowadays, swarm intelligence optimization has become an important optimization tool and wildly used in many fields of application. In contrast to many successful applications, the theoretical foundation is rather weak. Therefore, there are still many problems to be solved. One problem is how to quantify the performance of algorithm in finite time, that is, how to evaluate the solution quality got by algorithm for practical problems. It greatly limits the application in practical problems. A solution quality assessment method for intelligent optimization is proposed in this paper. It is an experimental analysis method based on the analysis of search space and characteristic of algorithm itself. Instead of “value performance,” the “ordinal performance” is used as evaluation criteria in this method. The feasible solutions were clustered according to distance to divide solution samples into several parts. Then, solution space and “good enough” set can be decomposed based on the clustering results. Last, using relative knowledge of statistics, the evaluation result can be got. To validate the proposed method, some intelligent algorithms such as ant colony optimization (ACO), particle swarm optimization (PSO), and artificial fish swarm algorithm (AFS) were taken to solve traveling salesman problem. Computational results indicate the feasibility of proposed method. PMID:25013845
MAKHA—A New Hybrid Swarm Intelligence Global Optimization Algorithm
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Ahmed M.E. Khalil
2015-06-01
Full Text Available The search for efficient and reliable bio-inspired optimization methods continues to be an active topic of research due to the wide application of the developed methods. In this study, we developed a reliable and efficient optimization method via the hybridization of two bio-inspired swarm intelligence optimization algorithms, namely, the Monkey Algorithm (MA and the Krill Herd Algorithm (KHA. The hybridization made use of the efficient steps in each of the two original algorithms and provided a better balance between the exploration/diversification steps and the exploitation/intensification steps. The new hybrid algorithm, MAKHA, was rigorously tested with 27 benchmark problems and its results were compared with the results of the two original algorithms. MAKHA proved to be considerably more reliable and more efficient in tested problems.
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Mehdi Neshat
2015-11-01
Full Text Available In this article, the objective was to present effective and optimal strategies aimed at improving the Swallow Swarm Optimization (SSO method. The SSO is one of the best optimization methods based on swarm intelligence which is inspired by the intelligent behaviors of swallows. It has been able to offer a relatively strong method for solving optimization problems. However, despite its many advantages, the SSO suffers from two shortcomings. Firstly, particles movement speed is not controlled satisfactorily during the search due to the lack of an inertia weight. Secondly, the variables of the acceleration coefficient are not able to strike a balance between the local and the global searches because they are not sufficiently flexible in complex environments. Therefore, the SSO algorithm does not provide adequate results when it searches in functions such as the Step or Quadric function. Hence, the fuzzy adaptive Swallow Swarm Optimization (FASSO method was introduced to deal with these problems. Meanwhile, results enjoy high accuracy which are obtained by using an adaptive inertia weight and through combining two fuzzy logic systems to accurately calculate the acceleration coefficients. High speed of convergence, avoidance from falling into local extremum, and high level of error tolerance are the advantages of proposed method. The FASSO was compared with eleven of the best PSO methods and SSO in 18 benchmark functions. Finally, significant results were obtained.
Luo, Yaqi; Zeng, Bi
2017-08-01
This paper researches the drainage routing problem in drainage pipe network, and propose an intelligent scheduling method. The method relates to the design of improved particle swarm optimization algorithm, the establishment of the corresponding model from the pipe network, and the process by using the algorithm based on improved particle swarm optimization to find the optimum drainage route in the current environment.
Algorithmic requirements for swarm intelligence in differently coupled collective systems.
Stradner, Jürgen; Thenius, Ronald; Zahadat, Payam; Hamann, Heiko; Crailsheim, Karl; Schmickl, Thomas
2013-05-01
Swarm systems are based on intermediate connectivity between individuals and dynamic neighborhoods. In natural swarms self-organizing principles bring their agents to that favorable level of connectivity. They serve as interesting sources of inspiration for control algorithms in swarm robotics on the one hand, and in modular robotics on the other hand. In this paper we demonstrate and compare a set of bio-inspired algorithms that are used to control the collective behavior of swarms and modular systems: BEECLUST, AHHS (hormone controllers), FGRN (fractal genetic regulatory networks), and VE (virtual embryogenesis). We demonstrate how such bio-inspired control paradigms bring their host systems to a level of intermediate connectivity, what delivers sufficient robustness to these systems for collective decentralized control. In parallel, these algorithms allow sufficient volatility of shared information within these systems to help preventing local optima and deadlock situations, this way keeping those systems flexible and adaptive in dynamic non-deterministic environments.
Swarm intelligence based on modified PSO algorithm for the optimization of axial-flow pump impeller
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Miao, Fuqing; Kim, Chol Min; Ahn, Seok Young [Pusan National University, Busan (Korea, Republic of); Park, Hong Seok [Ulsan University, Ulsan (Korea, Republic of)
2015-11-15
This paper presents a multi-objective optimization of the impeller shape of an axial-flow pump based on the Modified particle swarm optimization (MPSO) algorithm. At first, an impeller shape was designed and used as a reference in the optimization process then NPSHr and η of the axial flow pump were numerically investigated by using the commercial software ANSYS with the design variables concerning hub angle β{sub h}, chord angle β{sub c}, cascade solidity of chord σ{sub c} and maximum thickness of blade H. By using the Group method of data handling (GMDH) type neural networks in commercial software DTREG, the corresponding polynomial representation for NPSHr and η with respect to the design variables were obtained. A benchmark test was employed to evaluate the performance of the MPSO algorithm in comparison with other particle swarm algorithms. Later the MPSO approach was used for Pareto based optimization. Finally, the MPSO optimization result and CFD simulation result were compared in a re-evaluation process. By using swarm intelligence based on the modified PSO algorithm, better performance pump with higher efficiency and lower NPSHr could be obtained. This novel algorithm was successfully applied for the optimization of axial-flow pump impeller shape design.
Parameter estimation of Lorenz chaotic system using a hybrid swarm intelligence algorithm
Energy Technology Data Exchange (ETDEWEB)
Lazzús, Juan A., E-mail: jlazzus@dfuls.cl; Rivera, Marco; López-Caraballo, Carlos H.
2016-03-11
A novel hybrid swarm intelligence algorithm for chaotic system parameter estimation is present. For this purpose, the parameters estimation on Lorenz systems is formulated as a multidimensional problem, and a hybrid approach based on particle swarm optimization with ant colony optimization (PSO–ACO) is implemented to solve this problem. Firstly, the performance of the proposed PSO–ACO algorithm is tested on a set of three representative benchmark functions, and the impact of the parameter settings on PSO–ACO efficiency is studied. Secondly, the parameter estimation is converted into an optimization problem on a three-dimensional Lorenz system. Numerical simulations on Lorenz model and comparisons with results obtained by other algorithms showed that PSO–ACO is a very powerful tool for parameter estimation with high accuracy and low deviations. - Highlights: • PSO–ACO combined particle swarm optimization with ant colony optimization. • This study is the first research of PSO–ACO to estimate parameters of chaotic systems. • PSO–ACO algorithm can identify the parameters of the three-dimensional Lorenz system with low deviations. • PSO–ACO is a very powerful tool for the parameter estimation on other chaotic system.
Guo, Y C; Wang, H; Wu, H P; Zhang, M Q
2015-12-21
Aimed to address the defects of the large mean square error (MSE), and the slow convergence speed in equalizing the multi-modulus signals of the constant modulus algorithm (CMA), a multi-modulus algorithm (MMA) based on global artificial fish swarm (GAFS) intelligent optimization of DNA encoding sequences (GAFS-DNA-MMA) was proposed. To improve the convergence rate and reduce the MSE, this proposed algorithm adopted an encoding method based on DNA nucleotide chains to provide a possible solution to the problem. Furthermore, the GAFS algorithm, with its fast convergence and global search ability, was used to find the best sequence. The real and imaginary parts of the initial optimal weight vector of MMA were obtained through DNA coding of the best sequence. The simulation results show that the proposed algorithm has a faster convergence speed and smaller MSE in comparison with the CMA, the MMA, and the AFS-DNA-MMA.
Multi-Working Modes Product-Color Planning Based on Evolutionary Algorithms and Swarm Intelligence
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Man Ding
2010-01-01
Full Text Available In order to assist designer in color planning during product development, a novel synthesized evaluation method is presented to evaluate color-combination schemes of multi-working modes products (MMPs. The proposed evaluation method considers color-combination images in different working modes as evaluating attributes, to which the corresponding weights are assigned for synthesized evaluation. Then a mathematical model is developed to search for optimal color-combination schemes of MMP based on the proposed evaluation method and two powerful search techniques known as Evolution Algorithms (EAs and Swarm Intelligence (SI. In the experiments, we present a comparative study for two EAs, namely, Genetic Algorithm (GA and Difference Evolution (DE, and one SI algorithm, namely, Particle Swarm Optimization (PSO, on searching for color-combination schemes of MMP problem. All of the algorithms are evaluated against a test scenario, namely, an Arm-type aerial work platform, which has two working modes. The results show that the DE obtains the superior solution than the other two algorithms for color-combination scheme searching problem in terms of optimization accuracy and computation robustness. Simulation results demonstrate that the proposed method is feasible and efficient.
Simulation on Vessel Intelligent Collision Avoidance Based on Artificial Fish Swarm Algorithm
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Li Weifeng
2016-10-01
Full Text Available TAs the rapid development of the ship equipments and navigation technology, vessel intelligent collision avoidance theory was researched world widely. Meantime, more and more ship intelligent collision avoidance products are put into use. It not only makes the ship much safer, but also lighten the officers work intensity and improve the ship’s economy. The paper based on the International Regulation for Preventing Collision at sea and ship domain theories, with the ship proceeding distance when collision avoidance as the objective function, through the artificial fish swarm algorithm to optimize the collision avoidance path, and finally simulates overtaking situation, crossing situation and head-on situation three classic meeting situation of ships on the sea by VC++ computer language. Calculation and simulation results are basically consistent with the actual situation which certifies that its validity.
Hybrid Swarm Intelligence Energy Efficient Clustered Routing Algorithm for Wireless Sensor Networks
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Rajeev Kumar
2016-01-01
Full Text Available Currently, wireless sensor networks (WSNs are used in many applications, namely, environment monitoring, disaster management, industrial automation, and medical electronics. Sensor nodes carry many limitations like low battery life, small memory space, and limited computing capability. To create a wireless sensor network more energy efficient, swarm intelligence technique has been applied to resolve many optimization issues in WSNs. In many existing clustering techniques an artificial bee colony (ABC algorithm is utilized to collect information from the field periodically. Nevertheless, in the event based applications, an ant colony optimization (ACO is a good solution to enhance the network lifespan. In this paper, we combine both algorithms (i.e., ABC and ACO and propose a new hybrid ABCACO algorithm to solve a Nondeterministic Polynomial (NP hard and finite problem of WSNs. ABCACO algorithm is divided into three main parts: (i selection of optimal number of subregions and further subregion parts, (ii cluster head selection using ABC algorithm, and (iii efficient data transmission using ACO algorithm. We use a hierarchical clustering technique for data transmission; the data is transmitted from member nodes to the subcluster heads and then from subcluster heads to the elected cluster heads based on some threshold value. Cluster heads use an ACO algorithm to discover the best route for data transmission to the base station (BS. The proposed approach is very useful in designing the framework for forest fire detection and monitoring. The simulation results show that the ABCACO algorithm enhances the stability period by 60% and also improves the goodput by 31% against LEACH and WSNCABC, respectively.
Adaptive cockroach swarm algorithm
Obagbuwa, Ibidun C.; Abidoye, Ademola P.
2017-07-01
An adaptive cockroach swarm optimization (ACSO) algorithm is proposed in this paper to strengthen the existing cockroach swarm optimization (CSO) algorithm. The ruthless component of CSO algorithm is modified by the employment of blend crossover predator-prey evolution method which helps algorithm prevent any possible population collapse, maintain population diversity and create adaptive search in each iteration. The performance of the proposed algorithm on 16 global optimization benchmark function problems was evaluated and compared with the existing CSO, cuckoo search, differential evolution, particle swarm optimization and artificial bee colony algorithms.
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Xiaoming Wu
2015-01-01
Full Text Available Recent studies have shown that mobile sink can be a solution to solve the problem that energy consumption of sensor nodes is not balanced in wireless sensor networks (WSNs. Caused by the sink mobility, the paths between the sensor nodes and the sink change frequently and have profound influence on the lifetime of WSN. It is necessary to design a protocol that can find efficient routings between the mobile sink and nodes but does not consume too many network resources. In this paper, we propose a swarm intelligent algorithm based route maintaining protocol to resolve this issue. The protocol utilizes the concentric ring mechanism to guide the route researching direction and adopts the optimal routing selection to maintain the data delivery route in mobile sink WSN. Using the immune based artificial bee colony (IABC algorithm to optimize the forwarding path, the routing maintaining protocol could find an alternative routing path quickly and efficiently when the coordinate of sink is changed in WSN. The results of our extensive experiments demonstrate that our proposed route maintaining protocol is able to balance the network traffic load and prolong the network lifetime.
Swarm Intelligence Optimization and Its Applications
Ding, Caichang; Lu, Lu; Liu, Yuanchao; Peng, Wenxiu
Swarm Intelligence is a computational and behavioral metaphor for solving distributed problems inspired from biological examples provided by social insects such as ants, termites, bees, and wasps and by swarm, herd, flock, and shoal phenomena in vertebrates such as fish shoals and bird flocks. An example of successful research direction in Swarm Intelligence is ant colony optimization (ACO), which focuses on combinatorial optimization problems. Ant algorithms can be viewed as multi-agent systems (ant colony), where agents (individual ants) solve required tasks through cooperation in the same way that ants create complex social behavior from the combined efforts of individuals.
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V. Sedenka
2010-09-01
Full Text Available The paper deals with efficiency comparison of two global evolutionary optimization methods implemented in MATLAB. Attention is turned to an elitist Non-dominated Sorting Genetic Algorithm (NSGA-II and a novel multi-objective Particle Swarm Optimization (PSO. The performance of optimizers is compared on three different test functions and on a cavity resonator synthesis. The microwave resonator is modeled using the Finite Element Method (FEM. The hit rate and the quality of the Pareto front distribution are classified.
Effectively Tackling Reinsurance Problems by Using Evolutionary and Swarm Intelligence Algorithms
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Sancho Salcedo-Sanz
2014-04-01
Full Text Available This paper is focused on solving different hard optimization problems that arise in the field of insurance and, more specifically, in reinsurance problems. In this area, the complexity of the models and assumptions considered in the definition of the reinsurance rules and conditions produces hard black-box optimization problems (problems in which the objective function does not have an algebraic expression, but it is the output of a system (usually a computer program, which must be solved in order to obtain the optimal output of the reinsurance. The application of traditional optimization approaches is not possible in this kind of mathematical problem, so new computational paradigms must be applied to solve these problems. In this paper, we show the performance of two evolutionary and swarm intelligence techniques (evolutionary programming and particle swarm optimization. We provide an analysis in three black-box optimization problems in reinsurance, where the proposed approaches exhibit an excellent behavior, finding the optimal solution within a fraction of the computational cost used by inspection or enumeration methods.
Components of Swarm Intelligence
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David Bruemmer; Donald Dudenhoeffer; Matthew Anderson; Mark McKay
2004-03-01
This paper discusses the successes and failures over the past three years as efforts at the Idaho National Engineering and Environmental Laboratory (INEEL) have developed and evaluated robot behaviors that promote the emergence of swarm intelligence. Using a team of 12 small robots with the ability to respond to light and sound, the INEEL has investigated the fundamental advantages of swarm behavior as well as the limitations of this approach. The paper discusses the ways in which biology has inspired this work and the ways in which adherence to the biological model has proven to be both a benefit and hindrance to developing a fieldable system. The paper outlines how a hierarchical command and control structure can be imposed in order to permit human control at a level of group abstraction and discusses experimental results that show how group performance scales as different numbers of robots are utilized. Lastly, the paper outlines the applications for which the resulting capabilities have been applied and demonstrated.
A Hybrid Swarm Intelligence Algorithm for Intrusion Detection Using Significant Features
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P. Amudha
2015-01-01
Full Text Available Intrusion detection has become a main part of network security due to the huge number of attacks which affects the computers. This is due to the extensive growth of internet connectivity and accessibility to information systems worldwide. To deal with this problem, in this paper a hybrid algorithm is proposed to integrate Modified Artificial Bee Colony (MABC with Enhanced Particle Swarm Optimization (EPSO to predict the intrusion detection problem. The algorithms are combined together to find out better optimization results and the classification accuracies are obtained by 10-fold cross-validation method. The purpose of this paper is to select the most relevant features that can represent the pattern of the network traffic and test its effect on the success of the proposed hybrid classification algorithm. To investigate the performance of the proposed method, intrusion detection KDDCup’99 benchmark dataset from the UCI Machine Learning repository is used. The performance of the proposed method is compared with the other machine learning algorithms and found to be significantly different.
A Hybrid Swarm Intelligence Algorithm for Intrusion Detection Using Significant Features.
Amudha, P; Karthik, S; Sivakumari, S
2015-01-01
Intrusion detection has become a main part of network security due to the huge number of attacks which affects the computers. This is due to the extensive growth of internet connectivity and accessibility to information systems worldwide. To deal with this problem, in this paper a hybrid algorithm is proposed to integrate Modified Artificial Bee Colony (MABC) with Enhanced Particle Swarm Optimization (EPSO) to predict the intrusion detection problem. The algorithms are combined together to find out better optimization results and the classification accuracies are obtained by 10-fold cross-validation method. The purpose of this paper is to select the most relevant features that can represent the pattern of the network traffic and test its effect on the success of the proposed hybrid classification algorithm. To investigate the performance of the proposed method, intrusion detection KDDCup'99 benchmark dataset from the UCI Machine Learning repository is used. The performance of the proposed method is compared with the other machine learning algorithms and found to be significantly different.
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Zhilong Wang
2016-11-01
Full Text Available Accurate quantification and characterization of a wind energy potential assessment and forecasting is significant to optimal wind farm design, evaluation and scheduling. However, wind energy potential assessment and forecasting remain difficult and challenging research topics at present. Traditional wind energy assessment and forecasting models usually ignore the problem of data pre-processing as well as parameter optimization, which leads to low accuracy. Therefore, this paper aims to assess the potential of wind energy and forecast the wind speed in four locations in China based on the data pre-processing technique and swarm intelligent optimization algorithms. In the assessment stage, the cuckoo search (CS algorithm, ant colony (AC algorithm, firefly algorithm (FA and genetic algorithm (GA are used to estimate the two unknown parameters in the Weibull distribution. Then, the wind energy potential assessment results obtained by three data-preprocessing approaches are compared to recognize the best data-preprocessing approach and process the original wind speed time series. While in the forecasting stage, by considering the pre-processed wind speed time series as the original data, the CS and AC optimization algorithms are adopted to optimize three neural networks, namely, the Elman neural network, back propagation neural network, and wavelet neural network. The comparison results demonstrate that the new proposed wind energy assessment and speed forecasting techniques produce promising assessments and predictions and perform better than the single assessment and forecasting components.
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Daniel H. De La Iglesia
2017-10-01
Full Text Available The use of electric bikes (e-bikes has grown in popularity, especially in large cities where overcrowding and traffic congestion are common. This paper proposes an intelligent engine management system for e-bikes which uses the information collected from sensors to optimize battery energy and time. The intelligent engine management system consists of a built-in network of sensors in the e-bike, which is used for multi-sensor data fusion; the collected data is analysed and fused and on the basis of this information the system can provide the user with optimal and personalized assistance. The user is given recommendations related to battery consumption, sensors, and other parameters associated with the route travelled, such as duration, speed, or variation in altitude. To provide a user with these recommendations, artificial neural networks are used to estimate speed and consumption for each of the segments of a route. These estimates are incorporated into evolutionary algorithms in order to make the optimizations. A comparative analysis of the results obtained has been conducted for when routes were travelled with and without the optimization system. From the experiments, it is evident that the use of an engine management system results in significant energy and time savings. Moreover, user satisfaction increases as the level of assistance adapts to user behavior and the characteristics of the route.
Villarubia, Gabriel; De Paz, Juan F.; Bajo, Javier
2017-01-01
The use of electric bikes (e-bikes) has grown in popularity, especially in large cities where overcrowding and traffic congestion are common. This paper proposes an intelligent engine management system for e-bikes which uses the information collected from sensors to optimize battery energy and time. The intelligent engine management system consists of a built-in network of sensors in the e-bike, which is used for multi-sensor data fusion; the collected data is analysed and fused and on the basis of this information the system can provide the user with optimal and personalized assistance. The user is given recommendations related to battery consumption, sensors, and other parameters associated with the route travelled, such as duration, speed, or variation in altitude. To provide a user with these recommendations, artificial neural networks are used to estimate speed and consumption for each of the segments of a route. These estimates are incorporated into evolutionary algorithms in order to make the optimizations. A comparative analysis of the results obtained has been conducted for when routes were travelled with and without the optimization system. From the experiments, it is evident that the use of an engine management system results in significant energy and time savings. Moreover, user satisfaction increases as the level of assistance adapts to user behavior and the characteristics of the route. PMID:29088087
De La Iglesia, Daniel H; Villarrubia, Gabriel; De Paz, Juan F; Bajo, Javier
2017-10-31
The use of electric bikes (e-bikes) has grown in popularity, especially in large cities where overcrowding and traffic congestion are common. This paper proposes an intelligent engine management system for e-bikes which uses the information collected from sensors to optimize battery energy and time. The intelligent engine management system consists of a built-in network of sensors in the e-bike, which is used for multi-sensor data fusion; the collected data is analysed and fused and on the basis of this information the system can provide the user with optimal and personalized assistance. The user is given recommendations related to battery consumption, sensors, and other parameters associated with the route travelled, such as duration, speed, or variation in altitude. To provide a user with these recommendations, artificial neural networks are used to estimate speed and consumption for each of the segments of a route. These estimates are incorporated into evolutionary algorithms in order to make the optimizations. A comparative analysis of the results obtained has been conducted for when routes were travelled with and without the optimization system. From the experiments, it is evident that the use of an engine management system results in significant energy and time savings. Moreover, user satisfaction increases as the level of assistance adapts to user behavior and the characteristics of the route.
Glowworm swarm optimization theory, algorithms, and applications
Kaipa, Krishnanand N
2017-01-01
This book provides a comprehensive account of the glowworm swarm optimization (GSO) algorithm, including details of the underlying ideas, theoretical foundations, algorithm development, various applications, and MATLAB programs for the basic GSO algorithm. It also discusses several research problems at different levels of sophistication that can be attempted by interested researchers. The generality of the GSO algorithm is evident in its application to diverse problems ranging from optimization to robotics. Examples include computation of multiple optima, annual crop planning, cooperative exploration, distributed search, multiple source localization, contaminant boundary mapping, wireless sensor networks, clustering, knapsack, numerical integration, solving fixed point equations, solving systems of nonlinear equations, and engineering design optimization. The book is a valuable resource for researchers as well as graduate and undergraduate students in the area of swarm intelligence and computational intellige...
Organic Computing and Swarm Intelligence
Merkle, Daniel; Middendorf, Martin; Scheidler, Alexander
The relations between swarm intelligence and organic computing are discussed in this chapter. The aim of organic computing is to design and study computing systems that consist of many autonomous components and show forms of collective behavior. Such organic computing systems (OC systems) should possess self-x properties (e.g., self-healing, self-managing, self-optimizing), have a decentralized control, and be adaptive to changing requirements of their user. Examples of OC systems are described in this chapter and two case studies are presented that show in detail that OC systems share important properties with social insect colonies and how methods of swarm intelligence can be used to solve problems in organic computing.
Recent advances in swarm intelligence and evolutionary computation
2015-01-01
This timely review volume summarizes the state-of-the-art developments in nature-inspired algorithms and applications with the emphasis on swarm intelligence and bio-inspired computation. Topics include the analysis and overview of swarm intelligence and evolutionary computation, hybrid metaheuristic algorithms, bat algorithm, discrete cuckoo search, firefly algorithm, particle swarm optimization, and harmony search as well as convergent hybridization. Application case studies have focused on the dehydration of fruits and vegetables by the firefly algorithm and goal programming, feature selection by the binary flower pollination algorithm, job shop scheduling, single row facility layout optimization, training of feed-forward neural networks, damage and stiffness identification, synthesis of cross-ambiguity functions by the bat algorithm, web document clustering, truss analysis, water distribution networks, sustainable building designs and others. As a timely review, this book can serve as an ideal reference f...
Intelligent discrete particle swarm optimization for multiprocessor task scheduling problem
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S Sarathambekai
2017-03-01
Full Text Available Discrete particle swarm optimization is one of the most recently developed population-based meta-heuristic optimization algorithm in swarm intelligence that can be used in any discrete optimization problems. This article presents a discrete particle swarm optimization algorithm to efficiently schedule the tasks in the heterogeneous multiprocessor systems. All the optimization algorithms share a common algorithmic step, namely population initialization. It plays a significant role because it can affect the convergence speed and also the quality of the final solution. The random initialization is the most commonly used method in majority of the evolutionary algorithms to generate solutions in the initial population. The initial good quality solutions can facilitate the algorithm to locate the optimal solution or else it may prevent the algorithm from finding the optimal solution. Intelligence should be incorporated to generate the initial population in order to avoid the premature convergence. This article presents a discrete particle swarm optimization algorithm, which incorporates opposition-based technique to generate initial population and greedy algorithm to balance the load of the processors. Make span, flow time, and reliability cost are three different measures used to evaluate the efficiency of the proposed discrete particle swarm optimization algorithm for scheduling independent tasks in distributed systems. Computational simulations are done based on a set of benchmark instances to assess the performance of the proposed algorithm.
Swarm intelligence metaheuristics for enhanced data analysis and optimization.
Hanrahan, Grady
2011-09-21
The swarm intelligence (SI) computing paradigm has proven itself as a comprehensive means of solving complicated analytical chemistry problems by emulating biologically-inspired processes. As global optimum search metaheuristics, associated algorithms have been widely used in training neural networks, function optimization, prediction and classification, and in a variety of process-based analytical applications. The goal of this review is to provide readers with critical insight into the utility of swarm intelligence tools as methods for solving complex chemical problems. Consideration will be given to algorithm development, ease of implementation and model performance, detailing subsequent influences on a number of application areas in the analytical, bioanalytical and detection sciences.
Couceiro, Micael
2015-01-01
This book examines the bottom-up applicability of swarm intelligence to solving multiple problems, such as curve fitting, image segmentation, and swarm robotics. It compares the capabilities of some of the better-known bio-inspired optimization approaches, especially Particle Swarm Optimization (PSO), Darwinian Particle Swarm Optimization (DPSO) and the recently proposed Fractional Order Darwinian Particle Swarm Optimization (FODPSO), and comprehensively discusses their advantages and disadvantages. Further, it demonstrates the superiority and key advantages of using the FODPSO algorithm, suc
PERFORMANCE OF PID CONTROLLER OF NONLINEAR SYSTEM USING SWARM INTELLIGENCE TECHNIQUES
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Neeraj Jain
2016-07-01
Full Text Available In this paper swarm intelligence based PID controller tuning is proposed for a nonlinear ball and hoop system. Particle swarm optimization (PSO, Artificial bee colony (ABC, Bacterial foraging optimization (BFO is some example of swarm intelligence techniques which are focused for PID controller tuning. These algorithms are also tested on perturbed ball and hoop model. Integral square error (ISE based performance index is used for finding the best possible value of controller parameters. Matlab software is used for designing the ball and hoop model. It is found that these swarm intelligence techniques have easy implementation & lesser settling & rise time compare to conventional methods.
Solving Fractional Programming Problems based on Swarm Intelligence
Raouf, Osama Abdel; Hezam, Ibrahim M.
2014-04-01
This paper presents a new approach to solve Fractional Programming Problems (FPPs) based on two different Swarm Intelligence (SI) algorithms. The two algorithms are: Particle Swarm Optimization, and Firefly Algorithm. The two algorithms are tested using several FPP benchmark examples and two selected industrial applications. The test aims to prove the capability of the SI algorithms to solve any type of FPPs. The solution results employing the SI algorithms are compared with a number of exact and metaheuristic solution methods used for handling FPPs. Swarm Intelligence can be denoted as an effective technique for solving linear or nonlinear, non-differentiable fractional objective functions. Problems with an optimal solution at a finite point and an unbounded constraint set, can be solved using the proposed approach. Numerical examples are given to show the feasibility, effectiveness, and robustness of the proposed algorithm. The results obtained using the two SI algorithms revealed the superiority of the proposed technique among others in computational time. A better accuracy was remarkably observed in the solution results of the industrial application problems.
Heterogeneous architecture to process swarm optimization algorithms
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Maria A. Dávila-Guzmán
2014-01-01
Full Text Available Since few years ago, the parallel processing has been embedded in personal computers by including co-processing units as the graphics processing units resulting in a heterogeneous platform. This paper presents the implementation of swarm algorithms on this platform to solve several functions from optimization problems, where they highlight their inherent parallel processing and distributed control features. In the swarm algorithms, each individual and dimension problem are parallelized by the granularity of the processing system which also offer low communication latency between individuals through the embedded processing. To evaluate the potential of swarm algorithms on graphics processing units we have implemented two of them: the particle swarm optimization algorithm and the bacterial foraging optimization algorithm. The algorithms’ performance is measured using the acceleration where they are contrasted between a typical sequential processing platform and the NVIDIA GeForce GTX480 heterogeneous platform; the results show that the particle swarm algorithm obtained up to 36.82x and the bacterial foraging swarm algorithm obtained up to 9.26x. Finally, the effect to increase the size of the population is evaluated where we show both the dispersion and the quality of the solutions are decreased despite of high acceleration performance since the initial distribution of the individuals can converge to local optimal solution.
Creating Virtual Communities by Means of Swarm Intelligence
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Lucian Hancu
2011-01-01
Full Text Available
During centuries, observing the behavior of wild species has always been fascinating and full of mysteries. Modeling human interactions based on those revealed on the wilderness conducts to innovative solutions, but also poses unexpected issues. In this article, we describe our approach of creating communities of virtual entities by means of swarm intelligence. We discuss the algorithm of creating the virtual communities along with the issues that arise when modeling business entities as individuals of the swarm.
A comprehensive review of swarm optimization algorithms.
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Mohd Nadhir Ab Wahab
Full Text Available Many swarm optimization algorithms have been introduced since the early 60's, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known optimization algorithms. Selected algorithms are briefly explained and compared with each other comprehensively through experiments conducted using thirty well-known benchmark functions. Their advantages and disadvantages are also discussed. A number of statistical tests are then carried out to determine the significant performances. The results indicate the overall advantage of Differential Evolution (DE and is closely followed by Particle Swarm Optimization (PSO, compared with other considered approaches.
A comprehensive review of swarm optimization algorithms.
Ab Wahab, Mohd Nadhir; Nefti-Meziani, Samia; Atyabi, Adham
2015-01-01
Many swarm optimization algorithms have been introduced since the early 60's, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known optimization algorithms. Selected algorithms are briefly explained and compared with each other comprehensively through experiments conducted using thirty well-known benchmark functions. Their advantages and disadvantages are also discussed. A number of statistical tests are then carried out to determine the significant performances. The results indicate the overall advantage of Differential Evolution (DE) and is closely followed by Particle Swarm Optimization (PSO), compared with other considered approaches.
Dynamic Alliance of Agriculture Productslogistics Based on Swarm Intelligence
Yao, Xinsheng; Cui, Yan; Ying, Jilai; Wei, Jianguang
Along with the growing up of the Chinese generalized agriculture, the agriculture products logistics demands are increasing quickly in quality and quantity. Oppositely, the service of agriculture products logistics is slowly. It is very essential to study the logistics service mode suited to the tendency of the agriculture products logistics demand. The paper analyzes the common characteristic between the agriculture products logistics individual and the intelligence individual. Then, by the swarm intelligence, thedynamic alliance of agriculture products logistics is presented, the construction algorithm and the application method are given too. The paper provides a better operable development mode for the agriculture products logistics in China, which has directive meaning to improve the logistics efficiency for the socialistic new economy development and the New County Construction.
The Study of Intelligent Vehicle Navigation Path Based on Behavior Coordination of Particle Swarm.
Han, Gaining; Fu, Weiping; Wang, Wen
2016-01-01
In the behavior dynamics model, behavior competition leads to the shock problem of the intelligent vehicle navigation path, because of the simultaneous occurrence of the time-variant target behavior and obstacle avoidance behavior. Considering the safety and real-time of intelligent vehicle, the particle swarm optimization (PSO) algorithm is proposed to solve these problems for the optimization of weight coefficients of the heading angle and the path velocity. Firstly, according to the behavior dynamics model, the fitness function is defined concerning the intelligent vehicle driving characteristics, the distance between intelligent vehicle and obstacle, and distance of intelligent vehicle and target. Secondly, behavior coordination parameters that minimize the fitness function are obtained by particle swarm optimization algorithms. Finally, the simulation results show that the optimization method and its fitness function can improve the perturbations of the vehicle planning path and real-time and reliability.
Multi-Robot Motion Planning Using Swarm Intelligence
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Gerasimos G. Rigatos
2008-11-01
Full Text Available Swarm intelligence theory is proposed for motion planning of multi-robot systems. Multiple particles start from different points in the solutions space and interact to each other while moving towards the goal position. Swarm intelligence theory is a derivative-free approach to the problem of multi-robotcooperation which works by searching iteratively in regions defined by each robot's best previous move and the best previous move of its neighbors. The method's performance is evaluated through simulation tests.
Multi-Robot Motion Planning Using Swarm Intelligence
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Gerasimos G. Rigatos
2008-06-01
Full Text Available Swarm intelligence theory is proposed for motion planning of multi-robot systems. Multiple particles start from different points in the solutions space and interact to each other while moving towards the goal position. Swarm intelligence theory is a derivative-free approach to the problem of multi-robotcooperation which works by searching iteratively in regions defined by each robot's best previous move and the best previous move of its neighbors. The method's performance is evaluated through simulation tests.
Optimization of Transformation Coefficients Using Direct Search and Swarm Intelligence
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Manusov V.Z.
2017-04-01
Full Text Available This research considers optimization of tap position of transformers in power systems to reduce power losses. Now, methods based on heuristic rules and fuzzy logic, or methods that optimize parts of the whole system separately, are applied to this problem. The first approach requires expert knowledge about processes in the network. The second methods are not able to consider all the interrelations of system’s parts, while changes in segment affect the entire system. Both approaches are tough to implement and require adjustment to the tasks solved. It needs to implement algorithms that can take into account complex interrelations of optimized variables and self-adapt to optimization task. It is advisable to use algorithms given complex interrelations of optimized variables and independently adapting from optimization tasks. Such algorithms include Swarm Intelligence algorithms. Their main features are self-organization, which allows them to automatically adapt to conditions of tasks, and the ability to efficiently exit from local extremes. Thus, they do not require specialized knowledge of the system, in contrast to fuzzy logic. In addition, they can efficiently find quasi-optimal solutions converging to the global optimum. This research applies Particle Swarm Optimization algorithm (PSO. The model of Tajik power system used in experiments. It was found out that PSO is much more efficient than greedy heuristics and more flexible and easier to use than fuzzy logic. PSO allows reducing active power losses from 48.01 to 45.83 MW (4.5%. With al, the effect of using greedy heuristics or fuzzy logic is two times smaller (2.3%.
Multi-objective swarm intelligence theoretical advances and applications
Jagadev, Alok; Panda, Mrutyunjaya
2015-01-01
The aim of this book is to understand the state-of-the-art theoretical and practical advances of swarm intelligence. It comprises seven contemporary relevant chapters. In chapter 1, a review of Bacteria Foraging Optimization (BFO) techniques for both single and multiple criterions problem is presented. A survey on swarm intelligence for multiple and many objectives optimization is presented in chapter 2 along with a topical study on EEG signal analysis. Without compromising the extensive simulation study, a comparative study of variants of MOPSO is provided in chapter 3. Intractable problems like subset and job scheduling problems are discussed in chapters 4 and 7 by different hybrid swarm intelligence techniques. An attempt to study image enhancement by ant colony optimization is made in chapter 5. Finally, chapter 7 covers the aspect of uncertainty in data by hybrid PSO.
CFSO3: A New Supervised Swarm-Based Optimization Algorithm
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Antonino Laudani
2013-01-01
Full Text Available We present CFSO3, an optimization heuristic within the class of the swarm intelligence, based on a synergy among three different features of the Continuous Flock-of-Starlings Optimization. One of the main novelties is that this optimizer is no more a classical numerical algorithm since it now can be seen as a continuous dynamic system, which can be treated by using all the mathematical instruments available for managing state equations. In addition, CFSO3 allows passing from stochastic approaches to supervised deterministic ones since the random updating of parameters, a typical feature for numerical swam-based optimization algorithms, is now fully substituted by a supervised strategy: in CFSO3 the tuning of parameters is a priori designed for obtaining both exploration and exploitation. Indeed the exploration, that is, the escaping from a local minimum, as well as the convergence and the refinement to a solution can be designed simply by managing the eigenvalues of the CFSO state equations. Virtually in CFSO3, just the initial values of positions and velocities of the swarm members have to be randomly assigned. Both standard and parallel versions of CFSO3 together with validations on classical benchmarks are presented.
The Swarm Computing Approach to Business Intelligence
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Schumann Andrew
2015-07-01
Full Text Available We have proposed to use some features of swarm behaviours in modelling business processes. Due to these features we deal with a propagation of business processes in all accessible directions. This propagation is involved into our formalization instead of communicating sequential processes. As a result, we have constructed a business process diagram language based on the swarm behavior and an extension of that language in the form of reflexive management language.
Survey of Methods and Algorithms of Robot Swarm Aggregation
E Shlyakhov, N.; Vatamaniuk, I. V.; Ronzhin, A. L.
2017-01-01
The paper considers the problem of swarm aggregation of autonomous robots with the use of three methods based on the analogy of the behavior of biological objects. The algorithms substantiating the requirements for hardware realization of sensor, computer and network resources and propulsion devices are presented. Techniques for efficiency estimation of swarm aggregation via space-time characteristics are described. The developed model of the robot swarm reconfiguration into a predetermined three-dimensional shape is presented.
Particle swarm inspired optimization algorithm without velocity equation
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Mahmoud Mostafa El-Sherbiny
2011-03-01
Full Text Available This paper introduces Particle Swarm Without Velocity equation optimization algorithm (PSWV that significantly reduces the number of iterations required to reach good solutions for optimization problems. PSWV algorithm uses a set of particles as in particle swarm optimization algorithm but a different mechanism for finding the next position for each particle is used in order to reach a good solution in a minimum number of iterations. In PSWV algorithm, the new position of each particle is determined directly from the result of linear combination between its own best position and the swarm best position without using velocity equation. The results of PSWV algorithm and the results of different variations of particle swarm optimizer are experimentally compared. The performance of PSWV algorithm and the solution quality prove that PSWV is highly competitive and can be considered as a viable alternative to solve optimization problems.
Adaptive Fuzzy-Lyapunov Controller Using Biologically Inspired Swarm Intelligence
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Alejandro Carrasco Elizalde
2008-01-01
Full Text Available The collective behaviour of swarms produces smarter actions than those achieved by a single individual. Colonies of ants, flocks of birds and fish schools are examples of swarms interacting with their environment to achieve a common goal. This cooperative biological intelligence is the inspiration for an adaptive fuzzy controller developed in this paper. Swarm intelligence is used to adjust the parameters of the membership functions used in the adaptive fuzzy controller. The rules of the controller are designed using a computing-with-words approach called Fuzzy-Lyapunov synthesis to improve the stability and robustness of an adaptive fuzzy controller. Computing-with-words provides a powerful tool to manipulate numbers and symbols, like words in a natural language.
Handbook of swarm intelligence concepts, principles and applications
Shi, Yuhui; Panigrahi, Bijaya Ketan
2011-01-01
Recent work on the behavior of swarming creatures such as bees posits an innate collective intelligence that gives rise to myriad computational problem-solving techniques. This volume is both an introduction to the topic and a survey of leading-edge research.
A Swarm Optimization Genetic Algorithm Based on Quantum-Behaved Particle Swarm Optimization.
Sun, Tao; Xu, Ming-Hai
2017-01-01
Quantum-behaved particle swarm optimization (QPSO) algorithm is a variant of the traditional particle swarm optimization (PSO). The QPSO that was originally developed for continuous search spaces outperforms the traditional PSO in search ability. This paper analyzes the main factors that impact the search ability of QPSO and converts the particle movement formula to the mutation condition by introducing the rejection region, thus proposing a new binary algorithm, named swarm optimization genetic algorithm (SOGA), because it is more like genetic algorithm (GA) than PSO in form. SOGA has crossover and mutation operator as GA but does not need to set the crossover and mutation probability, so it has fewer parameters to control. The proposed algorithm was tested with several nonlinear high-dimension functions in the binary search space, and the results were compared with those from BPSO, BQPSO, and GA. The experimental results show that SOGA is distinctly superior to the other three algorithms in terms of solution accuracy and convergence.
Mohanasundaram, Ranganathan; Periasamy, Pappampalayam Sanmugam
2015-01-01
The current high profile debate with regard to data storage and its growth have become strategic task in the world of networking. It mainly depends on the sensor nodes called producers, base stations, and also the consumers (users and sensor nodes) to retrieve and use the data. The main concern dealt here is to find an optimal data storage position in wireless sensor networks. The works that have been carried out earlier did not utilize swarm intelligence based optimization approaches to find the optimal data storage positions. To achieve this goal, an efficient swam intelligence approach is used to choose suitable positions for a storage node. Thus, hybrid particle swarm optimization algorithm has been used to find the suitable positions for storage nodes while the total energy cost of data transmission is minimized. Clustering-based distributed data storage is utilized to solve clustering problem using fuzzy-C-means algorithm. This research work also considers the data rates and locations of multiple producers and consumers to find optimal data storage positions. The algorithm is implemented in a network simulator and the experimental results show that the proposed clustering and swarm intelligence based ODS strategy is more effective than the earlier approaches.
Analog Circuit Fault Diagnosis Approach Based on Improved Particle Swarm Optimization Algorithm
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Ming-Fang WANG
2014-07-01
Full Text Available The basic thought of particle swarm optimization is introduced firstly, then particle swarm optimization algorithm model is established. The application of the improved particle swarm optimization algorithm to power supply system fault diagnosis is analyzed in accordance with problem of the algorithm, and migration strategy is added to particle swarm optimization algorithm. Finally the parameters of the wide area damping controller are adjusted by the improved particle swarm optimization algorithm.
A Novel Particle Swarm Optimization Algorithm for Global Optimization.
Wang, Chun-Feng; Liu, Kui
2016-01-01
Particle Swarm Optimization (PSO) is a recently developed optimization method, which has attracted interest of researchers in various areas due to its simplicity and effectiveness, and many variants have been proposed. In this paper, a novel Particle Swarm Optimization algorithm is presented, in which the information of the best neighbor of each particle and the best particle of the entire population in the current iteration is considered. Meanwhile, to avoid premature, an abandoned mechanism is used. Furthermore, for improving the global convergence speed of our algorithm, a chaotic search is adopted in the best solution of the current iteration. To verify the performance of our algorithm, standard test functions have been employed. The experimental results show that the algorithm is much more robust and efficient than some existing Particle Swarm Optimization algorithms.
Particle swarm optimization algorithm based low cost magnetometer calibration
Ali, A. S.; Siddharth, S., Syed, Z., El-Sheimy, N.
2011-12-01
Inertial Navigation Systems (INS) consist of accelerometers, gyroscopes and a microprocessor provide inertial digital data from which position and orientation is obtained by integrating the specific forces and rotation rates. In addition to the accelerometers and gyroscopes, magnetometers can be used to derive the absolute user heading based on Earth's magnetic field. Unfortunately, the measurements of the magnetic field obtained with low cost sensors are corrupted by several errors including manufacturing defects and external electro-magnetic fields. Consequently, proper calibration of the magnetometer is required to achieve high accuracy heading measurements. In this paper, a Particle Swarm Optimization (PSO) based calibration algorithm is presented to estimate the values of the bias and scale factor of low cost magnetometer. The main advantage of this technique is the use of the artificial intelligence which does not need any error modeling or awareness of the nonlinearity. The estimated bias and scale factor errors from the proposed algorithm improve the heading accuracy and the results are also statistically significant. Also, it can help in the development of the Pedestrian Navigation Devices (PNDs) when combined with the INS and GPS/Wi-Fi especially in the indoor environments
Parameter estimation for chaotic systems using improved bird swarm algorithm
Xu, Chuangbiao; Yang, Renhuan
2017-12-01
Parameter estimation of chaotic systems is an important problem in nonlinear science and has aroused increasing interest of many research fields, which can be basically reduced to a multidimensional optimization problem. In this paper, an improved boundary bird swarm algorithm is used to estimate the parameters of chaotic systems. This algorithm can combine the good global convergence and robustness of the bird swarm algorithm and the exploitation capability of improved boundary learning strategy. Experiments are conducted on the Lorenz system and the coupling motor system. Numerical simulation results reveal the effectiveness and with desirable performance of IBBSA for parameter estimation of chaotic systems.
Swarm, genetic and evolutionary programming algorithms applied to multiuser detection
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Paul Jean Etienne Jeszensky
2005-02-01
Full Text Available In this paper, the particles swarm optimization technique, recently published in the literature, and applied to Direct Sequence/Code Division Multiple Access systems (DS/CDMA with multiuser detection (MuD is analyzed, evaluated and compared. The Swarm algorithm efficiency when applied to the DS-CDMA multiuser detection (Swarm-MuD is compared through the tradeoff performance versus computational complexity, being the complexity expressed in terms of the number of necessary operations in order to reach the performance obtained through the optimum detector or the Maximum Likelihood detector (ML. The comparison is accomplished among the genetic algorithm, evolutionary programming with cloning and Swarm algorithm under the same simulation basis. Additionally, it is proposed an heuristics-MuD complexity analysis through the number of computational operations. Finally, an analysis is carried out for the input parameters of the Swarm algorithm in the attempt to find the optimum parameters (or almost-optimum for the algorithm applied to the MuD problem.
Particle swarm optimization - Genetic algorithm (PSOGA) on linear transportation problem
Rahmalia, Dinita
2017-08-01
Linear Transportation Problem (LTP) is the case of constrained optimization where we want to minimize cost subject to the balance of the number of supply and the number of demand. The exact method such as northwest corner, vogel, russel, minimal cost have been applied at approaching optimal solution. In this paper, we use heurisitic like Particle Swarm Optimization (PSO) for solving linear transportation problem at any size of decision variable. In addition, we combine mutation operator of Genetic Algorithm (GA) at PSO to improve optimal solution. This method is called Particle Swarm Optimization - Genetic Algorithm (PSOGA). The simulations show that PSOGA can improve optimal solution resulted by PSO.
Mining Customer Change Model Based on Swarm Intelligence
Jin, Peng; Zhu, Yunlong
Understanding and adapting to changes of customer behavior is an important aspect of surviving in a continuously changing market environment for a modern company. The concept of customer change model mining is introduced and its process is analyzed in this paper. A customer change model mining method based on swarm intelligence is presented, and the strategies of pheromone updating and items searching are given. Finally, an examination on two customer datasets of a telecom company illuminates that this method can achieve customer change model efficiently.
Janaki, Sathya D.; Geetha, K.
2017-06-01
Interpreting Dynamic Contrast-Enhanced (DCE) MR images for signs of breast cancer is time consuming and complex, since the amount of data that needs to be examined by a radiologist in breast DCE-MRI to locate suspicious lesions is huge. Misclassifications can arise from either overlooking a suspicious region or from incorrectly interpreting a suspicious region. The segmentation of breast DCE-MRI for suspicious lesions in detection is thus attractive, because it drastically decreases the amount of data that needs to be examined. The new segmentation method for detection of suspicious lesions in DCE-MRI of the breast tissues is based on artificial fishes swarm clustering algorithm is presented in this paper. Artificial fish swarm optimization algorithm is a swarm intelligence algorithm, which performs a search based on population and neighborhood search combined with random search. The major criteria for segmentation are based on the image voxel values and the parameters of an empirical parametric model of segmentation algorithms. The experimental results show considerable impact on the performance of the segmentation algorithm, which can assist the physician with the task of locating suspicious regions at minimal time.
An External Archive-Guided Multiobjective Particle Swarm Optimization Algorithm.
Zhu, Qingling; Lin, Qiuzhen; Chen, Weineng; Wong, Ka-Chun; Coello Coello, Carlos A; Li, Jianqiang; Chen, Jianyong; Zhang, Jun
2017-09-01
The selection of swarm leaders (i.e., the personal best and global best), is important in the design of a multiobjective particle swarm optimization (MOPSO) algorithm. Such leaders are expected to effectively guide the swarm to approach the true Pareto optimal front. In this paper, we present a novel external archive-guided MOPSO algorithm (AgMOPSO), where the leaders for velocity update are all selected from the external archive. In our algorithm, multiobjective optimization problems (MOPs) are transformed into a set of subproblems using a decomposition approach, and then each particle is assigned accordingly to optimize each subproblem. A novel archive-guided velocity update method is designed to guide the swarm for exploration, and the external archive is also evolved using an immune-based evolutionary strategy. These proposed approaches speed up the convergence of AgMOPSO. The experimental results fully demonstrate the superiority of our proposed AgMOPSO in solving most of the test problems adopted, in terms of two commonly used performance measures. Moreover, the effectiveness of our proposed archive-guided velocity update method and immune-based evolutionary strategy is also experimentally validated on more than 30 test MOPs.
Progressive Particle Swarm Optimization Algorithm for Solving Reactive Power Problem
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Kanagasabai Lenin
2015-11-01
Full Text Available In this paper a Progressive particle swarm optimization algorithm (PPS is used to solve optimal reactive power problem. A Particle Swarm Optimization algorithm maintains a swarm of particles, where each particle has position vector and velocity vector which represents the potential solutions of the particles. These vectors are modernized from the information of global best (Gbest and personal best (Pbest of the swarm. All particles move in the search space to obtain optimal solution. In this paper a new concept is introduced of calculating the velocity of the particles with the help of Euclidian Distance conception. This new-fangled perception helps in finding whether the particle is closer to Pbest or Gbest and updates the velocity equation consequently. By this we plan to perk up the performance in terms of the optimal solution within a rational number of generations. The projected PPS has been tested on standard IEEE 30 bus test system and simulation results show clearly the better performance of the proposed algorithm in reducing the real power loss with control variables are within the limits.
A Parallel Particle Swarm Optimization Algorithm Accelerated by Asynchronous Evaluations
Venter, Gerhard; Sobieszczanski-Sobieski, Jaroslaw
2005-01-01
A parallel Particle Swarm Optimization (PSO) algorithm is presented. Particle swarm optimization is a fairly recent addition to the family of non-gradient based, probabilistic search algorithms that is based on a simplified social model and is closely tied to swarming theory. Although PSO algorithms present several attractive properties to the designer, they are plagued by high computational cost as measured by elapsed time. One approach to reduce the elapsed time is to make use of coarse-grained parallelization to evaluate the design points. Previous parallel PSO algorithms were mostly implemented in a synchronous manner, where all design points within a design iteration are evaluated before the next iteration is started. This approach leads to poor parallel speedup in cases where a heterogeneous parallel environment is used and/or where the analysis time depends on the design point being analyzed. This paper introduces an asynchronous parallel PSO algorithm that greatly improves the parallel e ciency. The asynchronous algorithm is benchmarked on a cluster assembled of Apple Macintosh G5 desktop computers, using the multi-disciplinary optimization of a typical transport aircraft wing as an example.
KANTS: a stigmergic ant algorithm for cluster analysis and swarm art.
Fernandes, Carlos M; Mora, Antonio M; Merelo, Juan J; Rosa, Agostinho C
2014-06-01
KANTS is a swarm intelligence clustering algorithm inspired by the behavior of social insects. It uses stigmergy as a strategy for clustering large datasets and, as a result, displays a typical behavior of complex systems: self-organization and global patterns emerging from the local interaction of simple units. This paper introduces a simplified version of KANTS and describes recent experiments with the algorithm in the context of a contemporary artistic and scientific trend called swarm art, a type of generative art in which swarm intelligence systems are used to create artwork or ornamental objects. KANTS is used here for generating color drawings from the input data that represent real-world phenomena, such as electroencephalogram sleep data. However, the main proposal of this paper is an art project based on well-known abstract paintings, from which the chromatic values are extracted and used as input. Colors and shapes are therefore reorganized by KANTS, which generates its own interpretation of the original artworks. The project won the 2012 Evolutionary Art, Design, and Creativity Competition.
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Hanning Chen
2014-01-01
Full Text Available The development of radio frequency identification (RFID technology generates the most challenging RFID network planning (RNP problem, which needs to be solved in order to operate the large-scale RFID network in an optimal fashion. RNP involves many objectives and constraints and has been proven to be a NP-hard multi-objective problem. The application of evolutionary algorithm (EA and swarm intelligence (SI for solving multiobjective RNP (MORNP has gained significant attention in the literature, but these algorithms always transform multiple objectives into a single objective by weighted coefficient approach. In this paper, we use multiobjective EA and SI algorithms to find all the Pareto optimal solutions and to achieve the optimal planning solutions by simultaneously optimizing four conflicting objectives in MORNP, instead of transforming multiobjective functions into a single objective function. The experiment presents an exhaustive comparison of three successful multiobjective EA and SI, namely, the recently developed multiobjective artificial bee colony algorithm (MOABC, the nondominated sorting genetic algorithm II (NSGA-II, and the multiobjective particle swarm optimization (MOPSO, on MORNP instances of different nature, namely, the two-objective and three-objective MORNP. Simulation results show that MOABC proves to be more superior for planning RFID networks than NSGA-II and MOPSO in terms of optimization accuracy and computation robustness.
Swarm intelligence of artificial bees applied to In-Core Fuel Management Optimization
Energy Technology Data Exchange (ETDEWEB)
Santos de Oliveira, Iona Maghali, E-mail: ioliveira@con.ufrj.br [Nuclear Engineering Program, Federal University of Rio de Janeiro, P.O. Box 68509, Zip Code 21945-970, Rio de Janeiro, RJ (Brazil); Schirru, Roberto, E-mail: schirru@lmp.ufrj.br [Nuclear Engineering Program, Federal University of Rio de Janeiro, P.O. Box 68509, Zip Code 21945-970, Rio de Janeiro, RJ (Brazil)
2011-05-15
Research highlights: > We present Artificial Bee Colony with Random Keys (ABCRK) for In-Core Fuel Management Optimization. > Its performance is examined through the optimization of a Brazilian '2-loop' PWR. > Feasibility of using ABCRK is shown against some well known population-based algorithms. > Additional advantage includes the utilization of fewer control parameters. - Abstract: Artificial Bee Colony (ABC) algorithm is a relatively new member of swarm intelligence. ABC tries to simulate the intelligent behavior of real honey bees in food foraging and can be used for solving continuous optimization and multi-dimensional numeric problems. This paper introduces the Artificial Bee Colony with Random Keys (ABCRK), a modified ABC algorithm for solving combinatorial problems such as the In-Core Fuel Management Optimization (ICFMO). The ICFMO is a hard combinatorial optimization problem in Nuclear Engineering which during many years has been solved by expert knowledge. It aims at getting the best arrangement of fuel in the nuclear reactor core that leads to a maximization of the operating time. As a consequence, the operation cost decreases and money is saved. In this study, ABCRK is used for optimizing the ICFMO problem of a Brazilian '2-loop' Pressurized Water Reactor (PWR) Nuclear Power Plant (NPP) and the results obtained with the proposed algorithm are compared with those obtained by Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). The results show that the performance of the ABCRK algorithm is better than or similar to that of other population-based algorithms, with the advantage of employing fewer control parameters.
Design and implementation of intelligent electronic warfare decision making algorithm
Peng, Hsin-Hsien; Chen, Chang-Kuo; Hsueh, Chi-Shun
2017-05-01
Electromagnetic signals and the requirements of timely response have been a rapid growth in modern electronic warfare. Although jammers are limited resources, it is possible to achieve the best electronic warfare efficiency by tactical decisions. This paper proposes the intelligent electronic warfare decision support system. In this work, we develop a novel hybrid algorithm, Digital Pheromone Particle Swarm Optimization, based on Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and Shuffled Frog Leaping Algorithm (SFLA). We use PSO to solve the problem and combine the concept of pheromones in ACO to accumulate more useful information in spatial solving process and speed up finding the optimal solution. The proposed algorithm finds the optimal solution in reasonable computation time by using the method of matrix conversion in SFLA. The results indicated that jammer allocation was more effective. The system based on the hybrid algorithm provides electronic warfare commanders with critical information to assist commanders in effectively managing the complex electromagnetic battlefield.
Hybrid Artificial Bee Colony Algorithm and Particle Swarm Search for Global Optimization
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Wang Chun-Feng
2014-01-01
Full Text Available Artificial bee colony (ABC algorithm is one of the most recent swarm intelligence based algorithms, which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. To overcome this problem, we propose a novel artificial bee colony algorithm based on particle swarm search mechanism. In this algorithm, for improving the convergence speed, the initial population is generated by using good point set theory rather than random selection firstly. Secondly, in order to enhance the exploitation ability, the employed bee, onlookers, and scouts utilize the mechanism of PSO to search new candidate solutions. Finally, for further improving the searching ability, the chaotic search operator is adopted in the best solution of the current iteration. Our algorithm is tested on some well-known benchmark functions and compared with other algorithms. Results show that our algorithm has good performance.
Swarm intelligence. A whole new way to think about business.
Bonabeau, E; Meyer, C
2001-05-01
What do ants and bees have to do with business? A great deal, it turns out. Individually, social insects are only minimally intelligent, and their work together is largely self-organized and unsupervised. Yet collectively they're capable of finding highly efficient solutions to difficult problems and can adapt automatically to changing environments. Over the past 20 years, the authors and other researchers have developed rigorous mathematical models to describe this phenomenon, which has been dubbed "swarm intelligence," and they are now applying them to business. Their research has already helped several companies develop more efficient ways to schedule factory equipment, divide tasks among workers, organize people, and even plot strategy. Emulating the way ants find the shortest path to a new food supply, for example, has led researchers at Hewlett-Packard to develop software programs that can find the most efficient way to route phone traffic over a telecommunications network. South-west Airlines has used a similar model to efficiently route cargo. To allocate labor, honeybees appear to follow one simple but powerful rule--they seem to specialize in a particular activity unless they perceive an important need to perform another function. Using that model, researchers at Northwestern University have devised a system for painting trucks that can automatically adapt to changing conditions. In the future, the authors speculate, a company might structure its entire business using the principles of swarm intelligence. The result, they believe, would be the ultimate self-organizing enterprise--one that could adapt quickly and instinctively to fast-changing markets.
Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms.
Garro, Beatriz A; Vázquez, Roberto A
2015-01-01
Artificial Neural Network (ANN) design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of synaptic weights. In this paper we present a methodology that automatically designs an ANN using particle swarm optimization algorithms such as Basic Particle Swarm Optimization (PSO), Second Generation of Particle Swarm Optimization (SGPSO), and a New Model of PSO called NMPSO. The aim of these algorithms is to evolve, at the same time, the three principal components of an ANN: the set of synaptic weights, the connections or architecture, and the transfer functions for each neuron. Eight different fitness functions were proposed to evaluate the fitness of each solution and find the best design. These functions are based on the mean square error (MSE) and the classification error (CER) and implement a strategy to avoid overtraining and to reduce the number of connections in the ANN. In addition, the ANN designed with the proposed methodology is compared with those designed manually using the well-known Back-Propagation and Levenberg-Marquardt Learning Algorithms. Finally, the accuracy of the method is tested with different nonlinear pattern classification problems.
A Novel Artificial Fish Swarm Algorithm for Recalibration of Fiber Optic Gyroscope Error Parameters
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Yanbin Gao
2015-05-01
Full Text Available The artificial fish swarm algorithm (AFSA is one of the state-of-the-art swarm intelligent techniques, which is widely utilized for optimization purposes. Fiber optic gyroscope (FOG error parameters such as scale factors, biases and misalignment errors are relatively unstable, especially with the environmental disturbances and the aging of fiber coils. These uncalibrated error parameters are the main reasons that the precision of FOG-based strapdown inertial navigation system (SINS degraded. This research is mainly on the application of a novel artificial fish swarm algorithm (NAFSA on FOG error coefficients recalibration/identification. First, the NAFSA avoided the demerits (e.g., lack of using artificial fishes’ pervious experiences, lack of existing balance between exploration and exploitation, and high computational cost of the standard AFSA during the optimization process. To solve these weak points, functional behaviors and the overall procedures of AFSA have been improved with some parameters eliminated and several supplementary parameters added. Second, a hybrid FOG error coefficients recalibration algorithm has been proposed based on NAFSA and Monte Carlo simulation (MCS approaches. This combination leads to maximum utilization of the involved approaches for FOG error coefficients recalibration. After that, the NAFSA is verified with simulation and experiments and its priorities are compared with that of the conventional calibration method and optimal AFSA. Results demonstrate high efficiency of the NAFSA on FOG error coefficients recalibration.
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Yanbin Gao
2015-01-01
Full Text Available Artificial fish swarm algorithm (AFSA is one of the state-of-the-art swarm intelligence techniques, which is widely utilized for optimization purposes. Triaxial accelerometer error coefficients are relatively unstable with the environmental disturbances and aging of the instrument. Therefore, identifying triaxial accelerometer error coefficients accurately and being with lower costs are of great importance to improve the overall performance of triaxial accelerometer-based strapdown inertial navigation system (SINS. In this study, a novel artificial fish swarm algorithm (NAFSA that eliminated the demerits (lack of using artificial fishes’ previous experiences, lack of existing balance between exploration and exploitation, and high computational cost of AFSA is introduced at first. In NAFSA, functional behaviors and overall procedure of AFSA have been improved with some parameters variations. Second, a hybrid accelerometer error coefficients identification algorithm has been proposed based on NAFSA and Monte Carlo simulation (MCS approaches. This combination leads to maximum utilization of the involved approaches for triaxial accelerometer error coefficients identification. Furthermore, the NAFSA-identified coefficients are testified with 24-position verification experiment and triaxial accelerometer-based SINS navigation experiment. The priorities of MCS-NAFSA are compared with that of conventional calibration method and optimal AFSA. Finally, both experiments results demonstrate high efficiency of MCS-NAFSA on triaxial accelerometer error coefficients identification.
A novel artificial fish swarm algorithm for recalibration of fiber optic gyroscope error parameters.
Gao, Yanbin; Guan, Lianwu; Wang, Tingjun; Sun, Yunlong
2015-05-05
The artificial fish swarm algorithm (AFSA) is one of the state-of-the-art swarm intelligent techniques, which is widely utilized for optimization purposes. Fiber optic gyroscope (FOG) error parameters such as scale factors, biases and misalignment errors are relatively unstable, especially with the environmental disturbances and the aging of fiber coils. These uncalibrated error parameters are the main reasons that the precision of FOG-based strapdown inertial navigation system (SINS) degraded. This research is mainly on the application of a novel artificial fish swarm algorithm (NAFSA) on FOG error coefficients recalibration/identification. First, the NAFSA avoided the demerits (e.g., lack of using artificial fishes' pervious experiences, lack of existing balance between exploration and exploitation, and high computational cost) of the standard AFSA during the optimization process. To solve these weak points, functional behaviors and the overall procedures of AFSA have been improved with some parameters eliminated and several supplementary parameters added. Second, a hybrid FOG error coefficients recalibration algorithm has been proposed based on NAFSA and Monte Carlo simulation (MCS) approaches. This combination leads to maximum utilization of the involved approaches for FOG error coefficients recalibration. After that, the NAFSA is verified with simulation and experiments and its priorities are compared with that of the conventional calibration method and optimal AFSA. Results demonstrate high efficiency of the NAFSA on FOG error coefficients recalibration.
Synthesizing Sierpinski Antenna by Genetic Algorithm and Swarm Optimization
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Z. Raida
2008-12-01
Full Text Available The paper discusses the synthesis of the Sierpinski antenna operating at three prescribed frequencies: 0.9 GHz, 1.8 GHz (both GSM and 2.4 GHz (Bluetooth. In order to synthesize the antenna, a genetic algorithm and a particle swarm optimization were used. The numerical model of the antenna was developed in Zeland IE3D, optimization scripts were programmed in MATLAB. Results of both the optimization methods are compared and experimentally verified.
OPTIMIZATION OF GRID RESOURCE SCHEDULING USING PARTICLE SWARM OPTIMIZATION ALGORITHM
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S. Selvakrishnan
2010-10-01
Full Text Available Job allocation process is one of the big issues in grid environment and it is one of the research areas in Grid Computing. Hence a new area of research is developed to design optimal methods. It focuses on new heuristic techniques that provide an optimal or near optimal solution for large grids. By learning grid resource scheduling and PSO (Particle Swarm Optimization algorithm, this proposed scheduler allocates an application to a host from a pool of available hosts and applications by selecting the best match. PSO-based algorithm is more effective in grid resources scheduling with the favor of reducing the executing time and completing time.
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Muhammad Asif Zahoor Raja
2012-01-01
Full Text Available A methodology for solution of Painlevé equation-I is presented using computational intelligence technique based on neural networks and particle swarm optimization hybridized with active set algorithm. The mathematical model of the equation is developed with the help of linear combination of feed-forward artificial neural networks that define the unsupervised error of the model. This error is minimized subject to the availability of appropriate weights of the networks. The learning of the weights is carried out using particle swarm optimization algorithm used as a tool for viable global search method, hybridized with active set algorithm for rapid local convergence. The accuracy, convergence rate, and computational complexity of the scheme are analyzed based on large number of independents runs and their comprehensive statistical analysis. The comparative studies of the results obtained are made with MATHEMATICA solutions, as well as, with variational iteration method and homotopy perturbation method.
Swarm of bees and particles algorithms in the problem of gradual failure reliability assurance
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M. F. Anop
2015-01-01
Full Text Available Probability-statistical framework of reliability theory uses models based on the chance failures analysis. These models are not functional and do not reflect relation of reliability characteristics to the object performance. At the same time, a significant part of the technical systems failures are gradual failures caused by degradation of the internal parameters of the system under the influence of various external factors.The paper shows how to provide the required level of reliability at the design stage using a functional model of a technical object. Paper describes the method for solving this problem under incomplete initial information, when there is no information about the patterns of technological deviations and degradation parameters, and the considered system model is a \\black box" one.To this end, we formulate the problem of optimal parametric synthesis. It lies in the choice of the nominal values of the system parameters to satisfy the requirements for its operation and take into account the unavoidable deviations of the parameters from their design values during operation. As an optimization criterion in this case we propose to use a deterministic geometric criterion \\reliability reserve", which is the minimum distance measured along the coordinate directions from the nominal parameter value to the acceptability region boundary rather than statistical values.The paper presents the results of the application of heuristic swarm intelligence methods to solve the formulated optimization problem. Efficiency of particle swarm algorithms and swarm of bees one compared with undirected random search algorithm in solving a number of test optimal parametric synthesis problems in three areas: reliability, convergence rate and operating time. The study suggests that the use of a swarm of bees method for solving the problem of the technical systems gradual failure reliability ensuring is preferred because of the greater flexibility of the
Motion Planning Using a Memetic Evolution Algorithm for Swarm Robots
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Chien-Chou Lin
2012-05-01
Full Text Available A hierarchical memetic algorithm (MA is proposed for the path planning and formation control of swarm robots. The proposed algorithm consists of a global path planner (GPP and a local motion planner (LMP. The GPP plans a trajectory within the Voronoi diagram (VD of the free space. An MA with a non-random initial population plans a series of configurations along the path given by the former stage. The MA locally adjusts the robot positions to search for better fitness along the gradient direction of the distance between the swarm robots and the intermediate goals (IGs. Once the optimal configuration is obtained, the best chromosomes are reserved as the initial population for the next generation. Since the proposed MA has a non-random initial population and local searching, it is more efficient and the planned path is faster compared to a traditional genetic algorithm (GA. The simulation results show that the proposed algorithm works well in terms of path smoothness and computation efficiency.
An Orthogonal Multi-Swarm Cooperative PSO Algorithm with a Particle Trajectory Knowledge Base
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Jun Yang
2017-01-01
Full Text Available A novel orthogonal multi-swarm cooperative particle swarm optimization (PSO algorithm with a particle trajectory knowledge base is presented in this paper. Different from the traditional PSO algorithms and other variants of PSO, the proposed orthogonal multi-swarm cooperative PSO algorithm not only introduces an orthogonal initialization mechanism and a particle trajectory knowledge base for multi-dimensional optimization problems, but also conceives a new adaptive cooperation mechanism to accomplish the information interaction among swarms and particles. Experiments are conducted on a set of benchmark functions, and the results show its better performance compared with traditional PSO algorithm in aspects of convergence, computational efficiency and avoiding premature convergence.
An, Meiyan; Wang, Zhaokui; Zhang, Yulin
2017-01-01
The self-organizing control strategy for asteroid intelligent detection swarm, which is considered as a space application instance of intelligent swarm, is developed. The leader-follower model for the asteroid intelligent detection swarm is established, and the further analysis is conducted for massive asteroid and small asteroid. For a massive asteroid, the leader spacecraft flies under the gravity field of the asteroid. For a small asteroid, the asteroid gravity is negligible, and a trajectory planning method is proposed based on elliptic cavity virtual potential field. The self-organizing control strategy for the follower spacecraft is developed based on a mechanism of velocity planning and velocity tracking. The simulation results show that the self-organizing control strategy is valid for both massive asteroid and small asteroid, and the exploration swarm forms a stable configuration.
Energy Technology Data Exchange (ETDEWEB)
Huang, Xiaobiao; Safranek, James
2014-09-01
Nonlinear dynamics optimization is carried out for a low emittance upgrade lattice of SPEAR3 in order to improve its dynamic aperture and Touschek lifetime. Two multi-objective optimization algorithms, a genetic algorithm and a particle swarm algorithm, are used for this study. The performance of the two algorithms are compared. The result shows that the particle swarm algorithm converges significantly faster to similar or better solutions than the genetic algorithm and it does not require seeding of good solutions in the initial population. These advantages of the particle swarm algorithm may make it more suitable for many accelerator optimization applications.
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Chika Yinka-Banjo
2014-01-01
Full Text Available Underground mining operations are carried out in hazardous environments. To prevent disasters from occurring, as often as they do in underground mines, and to prevent safety routine checkers from disasters during safety inspection checks, multirobots are suggested to do the job of safety inspection rather than human beings and single robots. Multirobots are preferred because the inspection task will be done in the minimum amount of time. This paper proposes a cooperative behaviour for a multirobot system (MRS to achieve a preentry safety inspection in underground terrains. A hybrid QLACS swarm intelligent model based on Q-Learning (QL and the Ant Colony System (ACS was proposed to achieve this cooperative behaviour in MRS. The intelligent model was developed by harnessing the strengths of both QL and ACS algorithms. The ACS optimizes the routes used for each robot while the QL algorithm enhances the cooperation between the autonomous robots. A description of a communicating variation within the QLACS model for cooperative behavioural purposes is presented. The performance of the algorithms in terms of without communication, with communication, computation time, path costs, and the number of robots used was evaluated by using a simulation approach. Simulation results show achieved cooperative behaviour between robots.
Artificial Fish Swarm Algorithm-Based Particle Filter for Li-Ion Battery Life Prediction
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Ye Tian
2014-01-01
Full Text Available An intelligent online prognostic approach is proposed for predicting the remaining useful life (RUL of lithium-ion (Li-ion batteries based on artificial fish swarm algorithm (AFSA and particle filter (PF, which is an integrated approach combining model-based method with data-driven method. The parameters, used in the empirical model which is based on the capacity fade trends of Li-ion batteries, are identified dependent on the tracking ability of PF. AFSA-PF aims to improve the performance of the basic PF. By driving the prior particles to the domain with high likelihood, AFSA-PF allows global optimization, prevents particle degeneracy, thereby improving particle distribution and increasing prediction accuracy and algorithm convergence. Data provided by NASA are used to verify this approach and compare it with basic PF and regularized PF. AFSA-PF is shown to be more accurate and precise.
Yan, Bailu; Zhao, Zheng; Zhou, Yingcheng; Yuan, Wenyan; Li, Jian; Wu, Jun; Cheng, Daojian
2017-10-01
Swarm intelligence optimization algorithms are mainstream algorithms for solving complex optimization problems. Among these algorithms, the particle swarm optimization (PSO) algorithm has the advantages of fast computation speed and few parameters. However, PSO is prone to premature convergence. To solve this problem, we develop a new PSO algorithm (RPSOLF) by combining the characteristics of random learning mechanism and Levy flight. The RPSOLF algorithm increases the diversity of the population by learning from random particles and random walks in Levy flight. On the one hand, we carry out a large number of numerical experiments on benchmark test functions, and compare these results with the PSO algorithm with Levy flight (PSOLF) algorithm and other PSO variants in previous reports. The results show that the optimal solution can be found faster and more efficiently by the RPSOLF algorithm. On the other hand, the RPSOLF algorithm can also be applied to optimize the Lennard-Jones clusters, and the results indicate that the algorithm obtains the optimal structure (2-60 atoms) with an extraordinary high efficiency. In summary, RPSOLF algorithm proposed in our paper is proved to be an extremely effective tool for global optimization.
Swarm Intelligence-Based Hybrid Models for Short-Term Power Load Prediction
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Jianzhou Wang
2014-01-01
Full Text Available Swarm intelligence (SI is widely and successfully applied in the engineering field to solve practical optimization problems because various hybrid models, which are based on the SI algorithm and statistical models, are developed to further improve the predictive abilities. In this paper, hybrid intelligent forecasting models based on the cuckoo search (CS as well as the singular spectrum analysis (SSA, time series, and machine learning methods are proposed to conduct short-term power load prediction. The forecasting performance of the proposed models is augmented by a rolling multistep strategy over the prediction horizon. The test results are representative of the out-performance of the SSA and CS in tuning the seasonal autoregressive integrated moving average (SARIMA and support vector regression (SVR in improving load forecasting, which indicates that both the SSA-based data denoising and SI-based intelligent optimization strategy can effectively improve the model’s predictive performance. Additionally, the proposed CS-SSA-SARIMA and CS-SSA-SVR models provide very impressive forecasting results, demonstrating their strong robustness and universal forecasting capacities in terms of short-term power load prediction 24 hours in advance.
Paramanandham, Nirmala; Rajendiran, Kishore
2018-01-01
A novel image fusion technique is presented for integrating infrared and visible images. Integration of images from the same or various sensing modalities can deliver the required information that cannot be delivered by viewing the sensor outputs individually and consecutively. In this paper, a swarm intelligence based image fusion technique using discrete cosine transform (DCT) domain is proposed for surveillance application which integrates the infrared image with the visible image for generating a single informative fused image. Particle swarm optimization (PSO) is used in the fusion process for obtaining the optimized weighting factor. These optimized weighting factors are used for fusing the DCT coefficients of visible and infrared images. Inverse DCT is applied for obtaining the initial fused image. An enhanced fused image is obtained through adaptive histogram equalization for a better visual understanding and target detection. The proposed framework is evaluated using quantitative metrics such as standard deviation, spatial frequency, entropy and mean gradient. The experimental results demonstrate the outperformance of the proposed algorithm over many other state- of- the- art techniques reported in literature.
Compressive Sensing Image Fusion Based on Particle Swarm Optimization Algorithm
Li, X.; Lv, J.; Jiang, S.; Zhou, H.
2017-09-01
In order to solve the problem that the spatial matching is difficult and the spectral distortion is large in traditional pixel-level image fusion algorithm. We propose a new method of image fusion that utilizes HIS transformation and the recently developed theory of compressive sensing that is called HIS-CS image fusion. In this algorithm, the particle swarm optimization algorithm is used to select the fusion coefficient ω. In the iterative process, the image fusion coefficient ω is taken as particle, and the optimal value is obtained by combining the optimal objective function. Then we use the compression-aware weighted fusion algorithm for remote sensing image fusion, taking the coefficient ω as the weight value. The algorithm ensures the optimal selection of fusion effect with a certain degree of self-adaptability. To evaluate the fused images, this paper uses five kinds of index parameters such as Entropy, Standard Deviation, Average Gradient, Degree of Distortion and Peak Signal-to-Noise Ratio. The experimental results show that the image fusion effect of the algorithm in this paper is better than that of traditional methods.
Improved multi-objective clustering algorithm using particle swarm optimization.
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Congcong Gong
Full Text Available Multi-objective clustering has received widespread attention recently, as it can obtain more accurate and reasonable solution. In this paper, an improved multi-objective clustering framework using particle swarm optimization (IMCPSO is proposed. Firstly, a novel particle representation for clustering problem is designed to help PSO search clustering solutions in continuous space. Secondly, the distribution of Pareto set is analyzed. The analysis results are applied to the leader selection strategy, and make algorithm avoid trapping in local optimum. Moreover, a clustering solution-improved method is proposed, which can increase the efficiency in searching clustering solution greatly. In the experiments, 28 datasets are used and nine state-of-the-art clustering algorithms are compared, the proposed method is superior to other approaches in the evaluation index ARI.
Bio Inspired Swarm Algorithm for Tumor Detection in Digital Mammogram
Dheeba, J.; Selvi, Tamil
Microcalcification clusters in mammograms is the significant early sign of breast cancer. Individual clusters are difficult to detect and hence an automatic computer aided mechanism will help the radiologist in detecting the microcalcification clusters in an easy and efficient way. This paper presents a new classification approach for detection of microcalcification in digital mammogram using particle swarm optimization algorithm (PSO) based clustering technique. Fuzzy C-means clustering technique, well defined for clustering data sets are used in combination with the PSO. We adopt the particle swarm optimization to search the cluster center in the arbitrary data set automatically. PSO can search the best solution from the probability option of the Social-only model and Cognition-only model. This method is quite simple and valid, and it can avoid the minimum local value. The proposed classification approach is applied to a database of 322 dense mammographic images, originating from the MIAS database. Results shows that the proposed PSO-FCM approach gives better detection performance compared to conventional approaches.
Multivariable optimization of liquid rocket engines using particle swarm algorithms
Jones, Daniel Ray
Liquid rocket engines are highly reliable, controllable, and efficient compared to other conventional forms of rocket propulsion. As such, they have seen wide use in the space industry and have become the standard propulsion system for launch vehicles, orbit insertion, and orbital maneuvering. Though these systems are well understood, historical optimization techniques are often inadequate due to the highly non-linear nature of the engine performance problem. In this thesis, a Particle Swarm Optimization (PSO) variant was applied to maximize the specific impulse of a finite-area combustion chamber (FAC) equilibrium flow rocket performance model by controlling the engine's oxidizer-to-fuel ratio and de Laval nozzle expansion and contraction ratios. In addition to the PSO-controlled parameters, engine performance was calculated based on propellant chemistry, combustion chamber pressure, and ambient pressure, which are provided as inputs to the program. The performance code was validated by comparison with NASA's Chemical Equilibrium with Applications (CEA) and the commercially available Rocket Propulsion Analysis (RPA) tool. Similarly, the PSO algorithm was validated by comparison with brute-force optimization, which calculates all possible solutions and subsequently determines which is the optimum. Particle Swarm Optimization was shown to be an effective optimizer capable of quick and reliable convergence for complex functions of multiple non-linear variables.
Industry Cluster's Adaptive Co-competition Behavior Modeling Inspired by Swarm Intelligence
Xiang, Wei; Ye, Feifan
Adaptation helps the individual enterprise to adjust its behavior to uncertainties in environment and hence determines a healthy growth of both the individuals and the whole industry cluster as well. This paper is focused on the study on co-competition adaptation behavior of industry cluster, which is inspired by swarm intelligence mechanisms. By referencing to ant cooperative transportation and ant foraging behavior and their related swarm intelligence approaches, the cooperative adaptation and competitive adaptation behavior are studied and relevant models are proposed. Those adaptive co-competition behaviors model can be integrated to the multi-agent system of industry cluster to make the industry cluster model more realistic.
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Indrajit Bhattacharya
2011-05-01
Full Text Available The present paper proposes a departmental store automation system based on Radio Frequency Identification (RFID technology and Particle Swarm Optimization (PSO algorithm. The items in the departmental store spanned over different sections and in multiple floors, are tagged with passive RFID tags. The floor is divided into number of zones depending on different types of items that are placed in their respective racks. Each of the zones is placed with one RFID reader, which constantly monitors the items in their zone and periodically sends that information to the application. The problem of systematic periodic monitoring of the store is addressed in this application so that the locations, distributions and demands of every item in the store can be invigilated with intelligence. The proposed application is successfully demonstrated on a simulated case study.
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Fereydoun Naghibi
2016-12-01
Full Text Available This paper presents an advanced method in urban growth modeling to discover transition rules of cellular automata (CA using the artificial bee colony (ABC optimization algorithm. Also, comparisons between the simulation results of CA models optimized by the ABC algorithm and the particle swarm optimization algorithms (PSO as intelligent approaches were performed to evaluate the potential of the proposed methods. According to previous studies, swarm intelligence algorithms for solving optimization problems such as discovering transition rules of CA in land use change/urban growth modeling can produce reasonable results. Modeling of urban growth as a dynamic process is not straightforward because of the existence of nonlinearity and heterogeneity among effective involved variables which can cause a number of challenges for traditional CA. ABC algorithm, the new powerful swarm based optimization algorithms, can be used to capture optimized transition rules of CA. This paper has proposed a methodology based on remote sensing data for modeling urban growth with CA calibrated by the ABC algorithm. The performance of ABC-CA, PSO-CA, and CA-logistic models in land use change detection is tested for the city of Urmia, Iran, between 2004 and 2014. Validations of the models based on statistical measures such as overall accuracy, figure of merit, and total operating characteristic were made. We showed that the overall accuracy of the ABC-CA model was 89%, which was 1.5% and 6.2% higher than those of the PSO-CA and CA-logistic model, respectively. Moreover, the allocation disagreement (simulation error of the simulation results for the ABC-CA, PSO-CA, and CA-logistic models are 11%, 12.5%, and 17.2%, respectively. Finally, for all evaluation indices including running time, convergence capability, flexibility, statistical measurements, and the produced spatial patterns, the ABC-CA model performance showed relative improvement and therefore its superiority was
Pitch Based Wind Turbine Intelligent Speed Setpoint Adjustment Algorithms
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Asier González-González
2014-06-01
Full Text Available This work is aimed at optimizing the wind turbine rotor speed setpoint algorithm. Several intelligent adjustment strategies have been investigated in order to improve a reward function that takes into account the power captured from the wind and the turbine speed error. After different approaches including Reinforcement Learning, the best results were obtained using a Particle Swarm Optimization (PSO-based wind turbine speed setpoint algorithm. A reward improvement of up to 10.67% has been achieved using PSO compared to a constant approach and 0.48% compared to a conventional approach. We conclude that the pitch angle is the most adequate input variable for the turbine speed setpoint algorithm compared to others such as rotor speed, or rotor angular acceleration.
Mandal, Sudip; Saha, Goutam; Pal, Rajat Kumar
2017-08-01
Correct inference of genetic regulations inside a cell from the biological database like time series microarray data is one of the greatest challenges in post genomic era for biologists and researchers. Recurrent Neural Network (RNN) is one of the most popular and simple approach to model the dynamics as well as to infer correct dependencies among genes. Inspired by the behavior of social elephants, we propose a new metaheuristic namely Elephant Swarm Water Search Algorithm (ESWSA) to infer Gene Regulatory Network (GRN). This algorithm is mainly based on the water search strategy of intelligent and social elephants during drought, utilizing the different types of communication techniques. Initially, the algorithm is tested against benchmark small and medium scale artificial genetic networks without and with presence of different noise levels and the efficiency was observed in term of parametric error, minimum fitness value, execution time, accuracy of prediction of true regulation, etc. Next, the proposed algorithm is tested against the real time gene expression data of Escherichia Coli SOS Network and results were also compared with others state of the art optimization methods. The experimental results suggest that ESWSA is very efficient for GRN inference problem and performs better than other methods in many ways.
Gold rush - A swarm dynamics in games
Zelinka, Ivan; Bukacek, Michal
2017-07-01
This paper is focused on swarm intelligence techniques and its practical use in computer games. The aim is to show how a swarm dynamics can be generated by multiplayer game, then recorded, analyzed and eventually controlled. In this paper we also discuss possibility to use swarm intelligence instead of game players. Based on our previous experiments two games, using swarm algorithms are mentioned briefly here. The first one is strategy game StarCraft: Brood War, and TicTacToe in which SOMA algorithm has also take a role of player against human player. Open research reported here has shown potential benefit of swarm computation in the field of strategy games and players strategy based on swarm behavior record and analysis. We propose new game called Gold Rush as an experimental environment for human or artificial swarm behavior and consequent analysis.
Intelligent perturbation algorithms for space scheduling optimization
Kurtzman, Clifford R.
1991-01-01
Intelligent perturbation algorithms for space scheduling optimization are presented in the form of the viewgraphs. The following subject areas are covered: optimization of planning, scheduling, and manifesting; searching a discrete configuration space; heuristic algorithms used for optimization; use of heuristic methods on a sample scheduling problem; intelligent perturbation algorithms are iterative refinement techniques; properties of a good iterative search operator; dispatching examples of intelligent perturbation algorithm and perturbation operator attributes; scheduling implementations using intelligent perturbation algorithms; major advances in scheduling capabilities; the prototype ISF (industrial Space Facility) experiment scheduler; optimized schedule (max revenue); multi-variable optimization; Space Station design reference mission scheduling; ISF-TDRSS command scheduling demonstration; and example task - communications check.
Ervin, Katherine; Shipman, Steven
2017-06-01
While rotational spectra can be rapidly collected, their analysis (especially for complex systems) is seldom straightforward, leading to a bottleneck. The AUTOFIT program was designed to serve that need by quickly matching rotational constants to spectra with little user input and supervision. This program can potentially be improved by incorporating an optimization algorithm in the search for a solution. The Particle Swarm Optimization Algorithm (PSO) was chosen for implementation. PSO is part of a family of optimization algorithms called heuristic algorithms, which seek approximate best answers. This is ideal for rotational spectra, where an exact match will not be found without incorporating distortion constants, etc., which would otherwise greatly increase the size of the search space. PSO was tested for robustness against five standard fitness functions and then applied to a custom fitness function created for rotational spectra. This talk will explain the Particle Swarm Optimization algorithm and how it works, describe how Autofit was modified to use PSO, discuss the fitness function developed to work with spectroscopic data, and show our current results. Seifert, N.A., Finneran, I.A., Perez, C., Zaleski, D.P., Neill, J.L., Steber, A.L., Suenram, R.D., Lesarri, A., Shipman, S.T., Pate, B.H., J. Mol. Spec. 312, 13-21 (2015)
Research on Multiple Particle Swarm Algorithm Based on Analysis of Scientific Materials
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Zhao Hongwei
2017-01-01
Full Text Available This paper proposed an improved particle swarm optimization algorithm based on analysis of scientific materials. The core thesis of MPSO (Multiple Particle Swarm Algorithm is to improve the single population PSO to interactive multi-swarms, which is used to settle the problem of being trapped into local minima during later iterations because it is lack of diversity. The simulation results show that the convergence rate is fast and the search performance is good, and it has achieved very good results.
Using swarm intelligence to boost the root cause analysis process and enhance patient safety.
2016-03-01
In an effort to strengthen patient safety, leadership at the University of Kentucky HealthCare (UKHC) decided to replace its traditional approach to root cause analysis (RCA) with a process based on swarm intelligence, a concept borrowed from other industries. Under this process, when a problem or error is identified, staff quickly hold a swarm--a meeting in which all those involved in the incident or problem quickly evaluate why the issue occurred and identify potential solutions for implementation. A pillar of the swarm concept is a mandate that there be no punishments or finger-pointing during the swarms. The idea is to encourage staff to be forthcoming to achieve effective solutions. Typically, swarms last for one hour and result in action plans designed to correct problems or deficiencies within a specific period of time. The ED was one of the first areas where UKHC applied swarms. For example, hospital administrators note that the approach has been used to address issues involving patient flow, triage protocols, assessments, overcrowding, and boarding. After seven years, incident reporting at UKHC has increased by 52%, and the health system has experienced a 37% decrease in the observed-to-expected mortality ratio.
Learning Intelligent Genetic Algorithms Using Japanese Nonograms
Tsai, Jinn-Tsong; Chou, Ping-Yi; Fang, Jia-Cen
2012-01-01
An intelligent genetic algorithm (IGA) is proposed to solve Japanese nonograms and is used as a method in a university course to learn evolutionary algorithms. The IGA combines the global exploration capabilities of a canonical genetic algorithm (CGA) with effective condensed encoding, improved fitness function, and modified crossover and…
Parallel particle swarm optimization algorithm in nuclear problems
Energy Technology Data Exchange (ETDEWEB)
Waintraub, Marcel; Pereira, Claudio M.N.A. [Instituto de Engenharia Nuclear (IEN/CNEN-RJ), Rio de Janeiro, RJ (Brazil)], e-mail: marcel@ien.gov.br, e-mail: cmnap@ien.gov.br; Schirru, Roberto [Coordenacao dos Programas de Pos-graduacao de Engenharia (COPPE/UFRJ), Rio de Janeiro, RJ (Brazil). Lab. de Monitoracao de Processos], e-mail: schirru@lmp.ufrj.br
2009-07-01
Particle Swarm Optimization (PSO) is a population-based metaheuristic (PBM), in which solution candidates evolve through simulation of a simplified social adaptation model. Putting together robustness, efficiency and simplicity, PSO has gained great popularity. Many successful applications of PSO are reported, in which PSO demonstrated to have advantages over other well-established PBM. However, computational costs are still a great constraint for PSO, as well as for all other PBMs, especially in optimization problems with time consuming objective functions. To overcome such difficulty, parallel computation has been used. The default advantage of parallel PSO (PPSO) is the reduction of computational time. Master-slave approaches, exploring this characteristic are the most investigated. However, much more should be expected. It is known that PSO may be improved by more elaborated neighborhood topologies. Hence, in this work, we develop several different PPSO algorithms exploring the advantages of enhanced neighborhood topologies implemented by communication strategies in multiprocessor architectures. The proposed PPSOs have been applied to two complex and time consuming nuclear engineering problems: reactor core design and fuel reload optimization. After exhaustive experiments, it has been concluded that: PPSO still improves solutions after many thousands of iterations, making prohibitive the efficient use of serial (non-parallel) PSO in such kind of realworld problems; and PPSO with more elaborated communication strategies demonstrated to be more efficient and robust than the master-slave model. Advantages and peculiarities of each model are carefully discussed in this work. (author)
Pebble bed reactor fuel cycle optimization using particle swarm algorithm
Energy Technology Data Exchange (ETDEWEB)
Tavron, Barak, E-mail: btavron@bgu.ac.il [Planning, Development and Technology Division, Israel Electric Corporation Ltd., P.O. Box 10, Haifa 31000 (Israel); Shwageraus, Eugene, E-mail: es607@cam.ac.uk [Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ (United Kingdom)
2016-10-15
Highlights: • Particle swarm method has been developed for fuel cycle optimization of PBR reactor. • Results show uranium utilization low sensitivity to fuel and core design parameters. • Multi-zone fuel loading pattern leads to a small improvement in uranium utilization. • Thorium mixes with highly enriched uranium yields the best uranium utilization. - Abstract: Pebble bed reactors (PBR) features, such as robust thermo-mechanical fuel design and on-line continuous fueling, facilitate wide range of fuel cycle alternatives. A range off fuel pebble types, containing different amounts of fertile or fissile fuel material, may be loaded into the reactor core. Several fuel loading zones may be used since radial mixing of the pebbles was shown to be limited. This radial separation suggests the possibility to implement the “seed-blanket” concept for the utilization of fertile fuels such as thorium, and for enhancing reactor fuel utilization. In this study, the particle-swarm meta-heuristic evolutionary optimization method (PSO) has been used to find optimal fuel cycle design which yields the highest natural uranium utilization. The PSO method is known for solving efficiently complex problems with non-linear objective function, continuous or discrete parameters and complex constrains. The VSOP system of codes has been used for PBR fuel utilization calculations and MATLAB script has been used to implement the PSO algorithm. Optimization of PBR natural uranium utilization (NUU) has been carried out for 3000 MWth High Temperature Reactor design (HTR) operating on the Once Trough Then Out (OTTO) fuel management scheme, and for 400 MWth Pebble Bed Modular Reactor (PBMR) operating on the multi-pass (MEDUL) fuel management scheme. Results showed only a modest improvement in the NUU (<5%) over reference designs. Investigation of thorium fuel cases showed that the use of HEU in combination with thorium results in the most favorable reactor performance in terms of
Autonomous intelligent vehicles theory, algorithms, and implementation
Cheng, Hong
2011-01-01
Here is the latest on intelligent vehicles, covering object and obstacle detection and recognition and vehicle motion control. Includes a navigation approach using global views; introduces algorithms for lateral and longitudinal motion control and more.
Directory of Open Access Journals (Sweden)
Qi Hu
2013-04-01
Full Text Available State-of-the-art heuristic algorithms to solve the vehicle routing problem with time windows (VRPTW usually present slow speeds during the early iterations and easily fall into local optimal solutions. Focusing on solving the above problems, this paper analyzes the particle encoding and decoding strategy of the particle swarm optimization algorithm, the construction of the vehicle route and the judgment of the local optimal solution. Based on these, a hybrid chaos-particle swarm optimization algorithm (HPSO is proposed to solve VRPTW. The chaos algorithm is employed to re-initialize the particle swarm. An efficient insertion heuristic algorithm is also proposed to build the valid vehicle route in the particle decoding process. A particle swarm premature convergence judgment mechanism is formulated and combined with the chaos algorithm and Gaussian mutation into HPSO when the particle swarm falls into the local convergence. Extensive experiments are carried out to test the parameter settings in the insertion heuristic algorithm and to evaluate that they are corresponding to the data’s real-distribution in the concrete problem. It is also revealed that the HPSO achieves a better performance than the other state-of-the-art algorithms on solving VRPTW.
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Jie Li
2014-01-01
Full Text Available Swarm intelligence is widely used in the application of communication networks. In this paper we adopt a biologically inspired strategy to investigate the data dissemination problem in the opportunistic cognitive networks (OCNs. We model the system as a centralized and distributed hybrid system including a location prediction server and a pervasive environment deploying the large-scale human-centric devices. To exploit such environment, data gathering and dissemination are fundamentally based on the contact opportunities. To tackle the lack of contemporaneous end-to-end connectivity in opportunistic networks, we apply ant colony optimization as a cognitive heuristic technology to formulate a self-adaptive dissemination-based routing scheme in opportunistic cognitive networks. This routing strategy has attempted to find the most appropriate nodes conveying messages to the destination node based on the location prediction information and intimacy between nodes, which uses the online unsupervised learning on geographical locations and the biologically inspired algorithm on the relationship of nodes to estimate the delivery probability. Extensive simulation is carried out on the real-world traces to evaluate the accuracy of the location prediction and the proposed scheme in terms of transmission cost, delivery ratio, average hops, and delivery latency, which achieves better routing performances compared to the typical routing schemes in OCNs.
Directory of Open Access Journals (Sweden)
Imran Rahman
2015-01-01
Full Text Available Recent researches towards the use of green technologies to reduce pollution and higher penetration of renewable energy sources in the transportation sector have been gaining popularity. In this wake, extensive participation of plug-in hybrid electric vehicles (PHEVs requires adequate charging allocation strategy using a combination of smart grid systems and smart charging infrastructures. Daytime charging stations will be needed for daily usage of PHEVs due to the limited all-electric range. Intelligent energy management is an important issue which has already drawn much attention of researchers. Most of these works require formulation of mathematical models with extensive use of computational intelligence-based optimization techniques to solve many technical problems. In this paper, gravitational search algorithm (GSA has been applied and compared with another member of swarm family, particle swarm optimization (PSO, considering constraints such as energy price, remaining battery capacity, and remaining charging time. Simulation results obtained for maximizing the highly nonlinear objective function evaluate the performance of both techniques in terms of best fitness.
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Xiaomin Xu
2015-01-01
Full Text Available Not only can the icing coat on transmission line cause the electrical fault of gap discharge and icing flashover but also it will lead to the mechanical failure of tower, conductor, insulators, and others. It will bring great harm to the people’s daily life and work. Thus, accurate prediction of ice thickness has important significance for power department to control the ice disaster effectively. Based on the analysis of standard support vector machine, this paper presents a weighted support vector machine regression model based on the similarity (WSVR. According to the different importance of samples, this paper introduces the weighted support vector machine and optimizes its parameters by hybrid swarm intelligence optimization algorithm with the particle swarm and ant colony (PSO-ACO, which improves the generalization ability of the model. In the case study, the actual data of ice thickness and climate in a certain area of Hunan province have been used to predict the icing thickness of the area, which verifies the validity and applicability of this proposed method. The predicted results show that the intelligent model proposed in this paper has higher precision and stronger generalization ability.
Applying Sequential Particle Swarm Optimization Algorithm to Improve Power Generation Quality
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Abdulhafid Sallama
2014-10-01
Full Text Available Swarm Optimization approach is a heuristic search method whose mechanics are inspired by the swarming or collaborative behaviour of biological populations. It is used to solve constrained, unconstrained, continuous and discrete problems. Swarm intelligence systems are widely used and very effective in solving standard and large-scale optimization, provided that the problem does not require multi solutions. In this paper, particle swarm optimisation technique is used to optimise fuzzy logic controller (FLC for stabilising a power generation and distribution network that consists of four generators. The system is subject to different types of faults (single and multi-phase. Simulation studies show that the optimised FLC performs well in stabilising the network after it recovers from a fault. The controller is compared to multi-band and standard controllers.
Scheduling of multi load AGVs in FMS by modified memetic particle swarm optimization algorithm
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V.K. Chawla
2018-01-01
Full Text Available Use of Automated guided vehicles (AGVs is highly significant in Flexible Manufacturing Sys-tem (FMS in which material handling in form of jobs is performed from one work center to an-other work center. A multifold increase in through put of FMS can be observed by application of multi load AGVs. In this paper, Particle Swarm Optimization (PSO integrated with Memetic Algorithm (MA named as Modified Memetic Particle Swarm Optimization Algorithm (MMP-SO is applied to yield initial feasible solutions for scheduling of multi load AGVs for minimum travel and waiting time in the FMS. The proposed MMPSO algorithm exhibits balanced explora-tion and exploitation for global search method of standard Particle Swarm Optimization (PSO algorithm and local search method of Memetic Algorithm (MA which further results into yield of efficient and effective initial feasible solutions for the multi load AGVs scheduling problem.
A Constructive Data Classification Version of the Particle Swarm Optimization Algorithm
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Alexandre Szabo
2013-01-01
Full Text Available The particle swarm optimization algorithm was originally introduced to solve continuous parameter optimization problems. It was soon modified to solve other types of optimization tasks and also to be applied to data analysis. In the latter case, however, there are few works in the literature that deal with the problem of dynamically building the architecture of the system. This paper introduces new particle swarm algorithms specifically designed to solve classification problems. The first proposal, named Particle Swarm Classifier (PSClass, is a derivation of a particle swarm clustering algorithm and its architecture, as in most classifiers, is pre-defined. The second proposal, named Constructive Particle Swarm Classifier (cPSClass, uses ideas from the immune system to automatically build the swarm. A sensitivity analysis of the growing procedure of cPSClass and an investigation into a proposed pruning procedure for this algorithm are performed. The proposals were applied to a wide range of databases from the literature and the results show that they are competitive in relation to other approaches, with the advantage of having a dynamically constructed architecture.
Knowledge Management and Problem Solving in Real Time: The Role of Swarm Intelligence
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Chris W Callaghan
2016-06-01
Full Text Available Knowledge management research applied to the development of real-time research capability, or capability to solve societal problems in hours and days instead of years and decades, is perhaps increasingly important, given persistent global problems such as the Zika virus and rapidly developing antibiotic resistance. Drawing on swarm intelligence theory, this paper presents an approach to real-time research problem-solving in the form of a framework for understanding the complexity of real-time research and the challenges associated with maximizing collaboration. The objective of this research is to make explicit certain theoretical, methodological, and practical implications deriving from new literature on emerging technologies and new forms of problem solving and to offer a model of real-time problem solving based on a synthesis of the literature. Drawing from ant colony, bee colony, and particle swarm optimization, as well as other population-based metaheuristics, swarm intelligence principles are derived in support of improved effectiveness and efficiency for multidisciplinary human swarm problem-solving. This synthesis seeks to offer useful insights into the research process, by offering a perspective of what maximized collaboration, as a system, implies for real-time problem solving.
Neighbor Selection in Peer-to-Peer Overlay Networks: A Swarm Intelligence Approach
Liu, Hongbo; Abraham, Ajith; Badr, Youakim
Peer-to-peer (P2P) topology has a significant influence on the performance, search efficiency and functionality, and scalability of the application. In this chapter, we investigate a multi-swarm approach to the problem of neighbor selection in P2P networks. Particle swarm share some common characteristics with P2P in the dynamic socially environment. Each particle encodes the upper half of the peer-connection matrix through the undirected graph, which reduces the search space dimension. The portion of the adjustment to the velocity influenced by the individual’s cognition, the group cognition from multi-swarms, and the social cognition from the whole swarm, makes an important influence on the particles’ ergodic and synergetic performance. We also attempt to theoretically prove that the multi-swarm optimization algorithm converges with a probability of 1 towards the global optima. The performance of our approach is evaluated and compared with other two different algorithms. The results indicate that it usually required shorter time to obtain better results than the other considered methods, specially for large scale problems.
Combined Intelligent Control (CIC an Intelligent Decision Making Algorithm
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Moteaal Asadi Shirzi
2007-03-01
Full Text Available The focus of this research is to introduce the concept of combined intelligent control (CIC as an effective architecture for decision-making and control of intelligent agents and multi-robot sets. Basically, the CIC is a combination of various architectures and methods from fields such as artificial intelligence, Distributed Artificial Intelligence (DAI, control and biological computing. Although any intelligent architecture may be very effective for some specific applications, it could be less for others. Therefore, CIC combines and arranges them in a way that the strengths of any approach cover the weaknesses of others. In this paper first, we introduce some intelligent architectures from a new aspect. Afterward, we offer the CIC by combining them. CIC has been executed in a multi-agent set. In this set, robots must cooperate to perform some various tasks in a complex and nondeterministic environment with a low sensory feedback and relationship. In order to investigate, improve, and correct the combined intelligent control method, simulation software has been designed which will be presented and considered. To show the ability of the CIC algorithm as a distributed architecture, a central algorithm is designed and compared with the CIC.
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Hossein Soleymani
2017-08-01
Full Text Available Interconnection of the power system utilities and grids offers a formidable dispute in front of design engineers. With the interconnections, power system has emerged as a more intricate and nonlinear system. Recent years small signal stability problems have achieved much significance along with the conventional transient constancy problems. Transient stability of the power system can be attained with high gain and fast acting Automatic Voltage Regulators (AVRs. Yet, AVRs establish negative damping in the system. Propagation of small signals is hazardous for system’s health and offers a potential threat to system’s oscillatory stability. These small signals have magnitude of 0.2 to 2 Hz. The professional control tactic to develop system damping is Power System Stabilizer (PSS.This paper presents application of swarm intelligence for PSS parameter estimation issue on standard IEEE 10 Generator 39 Bus power network (New England. Realization of the objective function is done with the help of interpolation investigation using MATLAB. The system performance is compared with the conventional optimization algorithms like Genetic Algorithm (GA and Particle Swarm Optimization (PSO based PSS controller. The strength of proposed controller is tested by examining various operating conditions. An Eigen property analysis is done on this system i.e. before installing PSS, and after the employment of GA and PSO tuned PSSs. A significant comparison is carried out with GA and PSO on the basis of convergence uniqueness and dynamic response of speed deviation curves of various generators.
Application of particle swarm optimization algorithm in the heating system planning problem.
Ma, Rong-Jiang; Yu, Nan-Yang; Hu, Jun-Yi
2013-01-01
Based on the life cycle cost (LCC) approach, this paper presents an integral mathematical model and particle swarm optimization (PSO) algorithm for the heating system planning (HSP) problem. The proposed mathematical model minimizes the cost of heating system as the objective for a given life cycle time. For the particularity of HSP problem, the general particle swarm optimization algorithm was improved. An actual case study was calculated to check its feasibility in practical use. The results show that the improved particle swarm optimization (IPSO) algorithm can more preferably solve the HSP problem than PSO algorithm. Moreover, the results also present the potential to provide useful information when making decisions in the practical planning process. Therefore, it is believed that if this approach is applied correctly and in combination with other elements, it can become a powerful and effective optimization tool for HSP problem.
An Improved Particle Swarm Optimization Algorithm and Its Application in the Community Division
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Jiang Hao
2016-01-01
Full Text Available With the deepening of the research on complex networks, the method of detecting and classifying social network is springing up. In this essay, the basic particle swarm algorithm is improved based on the GN algorithm. Modularity is taken as a measure of community division [1]. In view of the dynamic network community division, scrolling calculation method is put forward. Experiments show that using the improved particle swarm optimization algorithm can improve the accuracy of the community division and can also get higher value of the modularity in the dynamic community
DEFF Research Database (Denmark)
Vesterstrøm, Jacob Svaneborg; Thomsen, Rene
2004-01-01
Several extensions to evolutionary algorithms (EAs) and particle swarm optimization (PSO) have been suggested during the last decades offering improved performance on selected benchmark problems. Recently, another search heuristic termed differential evolution (DE) has shown superior performance...... outperforms the other algorithms. However, on two noisy functions, both DE and PSO were outperformed by the EA....
Ioannou, Christos C
2017-08-01
Larger groups often have a greater ability to solve cognitive tasks compared to smaller ones or lone individuals. This is well established in social insects, navigating flocks of birds, and in groups of prey collectively vigilant for predators. Research in social insects has convincingly shown that improved cognitive performance can arise from self-organised local interactions between individuals that integrates their contributions, often referred to as swarm intelligence. This emergent collective intelligence has gained in popularity and been directly applied to groups of other animals, including fish. Despite being a likely mechanism at least partially explaining group performance in vertebrates, I argue here that other possible explanations are rarely ruled out in empirical studies. Hence, evidence for self-organised collective (or 'swarm') intelligence in fish is not as strong as it would first appear. These other explanations, the 'pool-of-competence' and the greater cognitive ability of individuals when in larger groups, are also reviewed. Also discussed is why improved group performance in general may be less often observed in animals such as shoaling fish compared to social insects. This review intends to highlight the difficulties in exploring collective intelligence in animal groups, ideally leading to further empirical work to illuminate these issues. Copyright © 2016 The Author. Published by Elsevier B.V. All rights reserved.
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Weitian Lin
2014-01-01
Full Text Available Particle swarm optimization algorithm (PSOA is an advantage optimization tool. However, it has a tendency to get stuck in a near optimal solution especially for middle and large size problems and it is difficult to improve solution accuracy by fine-tuning parameters. According to the insufficiency, this paper researches the local and global search combine particle swarm algorithm (LGSCPSOA, and its convergence and obtains its convergence qualification. At the same time, it is tested with a set of 8 benchmark continuous functions and compared their optimization results with original particle swarm algorithm (OPSOA. Experimental results indicate that the LGSCPSOA improves the search performance especially on the middle and large size benchmark functions significantly.
Cao, Jianfang; Cui, Hongyan; Shi, Hao; Jiao, Lijuan
2016-01-01
A back-propagation (BP) neural network can solve complicated random nonlinear mapping problems; therefore, it can be applied to a wide range of problems. However, as the sample size increases, the time required to train BP neural networks becomes lengthy. Moreover, the classification accuracy decreases as well. To improve the classification accuracy and runtime efficiency of the BP neural network algorithm, we proposed a parallel design and realization method for a particle swarm optimization (PSO)-optimized BP neural network based on MapReduce on the Hadoop platform using both the PSO algorithm and a parallel design. The PSO algorithm was used to optimize the BP neural network's initial weights and thresholds and improve the accuracy of the classification algorithm. The MapReduce parallel programming model was utilized to achieve parallel processing of the BP algorithm, thereby solving the problems of hardware and communication overhead when the BP neural network addresses big data. Datasets on 5 different scales were constructed using the scene image library from the SUN Database. The classification accuracy of the parallel PSO-BP neural network algorithm is approximately 92%, and the system efficiency is approximately 0.85, which presents obvious advantages when processing big data. The algorithm proposed in this study demonstrated both higher classification accuracy and improved time efficiency, which represents a significant improvement obtained from applying parallel processing to an intelligent algorithm on big data.
Generating a Multiphase Equation of State with Swarm Intelligence
Cox, Geoffrey
2017-06-01
Hydrocode calculations require knowledge of the variation of pressure of a material with density and temperature, which is given by the equation of state. An accurate model needs to account for discontinuities in energy, density and properties of a material across a phase boundary. When generating a multiphase equation of state the modeller attempts to balance the agreement between the available data for compression, expansion and phase boundary location. However, this can prove difficult because minor adjustments in the equation of state for a single phase can have a large impact on the overall phase diagram. Recently, Cox and Christie described a method for combining statistical-mechanics-based condensed matter physics models with a stochastic analysis technique called particle swarm optimisation. The models produced show good agreement with experiment over a wide range of pressure-temperature space. This talk details the general implementation of this technique, shows example results, and describes the types of analysis that can be performed with this method.
Proportional–Integral–Derivative (PID Controller Tuning using Particle Swarm Optimization Algorithm
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J. S. Bassi
2012-08-01
Full Text Available The proportional-integral-derivative (PID controllers are the most popular controllers used in industry because of their remarkable effectiveness, simplicity of implementation and broad applicability. However, manual tuning of these controllers is time consuming, tedious and generally lead to poor performance. This tuning which is application specific also deteriorates with time as a result of plant parameter changes. This paper presents an artificial intelligence (AI method of particle swarm optimization (PSO algorithm for tuning the optimal proportional-integral derivative (PID controller parameters for industrial processes. This approach has superior features, including easy implementation, stable convergence characteristic and good computational efficiency over the conventional methods. Ziegler- Nichols, tuning method was applied in the PID tuning and results were compared with the PSO-Based PID for optimum control. Simulation results are presented to show that the PSO-Based optimized PID controller is capable of providing an improved closed-loop performance over the Ziegler- Nichols tuned PID controller Parameters. Compared to the heuristic PID tuning method of Ziegler-Nichols, the proposed method was more efficient in improving the step response characteristics such as, reducing the steady-states error; rise time, settling time and maximum overshoot in speed control of DC motor.
Hou, Rui; Yu, Junle
2011-12-01
Optical burst switching (OBS) has been regarded as the next generation optical switching technology. In this paper, the routing problem based on particle swarm optimization (PSO) algorithm in OBS has been studies and analyzed. Simulation results indicate that, the PSO based routing algorithm will optimal than the conversional shortest path first algorithm in space cost and calculation cost. Conclusions have certain theoretical significances for the improvement of OBS routing protocols.
Swarm Intelligence-Enhanced Detection of Non-Small-Cell Lung Cancer Using Tumor-Educated Platelets.
Best, Myron G; Sol, Nik; In 't Veld, Sjors G J G; Vancura, Adrienne; Muller, Mirte; Niemeijer, Anna-Larissa N; Fejes, Aniko V; Tjon Kon Fat, Lee-Ann; Huis In 't Veld, Anna E; Leurs, Cyra; Le Large, Tessa Y; Meijer, Laura L; Kooi, Irsan E; Rustenburg, François; Schellen, Pepijn; Verschueren, Heleen; Post, Edward; Wedekind, Laurine E; Bracht, Jillian; Esenkbrink, Michelle; Wils, Leon; Favaro, Francesca; Schoonhoven, Jilian D; Tannous, Jihane; Meijers-Heijboer, Hanne; Kazemier, Geert; Giovannetti, Elisa; Reijneveld, Jaap C; Idema, Sander; Killestein, Joep; Heger, Michal; de Jager, Saskia C; Urbanus, Rolf T; Hoefer, Imo E; Pasterkamp, Gerard; Mannhalter, Christine; Gomez-Arroyo, Jose; Bogaard, Harm-Jan; Noske, David P; Vandertop, W Peter; van den Broek, Daan; Ylstra, Bauke; Nilsson, R Jonas A; Wesseling, Pieter; Karachaliou, Niki; Rosell, Rafael; Lee-Lewandrowski, Elizabeth; Lewandrowski, Kent B; Tannous, Bakhos A; de Langen, Adrianus J; Smit, Egbert F; van den Heuvel, Michel M; Wurdinger, Thomas
2017-08-14
Blood-based liquid biopsies, including tumor-educated blood platelets (TEPs), have emerged as promising biomarker sources for non-invasive detection of cancer. Here we demonstrate that particle-swarm optimization (PSO)-enhanced algorithms enable efficient selection of RNA biomarker panels from platelet RNA-sequencing libraries (n = 779). This resulted in accurate TEP-based detection of early- and late-stage non-small-cell lung cancer (n = 518 late-stage validation cohort, accuracy, 88%; AUC, 0.94; 95% CI, 0.92-0.96; p < 0.001; n = 106 early-stage validation cohort, accuracy, 81%; AUC, 0.89; 95% CI, 0.83-0.95; p < 0.001), independent of age of the individuals, smoking habits, whole-blood storage time, and various inflammatory conditions. PSO enabled selection of gene panels to diagnose cancer from TEPs, suggesting that swarm intelligence may also benefit the optimization of diagnostics readout of other liquid biopsy biosources. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.
Algorithms and architectures of artificial intelligence
Tyugu, E
2007-01-01
This book gives an overview of methods developed in artificial intelligence for search, learning, problem solving and decision-making. It gives an overview of algorithms and architectures of artificial intelligence that have reached the degree of maturity when a method can be presented as an algorithm, or when a well-defined architecture is known, e.g. in neural nets and intelligent agents. It can be used as a handbook for a wide audience of application developers who are interested in using artificial intelligence methods in their software products. Parts of the text are rather independent, so that one can look into the index and go directly to a description of a method presented in the form of an abstract algorithm or an architectural solution. The book can be used also as a textbook for a course in applied artificial intelligence. Exercises on the subject are added at the end of each chapter. Neither programming skills nor specific knowledge in computer science are expected from the reader. However, some p...
Yang, Zhen-Lun; Wu, Angus; Min, Hua-Qing
2015-01-01
An improved quantum-behaved particle swarm optimization with elitist breeding (EB-QPSO) for unconstrained optimization is presented and empirically studied in this paper. In EB-QPSO, the novel elitist breeding strategy acts on the elitists of the swarm to escape from the likely local optima and guide the swarm to perform more efficient search. During the iterative optimization process of EB-QPSO, when criteria met, the personal best of each particle and the global best of the swarm are used to generate new diverse individuals through the transposon operators. The new generated individuals with better fitness are selected to be the new personal best particles and global best particle to guide the swarm for further solution exploration. A comprehensive simulation study is conducted on a set of twelve benchmark functions. Compared with five state-of-the-art quantum-behaved particle swarm optimization algorithms, the proposed EB-QPSO performs more competitively in all of the benchmark functions in terms of better global search capability and faster convergence rate.
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Zhen-Lun Yang
2015-01-01
Full Text Available An improved quantum-behaved particle swarm optimization with elitist breeding (EB-QPSO for unconstrained optimization is presented and empirically studied in this paper. In EB-QPSO, the novel elitist breeding strategy acts on the elitists of the swarm to escape from the likely local optima and guide the swarm to perform more efficient search. During the iterative optimization process of EB-QPSO, when criteria met, the personal best of each particle and the global best of the swarm are used to generate new diverse individuals through the transposon operators. The new generated individuals with better fitness are selected to be the new personal best particles and global best particle to guide the swarm for further solution exploration. A comprehensive simulation study is conducted on a set of twelve benchmark functions. Compared with five state-of-the-art quantum-behaved particle swarm optimization algorithms, the proposed EB-QPSO performs more competitively in all of the benchmark functions in terms of better global search capability and faster convergence rate.
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Hao Yin
2014-01-01
Full Text Available For SLA-aware service composition problem (SSC, an optimization model for this algorithm is built, and a hybrid multiobjective discrete particle swarm optimization algorithm (HMDPSO is also proposed in this paper. According to the characteristic of this problem, a particle updating strategy is designed by introducing crossover operator. In order to restrain particle swarm’s premature convergence and increase its global search capacity, the swarm diversity indicator is introduced and a particle mutation strategy is proposed to increase the swarm diversity. To accelerate the process of obtaining the feasible particle position, a local search strategy based on constraint domination is proposed and incorporated into the proposed algorithm. At last, some parameters in the algorithm HMDPSO are analyzed and set with relative proper values, and then the algorithm HMDPSO and the algorithm HMDPSO+ incorporated by local search strategy are compared with the recently proposed related algorithms on different scale cases. The results show that algorithm HMDPSO+ can solve the SSC problem more effectively.
Configurable intelligent optimization algorithm design and practice in manufacturing
Tao, Fei; Laili, Yuanjun
2014-01-01
Presenting the concept and design and implementation of configurable intelligent optimization algorithms in manufacturing systems, this book provides a new configuration method to optimize manufacturing processes. It provides a comprehensive elaboration of basic intelligent optimization algorithms, and demonstrates how their improvement, hybridization and parallelization can be applied to manufacturing. Furthermore, various applications of these intelligent optimization algorithms are exemplified in detail, chapter by chapter. The intelligent optimization algorithm is not just a single algorit
V.A.F. Dallagnol (V. A F); J.H. van den Berg (Jan); L. Mous (Lonneke)
2009-01-01
textabstractIn this paper, it is shown a comparison of the application of particle swarm optimization and genetic algorithms to portfolio management, in a constrained portfolio optimization problem where no short sales are allowed. The objective function to be minimized is the value at risk
Design of Wire Antennas by Using an Evolved Particle Swarm Optimization Algorithm
Lepelaars, E.S.A.M.; Zwamborn, A.P.M.; Rogovic, A.; Marasini, C.; Monorchio, A.
2007-01-01
A Particle Swarm Optimization (PSO) algorithm has been used in conjunction with a full-wave numerical code based on the Method of Moments (MoM) to design and optimize wire antennas. The PSO is a robust stochastic evolutionary numerical technique that is very effective in optimizing multidimensional
Competitive Swarm Optimizer Based Gateway Deployment Algorithm in Cyber-Physical Systems
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Shuqiang Huang
2017-01-01
Full Text Available Wireless sensor network topology optimization is a highly important issue, and topology control through node selection can improve the efficiency of data forwarding, while saving energy and prolonging lifetime of the network. To address the problem of connecting a wireless sensor network to the Internet in cyber-physical systems, here we propose a geometric gateway deployment based on a competitive swarm optimizer algorithm. The particle swarm optimization (PSO algorithm has a continuous search feature in the solution space, which makes it suitable for finding the geometric center of gateway deployment; however, its search mechanism is limited to the individual optimum (pbest and the population optimum (gbest; thus, it easily falls into local optima. In order to improve the particle search mechanism and enhance the search efficiency of the algorithm, we introduce a new competitive swarm optimizer (CSO algorithm. The CSO search algorithm is based on an inter-particle competition mechanism and can effectively avoid trapping of the population falling into a local optimum. With the improvement of an adaptive opposition-based search and its ability to dynamically parameter adjustments, this algorithm can maintain the diversity of the entire swarm to solve geometric K-center gateway deployment problems. The simulation results show that this CSO algorithm has a good global explorative ability as well as convergence speed and can improve the network quality of service (QoS level of cyber-physical systems by obtaining a minimum network coverage radius. We also find that the CSO algorithm is more stable, robust and effective in solving the problem of geometric gateway deployment as compared to the PSO or Kmedoids algorithms.
Competitive Swarm Optimizer Based Gateway Deployment Algorithm in Cyber-Physical Systems.
Huang, Shuqiang; Tao, Ming
2017-01-22
Wireless sensor network topology optimization is a highly important issue, and topology control through node selection can improve the efficiency of data forwarding, while saving energy and prolonging lifetime of the network. To address the problem of connecting a wireless sensor network to the Internet in cyber-physical systems, here we propose a geometric gateway deployment based on a competitive swarm optimizer algorithm. The particle swarm optimization (PSO) algorithm has a continuous search feature in the solution space, which makes it suitable for finding the geometric center of gateway deployment; however, its search mechanism is limited to the individual optimum (pbest) and the population optimum (gbest); thus, it easily falls into local optima. In order to improve the particle search mechanism and enhance the search efficiency of the algorithm, we introduce a new competitive swarm optimizer (CSO) algorithm. The CSO search algorithm is based on an inter-particle competition mechanism and can effectively avoid trapping of the population falling into a local optimum. With the improvement of an adaptive opposition-based search and its ability to dynamically parameter adjustments, this algorithm can maintain the diversity of the entire swarm to solve geometric K-center gateway deployment problems. The simulation results show that this CSO algorithm has a good global explorative ability as well as convergence speed and can improve the network quality of service (QoS) level of cyber-physical systems by obtaining a minimum network coverage radius. We also find that the CSO algorithm is more stable, robust and effective in solving the problem of geometric gateway deployment as compared to the PSO or Kmedoids algorithms.
Improved image quality of digital lithography using modified particle swarm optimization algorithm
Zhang, Liang; Shi, ZhaoJun; Li, Qishen
2017-02-01
Image distortion problem is key issue in DMD digital lithography system, in this paper, quality optimization algorithm of digital lithography based on improved particle swarm optimization algorithm is proposed. The fidelity is adopted as the fitness function. The pixels in the mask pattern are used as particles, and then optimization is implemented by updating the velocities and positions of these particles. Two different graphs are used to verify the method, image quality optimization of the standard particle swarm optimization algorithm and the steepest descent gradient descent algorithm, the pattern errors are reduced by 95.48%, 91.95% and 92.78%, 87.28%, respectively. The quality of image is improved, and the convergence speed is faster.
Optimization of wireless sensor networks based on chicken swarm optimization algorithm
Wang, Qingxi; Zhu, Lihua
2017-05-01
In order to reduce the energy consumption of wireless sensor network and improve the survival time of network, the clustering routing protocol of wireless sensor networks based on chicken swarm optimization algorithm was proposed. On the basis of LEACH agreement, it was improved and perfected that the points on the cluster and the selection of cluster head using the chicken group optimization algorithm, and update the location of chicken which fall into the local optimum by Levy flight, enhance population diversity, ensure the global search capability of the algorithm. The new protocol avoided the die of partial node of intensive using by making balanced use of the network nodes, improved the survival time of wireless sensor network. The simulation experiments proved that the protocol is better than LEACH protocol on energy consumption, also is better than that of clustering routing protocol based on particle swarm optimization algorithm.
A Parallel Adaptive Particle Swarm Optimization Algorithm for Economic/Environmental Power Dispatch
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Jinchao Li
2012-01-01
Full Text Available A parallel adaptive particle swarm optimization algorithm (PAPSO is proposed for economic/environmental power dispatch, which can overcome the premature characteristic, the slow-speed convergence in the late evolutionary phase, and lacking good direction in particles’ evolutionary process. A search population is randomly divided into several subpopulations. Then for each subpopulation, the optimal solution is searched synchronously using the proposed method, and thus parallel computing is realized. To avoid converging to a local optimum, a crossover operator is introduced to exchange the information among the subpopulations and the diversity of population is sustained simultaneously. Simulation results show that the proposed algorithm can effectively solve the economic/environmental operation problem of hydropower generating units. Performance comparisons show that the solution from the proposed method is better than those from the conventional particle swarm algorithm and other optimization algorithms.
Solution to Electric Power Dispatch Problem Using Fuzzy Particle Swarm Optimization Algorithm
Chaturvedi, D. K.; Kumar, S.
2015-03-01
This paper presents the application of fuzzy particle swarm optimization to constrained economic load dispatch (ELD) problem of thermal units. Several factors such as quadratic cost functions with valve point loading, ramp rate limits and prohibited operating zone are considered in the computation models. The Fuzzy particle swarm optimization (FPSO) provides a new mechanism to avoid premature convergence problem. The performance of proposed algorithm is evaluated on four test systems. Results obtained by proposed method have been compared with those obtained by PSO method and literature results. The experimental results show that proposed FPSO method is capable of obtaining minimum fuel costs in fewer numbers of iterations.
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Ćosić Aleksandar
2012-01-01
Full Text Available An approach to intelligent robot motion planning and tracking in known and static environments is presented in this paper. This complex problem is divided into several simpler problems. The first is generation of a collision free path from starting to destination point, which is solved using a particle swarm optimization (PSO algorithm. The second is interpolation of the obtained collision-free path, which is solved using a radial basis function neural network (RBFNN, and trajectory generation, based on the interpolated path. The last is a trajectory tracking problem, which is solved using a proportional-integral (PI controller. Due to uncertainties, obstacle avoidance is still not ensured, so an additional fuzzy controller is introduced, which corrects the control action of the PI controller. The proposed solution can be used even in dynamic environments, where obstacles change their position in time. Simulation studies were realized to validate and illustrate this approach.
Weerathunga, Thilina Shihan
2017-08-01
Gravitational waves are a fundamental prediction of Einstein's General Theory of Relativity. The first experimental proof of their existence was provided by the Nobel Prize winning discovery by Taylor and Hulse of orbital decay in a binary pulsar system. The first detection of gravitational waves incident on earth from an astrophysical source was announced in 2016 by the LIGO Scientific Collaboration, launching the new era of gravitational wave (GW) astronomy. The signal detected was from the merger of two black holes, which is an example of sources called Compact Binary Coalescences (CBCs). Data analysis strategies used in the search for CBC signals are derivatives of the Maximum-Likelihood (ML) method. The ML method applied to data from a network of geographically distributed GW detectors--called fully coherent network analysis--is currently the best approach for estimating source location and GW polarization waveforms. However, in the case of CBCs, especially for lower mass systems (O(1M solar masses)) such as double neutron star binaries, fully coherent network analysis is computationally expensive. The ML method requires locating the global maximum of the likelihood function over a nine dimensional parameter space, where the computation of the likelihood at each point requires correlations involving O(104) to O(106) samples between the data and the corresponding candidate signal waveform template. Approximations, such as semi-coherent coincidence searches, are currently used to circumvent the computational barrier but incur a concomitant loss in sensitivity. We explored the effectiveness of Particle Swarm Optimization (PSO), a well-known algorithm in the field of swarm intelligence, in addressing the fully coherent network analysis problem. As an example, we used a four-detector network consisting of the two LIGO detectors at Hanford and Livingston, Virgo and Kagra, all having initial LIGO noise power spectral densities, and show that PSO can locate the global
A hybrid search algorithm for swarm robots searching in an unknown environment.
Li, Shoutao; Li, Lina; Lee, Gordon; Zhang, Hao
2014-01-01
This paper proposes a novel method to improve the efficiency of a swarm of robots searching in an unknown environment. The approach focuses on the process of feeding and individual coordination characteristics inspired by the foraging behavior in nature. A predatory strategy was used for searching; hence, this hybrid approach integrated a random search technique with a dynamic particle swarm optimization (DPSO) search algorithm. If a search robot could not find any target information, it used a random search algorithm for a global search. If the robot found any target information in a region, the DPSO search algorithm was used for a local search. This particle swarm optimization search algorithm is dynamic as all the parameters in the algorithm are refreshed synchronously through a communication mechanism until the robots find the target position, after which, the robots fall back to a random searching mode. Thus, in this searching strategy, the robots alternated between two searching algorithms until the whole area was covered. During the searching process, the robots used a local communication mechanism to share map information and DPSO parameters to reduce the communication burden and overcome hardware limitations. If the search area is very large, search efficiency may be greatly reduced if only one robot searches an entire region given the limited resources available and time constraints. In this research we divided the entire search area into several subregions, selected a target utility function to determine which subregion should be initially searched and thereby reduced the residence time of the target to improve search efficiency.
A novel progressively swarmed mixed integer genetic algorithm for ...
African Journals Online (AJOL)
MIGA) which inherits the advantages of binary and real coded Genetic Algorithm approach. The proposed algorithm is applied for the conventional generation cost minimization Optimal Power Flow (OPF) problem and for the Security ...
Machining Parameters Optimization using Hybrid Firefly Algorithm and Particle Swarm Optimization
Farahlina Johari, Nur; Zain, Azlan Mohd; Haszlinna Mustaffa, Noorfa; Udin, Amirmudin
2017-09-01
Firefly Algorithm (FA) is a metaheuristic algorithm that is inspired by the flashing behavior of fireflies and the phenomenon of bioluminescent communication and the algorithm is used to optimize the machining parameters (feed rate, depth of cut, and spindle speed) in this research. The algorithm is hybridized with Particle Swarm Optimization (PSO) to discover better solution in exploring the search space. Objective function of previous research is used to optimize the machining parameters in turning operation. The optimal machining cutting parameters estimated by FA that lead to a minimum surface roughness are validated using ANOVA test.
Rayleigh wave dispersion curve inversion by using particle swarm optimization and genetic algorithm
Buyuk, Ersin; Zor, Ekrem; Karaman, Abdullah
2017-04-01
Inversion of surface wave dispersion curves with its highly nonlinear nature has some difficulties using traditional linear inverse methods due to the need and strong dependence to the initial model, possibility of trapping in local minima and evaluation of partial derivatives. There are some modern global optimization methods to overcome of these difficulties in surface wave analysis such as Genetic algorithm (GA) and Particle Swarm Optimization (PSO). GA is based on biologic evolution consisting reproduction, crossover and mutation operations, while PSO algorithm developed after GA is inspired from the social behaviour of birds or fish of swarms. Utility of these methods require plausible convergence rate, acceptable relative error and optimum computation cost that are important for modelling studies. Even though PSO and GA processes are similar in appearence, the cross-over operation in GA is not used in PSO and the mutation operation is a stochastic process for changing the genes within chromosomes in GA. Unlike GA, the particles in PSO algorithm changes their position with logical velocities according to particle's own experience and swarm's experience. In this study, we applied PSO algorithm to estimate S wave velocities and thicknesses of the layered earth model by using Rayleigh wave dispersion curve and also compared these results with GA and we emphasize on the advantage of using PSO algorithm for geophysical modelling studies considering its rapid convergence, low misfit error and computation cost.
A Comparison of Selected Modifications of the Particle Swarm Optimization Algorithm
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Michala Jakubcová
2014-01-01
Full Text Available We compare 27 modifications of the original particle swarm optimization (PSO algorithm. The analysis evaluated nine basic PSO types, which differ according to the swarm evolution as controlled by various inertia weights and constriction factor. Each of the basic PSO modifications was analyzed using three different distributed strategies. In the first strategy, the entire swarm population is considered as one unit (OC-PSO, the second strategy periodically partitions the population into equally large complexes according to the particle’s functional value (SCE-PSO, and the final strategy periodically splits the swarm population into complexes using random permutation (SCERand-PSO. All variants are tested using 11 benchmark functions that were prepared for the special session on real-parameter optimization of CEC 2005. It was found that the best modification of the PSO algorithm is a variant with adaptive inertia weight. The best distribution strategy is SCE-PSO, which gives better results than do OC-PSO and SCERand-PSO for seven functions. The sphere function showed no significant difference between SCE-PSO and SCERand-PSO. It follows that a shuffling mechanism improves the optimization process.
Oyekan, John Oluwagbemiga
2016-01-01
The book discusses new algorithms capable of searching for, tracking, mapping and providing a visualization of invisible substances. It reports on the realization of a bacterium-inspired robotic controller that can be used by an agent to search for any environmental spatial function such as temperature or pollution. Using the parameters of a mathematical model, the book shows that it is possible to control the exploration, exploitation and sensitivity of the agent. This feature sets the work apart from the usual method of applying the bacterium behavior to robotic agents. The book also discusses how a computationally tractable multi-agent robotic controller was developed and used to track as well as provide a visual map of a spatio-temporal distribution of a substance. On the one hand, this book provides biologists and ecologists with a basis to perform simulations related to how individual organisms respond to spatio-temporal factors in their environment as well as predict and analyze the behavior of organis...
Directory of Open Access Journals (Sweden)
Xun Zhang
2014-01-01
Full Text Available Optimal sensor placement is a key issue in the structural health monitoring of large-scale structures. However, some aspects in existing approaches require improvement, such as the empirical and unreliable selection of mode and sensor numbers and time-consuming computation. A novel improved particle swarm optimization (IPSO algorithm is proposed to address these problems. The approach firstly employs the cumulative effective modal mass participation ratio to select mode number. Three strategies are then adopted to improve the PSO algorithm. Finally, the IPSO algorithm is utilized to determine the optimal sensors number and configurations. A case study of a latticed shell model is implemented to verify the feasibility of the proposed algorithm and four different PSO algorithms. The effective independence method is also taken as a contrast experiment. The comparison results show that the optimal placement schemes obtained by the PSO algorithms are valid, and the proposed IPSO algorithm has better enhancement in convergence speed and precision.
OPTIMIZATION OF PLY STACKING SEQUENCE OF COMPOSITE DRIVE SHAFT USING PARTICLE SWARM ALGORITHM
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CHANNAKESHAVA K. R.
2011-06-01
Full Text Available In this paper an attempt has been made to optimize ply stacking sequence of single piece E-Glass/Epoxy and Boron /Epoxy composite drive shafts using Particle swarm algorithm (PSA. PSA is a population based evolutionary stochastic optimization technique which is a resent heuristic search method, where mechanics are inspired by swarming or collaborative behavior of biological population. PSA programme is developed to optimize the ply stacking sequence with an objective of weight minimization by considering design constraints as torque transmission capacity, fundamental natural frequency, lateral vibration and torsional buckling strength having number of laminates, ply thickness and stacking sequence as design variables. The weight savings of the E-Glass/epoxy and Boron /Epoxy shaft from PAS were 51% and 85 % of the steel shaft respectively. The optimum results of PSA obtained are compared with results of genetic algorithm (GA results and found that PSA yields better results than GA.
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Ezhil E. Nithila
2017-06-01
Full Text Available Lung Cancer is one of the most dangerous diseases that cause a large number of deaths. Early detection and analysis will be the only remedy. Computer-Aided Diagnosis (CAD plays a key role in the early detection and diagnosis of lung cancer. This paper develops a CAD system that focus on new heuristic search algorithm to optimize the Back Propagation Neural Network (BPNN in characterizing nodule from non-nodules. The proposed CAD system consists of four main stages: (i image acquisition (ii lesion detection, (iii texture feature extraction and (iv tumor characterization using a classifier. The optimization mechanism employs Particle Swarm Optimization (PSO with new inertia weight for NN in order to investigate the classification rate of these algorithms in reducing the problems of trapping in local minima and the slow convergence rate of current evolutionary learning algorithms. The experiments were conducted on CT images to classify into nodule and non-nodule from the tumor region of interest. The performance of the CAD system was evaluated for the texture characterized images taken from LIDC-IDRI and SPIE-AAPM databases. Due to improved inertia weight used in Particle Swarm (PS the CAD achieves highest classification accuracy of 98% for solid nodules, 99.5% for part solid nodules and 97.2% for non solid nodules respectively. The experimental results suggest that the developed CAD system has great potential and promise in the automatic diagnosis of tumors of lung.
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Peng Yinghui
2017-01-01
Full Text Available This paper adopt improved particle swarm algorithm to solve multi-objective optimization problem; so this project can determine vocational skills certification examination test of the different professions, different types and different levels of difficulty; the making test paper covers a wide range of knowledge and chapter score; and the difficult level of making test paper conform to normal distribution, meet different groups requirements of vocational skills certification examination.
Applications of Parallelism to Current Algorithms for Intelligence Analysis
1987-07-10
ALGORITHMS FOR INTELLIGENCE ANALYSIS Martha Ann Griesel * J. Steven Hughes Beth R. Moore 10 July 1987 National Aeronautics and Space Administration...INTELLIGENCE CENTER AND SCHOOL Software Analysis and Management System Applications of Parallelism to Current Algorithms for Intelligence Analysis 10 July 1987...rates and a growing diversity of IEW information sources create a void in current intelligence analysis methods. New ways are needed to quickly
Application of Swarm Intelligence Based Routingprotocols for Wireless Adhoc Sensor Network
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Mrutyunjaya PANDA
2011-07-01
Full Text Available The enormous growth of wireless sensor network (WSN research has opined challenges about their ease in implementation and performance evaluation. Efficient swarm intelligence based routing protocols that can be used to obtain the application specific service guarantee are the key design issues in designing a WSN model. In this paper, an experimental testbed is designed with 100 sensor nodes deployed in a dense environment to address the scalability and performance issues of WSN. In this paper, we use Flooded Piggyback (FP and SC-MCBR ant colony based routing along with AODV and MCBR Tree in order to design an efficient WSN model. Finally, simulation results are presented with various performance measures to understand the efficacy of the proposed WSN design.
A Dynamic Recommender System for Improved Web Usage Mining and CRM Using Swarm Intelligence.
Alphy, Anna; Prabakaran, S
2015-01-01
In modern days, to enrich e-business, the websites are personalized for each user by understanding their interests and behavior. The main challenges of online usage data are information overload and their dynamic nature. In this paper, to address these issues, a WebBluegillRecom-annealing dynamic recommender system that uses web usage mining techniques in tandem with software agents developed for providing dynamic recommendations to users that can be used for customizing a website is proposed. The proposed WebBluegillRecom-annealing dynamic recommender uses swarm intelligence from the foraging behavior of a bluegill fish. It overcomes the information overload by handling dynamic behaviors of users. Our dynamic recommender system was compared against traditional collaborative filtering systems. The results show that the proposed system has higher precision, coverage, F1 measure, and scalability than the traditional collaborative filtering systems. Moreover, the recommendations given by our system overcome the overspecialization problem by including variety in recommendations.
A Dynamic Recommender System for Improved Web Usage Mining and CRM Using Swarm Intelligence
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Anna Alphy
2015-01-01
Full Text Available In modern days, to enrich e-business, the websites are personalized for each user by understanding their interests and behavior. The main challenges of online usage data are information overload and their dynamic nature. In this paper, to address these issues, a WebBluegillRecom-annealing dynamic recommender system that uses web usage mining techniques in tandem with software agents developed for providing dynamic recommendations to users that can be used for customizing a website is proposed. The proposed WebBluegillRecom-annealing dynamic recommender uses swarm intelligence from the foraging behavior of a bluegill fish. It overcomes the information overload by handling dynamic behaviors of users. Our dynamic recommender system was compared against traditional collaborative filtering systems. The results show that the proposed system has higher precision, coverage, F1 measure, and scalability than the traditional collaborative filtering systems. Moreover, the recommendations given by our system overcome the overspecialization problem by including variety in recommendations.
Srinivas, Nisha; Mallick, Mahendra; Osadciw, Lisa A.
2010-04-01
Raman spectroscopy is a powerful technique for determining the chemical composition of a substance. Our objective is to determine the chemical composition of an unknown substance given a reference library of Raman spectra. The unknown spectrum is expressed as a linear combination of the reference library spectra and the non-zero mixing coefficients represent the presence of individual substances, which are not known. This approach is known as the supervised learning method. The mixing coefficients are usually estimated using the nonnegative least squares (NNLS) or nonnegative weighted least squares (NNWLS). This problem is a constrained estimation problem due to the presence of the nonnegativity constraint. In this paper, we present a swarm based algorithm, the particle swarm optimization (PSO), to estimate the mixing coefficients and Raman spectra. The PSO is used to determine the mixing coefficients. PSO efficiently finds an optimum solution. Results are presented for simulated data obtained from the Jennifer Kelly Raman spectra library. The reference library consists of Raman spectra for nine minerals and the measured spectrum is simulated by using spectrum/spectra of single/multiple minerals. We compare the root mean square error (RMSE) for parameter estimation and measurement residual and computational time of the NNWLS and nonnegative weighted PSO (NNWPSO) algorithms.
Analysis of Population Diversity of Dynamic Probabilistic Particle Swarm Optimization Algorithms
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Qingjian Ni
2014-01-01
Full Text Available In evolutionary algorithm, population diversity is an important factor for solving performance. In this paper, combined with some population diversity analysis methods in other evolutionary algorithms, three indicators are introduced to be measures of population diversity in PSO algorithms, which are standard deviation of population fitness values, population entropy, and Manhattan norm of standard deviation in population positions. The three measures are used to analyze the population diversity in a relatively new PSO variant—Dynamic Probabilistic Particle Swarm Optimization (DPPSO. The results show that the three measure methods can fully reflect the evolution of population diversity in DPPSO algorithms from different angles, and we also discuss the impact of population diversity on the DPPSO variants. The relevant conclusions of the population diversity on DPPSO can be used to analyze, design, and improve the DPPSO algorithms, thus improving optimization performance, which could also be beneficial to understand the working mechanism of DPPSO theoretically.
Computing of network tenacity based on modified binary particle swarm optimization algorithm
Shen, Maoxing; Sun, Chengyu
2017-05-01
For rapid calculation of network node tenacity, which can depict the invulnerability performance of network, this paper designs a computational method based on modified binary particle swarm optimization (BPSO) algorithm. Firstly, to improve the astringency of the BPSO algorithm, the algorithm adopted an improved bit transfer probability function and location updating formula. Secondly, algorithm for fitness function value of BPSO based on the breadth-first search is designed. Thirdly, the computing method for network tenacity based on the modified BPSO algorithm is presented. Results of experiment conducted in the Advanced Research Project Agency (ARPA) network and Tactical Support Communication (TCS) network illustrate that the computing method is impactful and high-performance to calculate network tenacity.
DEFF Research Database (Denmark)
Nica, Florin Valentin Traian; Ritchie, Ewen; Leban, Krisztina Monika
2013-01-01
Nowadays the requirements imposed by the industry and economy ask for better quality and performance while the price must be maintained in the same range. To achieve this goal optimization must be introduced in the design process. Two of the best known optimization algorithms for machine design......, genetic algorithm and particle swarm are shortly presented in this paper. These two algorithms are tested to determine their performance on five different benchmark test functions. The algorithms are tested based on three requirements: precision of the result, number of iterations and calculation time....... Both algorithms are also tested on an analytical design process of a Transverse Flux Permanent Magnet Generator to observe their performances in an electrical machine design application....
A particle swarm algorithm for the optimal power flow problem
Energy Technology Data Exchange (ETDEWEB)
Mantawy, A.H. [King Fahd Univ. of Petroleum and Minerals, Dhahran (Saudi Arabia). Dept. of Electrical Engineering; Al-Ghamdi, M.S. [Saudi Aramco, Dhahran (Saudi Arabia). Consulting Services Dept.
2006-07-01
Deregulated and open access electric utilities are faced with the challenge of optimizing the operation of their generation and transmission systems. This paper addressed the Optimal Power Flow (OPF) problem which is a generalized formulation of the economic dispatch problem involving voltage and other operating constraints. A typical OPF problem seeks a dispatch of active power (P) and/or reactive power (Q) by adjusting the appropriate control variables, so that a specific objective in operating a power system network is optimized. The OPF problem in electrical power systems is considered as a static non-linear and a non-convex optimization problem with both continuous and discrete control variables. In this work, the particle swam algorithm was used to solve the optimal power flow problem. The active power losses and generation fuel cost were minimized and compared using the IEEE 30-bus system. System operating constraints, including constraints dictated by the electrical network were satisfied. The proposed algorithm offered many advantages, such as flexibility in adding or deleting any system constraints and objective functions. It calculated the optimum generation pattern as well as all control variables in order to minimize the specified objective function and satisfy the system constraints. The control variables included: active power generation except the slack bus; all PV-bus voltages; all transformer load tap changers; and, the setting of all switched reactors or static VAR components. The flexibility of the algorithm allows operators and control engineers to relieve any overload or voltage violation of any component in the system during normal operation and in emergency situations. Additional objective functions and constraints can be readily added to the developed software package. 14 refs., 3 tabs.
Swarm intelligence for multi-objective optimization of synthesis gas production
Ganesan, T.; Vasant, P.; Elamvazuthi, I.; Ku Shaari, Ku Zilati
2012-11-01
In the chemical industry, the production of methanol, ammonia, hydrogen and higher hydrocarbons require synthesis gas (or syn gas). The main three syn gas production methods are carbon dioxide reforming (CRM), steam reforming (SRM) and partial-oxidation of methane (POM). In this work, multi-objective (MO) optimization of the combined CRM and POM was carried out. The empirical model and the MO problem formulation for this combined process were obtained from previous works. The central objectives considered in this problem are methane conversion, carbon monoxide selectivity and the hydrogen to carbon monoxide ratio. The MO nature of the problem was tackled using the Normal Boundary Intersection (NBI) method. Two techniques (Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO)) were then applied in conjunction with the NBI method. The performance of the two algorithms and the quality of the solutions were gauged by using two performance metrics. Comparative studies and results analysis were then carried out on the optimization results.
Directory of Open Access Journals (Sweden)
Álvaro Gutiérrez
2011-11-01
Full Text Available Swarms of robots can use their sensing abilities to explore unknown environments and deploy on sites of interest. In this task, a large number of robots is more effective than a single unit because of their ability to quickly cover the area. However, the coordination of large teams of robots is not an easy problem, especially when the resources for the deployment are limited. In this paper, the Distributed Bees Algorithm (DBA, previously proposed by the authors, is optimized and applied to distributed target allocation in swarms of robots. Improved target allocation in terms of deployment cost efficiency is achieved through optimization of the DBA’s control parameters by means of a Genetic Algorithm. Experimental results show that with the optimized set of parameters, the deployment cost measured as the average distance traveled by the robots is reduced. The cost-efficient deployment is in some cases achieved at the expense of increased robots’ distribution error. Nevertheless, the proposed approach allows the swarm to adapt to the operating conditions when available resources are scarce.
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Hsu-Chih Huang
2013-01-01
Full Text Available This paper presents an intelligent motion controller for four-wheeled holonomic mobile robots with four driving omnidirectional wheels equally spaced at 90 degrees from one another by using field-programmable gate array (FPGA-based artificial immune system (AIS algorithm. Both the nature-inspired AIS computational approach and motion controller are implemented in one FPGA chip to address the optimal control problem of real-world mobile robotics application. The proposed FPGA-based AIS method takes the advantages of artificial intelligence and FPGA technology by using system-on-a-programmable chip (SoPC methodology. Experimental results are conducted to show the effectiveness and merit of the proposed FPGA-based AIS intelligent motion controller for four-wheeled omnidirectional mobile robots. This FPGA-based AIS autotuning intelligent controller outperforms the conventional nonoptimal controllers, the genetic algorithm (GA controller, and the particle swarm optimization (PSO controller.
A Novel Cluster Head Selection Algorithm Based on Fuzzy Clustering and Particle Swarm Optimization.
Ni, Qingjian; Pan, Qianqian; Du, Huimin; Cao, Cen; Zhai, Yuqing
2017-01-01
An important objective of wireless sensor network is to prolong the network life cycle, and topology control is of great significance for extending the network life cycle. Based on previous work, for cluster head selection in hierarchical topology control, we propose a solution based on fuzzy clustering preprocessing and particle swarm optimization. More specifically, first, fuzzy clustering algorithm is used to initial clustering for sensor nodes according to geographical locations, where a sensor node belongs to a cluster with a determined probability, and the number of initial clusters is analyzed and discussed. Furthermore, the fitness function is designed considering both the energy consumption and distance factors of wireless sensor network. Finally, the cluster head nodes in hierarchical topology are determined based on the improved particle swarm optimization. Experimental results show that, compared with traditional methods, the proposed method achieved the purpose of reducing the mortality rate of nodes and extending the network life cycle.
Multi-Objective Optimization of Wire Antennas: Genetic Algorithms Versus Particle Swarm Optimization
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Z. Raida
2005-12-01
Full Text Available The paper is aimed to the multi-objective optimization of wiremulti-band antennas. Antennas are numerically modeled using time-domainintegral-equation method. That way, the designed antennas can becharacterized in a wide band of frequencies within a single run of theanalysis. Antennas are optimized to reach the prescribed matching, toexhibit the omni-directional constant gain and to have the satisfactorypolarization purity. Results of the design are experimentally verified. The multi-objective cost function is minimized by the genetic algorithmand by the particle swarm optimization. Results of the optimization byboth the multi-objective methods are in detail compared. The combination of the time domain analysis and global optimizationmethods for the broadband antenna design and the detailed comparison ofthe multi-objective particle swarm optimization with themulti-objective genetic algorithm are the original contributions of thepaper.
Combined Intelligent Control (CIC): An Intelligent decision making algorithm
Moteaal Asadi Shirzi; M. R. Hairi Yazdi; Caro Lucas
2008-01-01
The focus of this research is to introduce the concept of combined intelligent control (CIC) as an effective architecture for decision making and control of intelligent agents and multi robot sets. Basically, the CIC is a combination of various architectures and methods from fields such as artificial intelligence, Distributed Artificial Intelligence (DAI), control and biological computing. Although any intelligent architecture may be very effective for some specific applications, it could be ...
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Keivan Borna
2015-12-01
Full Text Available Traveling salesman problem (TSP is a well-established NP-complete problem and many evolutionary techniques like particle swarm optimization (PSO are used to optimize existing solutions for that. PSO is a method inspired by the social behavior of birds. In PSO, each member will change its position in the search space, according to personal or social experience of the whole society. In this paper, we combine the principles of PSO and crossover operator of genetic algorithm to propose a heuristic algorithm for solving the TSP more efficiently. Finally, some experimental results on our algorithm are applied in some instances in TSPLIB to demonstrate the effectiveness of our methods which also show that our algorithm can achieve better results than other approaches.
Multi-objective Reactive Power Optimization Based on Improved Particle Swarm Algorithm
Cui, Xue; Gao, Jian; Feng, Yunbin; Zou, Chenlu; Liu, Huanlei
2018-01-01
In this paper, an optimization model with the minimum active power loss and minimum voltage deviation of node and maximum static voltage stability margin as the optimization objective is proposed for the reactive power optimization problems. By defining the index value of reactive power compensation, the optimal reactive power compensation node was selected. The particle swarm optimization algorithm was improved, and the selection pool of global optimal and the global optimal of probability (p-gbest) were introduced. A set of Pareto optimal solution sets is obtained by this algorithm. And by calculating the fuzzy membership value of the pareto optimal solution sets, individuals with the smallest fuzzy membership value were selected as the final optimization results. The above improved algorithm is used to optimize the reactive power of IEEE14 standard node system. Through the comparison and analysis of the results, it has been proven that the optimization effect of this algorithm was very good.
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Weizhe Zhang
2014-01-01
Full Text Available Energy consumption in computer systems has become a more and more important issue. High energy consumption has already damaged the environment to some extent, especially in heterogeneous multiprocessors. In this paper, we first formulate and describe the energy-aware real-time task scheduling problem in heterogeneous multiprocessors. Then we propose a particle swarm optimization (PSO based algorithm, which can successfully reduce the energy cost and the time for searching feasible solutions. Experimental results show that the PSO-based energy-aware metaheuristic uses 40%–50% less energy than the GA-based and SFLA-based algorithms and spends 10% less time than the SFLA-based algorithm in finding the solutions. Besides, it can also find 19% more feasible solutions than the SFLA-based algorithm.
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Li Mao
2016-01-01
Full Text Available Artificial bee colony (ABC algorithm has good performance in discovering the optimal solutions to difficult optimization problems, but it has weak local search ability and easily plunges into local optimum. In this paper, we introduce the chemotactic behavior of Bacterial Foraging Optimization into employed bees and adopt the principle of moving the particles toward the best solutions in the particle swarm optimization to improve the global search ability of onlooker bees and gain a hybrid artificial bee colony (HABC algorithm. To obtain a global optimal solution efficiently, we make HABC algorithm converge rapidly in the early stages of the search process, and the search range contracts dynamically during the late stages. Our experimental results on 16 benchmark functions of CEC 2014 show that HABC achieves significant improvement at accuracy and convergence rate, compared with the standard ABC, best-so-far ABC, directed ABC, Gaussian ABC, improved ABC, and memetic ABC algorithms.
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Runhai Jiao
2015-01-01
Full Text Available This paper puts forward a novel particle swarm optimization algorithm with quantum behavior (QPSO to solve reactive power optimization in power system with distributed generation. Moreover, differential evolution (DE operators are applied to enhance the algorithm (DQPSO. This paper focuses on the minimization of active power loss, respectively, and uses QPSO and DQPSO to determine terminal voltage of generators, and ratio of transformers, switching group number of capacitors to achieve optimal reactive power flow. The proposed algorithms are validated through three IEEE standard examples. Comparing the results obtained from QPSO and DQPSO with those obtained from PSO, we find that our algorithms are more likely to get the global optimal solution and have a better convergence. What is more, DQPSO is better than QPSO. Furthermore, with the integration of distributed generation, active power loss has decreased significantly. Specifically, PV distributed generations can suppress voltage fluctuation better than PQ distributed generations.
Jin, Junchen
2016-01-01
The shunting schedule of electric multiple units depot (SSED) is one of the essential plans for high-speed train maintenance activities. This paper presents a 0-1 programming model to address the problem of determining an optimal SSED through automatic computing. The objective of the model is to minimize the number of shunting movements and the constraints include track occupation conflicts, shunting routes conflicts, time durations of maintenance processes, and shunting running time. An enhanced particle swarm optimization (EPSO) algorithm is proposed to solve the optimization problem. Finally, an empirical study from Shanghai South EMU Depot is carried out to illustrate the model and EPSO algorithm. The optimization results indicate that the proposed method is valid for the SSED problem and that the EPSO algorithm outperforms the traditional PSO algorithm on the aspect of optimality. PMID:27436998
Xu, Sheng-Hua; Liu, Ji-Ping; Zhang, Fu-Hao; Wang, Liang; Sun, Li-Jian
2015-01-01
A combination of genetic algorithm and particle swarm optimization (PSO) for vehicle routing problems with time windows (VRPTW) is proposed in this paper. The improvements of the proposed algorithm include: using the particle real number encoding method to decode the route to alleviate the computation burden, applying a linear decreasing function based on the number of the iterations to provide balance between global and local exploration abilities, and integrating with the crossover operator of genetic algorithm to avoid the premature convergence and the local minimum. The experimental results show that the proposed algorithm is not only more efficient and competitive with other published results but can also obtain more optimal solutions for solving the VRPTW issue. One new well-known solution for this benchmark problem is also outlined in the following. PMID:26343655
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Jiaxi Wang
2016-01-01
Full Text Available The shunting schedule of electric multiple units depot (SSED is one of the essential plans for high-speed train maintenance activities. This paper presents a 0-1 programming model to address the problem of determining an optimal SSED through automatic computing. The objective of the model is to minimize the number of shunting movements and the constraints include track occupation conflicts, shunting routes conflicts, time durations of maintenance processes, and shunting running time. An enhanced particle swarm optimization (EPSO algorithm is proposed to solve the optimization problem. Finally, an empirical study from Shanghai South EMU Depot is carried out to illustrate the model and EPSO algorithm. The optimization results indicate that the proposed method is valid for the SSED problem and that the EPSO algorithm outperforms the traditional PSO algorithm on the aspect of optimality.
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Yasear Shaymah
2017-01-01
Full Text Available Mode division multiplexing (MDM technique has been introduced as a promising solution to the rapid increase of data traffic. However, although MDM has the potential to increase transmission capacity and significantly reduce the cost and complexity of parallel systems, it also has its challenges. Along the optical fibre link, the deficient characteristics always exist. These characteristics, damage the orthogonality of the modes and lead to mode coupling, causing Inter-symbol interference (SI which limit the capacity of MDM system. In order to mitigate the effects of mode coupling, an adaptive equalization scheme based on particle swarm optimization (PSO algorithm has been proposed. Compared to other traditional algorithms that have been used in the equalization process on the MDM system such as least mean square (LMS and recursive least squares (RLS algorithms, simulation results demonstrate that the PSO algorithm has flexibility and higher convergence speed for mitigating the effects of nonlinear mode coupling.
Yasear, Shaymah; Amphawan, Angela
2017-11-01
Mode division multiplexing (MDM) technique has been introduced as a promising solution to the rapid increase of data traffic. However, although MDM has the potential to increase transmission capacity and significantly reduce the cost and complexity of parallel systems, it also has its challenges. Along the optical fibre link, the deficient characteristics always exist. These characteristics, damage the orthogonality of the modes and lead to mode coupling, causing Inter-symbol interference (SI) which limit the capacity of MDM system. In order to mitigate the effects of mode coupling, an adaptive equalization scheme based on particle swarm optimization (PSO) algorithm has been proposed. Compared to other traditional algorithms that have been used in the equalization process on the MDM system such as least mean square (LMS) and recursive least squares (RLS) algorithms, simulation results demonstrate that the PSO algorithm has flexibility and higher convergence speed for mitigating the effects of nonlinear mode coupling.
Xu, Sheng-Hua; Liu, Ji-Ping; Zhang, Fu-Hao; Wang, Liang; Sun, Li-Jian
2015-08-27
A combination of genetic algorithm and particle swarm optimization (PSO) for vehicle routing problems with time windows (VRPTW) is proposed in this paper. The improvements of the proposed algorithm include: using the particle real number encoding method to decode the route to alleviate the computation burden, applying a linear decreasing function based on the number of the iterations to provide balance between global and local exploration abilities, and integrating with the crossover operator of genetic algorithm to avoid the premature convergence and the local minimum. The experimental results show that the proposed algorithm is not only more efficient and competitive with other published results but can also obtain more optimal solutions for solving the VRPTW issue. One new well-known solution for this benchmark problem is also outlined in the following.
Directory of Open Access Journals (Sweden)
Shao-Fei Jiang
2014-01-01
Full Text Available Optimization techniques have been applied to structural health monitoring and damage detection of civil infrastructures for two decades. The standard particle swarm optimization (PSO is easy to fall into the local optimum and such deficiency also exists in the multiparticle swarm coevolution optimization (MPSCO. This paper presents an improved MPSCO algorithm (IMPSCO firstly and then integrates it with Newmark’s algorithm to localize and quantify the structural damage by using the damage threshold proposed. To validate the proposed method, a numerical simulation and an experimental study of a seven-story steel frame were employed finally, and a comparison was made between the proposed method and the genetic algorithm (GA. The results show threefold: (1 the proposed method not only is capable of localization and quantification of damage, but also has good noise-tolerance; (2 the damage location can be accurately detected using the damage threshold proposed in this paper; and (3 compared with the GA, the IMPSCO algorithm is more efficient and accurate for damage detection problems in general. This implies that the proposed method is applicable and effective in the community of damage detection and structural health monitoring.
New hybrid genetic particle swarm optimization algorithm to design multi-zone binary filter.
Lin, Jie; Zhao, Hongyang; Ma, Yuan; Tan, Jiubin; Jin, Peng
2016-05-16
The binary phase filters have been used to achieve an optical needle with small lateral size. Designing a binary phase filter is still a scientific challenge in such fields. In this paper, a hybrid genetic particle swarm optimization (HGPSO) algorithm is proposed to design the binary phase filter. The HGPSO algorithm includes self-adaptive parameters, recombination and mutation operations that originated from the genetic algorithm. Based on the benchmark test, the HGPSO algorithm has achieved global optimization and fast convergence. In an easy-to-perform optimizing procedure, the iteration number of HGPSO is decreased to about a quarter of the original particle swarm optimization process. A multi-zone binary phase filter is designed by using the HGPSO. The long depth of focus and high resolution are achieved simultaneously, where the depth of focus and focal spot transverse size are 6.05λ and 0.41λ, respectively. Therefore, the proposed HGPSO can be applied to the optimization of filter with multiple parameters.
Hybrid Swarm Algorithms for Parameter Identification of an Actuator Model in an Electrical Machine
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Ying Wu
2011-01-01
Full Text Available Efficient identification and control algorithms are needed, when active vibration suppression techniques are developed for industrial machines. In the paper a new actuator for reducing rotor vibrations in electrical machines is investigated. Model-based control is needed in designing the algorithm for voltage input, and therefore proper models for the actuator must be available. In addition to the traditional prediction error method a new knowledge-based Artificial Fish-Swarm optimization algorithm (AFA with crossover, CAFAC, is proposed to identify the parameters in the new model. Then, in order to obtain a fast convergence of the algorithm in the case of a 30 kW two-pole squirrel cage induction motor, we combine the CAFAC and Particle Swarm Optimization (PSO to identify parameters of the machine to construct a linear time-invariant(LTI state-space model. Besides that, the prediction error method (PEM is also employed to identify the induction motor to produce a black box model with correspondence to input-output measurements.
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Zhou Feng
2013-09-01
Full Text Available A based on Rapidly-exploring Random Tree(RRT and Particle Swarm Optimizer (PSO for path planning of the robot is proposed.First the grid method is built to describe the working space of the mobile robot,then the Rapidly-exploring Random Tree algorithm is used to obtain the global navigation path,and the Particle Swarm Optimizer algorithm is adopted to get the better path.Computer experiment results demonstrate that this novel algorithm can plan an optimal path rapidly in a cluttered environment.The successful obstacle avoidance is achieved,and the model is robust and performs reliably.
DEFF Research Database (Denmark)
Ren, Jingzheng; Tan, Shiyu; Dong, Lichun
2010-01-01
the searching ability of basic particle swarm algorithm significantly. An example of utilizing the improved algorithm to solve the mathematical model was demonstrated; the result showed that it is efficient and convenient to optimize the reflux ratio for a distillation column by using the mathematical model......A mathematical model relating operation profits with reflux ratio of a stage distillation column was established. In order to optimize the reflux ratio by solving the nonlinear objective function, an improved particle swarm algorithm was developed and has been proved to be able to enhance...
Swarm Intelligence-Enhanced Detection of Non-Small-Cell Lung Cancer Using Tumor-Educated Platelets
Best, Myron G.; Sol, Nik; In ‘t Veld, Sjors G.J.G.; Vancura, Adrienne; Muller, Mirte; Niemeijer, Anna Larissa N.; Fejes, Aniko V.; Tjon Kon Fat, Lee Ann; Huis in 't Veld, Anna E; Leurs, Cyra; Le Large, Tessa Y.; Meijer, Laura L.; Kooi, Irsan E.; Rustenburg, François; Schellen, Pepijn; Verschueren, Heleen; Post, Edward; Wedekind, Laurine E.; Bracht, Jillian; Esenkbrink, Michelle; Wils, Leon; Favaro, Francesca; Schoonhoven, Jilian D.; Tannous, Jihane; Meijers-Heijboer, Hanne; Kazemier, Geert; Giovannetti, Elisa; Reijneveld, Jaap C.; Idema, Sander; Killestein, Joep; Heger, Michal; de Jager, Saskia C.; Urbanus, Rolf T.; Hoefer, Imo E.; Pasterkamp, Gerard; Mannhalter, Christine; Gomez-Arroyo, Jose; Bogaard, Harm-Jan; Noske, David P.; Vandertop, W. Peter; van den Broek, Daan; Ylstra, Bauke; Nilsson, R. Jonas A; Wesseling, Pieter; Karachaliou, Niki; Rosell, Rafael; Lee-Lewandrowski, Elizabeth; Lewandrowski, Kent B.; Tannous, Bakhos A.; de Langen, Adrianus J.; Smit, Egbert F.; van den Heuvel, Michel M; Wurdinger, Thomas
2017-01-01
Blood-based liquid biopsies, including tumor-educated blood platelets (TEPs), have emerged as promising biomarker sources for non-invasive detection of cancer. Here we demonstrate that particle-swarm optimization (PSO)-enhanced algorithms enable efficient selection of RNA biomarker panels from
Ye, Zhiwei; Wang, Mingwei; Hu, Zhengbing; Liu, Wei
2015-01-01
Image enhancement is an important procedure of image processing and analysis. This paper presents a new technique using a modified measure and blending of cuckoo search and particle swarm optimization (CS-PSO) for low contrast images to enhance image adaptively. In this way, contrast enhancement is obtained by global transformation of the input intensities; it employs incomplete Beta function as the transformation function and a novel criterion for measuring image quality considering three factors which are threshold, entropy value, and gray-level probability density of the image. The enhancement process is a nonlinear optimization problem with several constraints. CS-PSO is utilized to maximize the objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The performance of the proposed method has been compared with other existing techniques such as linear contrast stretching, histogram equalization, and evolutionary computing based image enhancement methods like backtracking search algorithm, differential search algorithm, genetic algorithm, and particle swarm optimization in terms of processing time and image quality. Experimental results demonstrate that the proposed method is robust and adaptive and exhibits the better performance than other methods involved in the paper. PMID:25784928
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Zhiwei Ye
2015-01-01
Full Text Available Image enhancement is an important procedure of image processing and analysis. This paper presents a new technique using a modified measure and blending of cuckoo search and particle swarm optimization (CS-PSO for low contrast images to enhance image adaptively. In this way, contrast enhancement is obtained by global transformation of the input intensities; it employs incomplete Beta function as the transformation function and a novel criterion for measuring image quality considering three factors which are threshold, entropy value, and gray-level probability density of the image. The enhancement process is a nonlinear optimization problem with several constraints. CS-PSO is utilized to maximize the objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The performance of the proposed method has been compared with other existing techniques such as linear contrast stretching, histogram equalization, and evolutionary computing based image enhancement methods like backtracking search algorithm, differential search algorithm, genetic algorithm, and particle swarm optimization in terms of processing time and image quality. Experimental results demonstrate that the proposed method is robust and adaptive and exhibits the better performance than other methods involved in the paper.
Ye, Zhiwei; Wang, Mingwei; Hu, Zhengbing; Liu, Wei
2015-01-01
Image enhancement is an important procedure of image processing and analysis. This paper presents a new technique using a modified measure and blending of cuckoo search and particle swarm optimization (CS-PSO) for low contrast images to enhance image adaptively. In this way, contrast enhancement is obtained by global transformation of the input intensities; it employs incomplete Beta function as the transformation function and a novel criterion for measuring image quality considering three factors which are threshold, entropy value, and gray-level probability density of the image. The enhancement process is a nonlinear optimization problem with several constraints. CS-PSO is utilized to maximize the objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The performance of the proposed method has been compared with other existing techniques such as linear contrast stretching, histogram equalization, and evolutionary computing based image enhancement methods like backtracking search algorithm, differential search algorithm, genetic algorithm, and particle swarm optimization in terms of processing time and image quality. Experimental results demonstrate that the proposed method is robust and adaptive and exhibits the better performance than other methods involved in the paper.
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Wei Han
2013-01-01
Full Text Available An implicit reserve constraint unit commitment (IRCUC model is presented in this paper. Different from the traditional unit commitment (UC model, the constraint of spinning reserve is not given explicitly but implicitly in a trade-off between the production cost and the outage loss. An analytical method is applied to evaluate the reliability of UC solutions and to estimate the outage loss. The stochastic failures of generating units and uncertainties of load demands are considered while assessing the reliability. The artificial fish swarm algorithm (AFSA is employed to solve this proposed model. In addition to the regular operation, a mutation operator (MO is designed to enhance the searching performance of the algorithm. The feasibility of the proposed method is demonstrated from 10 to 100 units system, and the testing results are compared with those obtained by genetic algorithm (GA, particle swarm optimization (PSO, and ant colony optimization (ACO in terms of total production cost and computational time. The simulation results show that the proposed method is capable of obtaining higher quality solutions.
Sung, Wen-Tsai; Chiang, Yen-Chun
2012-12-01
This study examines wireless sensor network with real-time remote identification using the Android study of things (HCIOT) platform in community healthcare. An improved particle swarm optimization (PSO) method is proposed to efficiently enhance physiological multi-sensors data fusion measurement precision in the Internet of Things (IOT) system. Improved PSO (IPSO) includes: inertia weight factor design, shrinkage factor adjustment to allow improved PSO algorithm data fusion performance. The Android platform is employed to build multi-physiological signal processing and timely medical care of things analysis. Wireless sensor network signal transmission and Internet links allow community or family members to have timely medical care network services.
Wang, Deguang; Han, Baochang; Huang, Ming
Computer forensics is the technology of applying computer technology to access, investigate and analysis the evidence of computer crime. It mainly include the process of determine and obtain digital evidence, analyze and take data, file and submit result. And the data analysis is the key link of computer forensics. As the complexity of real data and the characteristics of fuzzy, evidence analysis has been difficult to obtain the desired results. This paper applies fuzzy c-means clustering algorithm based on particle swarm optimization (FCMP) in computer forensics, and it can be more satisfactory results.
An Image Filter Based on Shearlet Transformation and Particle Swarm Optimization Algorithm
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Kai Hu
2015-01-01
Full Text Available Digital image is always polluted by noise and made data postprocessing difficult. To remove noise and preserve detail of image as much as possible, this paper proposed image filter algorithm which combined the merits of Shearlet transformation and particle swarm optimization (PSO algorithm. Firstly, we use classical Shearlet transform to decompose noised image into many subwavelets under multiscale and multiorientation. Secondly, we gave weighted factor to those subwavelets obtained. Then, using classical Shearlet inverse transform, we obtained a composite image which is composed of those weighted subwavelets. After that, we designed fast and rough evaluation method to evaluate noise level of the new image; by using this method as fitness, we adopted PSO to find the optimal weighted factor we added; after lots of iterations, by the optimal factors and Shearlet inverse transform, we got the best denoised image. Experimental results have shown that proposed algorithm eliminates noise effectively and yields good peak signal noise ratio (PSNR.
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Yu Huang
Full Text Available Parameter estimation for fractional-order chaotic systems is an important issue in fractional-order chaotic control and synchronization and could be essentially formulated as a multidimensional optimization problem. A novel algorithm called quantum parallel particle swarm optimization (QPPSO is proposed to solve the parameter estimation for fractional-order chaotic systems. The parallel characteristic of quantum computing is used in QPPSO. This characteristic increases the calculation of each generation exponentially. The behavior of particles in quantum space is restrained by the quantum evolution equation, which consists of the current rotation angle, individual optimal quantum rotation angle, and global optimal quantum rotation angle. Numerical simulation based on several typical fractional-order systems and comparisons with some typical existing algorithms show the effectiveness and efficiency of the proposed algorithm.
Huang, Yu; Guo, Feng; Li, Yongling; Liu, Yufeng
2015-01-01
Parameter estimation for fractional-order chaotic systems is an important issue in fractional-order chaotic control and synchronization and could be essentially formulated as a multidimensional optimization problem. A novel algorithm called quantum parallel particle swarm optimization (QPPSO) is proposed to solve the parameter estimation for fractional-order chaotic systems. The parallel characteristic of quantum computing is used in QPPSO. This characteristic increases the calculation of each generation exponentially. The behavior of particles in quantum space is restrained by the quantum evolution equation, which consists of the current rotation angle, individual optimal quantum rotation angle, and global optimal quantum rotation angle. Numerical simulation based on several typical fractional-order systems and comparisons with some typical existing algorithms show the effectiveness and efficiency of the proposed algorithm.
Nigam, Nilay; Bessie Amali, D. Geraldine
2017-11-01
Pollutant Level Predicator is a system which helps in predicting the amount of pollutants in a specific region. The system uses historic data in order to predict the value for the new input. The prediction system uses Artificial Neural Networks (ANN) trained with different optimization algorithms to classify the pollution level into several classes. This research paper assesses and analyses various techniques which can be used to predict the level of pollutant in Delhi. This study uses daily mean air temperature, relative humidity, wind speed and concentration of PM2.5 in Anand Vihar area of Delhi for a period of 2 years (2015 to 2016). Experimental results show that a ANN trained with Galactic swarm optimization algorithm produces a more accurate predication compared to other optimization algorithms.
Yu, Chaoyin; Yuan, Zhengwu; Wu, Yuanfeng
2017-10-01
Hyperspectral image unmixing is an important part of hyperspectral data analysis. The mixed pixel decomposition consists of two steps, endmember (the unique signatures of pure ground components) extraction and abundance (the proportion of each endmember in each pixel) estimation. Recently, a Discrete Particle Swarm Optimization algorithm (DPSO) was proposed for accurately extract endmembers with high optimal performance. However, the DPSO algorithm shows very high computational complexity, which makes the endmember extraction procedure very time consuming for hyperspectral image unmixing. Thus, in this paper, the DPSO endmember extraction algorithm was parallelized, implemented on the CUDA (GPU K20) platform, and evaluated by real hyperspectral remote sensing data. The experimental results show that with increasing the number of particles the parallelized version obtained much higher computing efficiency while maintain the same endmember exaction accuracy.
Hybrid particle swarm global optimization algorithm for phase diversity phase retrieval.
Zhang, P G; Yang, C L; Xu, Z H; Cao, Z L; Mu, Q Q; Xuan, L
2016-10-31
The core problem of phase diversity phase retrieval (PDPR) is to find suitable optimization algorithms for wave-front sensing of different scales, especially for large-scale wavefront sensing. When dealing with large-scale wave-front sensing, existing gradient-based local optimization algorithms used in PDPR are easily trapped in local minimums near initial positions, and available global optimization algorithms possess low convergence efficiency. We construct a practicable optimization algorithm used in PDPR for large-scale wave-front sensing. This algorithm, named EPSO-BFGS, is a two-step hybrid global optimization algorithm based on the combination of evolutionary particle swarm optimization (EPSO) and the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. Firstly, EPSO provides global search and obtains a rough global minimum position in limited search steps. Then, BFGS initialized by the rough global minimum position approaches the global minimum with high accuracy and fast convergence speed. Numerical examples testify to the feasibility and reliability of EPSO-BFGS for wave-front sensing of different scales. Two numerical cases also validate the ability of EPSO-BFGS for large-scale wave-front sensing. The effectiveness of EPSO-BFGS is further affirmed by performing a verification experiment.
Kumar, Gaurav; Kumar, Ashok
2017-11-01
Structural control has gained significant attention in recent times. The standalone issue of power requirement during an earthquake has already been solved up to a large extent by designing semi-active control systems using conventional linear quadratic control theory, and many other intelligent control algorithms such as fuzzy controllers, artificial neural networks, etc. In conventional linear-quadratic regulator (LQR) theory, it is customary to note that the values of the design parameters are decided at the time of designing the controller and cannot be subsequently altered. During an earthquake event, the response of the structure may increase or decrease, depending the quasi-resonance occurring between the structure and the earthquake. In this case, it is essential to modify the value of the design parameters of the conventional LQR controller to obtain optimum control force to mitigate the vibrations due to the earthquake. A few studies have been done to sort out this issue but in all these studies it was necessary to maintain a database of the earthquake. To solve this problem and to find the optimized design parameters of the LQR controller in real time, a fast Fourier transform and particle swarm optimization based modified linear quadratic regulator method is presented here. This method comprises four different algorithms: particle swarm optimization (PSO), the fast Fourier transform (FFT), clipped control algorithm and the LQR. The FFT helps to obtain the dominant frequency for every time window. PSO finds the optimum gain matrix through the real-time update of the weighting matrix R, thereby, dispensing with the experimentation. The clipped control law is employed to match the magnetorheological (MR) damper force with the desired force given by the controller. The modified Bouc-Wen phenomenological model is taken to recognize the nonlinearities in the MR damper. The assessment of the advised method is done by simulation of a three-story structure
PSOVina: The hybrid particle swarm optimization algorithm for protein-ligand docking.
Ng, Marcus C K; Fong, Simon; Siu, Shirley W I
2015-06-01
Protein-ligand docking is an essential step in modern drug discovery process. The challenge here is to accurately predict and efficiently optimize the position and orientation of ligands in the binding pocket of a target protein. In this paper, we present a new method called PSOVina which combined the particle swarm optimization (PSO) algorithm with the efficient Broyden-Fletcher-Goldfarb-Shannon (BFGS) local search method adopted in AutoDock Vina to tackle the conformational search problem in docking. Using a diverse data set of 201 protein-ligand complexes from the PDBbind database and a full set of ligands and decoys for four representative targets from the directory of useful decoys (DUD) virtual screening data set, we assessed the docking performance of PSOVina in comparison to the original Vina program. Our results showed that PSOVina achieves a remarkable execution time reduction of 51-60% without compromising the prediction accuracies in the docking and virtual screening experiments. This improvement in time efficiency makes PSOVina a better choice of a docking tool in large-scale protein-ligand docking applications. Our work lays the foundation for the future development of swarm-based algorithms in molecular docking programs. PSOVina is freely available to non-commercial users at http://cbbio.cis.umac.mo .
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Huan Zhang
2017-01-01
Full Text Available For the problem of multiaircraft cooperative suppression interference array (MACSIA against the enemy air defense radar network in electronic warfare mission planning, firstly, the concept of route planning security zone is proposed and the solution to get the minimum width of security zone based on mathematical morphology is put forward. Secondly, the minimum width of security zone and the sum of the distance between each jamming aircraft and the center of radar network are regarded as objective function, and the multiobjective optimization model of MACSIA is built, and then an improved multiobjective particle swarm optimization algorithm is used to solve the model. The decomposition mechanism is adopted and the proportional distribution is used to maintain diversity of the new found nondominated solutions. Finally, the Pareto optimal solutions are analyzed by simulation, and the optimal MACSIA schemes of each jamming aircraft suppression against the enemy air defense radar network are obtained and verify that the built multiobjective optimization model is corrected. It also shows that the improved multiobjective particle swarm optimization algorithm for solving the problem of MACSIA is feasible and effective.
A Particle Swarm Optimization Algorithm for Neural Networks in Recognition of Maize Leaf Diseases
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Zhiyong ZHANG
2014-03-01
Full Text Available The neural networks have significance on recognition of crops disease diagnosis? but it has disadvantage of slow convergent speed and shortcoming of local optimum. In order to identify the maize leaf diseases by using machine vision more accurately, we propose an improved particle swarm optimization algorithm for neural networks. With the algorithm, the neural network property is improved. It reasonably confirms threshold and connection weight of neural network, and improves capability of solving problems in the image recognition. At last, an example of the emulation shows that neural network model based on recognizes significantly better than without optimization. Model accuracy has been improved to a certain extent to meet the actual needs of maize leaf diseases recognition.
Optimization of the Infrastructure of Reinforced Concrete Reservoirs by a Particle Swarm Algorithm
Kia, Saeed; Sebt, Mohammad Hassan; Shahhosseini, Vahid
2015-03-01
Optimization techniques may be effective in finding the best modeling and shapes for reinforced concrete reservoirs (RCR) to improve their durability and mechanical behavior, particularly for avoiding or reducing the bending moments in these structures. RCRs are one of the major structures applied for reserving fluids to be used in drinking water networks. Usually, these structures have fixed shapes which are designed and calculated based on input discharges, the conditions of the structure's topology, and geotechnical locations with various combinations of static and dynamic loads. In this research, the elements of reservoir walls are first typed according to the performance analyzed; then the range of the membrane based on the thickness and the minimum and maximum cross sections of the bar used are determined in each element. This is done by considering the variable constraints, which are estimated by the maximum stress capacity. In the next phase, based on the reservoir analysis and using the algorithm of the PARIS connector, the related information is combined with the code for the PSO algorithm, i.e., an algorithm for a swarming search, to determine the optimum thickness of the cross sections for the reservoir membrane's elements and the optimum cross section of the bar used. Based on very complex mathematical linear models for the correct embedding and angles related to achain of peripheral strengthening membranes, which optimize the vibration of the structure, a mutual relation is selected between the modeling software and the code for a particle swarm optimization algorithm. Finally, the comparative weight of the concrete reservoir optimized by the peripheral strengthening membrane is analyzed using common methods. This analysis shows a 19% decrease in the bar's weight, a 20% decrease in the concrete's weight, and a minimum 13% saving in construction costs according to the items of a checklist for a concrete reservoir at 10,000 m3.
Optimization of the Infrastructure of Reinforced Concrete Reservoirs by a Particle Swarm Algorithm
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Kia Saeed
2015-03-01
Full Text Available Optimization techniques may be effective in finding the best modeling and shapes for reinforced concrete reservoirs (RCR to improve their durability and mechanical behavior, particularly for avoiding or reducing the bending moments in these structures. RCRs are one of the major structures applied for reserving fluids to be used in drinking water networks. Usually, these structures have fixed shapes which are designed and calculated based on input discharges, the conditions of the structure's topology, and geotechnical locations with various combinations of static and dynamic loads. In this research, the elements of reservoir walls are first typed according to the performance analyzed; then the range of the membrane based on the thickness and the minimum and maximum cross sections of the bar used are determined in each element. This is done by considering the variable constraints, which are estimated by the maximum stress capacity. In the next phase, based on the reservoir analysis and using the algorithm of the PARIS connector, the related information is combined with the code for the PSO algorithm, i.e., an algorithm for a swarming search, to determine the optimum thickness of the cross sections for the reservoir membrane’s elements and the optimum cross section of the bar used. Based on very complex mathematical linear models for the correct embedding and angles related to achain of peripheral strengthening membranes, which optimize the vibration of the structure, a mutual relation is selected between the modeling software and the code for a particle swarm optimization algorithm. Finally, the comparative weight of the concrete reservoir optimized by the peripheral strengthening membrane is analyzed using common methods. This analysis shows a 19% decrease in the bar’s weight, a 20% decrease in the concrete’s weight, and a minimum 13% saving in construction costs according to the items of a checklist for a concrete reservoir at 10,000 m3.
Energy Technology Data Exchange (ETDEWEB)
Villas Boas Junior, Manoel; Strauss, Edilberto, E-mail: junior@lmp.ufrj.b [Instituto Federal de Educacao, Ciencia e Tecnologia do Ceara/ Universidade do Estado do Ceara, Itaperi, CE (Brazil). Mestrado Integrado em Computacao Aplicada; Nicolau, Andressa dos Santos; Schirru, Roberto, E-mail: andressa@lmp.ufrj.b [Coordenacao dos Programas de Pos-Graduacao de Engenharia (PEN/COPPE/UFRJ), Rio de Janeiro, RJ (Brazil). Programa de Engenharia Nuclear; Mello, Flavio Luis de [Universidade Federal do Rio de Janeiro (POLI/UFRJ), RJ (Brazil). Escola Politecnica. Dept. de Engenharia Eletronica e Computacao
2011-07-01
This article presents a computational model of the diagnostic system of transient. The model makes use of segmentation techniques applied to support decision making, based on identification of classes and optimized by Particle Swarm Optimization algorithm (PSO). The method proposed aims to classify an anomalous event in the signatures of three classes of the design basis transients postulated for the Angra 2 nuclear plant, where the PSO algorithm is used as a method of separation of classes, being responsible for finding the best centroid prototype vector of each accident/transient, ie equivalent to Voronoi vector that maximizes the number of correct classifications. To make the calculation of similarity between the set of the variables anomalous event in a given time t, and the prototype vector of variables of accident/transients, the metrics of Manhattan, Euclidean and Minkowski were used. The results obtained by the method proposed were compatible with others methods reported in the literature, allowing a solution that approximates the ideal solution, ie the Voronoi vectors. (author)
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Ahmed R. Abas
2012-07-01
Full Text Available In this paper, a new algorithm is presented for unsupervised learning of finite mixture models (FMMs using data set with missing values. This algorithm overcomes the local optima problem of the Expectation-Maximization (EM algorithm via integrating the EM algorithm with Particle Swarm Optimization (PSO. In addition, the proposed algorithm overcomes the problem of biased estimation due to overlapping clusters in estimating missing values in the input data set by integrating locally-tuned general regression neural networks with Optimal Completion Strategy (OCS. A comparison study shows the superiority of the proposed algorithm over other algorithms commonly used in the literature in unsupervised learning of FMM parameters that result in minimum mis-classification errors when used in clustering incomplete data set that is generated from overlapping clusters and these clusters are largely different in their sizes.
Swarm Optimization Methods in Microwave Imaging
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Andrea Randazzo
2012-01-01
Full Text Available Swarm intelligence denotes a class of new stochastic algorithms inspired by the collective social behavior of natural entities (e.g., birds, ants, etc.. Such approaches have been proven to be quite effective in several applicative fields, ranging from intelligent routing to image processing. In the last years, they have also been successfully applied in electromagnetics, especially for antenna synthesis, component design, and microwave imaging. In this paper, the application of swarm optimization methods to microwave imaging is discussed, and some recent imaging approaches based on such methods are critically reviewed.
Algorithmic Randomness as Foundation of Inductive Reasoning and Artificial Intelligence
Hutter, Marcus
2011-01-01
This article is a brief personal account of the past, present, and future of algorithmic randomness, emphasizing its role in inductive inference and artificial intelligence. It is written for a general audience interested in science and philosophy. Intuitively, randomness is a lack of order or predictability. If randomness is the opposite of determinism, then algorithmic randomness is the opposite of computability. Besides many other things, these concepts have been used to quantify Ockham's razor, solve the induction problem, and define intelligence.
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Patel G.C.M.
2016-09-01
Full Text Available The near net shaped manufacturing ability of squeeze casting process requiresto set the process variable combinations at their optimal levels to obtain both aesthetic appearance and internal soundness of the cast parts. The aesthetic and internal soundness of cast parts deal with surface roughness and tensile strength those can readily put the part in service without the requirement of costly secondary manufacturing processes (like polishing, shot blasting, plating, hear treatment etc.. It is difficult to determine the levels of the process variable (that is, pressure duration, squeeze pressure, pouring temperature and die temperature combinations for extreme values of the responses (that is, surface roughness, yield strength and ultimate tensile strength due to conflicting requirements. In the present manuscript, three population based search and optimization methods, namely genetic algorithm (GA, particle swarm optimization (PSO and multi-objective particle swarm optimization based on crowding distance (MOPSO-CD methods have been used to optimize multiple outputs simultaneously. Further, validation test has been conducted for the optimal casting conditions suggested by GA, PSO and MOPSO-CD. The results showed that PSO outperformed GA with regard to computation time.
An Accelerated Particle Swarm Optimization Algorithm on Parametric Optimization of WEDM of Die-Steel
Muthukumar, V.; Suresh Babu, A.; Venkatasamy, R.; Senthil Kumar, N.
2015-01-01
This study employed Accelerated Particle Swarm Optimization (APSO) algorithm to optimize the machining parameters that lead to a maximum Material Removal Rate (MRR), minimum surface roughness and minimum kerf width values for Wire Electrical Discharge Machining (WEDM) of AISI D3 die-steel. Four machining parameters that are optimized using APSO algorithm include Pulse on-time, Pulse off-time, Gap voltage, Wire feed. The machining parameters are evaluated by Taguchi's L9 Orthogonal Array (OA). Experiments are conducted on a CNC WEDM and output responses such as material removal rate, surface roughness and kerf width are determined. The empirical relationship between control factors and output responses are established by using linear regression models using Minitab software. Finally, APSO algorithm, a nature inspired metaheuristic technique, is used to optimize the WEDM machining parameters for higher material removal rate and lower kerf width with surface roughness as constraint. The confirmation experiments carried out with the optimum conditions show that the proposed algorithm was found to be potential in finding numerous optimal input machining parameters which can fulfill wide requirements of a process engineer working in WEDM industry.
Energy Technology Data Exchange (ETDEWEB)
Wang, Cheng-Der, E-mail: jdwang@iner.gov.tw [Nuclear Engineering Division, Institute of Nuclear Energy Research, No. 1000, Wenhua Rd., Jiaan Village, Longtan Township, Taoyuan County 32546, Taiwan, ROC (China); Lin, Chaung [National Tsing Hua University, Department of Engineering and System Science, 101, Section 2, Kuang Fu Road, Hsinchu 30013, Taiwan (China)
2013-02-15
Highlights: ► The PSO algorithm was adopted to automatically design a BWR CRP. ► The local search procedure was added to improve the result of PSO algorithm. ► The results show that the obtained CRP is the same good as that in the previous work. -- Abstract: This study developed a method for the automatic design of a boiling water reactor (BWR) control rod pattern (CRP) using the particle swarm optimization (PSO) algorithm. The PSO algorithm is more random compared to the rank-based ant system (RAS) that was used to solve the same BWR CRP design problem in the previous work. In addition, the local search procedure was used to make improvements after PSO, by adding the single control rod (CR) effect. The design goal was to obtain the CRP so that the thermal limits and shutdown margin would satisfy the design requirement and the cycle length, which is implicitly controlled by the axial power distribution, would be acceptable. The results showed that the same acceptable CRP found in the previous work could be obtained.
Guang, Chen; Qibo, Feng; Keqin, Ding; Zhan, Gao
2017-10-01
A subpixel displacement measurement method based on the combination of particle swarm optimization (PSO) and gradient algorithm (GA) was proposed for accuracy and speed optimization in GA, which is a subpixel displacement measurement method better applied in engineering practice. An initial integer-pixel value was obtained according to the global searching ability of PSO, and then gradient operators were adopted for a subpixel displacement search. A comparison was made between this method and GA by simulated speckle images and rigid-body displacement in metal specimens. The results showed that the computational accuracy of the combination of PSO and GA method reached 0.1 pixel in the simulated speckle images, or even 0.01 pixels in the metal specimen. Also, computational efficiency and the antinoise performance of the improved method were markedly enhanced.
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A. Muthukumar
2012-02-01
Full Text Available In general, the identification and verification are done by passwords, pin number, etc., which is easily cracked by others. In order to overcome this issue biometrics is a unique tool for authenticate an individual person. Nevertheless, unimodal biometric is suffered due to noise, intra class variations, spoof attacks, non-universality and some other attacks. In order to avoid these attacks, the multimodal biometrics i.e. combining of more modalities is adapted. In a biometric authentication system, the acceptance or rejection of an entity is dependent on the similarity score falling above or below the threshold. Hence this paper has focused on the security of the biometric system, because compromised biometric templates cannot be revoked or reissued and also this paper has proposed a multimodal system based on an evolutionary algorithm, Particle Swarm Optimization that adapts for varying security environments. With these two concerns, this paper had developed a design incorporating adaptability, authenticity and security.
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Mostafa Lotfi Forushani
2012-04-01
Full Text Available This paper presents an optimized controller around the longitudinal axis of multivariable system in one of the aircraft flight conditions. The controller is introduced in order to control the angle of attack from the pitch attitude angle independently (that is required for designing a set of direct force-modes for the longitudinal axis based on particle swarm optimization (PSO algorithm. The autopilot system for military or civil aircraft is an essential component and in this paper, the autopilot system via 6 degree of freedom model for the control and guidance of aircraft in which the autopilot design will perform based on defining the longitudinal and the lateral-directional axes are supposed. The effectiveness of the proposed controller is illustrated by considering HIMAT aircraft. The simulation results verify merits of the proposed controller.
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Saraiva J. T.
2012-10-01
Full Text Available The basic objective of Transmission Expansion Planning (TEP is to schedule a number of transmission projects along an extended planning horizon minimizing the network construction and operational costs while satisfying the requirement of delivering power safely and reliably to load centres along the horizon. This principle is quite simple, but the complexity of the problem and the impact on society transforms TEP on a challenging issue. This paper describes a new approach to solve the dynamic TEP problem, based on an improved discrete integer version of the Evolutionary Particle Swarm Optimization (EPSO meta-heuristic algorithm. The paper includes sections describing in detail the EPSO enhanced approach, the mathematical formulation of the TEP problem, including the objective function and the constraints, and a section devoted to the application of the developed approach to this problem. Finally, the use of the developed approach is illustrated using a case study based on the IEEE 24 bus 38 branch test system.
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Pang, X., E-mail: xpang@lanl.gov; Rybarcyk, L.J.
2014-03-21
Particle swarm optimization (PSO) and genetic algorithm (GA) are both nature-inspired population based optimization methods. Compared to GA, whose long history can trace back to 1975, PSO is a relatively new heuristic search method first proposed in 1995. Due to its fast convergence rate in single objective optimization domain, the PSO method has been extended to optimize multi-objective problems. In this paper, we will introduce the PSO method and its multi-objective extension, the MOPSO, apply it along with the MOGA (mainly the NSGA-II) to simulations of the LANSCE linac and operational set point optimizations. Our tests show that both methods can provide very similar Pareto fronts but the MOPSO converges faster.
Garlapati, Vijay Kumar; Vundavilli, Pandu Ranga; Banerjee, Rintu
2010-11-01
This paper presents the nature-inspired genetic algorithm (GA) and particle swarm optimization (PSO) approaches for optimization of fermentation conditions of lipase production for enhanced lipase activity. The central composite non-linear regression model of lipase production served as the optimization problem for PSO and GA approaches. The overall optimized fermentation conditions obtained thereby, when verified experimentally, have brought about a significant improvement (more than 15 U/gds (gram dry substrate)) in the lipase titer value. The performance of both optimization approaches in terms of computational time and convergence rate has been compared. The results show that the PSO approach (96.18 U/gds in 46 generations) has slightly better performance and possesses better convergence and computational efficiency than the GA approach (95.34 U/gds in 337 generations). Hence, the proposed PSO approach with the minimal parameter tuning is a viable tool for optimization of fermentation conditions of enzyme production.
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Jude Hemanth Duraisamy
2016-01-01
Full Text Available Image steganography is one of the ever growing computational approaches which has found its application in many fields. The frequency domain techniques are highly preferred for image steganography applications. However, there are significant drawbacks associated with these techniques. In transform based approaches, the secret data is embedded in random manner in the transform coefficients of the cover image. These transform coefficients may not be optimal in terms of the stego image quality and embedding capacity. In this work, the application of Genetic Algorithm (GA and Particle Swarm Optimization (PSO have been explored in the context of determining the optimal coefficients in these transforms. Frequency domain transforms such as Bandelet Transform (BT and Finite Ridgelet Transform (FRIT are used in combination with GA and PSO to improve the efficiency of the image steganography system.
A Survey on Evolutionary Algorithm Based Hybrid Intelligence in Bioinformatics
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Shan Li
2014-01-01
Full Text Available With the rapid advance in genomics, proteomics, metabolomics, and other types of omics technologies during the past decades, a tremendous amount of data related to molecular biology has been produced. It is becoming a big challenge for the bioinformatists to analyze and interpret these data with conventional intelligent techniques, for example, support vector machines. Recently, the hybrid intelligent methods, which integrate several standard intelligent approaches, are becoming more and more popular due to their robustness and efficiency. Specifically, the hybrid intelligent approaches based on evolutionary algorithms (EAs are widely used in various fields due to the efficiency and robustness of EAs. In this review, we give an introduction about the applications of hybrid intelligent methods, in particular those based on evolutionary algorithm, in bioinformatics. In particular, we focus on their applications to three common problems that arise in bioinformatics, that is, feature selection, parameter estimation, and reconstruction of biological networks.
Kun Zhang; Zhao Hu; Xiao-Ting Gan; Jian-Bo Fang
2016-01-01
Due to the fact that the fluctuation of network traffic is affected by various factors, accurate prediction of network traffic is regarded as a challenging task of the time series prediction process. For this purpose, a novel prediction method of network traffic based on QPSO algorithm and fuzzy wavelet neural network is proposed in this paper. Firstly, quantum-behaved particle swarm optimization (QPSO) was introduced. Then, the structure and operation algorithms of WFNN are presented. The pa...
Chicken Swarm Optimization Based on Elite Opposition-Based Learning
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Chiwen Qu
2017-01-01
Full Text Available Chicken swarm optimization is a new intelligent bionic algorithm, simulating the chicken swarm searching for food in nature. Basic algorithm is likely to fall into a local optimum and has a slow convergence rate. Aiming at these deficiencies, an improved chicken swarm optimization algorithm based on elite opposition-based learning is proposed. In cock swarm, random search based on adaptive t distribution is adopted to replace that based on Gaussian distribution so as to balance the global exploitation ability and local development ability of the algorithm. In hen swarm, elite opposition-based learning is introduced to promote the population diversity. Dimension-by-dimension greedy search mode is used to do local search for individual of optimal chicken swarm in order to improve optimization precision. According to the test results of 18 standard test functions and 2 engineering structure optimization problems, this algorithm has better effect on optimization precision and speed compared with basic chicken algorithm and other intelligent optimization algorithms.
A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications
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Yudong Zhang
2015-01-01
Full Text Available Particle swarm optimization (PSO is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO, population topology (as fully connected, von Neumann, ring, star, random, etc., hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization, extensions (to multiobjective, constrained, discrete, and binary optimization, theoretical analysis (parameter selection and tuning, and convergence analysis, and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms. On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.
Designing a mirrored Howland circuit with a particle swarm optimisation algorithm
Bertemes-Filho, Pedro; Negri, Lucas H.; Vincence, Volney C.
2016-06-01
Electrical impedance spectroscopy usually requires a wide bandwidth current source with high output impedance. Non-idealities of the operational amplifier (op-amp) degrade its performance. This work presents a particle swarm algorithm for extracting the main AC characteristics of the op-amp used to design a mirrored modified Howland current source circuit which satisfies both the output current and the impedance spectra required. User specifications were accommodated. Both resistive and biological loads were used in the simulations. The results showed that the algorithm can correctly identify the open-loop gain and the input and output resistance of the op-amp which best fit the performance requirements of the circuit. It was also shown that the higher the open-loop gain corner frequency the higher the output impedance of the circuit. The algorithm could be a powerful tool for developing a desirable current source for different bioimpedance medical and clinical applications, such as cancer tissue characterisation and tissue cell measurements.
Service Composition Optimization Method Based on Parallel Particle Swarm Algorithm on Spark
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Xing Guo
2017-01-01
Full Text Available Web service composition is one of the core technologies of realizing service-oriented computing. Web service composition satisfies the requirements of users to form new value-added services by composing existing services. As Cloud Computing develops, the emergence of Web services with different quality yet similar functionality has brought new challenges to service composition optimization problem. How to solve large-scale service composition in the Cloud Computing environment has become an urgent problem. To tackle this issue, this paper proposes a parallel optimization approach based on Spark distributed environment. Firstly, the parallel covering algorithm is used to cluster the Web services. Next, the multiple clustering centers obtained are used as the starting point of the particles to improve the diversity of the initial population. Then, according to the parallel data coding rules of resilient distributed dataset (RDD, the large-scale combination service is generated with the proposed algorithm named Spark Particle Swarm Optimization Algorithm (SPSO. Finally, the usage of particle elite selection strategy removes the inert particles to optimize the performance of the combination of service selection. This paper adopts real data set WS-Dream to prove the validity of the proposed method with a large number of experimental results.
Yang, Jie; Zhang, Pengcheng; Zhang, Liyuan; Shu, Huazhong; Li, Baosheng; Gui, Zhiguo
2017-01-01
In inverse treatment planning of intensity-modulated radiation therapy (IMRT), the objective function is typically the sum of the weighted sub-scores, where the weights indicate the importance of the sub-scores. To obtain a high-quality treatment plan, the planner manually adjusts the objective weights using a trial-and-error procedure until an acceptable plan is reached. In this work, a new particle swarm optimization (PSO) method which can adjust the weighting factors automatically was investigated to overcome the requirement of manual adjustment, thereby reducing the workload of the human planner and contributing to the development of a fully automated planning process. The proposed optimization method consists of three steps. (i) First, a swarm of weighting factors (i.e., particles) is initialized randomly in the search space, where each particle corresponds to a global objective function. (ii) Then, a plan optimization solver is employed to obtain the optimal solution for each particle, and the values of the evaluation functions used to determine the particle's location and the population global location for the PSO are calculated based on these results. (iii) Next, the weighting factors are updated based on the particle's location and the population global location. Step (ii) is performed alternately with step (iii) until the termination condition is reached. In this method, the evaluation function is a combination of several key points on the dose volume histograms. Furthermore, a perturbation strategy - the crossover and mutation operator hybrid approach - is employed to enhance the population diversity, and two arguments are applied to the evaluation function to improve the flexibility of the algorithm. In this study, the proposed method was used to develop IMRT treatment plans involving five unequally spaced 6MV photon beams for 10 prostate cancer cases. The proposed optimization algorithm yielded high-quality plans for all of the cases, without human
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A. A. Heidari
2017-09-01
Full Text Available Yin-Yang-pair optimization (YYPO is one of the latest metaheuristic algorithms (MA proposed in 2015 that tries to inspire the philosophy of balance between conflicting concepts. Particle swarm optimizer (PSO is one of the first population-based MA inspired by social behaviors of birds. In spite of PSO, the YYPO is not a nature inspired optimizer. It has a low complexity and starts with only two initial positions and can produce more points with regard to the dimension of target problem. Due to unique advantages of these methodologies and to mitigate the immature convergence and local optima (LO stagnation problems in PSO, in this work, a continuous hybrid strategy based on the behaviors of PSO and YYPO is proposed to attain the suboptimal solutions of uncapacitated warehouse location (UWL problems. This efficient hierarchical PSO-based optimizer (PSOYPO can improve the effectiveness of PSO on spatial optimization tasks such as the family of UWL problems. The performance of the proposed PSOYPO is verified according to some UWL benchmark cases. These test cases have been used in several works to evaluate the efficacy of different MA. Then, the PSOYPO is compared to the standard PSO, genetic algorithm (GA, harmony search (HS, modified HS (OBCHS, and evolutionary simulated annealing (ESA. The experimental results demonstrate that the PSOYPO can reveal a better or competitive efficacy compared to the PSO and other MA.
a New Hybrid Yin-Yang Swarm Optimization Algorithm for Uncapacitated Warehouse Location Problems
Heidari, A. A.; Kazemizade, O.; Hakimpour, F.
2017-09-01
Yin-Yang-pair optimization (YYPO) is one of the latest metaheuristic algorithms (MA) proposed in 2015 that tries to inspire the philosophy of balance between conflicting concepts. Particle swarm optimizer (PSO) is one of the first population-based MA inspired by social behaviors of birds. In spite of PSO, the YYPO is not a nature inspired optimizer. It has a low complexity and starts with only two initial positions and can produce more points with regard to the dimension of target problem. Due to unique advantages of these methodologies and to mitigate the immature convergence and local optima (LO) stagnation problems in PSO, in this work, a continuous hybrid strategy based on the behaviors of PSO and YYPO is proposed to attain the suboptimal solutions of uncapacitated warehouse location (UWL) problems. This efficient hierarchical PSO-based optimizer (PSOYPO) can improve the effectiveness of PSO on spatial optimization tasks such as the family of UWL problems. The performance of the proposed PSOYPO is verified according to some UWL benchmark cases. These test cases have been used in several works to evaluate the efficacy of different MA. Then, the PSOYPO is compared to the standard PSO, genetic algorithm (GA), harmony search (HS), modified HS (OBCHS), and evolutionary simulated annealing (ESA). The experimental results demonstrate that the PSOYPO can reveal a better or competitive efficacy compared to the PSO and other MA.
A bio-inspired swarm robot coordination algorithm for multiple target searching
Meng, Yan; Gan, Jing; Desai, Sachi
2008-04-01
The coordination of a multi-robot system searching for multi targets is challenging under dynamic environment since the multi-robot system demands group coherence (agents need to have the incentive to work together faithfully) and group competence (agents need to know how to work together well). In our previous proposed bio-inspired coordination method, Local Interaction through Virtual Stigmergy (LIVS), one problem is the considerable randomness of the robot movement during coordination, which may lead to more power consumption and longer searching time. To address these issues, an adaptive LIVS (ALIVS) method is proposed in this paper, which not only considers the travel cost and target weight, but also predicting the target/robot ratio and potential robot redundancy with respect to the detected targets. Furthermore, a dynamic weight adjustment is also applied to improve the searching performance. This new method a truly distributed method where each robot makes its own decision based on its local sensing information and the information from its neighbors. Basically, each robot only communicates with its neighbors through a virtual stigmergy mechanism and makes its local movement decision based on a Particle Swarm Optimization (PSO) algorithm. The proposed ALIVS algorithm has been implemented on the embodied robot simulator, Player/Stage, in a searching target. The simulation results demonstrate the efficiency and robustness in a power-efficient manner with the real-world constraints.
A Novel Strategy for Minimum Attribute Reduction Based on Rough Set Theory and Fish Swarm Algorithm
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Yuebin Su
2017-01-01
Full Text Available For data mining, reducing the unnecessary redundant attributes which was known as attribute reduction (AR, in particular, reducts with minimal cardinality, is an important preprocessing step. In the paper, by a coding method of combination subset of attributes set, a novel search strategy for minimal attribute reduction based on rough set theory (RST and fish swarm algorithm (FSA is proposed. The method identifies the core attributes by discernibility matrix firstly and all the subsets of noncore attribute sets with the same cardinality were encoded into integers as the individuals of FSA. Then, the evolutionary direction of the individual is limited to a certain extent by the coding method. The fitness function of an individual is defined based on the attribute dependency of RST, and FSA was used to find the optimal set of reducts. In each loop, if the maximum attribute dependency and the attribute dependency of condition attribute set are equal, then the algorithm terminates, otherwise adding a single attribute to the next loop. Some well-known datasets from UCI were selected to verify this method. The experimental results show that the proposed method searches the minimal attribute reduction set effectively and it has the excellent global search ability.
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Xu Jingqi
2014-04-01
Full Text Available The performance of improved glowworm swarm optimization (GSO algorithm and its application in coverage optimization of WSNs are analyzed in this paper. The global convergence analysis of basic GSO is made. In order to improve the GSO convergence efficiency, an improved GSO (IGSO is presented, and it is proved to be guaranteed to the global optimization with probability one. Further, a new coverage optimization algorithm for WSNS, based on IGSO, is presented according to the analysis of GSO. A model of coverage optimization in WSNS is built up by taking node uniformity and network coverage rate as the criterion, and the relationship between node redundancy and network coverage rate and the node dormancy strategy are presented. Then the deployment of nodes is divided into different stages, and the IGSO is used to solve the model in each stage. Through testing classical test functions and optimizing the problems of coverage in WSNS, the simulation results show that the IGSO achieves more reasonable results and can effectively provide the optimal solution of network coverage.
OPTIMIZATION OF A PULTRUSION PROCESS USING FINITE DIFFERENCE AND PARTICLE SWARM ALGORITHMS
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L. S. Santos
2015-06-01
Full Text Available AbstractPultrusion is one of several manufacturing processes for reinforced polymer composites. In this process fibers are continuously pulled through a resin bath and, after impregnation, the fiber-resin assembly is cured in a heated forming die. In order to obtain a polymeric composite with good properties (high and uniform degree of cure and a process with a minimum of wasted energy, an optimization procedure is necessary to calculate the optimal temperature profile. The present work suggests a new strategy to minimize the energy rate taking into account the final quality of the product. For this purpose the particle swarm optimization (PSO algorithm and the computer code DASSL were used to solve the differential algebraic equation that represents the mathematical model. The results of the optimization procedure were compared with results reported in the literature and showed that this strategy may be a good alternative to find the best operational point and to test other heat policies in order to improve the material quality and minimize the energy cost. In addition, the robustness and fast convergence of the algorithm encourage industrial implementation for the inference of the degree of cure and optimization.
Wang, Ji; Zhang, Ru; Yan, Yuting; Dong, Xiaoqiang; Li, Jun Ming
2017-05-01
Hazardous gas leaks in the atmosphere can cause significant economic losses in addition to environmental hazards, such as fires and explosions. A three-stage hazardous gas leak source localization method was developed that uses movable and stationary gas concentration sensors. The method calculates a preliminary source inversion with a modified genetic algorithm (MGA) and has the potential to crossover with eliminated individuals from the population, following the selection of the best candidate. The method then determines a search zone using Markov Chain Monte Carlo (MCMC) sampling, utilizing a partial evaluation strategy. The leak source is then accurately localized using a modified guaranteed convergence particle swarm optimization algorithm with several bad-performing individuals, following selection of the most successful individual with dynamic updates. The first two stages are based on data collected by motionless sensors, and the last stage is based on data from movable robots with sensors. The measurement error adaptability and the effect of the leak source location were analyzed. The test results showed that this three-stage localization process can localize a leak source within 1.0 m of the source for different leak source locations, with measurement error standard deviation smaller than 2.0.
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Sahazati Md Rozali
2014-01-01
Full Text Available This paper presents backstepping controller design for tracking purpose of nonlinear system. Since the performance of the designed controller depends on the value of control parameters, gravitational search algorithm (GSA and particle swarm optimization (PSO techniques are used to optimise these parameters in order to achieve a predefined system performance. The performance is evaluated based on the tracking error between reference input given to the system and the system output. Then, the efficacy of the backstepping controller is verified in simulation environment under various system setup including both the system subjected to external disturbance and without disturbance. The simulation results show that backstepping with particle swarm optimization technique performs better than the similar controller with gravitational search algorithm technique in terms of output response and tracking error.
Using Genetic Algorithms in Secured Business Intelligence Mobile Applications
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Silvia TRIF
2011-01-01
Full Text Available The paper aims to assess the use of genetic algorithms for training neural networks used in secured Business Intelligence Mobile Applications. A comparison is made between classic back-propagation method and a genetic algorithm based training. The design of these algorithms is presented. A comparative study is realized for determining the better way of training neural networks, from the point of view of time and memory usage. The results show that genetic algorithms based training offer better performance and memory usage than back-propagation and they are fit to be implemented on mobile devices.
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Castellano, T.; De Palma, L.; Laneve, D.; Strippoli, V.; Cuccovilllo, A.; Prudenzano, F. [Electrical and Information Engineering Department (DEI), Polytechnic Institute of Bari, 4 Orabona Street, CAP 70125, Bari, (Italy); Dimiccoli, V.; Losito, O.; Prisco, R. [ITEL Telecomunicazioni, 39 Labriola Street, CAP 70037, Ruvo di Puglia, Bari, (Italy)
2015-07-01
A homemade computer code for designing a Side- Coupled Linear Accelerator (SCL) is written. It integrates a simplified model of SCL tanks with the Particle Swarm Optimization (PSO) algorithm. The computer code main aim is to obtain useful guidelines for the design of Linear Accelerator (LINAC) resonant cavities. The design procedure, assisted via the aforesaid approach seems very promising, allowing future improvements towards the optimization of actual accelerating geometries. (authors)
Wolf Pack Algorithm for Unconstrained Global Optimization
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Hu-Sheng Wu
2014-01-01
Full Text Available The wolf pack unites and cooperates closely to hunt for the prey in the Tibetan Plateau, which shows wonderful skills and amazing strategies. Inspired by their prey hunting behaviors and distribution mode, we abstracted three intelligent behaviors, scouting, calling, and besieging, and two intelligent rules, winner-take-all generation rule of lead wolf and stronger-survive renewing rule of wolf pack. Then we proposed a new heuristic swarm intelligent method, named wolf pack algorithm (WPA. Experiments are conducted on a suit of benchmark functions with different characteristics, unimodal/multimodal, separable/nonseparable, and the impact of several distance measurements and parameters on WPA is discussed. What is more, the compared simulation experiments with other five typical intelligent algorithms, genetic algorithm, particle swarm optimization algorithm, artificial fish swarm algorithm, artificial bee colony algorithm, and firefly algorithm, show that WPA has better convergence and robustness, especially for high-dimensional functions.
Abedi, Kambiz; Mirjalili, Seyed Mohammad
2015-03-01
Recently, majority of current research in the field of designing Phonic Crystal Waveguides (PCW) focus in extracting the relations between output slow light properties of PCW and structural parameters through a huge number of tedious non-systematic simulations in order to introduce better designs. This paper proposes a novel systematic approach which can be considered as a shortcut to alleviate the difficulties and human involvements in designing PCWs. In the proposed method, the problem of PCW design is first formulated as an optimization problem. Then, an optimizer is employed in order to automatically find the optimum design for the formulated PCWs. Meanwhile, different constraints are also considered during optimization with the purpose of applying physical limitations to the final optimum structure. As a case study, the structure of a Bragg-like Corrugation Slotted PCWs (BCSPCW) is optimized by using the proposed method. One of the most computationally powerful techniques in Computational Intelligence (CI) called Particle Swarm Optimization (PSO) is employed as an optimizer to automatically find the optimum structure for BCSPCW. The optimization process is done by considering five constraints to guarantee the feasibility of the final optimized structures and avoid band mixing. Numerical results demonstrate that the proposed method is able to find an optimum structure for BCSPCW with 172% and 100% substantial improvements in the bandwidth and Normalized Delay-Bandwidth Product (NDBP) respectively compared to the best current structure in the literature. Moreover, there is a time domain analysis at the end of the paper which verifies the performance of the optimized structure and proves that this structure has low distortion and attenuation simultaneously.
Time domain electromagnetic 1D inversion using genetic algorithm and particle swarm optimization
Yogi, Ida Bagus Suananda; Widodo
2017-07-01
Most of geophysical data needs to be inverted, so that the inversion results can be interpreted further. There are two mayor methods to approach inversion calculation, they are least square methods with its derivative, and global optimization methods. The global optimization methods have two advantages over least square methods and its derivative; the inversion result are not sensitive to the starting model and this method can get results from global minimum instead of local minimum. These advantages make the global optimization methods give better results when priori data is unavailable. In this research we tried to implement genetic algorithm (GA) and particle swarm optimization (PSO) to do inversion of time domain electromagnetic (TDEM) 1D data with central loop configuration. The inversions were applied for synthetic. The starting models were varied from the closest to the furthest from the real synthetic models. After that, the results and the processes from the two global optimization methods were compared. This comparison results showed that the results and the performance were not much different. Both inversion results give similar models with the synthetic models. At the end of the research, the global optimizations methods were applied to TDEM real data from Volvi Basin, Greece. The inversion results of real data showed that there are two mayor resistivity layers for three layers model inversion and four resistivity layers for four and five layers model inversion. Overall PSO and GA gave similar results. However, PSO gave easier adjustment for the inversion process than the GA.
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Michala Jakubcová
2015-01-01
Full Text Available The presented paper provides the analysis of selected versions of the particle swarm optimization (PSO algorithm. The tested versions of the PSO were combined with the shuffling mechanism, which splits the model population into complexes and performs distributed PSO optimization. One of them is a new proposed PSO modification, APartW, which enhances the global exploration and local exploitation in the parametric space during the optimization process through the new updating mechanism applied on the PSO inertia weight. The performances of four selected PSO methods were tested on 11 benchmark optimization problems, which were prepared for the special session on single-objective real-parameter optimization CEC 2005. The results confirm that the tested new APartW PSO variant is comparable with other existing distributed PSO versions, AdaptW and LinTimeVarW. The distributed PSO versions were developed for finding the solution of inverse problems related to the estimation of parameters of hydrological model Bilan. The results of the case study, made on the selected set of 30 catchments obtained from MOPEX database, show that tested distributed PSO versions provide suitable estimates of Bilan model parameters and thus can be used for solving related inverse problems during the calibration process of studied water balance hydrological model.
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Hongjun Li
2012-01-01
Full Text Available This paper proposes a modified particle swarm optimization algorithm coupled with the finite element limit equilibrium method (FELEM for the minimum factor of safety and the location of associated noncircular critical failure surfaces for various geotechnical practices. During the search process, the stress compatibility constraints coupled with the geometrical and kinematical compatibility constraints are firstly established based on the features of slope geometry and stress distribution to guarantee realistic slip surfaces from being unreasonable. Furthermore, in the FELEM, based on rigorous theoretical analyses and derivation, it is noted that the physical meaning of the factor of safety can be formulated on the basis of strength reserving theory rather than the overloading theory. Consequently, compared with the limit equilibrium method (LEM and the shear strength reduction method (SSRM through several numerical examples, the FELEM in conjunction with the improved search strategy is proved to be an effective and efficient approach to routine analysis and design in geotechnical practices with a high level of confidence.
Particle swarm optimization algorithm for optimizing assignment of blood in blood banking system.
Olusanya, Micheal O; Arasomwan, Martins A; Adewumi, Aderemi O
2015-01-01
This paper reports the performance of particle swarm optimization (PSO) for the assignment of blood to meet patients' blood transfusion requests for blood transfusion. While the drive for blood donation lingers, there is need for effective and efficient management of available blood in blood banking systems. Moreover, inherent danger of transfusing wrong blood types to patients, unnecessary importation of blood units from external sources, and wastage of blood products due to nonusage necessitate the development of mathematical models and techniques for effective handling of blood distribution among available blood types in order to minimize wastages and importation from external sources. This gives rise to the blood assignment problem (BAP) introduced recently in literature. We propose a queue and multiple knapsack models with PSO-based solution to address this challenge. Simulation is based on sets of randomly generated data that mimic real-world population distribution of blood types. Results obtained show the efficiency of the proposed algorithm for BAP with no blood units wasted and very low importation, where necessary, from outside the blood bank. The result therefore can serve as a benchmark and basis for decision support tools for real-life deployment.
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Xiangdong Qian
2012-01-01
Full Text Available Delamination is a type of representative damage in composite structures, severely degrading structural integrity and reliability. The identification of delamination is commonly treated as an issue of nondestructive testing. Differing from existing studies, a hybrid optimization algorithm (HOA, combining particle swarm optimization (PSO with simplex method (SM, is proposed to identify delamination in laminated beams. The objective function of the optimization problem is created using delamination variables (optimization parameters together with actually measured modal frequencies. The HOA adopts a hierarchical and cooperative regime of global search and local search to optimize the objective function. The PSO performs global search for objective function space to achieve a preliminary solution specifying a local potential space. Initialized by this preliminary solution, the SM executes local search for the local potential space to explore the optimal solution. The HOA is validated by a series of simulated delamination scenarios, and the results show that it can identify delamination in laminated beams with decent accuracy, reliability and efficiency. The method proposed holds promise for establishing online damage detection system beneficial for health monitoring of laminated composite structures.
Aranza, M. F.; Kustija, J.; Trisno, B.; Hakim, D. L.
2016-04-01
PID Controller (Proportional Integral Derivative) was invented since 1910, but till today still is used in industries, even though there are many kind of modern controllers like fuzz controller and neural network controller are being developed. Performance of PID controller is depend on on Proportional Gain (Kp), Integral Gain (Ki) and Derivative Gain (Kd). These gains can be got by using method Ziegler-Nichols (ZN), gain-phase margin, Root Locus, Minimum Variance dan Gain Scheduling however these methods are not optimal to control systems that nonlinear and have high-orde, in addition, some methods relative hard. To solve those obstacles, particle swarm optimization (PSO) algorithm is proposed to get optimal Kp, Ki and Kd. PSO is proposed because PSO has convergent result and not require many iterations. On this research, PID controller is applied on AVR (Automatic Voltage Regulator). Based on result of analyzing transient, stability Root Locus and frequency response, performance of PID controller is better than Ziegler-Nichols.
Particle Swarm Optimization Algorithm for Optimizing Assignment of Blood in Blood Banking System
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Micheal O. Olusanya
2015-01-01
Full Text Available This paper reports the performance of particle swarm optimization (PSO for the assignment of blood to meet patients’ blood transfusion requests for blood transfusion. While the drive for blood donation lingers, there is need for effective and efficient management of available blood in blood banking systems. Moreover, inherent danger of transfusing wrong blood types to patients, unnecessary importation of blood units from external sources, and wastage of blood products due to nonusage necessitate the development of mathematical models and techniques for effective handling of blood distribution among available blood types in order to minimize wastages and importation from external sources. This gives rise to the blood assignment problem (BAP introduced recently in literature. We propose a queue and multiple knapsack models with PSO-based solution to address this challenge. Simulation is based on sets of randomly generated data that mimic real-world population distribution of blood types. Results obtained show the efficiency of the proposed algorithm for BAP with no blood units wasted and very low importation, where necessary, from outside the blood bank. The result therefore can serve as a benchmark and basis for decision support tools for real-life deployment.
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A. M. Dalavi
2015-09-01
Full Text Available Optimization of hole-making operations plays a crucial role in which tool travel and tool switch scheduling are the two major issues. Industrial applications such as moulds, dies, engine block etc. consist of large number of holes having different diameters, depths and surface finish. This results into to a large number of machining operations like drilling, reaming or tapping to achieve the final size of individual hole. Optimal sequence of operations and associated cutting speeds, which reduce the overall processing cost of these hole-making operations are essential to reach desirable products. In order to achieve this, an attempt is made by developing an effective methodology. An example of the injection mould is considered to demonstrate the proposed approach. The optimization of this example is carried out using recently developed particle swarm optimization (PSO algorithm. The results obtained using PSO are compared with those obtained using tabu search method. It is observed that results obtained using PSO are slightly better than those obtained using tabu search method.
The application of cat swarm optimisation algorithm in classifying small loan performance
Kencana, Eka N.; Kiswanti, Nyoman; Sari, Kartika
2017-10-01
It is common for banking system to analyse the feasibility of credit application before its approval. Although this process has been carefully done, there is no warranty that all credits will be repaid smoothly. This study aimed to know the accuracy of Cat Swarm Optimisation (CSO) algorithm in classifying small loans’ performance that is approved by Bank Rakyat Indonesia (BRI), one of several public banks in Indonesia. Data collected from 200 lenders were used in this work. The data matrix consists of 9 independent variables that represent profile of the credit, and one categorical dependent variable reflects credit’s performance. Prior to the analyses, data was divided into two data subset with equal size. Ordinal logistic regression (OLR) procedure is applied for the first subset and gave 3 out of 9 independent variables i.e. the amount of credit, credit’s period, and income per month of lender proved significantly affect credit performance. By using significantly estimated parameters from OLR procedure as the initial values for observations at the second subset, CSO procedure started. This procedure gave 76 percent of classification accuracy of credit performance, slightly better compared to 64 percent resulted from OLR procedure.
Guo, Y C; Wang, H; Zhang, B L
2015-10-02
This study proposes use of the DNA genetic artificial fish swarm constant modulus blind equalization algorithm (DNA-G-AFS-CMBEA) to overcome the local convergence of the CMBEA. In this proposed algorithm, after the fusion of the fast convergence of the AFS algorithm and the global search capability of the DNA-G algorithm to drastically optimize the position vector of the artificial fish, the global optimal position vector is obtained and used as the initial optimal weight vector of the CMBEA. The result of application of this improved method in medical image processing demonstrates that the proposed algorithm outperforms the CMBEA and the AFS-CMBEA in removing the noise in a medical image and improving the peak signal to noise ratio.
Wang, Yan; Huang, Song; Ji, Zhicheng
2017-07-01
This paper presents a hybrid particle swarm optimization and gravitational search algorithm based on hybrid mutation strategy (HGSAPSO-M) to optimize economic dispatch (ED) including distributed generations (DGs) considering market-based energy pricing. A daily ED model was formulated and a hybrid mutation strategy was adopted in HGSAPSO-M. The hybrid mutation strategy includes two mutation operators, chaotic mutation, Gaussian mutation. The proposed algorithm was tested on IEEE-33 bus and results show that the approach is effective for this problem.
Song, Lei; Zhang, Bo
2017-07-01
Nowadays, the grid faces much more challenges caused by wind power and the accessing of electric vehicles (EVs). Based on the potentiality of coordinated dispatch, a model of wind-EVs coordinated dispatch was developed. Then, A bi-level particle swarm optimization algorithm for solving the model was proposed in this paper. The application of this algorithm to 10-unit test system carried out that coordinated dispatch can benefit the power system from the following aspects: (1) Reducing operating costs; (2) Improving the utilization of wind power; (3) Stabilizing the peak-valley difference.
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Zhigang Lian
2010-01-01
Full Text Available The Job-shop scheduling problem (JSSP is a branch of production scheduling, which is among the hardest combinatorial optimization problems. Many different approaches have been applied to optimize JSSP, but for some JSSP even with moderate size cannot be solved to guarantee optimality. The original particle swarm optimization algorithm (OPSOA, generally, is used to solve continuous problems, and rarely to optimize discrete problems such as JSSP. In OPSOA, through research I find that it has a tendency to get stuck in a near optimal solution especially for middle and large size problems. The local and global search combine particle swarm optimization algorithm (LGSCPSOA is used to solve JSSP, where particle-updating mechanism benefits from the searching experience of one particle itself, the best of all particles in the swarm, and the best of particles in neighborhood population. The new coding method is used in LGSCPSOA to optimize JSSP, and it gets all sequences are feasible solutions. Three representative instances are made computational experiment, and simulation shows that the LGSCPSOA is efficacious for JSSP to minimize makespan.
Pineda, L.E.P.U.; Eiben, A.E.; van Steen, M.R.
2012-01-01
Robotic swarms offer flexibility, robustness, and scalability. For successful operation they need appropriate communication strategies that should be dynamically adaptable to possibly changing environmental requirements. In this paper we try to achieve this through evolving communication on-the-fly.
Venter, Gerhard; Sobieszczanski-Sobieski Jaroslaw
2002-01-01
The purpose of this paper is to show how the search algorithm known as particle swarm optimization performs. Here, particle swarm optimization is applied to structural design problems, but the method has a much wider range of possible applications. The paper's new contributions are improvements to the particle swarm optimization algorithm and conclusions and recommendations as to the utility of the algorithm, Results of numerical experiments for both continuous and discrete applications are presented in the paper. The results indicate that the particle swarm optimization algorithm does locate the constrained minimum design in continuous applications with very good precision, albeit at a much higher computational cost than that of a typical gradient based optimizer. However, the true potential of particle swarm optimization is primarily in applications with discrete and/or discontinuous functions and variables. Additionally, particle swarm optimization has the potential of efficient computation with very large numbers of concurrently operating processors.
Sheykhizadeh, Saheleh; Naseri, Abdolhossein
2018-04-05
Variable selection plays a key role in classification and multivariate calibration. Variable selection methods are aimed at choosing a set of variables, from a large pool of available predictors, relevant to the analyte concentrations estimation, or to achieve better classification results. Many variable selection techniques have now been introduced among which, those which are based on the methodologies of swarm intelligence optimization have been more respected during a few last decades since they are mainly inspired by nature. In this work, a simple and new variable selection algorithm is proposed according to the invasive weed optimization (IWO) concept. IWO is considered a bio-inspired metaheuristic mimicking the weeds ecological behavior in colonizing as well as finding an appropriate place for growth and reproduction; it has been shown to be very adaptive and powerful to environmental changes. In this paper, the first application of IWO, as a very simple and powerful method, to variable selection is reported using different experimental datasets including FTIR and NIR data, so as to undertake classification and multivariate calibration tasks. Accordingly, invasive weed optimization - linear discrimination analysis (IWO-LDA) and invasive weed optimization- partial least squares (IWO-PLS) are introduced for multivariate classification and calibration, respectively. Copyright © 2018 Elsevier B.V. All rights reserved.
National Research Council Canada - National Science Library
Claudia Eugenia Toca Torres
2014-01-01
By using a review of English literature on Swarm Intelligence and other meta-heuristics over the last sixteen years, the state of the art of three of its features, self-organization, flexibility and...
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D. Ramyachitra
2015-09-01
Full Text Available Microarray technology allows simultaneous measurement of the expression levels of thousands of genes within a biological tissue sample. The fundamental power of microarrays lies within the ability to conduct parallel surveys of gene expression using microarray data. The classification of tissue samples based on gene expression data is an important problem in medical diagnosis of diseases such as cancer. In gene expression data, the number of genes is usually very high compared to the number of data samples. Thus the difficulty that lies with data are of high dimensionality and the sample size is small. This research work addresses the problem by classifying resultant dataset using the existing algorithms such as Support Vector Machine (SVM, K-nearest neighbor (KNN, Interval Valued Classification (IVC and the improvised Interval Value based Particle Swarm Optimization (IVPSO algorithm. Thus the results show that the IVPSO algorithm outperformed compared with other algorithms under several performance evaluation functions.
Ramyachitra, D; Sofia, M; Manikandan, P
2015-09-01
Microarray technology allows simultaneous measurement of the expression levels of thousands of genes within a biological tissue sample. The fundamental power of microarrays lies within the ability to conduct parallel surveys of gene expression using microarray data. The classification of tissue samples based on gene expression data is an important problem in medical diagnosis of diseases such as cancer. In gene expression data, the number of genes is usually very high compared to the number of data samples. Thus the difficulty that lies with data are of high dimensionality and the sample size is small. This research work addresses the problem by classifying resultant dataset using the existing algorithms such as Support Vector Machine (SVM), K-nearest neighbor (KNN), Interval Valued Classification (IVC) and the improvised Interval Value based Particle Swarm Optimization (IVPSO) algorithm. Thus the results show that the IVPSO algorithm outperformed compared with other algorithms under several performance evaluation functions.
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Li Ran
2017-01-01
Full Text Available Optimal allocation of generalized power sources in distribution network is researched. A simple index of voltage stability is put forward. Considering the investment and operation benefit, the stability of voltage and the pollution emissions of generalized power sources in distribution network, a multi-objective optimization planning model is established. A multi-objective particle swarm optimization algorithm is proposed to solve the optimal model. In order to improve the global search ability, the strategies of fast non-dominated sorting, elitism and crowding distance are adopted in this algorithm. Finally, tested the model and algorithm by IEEE-33 node system to find the best configuration of GP, the computed result shows that with the generalized power reasonable access to the active distribution network, the investment benefit and the voltage stability of the system is improved, and the proposed algorithm has better global search capability.
Rasmussen, Thomas Kiel; Krink, Thiemo
2003-11-01
Multiple sequence alignment (MSA) is one of the basic problems in computational biology. Realistic problem instances of MSA are computationally intractable for exact algorithms. One way to tackle MSA is to use Hidden Markov Models (HMMs), which are known to be very powerful in the related problem domain of speech recognition. However, the training of HMMs is computationally hard and there is no known exact method that can guarantee optimal training within reasonable computing time. Perhaps the most powerful training method is the Baum-Welch algorithm, which is fast, but bears the problem of stagnation at local optima. In the study reported in this paper, we used a hybrid algorithm combining particle swarm optimization with evolutionary algorithms to train HMMs for the alignment of protein sequences. Our experiments show that our approach yields better alignments for a set of benchmark protein sequences than the most commonly applied HMM training methods, such as Baum-Welch and Simulated Annealing.
Gholami, Ali; Honarvar, Farhang; Abrishami Moghaddam, Hamid
2017-06-01
This paper presents an accurate and easy-to-implement algorithm for estimating the parameters of the asymmetric Gaussian chirplet model (AGCM) used for modeling echoes measured in ultrasonic nondestructive testing (NDT) of materials. The proposed algorithm is a combination of particle swarm optimization (PSO) and Levenberg-Marquardt (LM) algorithms. PSO does not need an accurate initial guess and quickly converges to a reasonable output while LM needs a good initial guess in order to provide an accurate output. In the combined algorithm, PSO is run first to provide a rough estimate of the output and this result is consequently inputted to the LM algorithm for more accurate estimation of parameters. To apply the algorithm to signals with multiple echoes, the space alternating generalized expectation maximization (SAGE) is used. The proposed combined algorithm is robust and accurate. To examine the performance of the proposed algorithm, it is applied to a number of simulated echoes having various signal to noise ratios. The combined algorithm is also applied to a number of experimental ultrasonic signals. The results corroborate the accuracy and reliability of the proposed combined algorithm.
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Chao Li
2017-11-01
Full Text Available Of all the renewable power generation technologies, solar tower power system is expected to be the most promising technology that is capable of large-scale electricity production. However, the optimization of heliostat field layout is a complicated process, in which thousands of heliostats have to be considered for any heliostat field optimization process. Therefore, in this paper, in order to optimize the heliostat field to obtain the highest energy collected per unit cost (ECUC, a mathematical model of a heliostat field and a hybrid algorithm combining particle swarm optimization algorithm and genetic algorithm (PSO-GA are coded in Matlab and the heliostat field in Lhasa is investigated as an example. The results show that, after optimization, the annual efficiency of the heliostat field increases by approximately six percentage points, and the ECUC increases from 12.50 MJ/USD to 12.97 MJ/USD, increased about 3.8%. Studies on the key parameters indicate that: for un-optimized filed, ECUC first peaks and then decline with the increase of the number of heliostats in the first row of the field (Nhel1. By contrast, for optimized field, ECUC increases with Nhel1. What is more, for both the un-optimized and optimized field, ECUC increases with tower height and decreases with the cost of heliostat mirror collector.
AN INTELLIGENT VERTICAL HANDOVER DECISION ALGORITHM FOR WIRELESS HETEROGENEOUS NETWORKS
V. Anantha Narayanan; Rajeswari, A; Sureshkumar, V.
2014-01-01
The Next Generation Wireless Networks (NGWN) should be compatible with other communication technologies to offer the best connectivity to the mobile terminal which can access any IP based services at any time from any network without the knowledge of its user. It requires an intelligent vertical handover decision making algorithm to migrate between technologies that enable seamless mobility, always best connection and minimal terminal power consumption. Currently existing decision engines are...
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Gys Albertus Marthinus Meiring
2015-12-01
Full Text Available In this paper the various driving style analysis solutions are investigated. An in-depth investigation is performed to identify the relevant machine learning and artificial intelligence algorithms utilised in current driver behaviour and driving style analysis systems. This review therefore serves as a trove of information, and will inform the specialist and the student regarding the current state of the art in driver style analysis systems, the application of these systems and the underlying artificial intelligence algorithms applied to these applications. The aim of the investigation is to evaluate the possibilities for unique driver identification utilizing the approaches identified in other driver behaviour studies. It was found that Fuzzy Logic inference systems, Hidden Markov Models and Support Vector Machines consist of promising capabilities to address unique driver identification algorithms if model complexity can be reduced.
Meiring, Gys Albertus Marthinus; Myburgh, Hermanus Carel
2015-12-04
In this paper the various driving style analysis solutions are investigated. An in-depth investigation is performed to identify the relevant machine learning and artificial intelligence algorithms utilised in current driver behaviour and driving style analysis systems. This review therefore serves as a trove of information, and will inform the specialist and the student regarding the current state of the art in driver style analysis systems, the application of these systems and the underlying artificial intelligence algorithms applied to these applications. The aim of the investigation is to evaluate the possibilities for unique driver identification utilizing the approaches identified in other driver behaviour studies. It was found that Fuzzy Logic inference systems, Hidden Markov Models and Support Vector Machines consist of promising capabilities to address unique driver identification algorithms if model complexity can be reduced.
Livari, As. Ali; Malekynia, B.; Livari, Ak. A.; Khoda-Bakhsh, R.
2017-11-01
When it was found out that the ignition of nuclear fusion hinges upon input energy laser, the efforts in order to make giant lasers began, and energy gains of DT fuel were obtained. But due to the neutrons generation and emitted radioactivity from DT reaction, gains of fuels like Proton-Lithium (7) were also adverted. Therefore, making larger and powerful lasers was followed. Here, using new versions of particle swarm optimization algorithm, it will be shown that available maximum gain of Proton-Lithium (7) is reached only at energies about 1014 J, and not only the highest input energy is not helpful but the efficiency is also decreased.
Janaki Sathya, D.; Geetha, K.
2017-12-01
Automatic mass or lesion classification systems are developed to aid in distinguishing between malignant and benign lesions present in the breast DCE-MR images, the systems need to improve both the sensitivity and specificity of DCE-MR image interpretation in order to be successful for clinical use. A new classifier (a set of features together with a classification method) based on artificial neural networks trained using artificial fish swarm optimization (AFSO) algorithm is proposed in this paper. The basic idea behind the proposed classifier is to use AFSO algorithm for searching the best combination of synaptic weights for the neural network. An optimal set of features based on the statistical textural features is presented. The investigational outcomes of the proposed suspicious lesion classifier algorithm therefore confirm that the resulting classifier performs better than other such classifiers reported in the literature. Therefore this classifier demonstrates that the improvement in both the sensitivity and specificity are possible through automated image analysis.
Hierarchical Swarm Model: A New Approach to Optimization
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Hanning Chen
2010-01-01
Full Text Available This paper presents a novel optimization model called hierarchical swarm optimization (HSO, which simulates the natural hierarchical complex system from where more complex intelligence can emerge for complex problems solving. This proposed model is intended to suggest ways that the performance of HSO-based algorithms on complex optimization problems can be significantly improved. This performance improvement is obtained by constructing the HSO hierarchies, which means that an agent in a higher level swarm can be composed of swarms of other agents from lower level and different swarms of different levels evolve on different spatiotemporal scale. A novel optimization algorithm (named PS2O, based on the HSO model, is instantiated and tested to illustrate the ideas of HSO model clearly. Experiments were conducted on a set of 17 benchmark optimization problems including both continuous and discrete cases. The results demonstrate remarkable performance of the PS2O algorithm on all chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms.
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Wenliao Du
2013-01-01
Full Text Available Promptly and accurately dealing with the equipment breakdown is very important in terms of enhancing reliability and decreasing downtime. A novel fault diagnosis method PSO-RVM based on relevance vector machines (RVM with particle swarm optimization (PSO algorithm for plunger pump in truck crane is proposed. The particle swarm optimization algorithm is utilized to determine the kernel width parameter of the kernel function in RVM, and the five two-class RVMs with binary tree architecture are trained to recognize the condition of mechanism. The proposed method is employed in the diagnosis of plunger pump in truck crane. The six states, including normal state, bearing inner race fault, bearing roller fault, plunger wear fault, thrust plate wear fault, and swash plate wear fault, are used to test the classification performance of the proposed PSO-RVM model, which compared with the classical models, such as back-propagation artificial neural network (BP-ANN, ant colony optimization artificial neural network (ANT-ANN, RVM, and support vectors, machines with particle swarm optimization (PSO-SVM, respectively. The experimental results show that the PSO-RVM is superior to the first three classical models, and has a comparative performance to the PSO-SVM, the corresponding diagnostic accuracy achieving as high as 99.17% and 99.58%, respectively. But the number of relevance vectors is far fewer than that of support vector, and the former is about 1/12–1/3 of the latter, which indicates that the proposed PSO-RVM model is more suitable for applications that require low complexity and real-time monitoring.
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Vanaja Gokul
2012-01-01
Full Text Available In distributed systems real time optimizations need to be performed dynamically for better utilization of the network resources. Real time optimizations can be performed effectively by using Cross Layer Optimization (CLO within the network operating system. This paper presents the performance evaluation of Cross Layer Optimization (CLO in comparison with the traditional approach of Single-Layer Optimization (SLO. In the parallel implementation of the approaches the experimental study carried out indicates that the CLO results in a significant improvement in network utilization when compared to SLO. A variant of the Particle Swarm Optimization technique that utilizes Digital Pheromones (PSODP for better performance has been used here. A significantly higher speed up in performance was observed from the parallel implementation of CLO that used PSODP on a cluster of nodes.
Gravity inversion of a fault by Particle swarm optimization (PSO).
Toushmalani, Reza
2013-01-01
Particle swarm optimization is a heuristic global optimization method and also an optimization algorithm, which is based on swarm intelligence. It comes from the research on the bird and fish flock movement behavior. In this paper we introduce and use this method in gravity inverse problem. We discuss the solution for the inverse problem of determining the shape of a fault whose gravity anomaly is known. Application of the proposed algorithm to this problem has proven its capability to deal with difficult optimization problems. The technique proved to work efficiently when tested to a number of models.
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Hamid Reza Mohammadi
2014-01-01
Full Text Available A cost effective off-line method for equivalent circuit parameter estimation of an induction motor using hybrid of genetic algorithm and particle swarm optimization (HGAPSO is proposed. The HGAPSO inherits the advantages of both genetic algorithm (GA and particle swarm optimization (PSO. The parameter estimation methodology describes a method for estimating the steady-state equivalent circuit parameters from the motor performance characteristics, which is normally available from the nameplate data or experimental tests. In this paper, the problem formulation uses the starting torque, the full load torque, the maximum torque, and the full load power factor which are normally available from the manufacturer data. The proposed method is used to estimate the stator and rotor resistances, the stator and rotor leakage reactances, and the magnetizing reactance in the steady-state equivalent circuit. The optimization problem is formulated to minimize an objective function containing the error between the estimated and the manufacturer data. The validity of the proposed method is demonstrated for a preset model of induction motor in MATLAB/Simulink. Also, the performance evaluation of the proposed method is carried out by comparison between the results of the HGAPSO, GA, and PSO.
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Yifan Hu
2012-01-01
Full Text Available The fault-tolerant routing problem is important consideration in the design of heterogeneous wireless sensor networks (H-WSNs applications, and has recently been attracting growing research interests. In order to maintain k disjoint communication paths from source sensors to the macronodes, we present a hybrid routing scheme and model, in which multiple paths are calculated and maintained in advance, and alternate paths are created once the previous routing is broken. Then, we propose an immune cooperative particle swarm optimization algorithm (ICPSOA in the model to provide the fast routing recovery and reconstruct the network topology for path failure in H-WSNs. In the ICPSOA, mutation direction of the particle is determined by multi-swarm evolution equation, and its diversity is improved by immune mechanism, which can enhance the capacity of global search and improve the converging rate of the algorithm. Then we validate this theoretical model with simulation results. The results indicate that the ICPSOA-based fault-tolerant routing protocol outperforms several other protocols due to its capability of fast routing recovery mechanism, reliable communications, and prolonging the lifetime of WSNs.
An Intelligent Model for Pairs Trading Using Genetic Algorithms.
Huang, Chien-Feng; Hsu, Chi-Jen; Chen, Chi-Chung; Chang, Bao Rong; Li, Chen-An
2015-01-01
Pairs trading is an important and challenging research area in computational finance, in which pairs of stocks are bought and sold in pair combinations for arbitrage opportunities. Traditional methods that solve this set of problems mostly rely on statistical methods such as regression. In contrast to the statistical approaches, recent advances in computational intelligence (CI) are leading to promising opportunities for solving problems in the financial applications more effectively. In this paper, we present a novel methodology for pairs trading using genetic algorithms (GA). Our results showed that the GA-based models are able to significantly outperform the benchmark and our proposed method is capable of generating robust models to tackle the dynamic characteristics in the financial application studied. Based upon the promising results obtained, we expect this GA-based method to advance the research in computational intelligence for finance and provide an effective solution to pairs trading for investment in practice.
An Intelligent Model for Pairs Trading Using Genetic Algorithms
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Chien-Feng Huang
2015-01-01
Full Text Available Pairs trading is an important and challenging research area in computational finance, in which pairs of stocks are bought and sold in pair combinations for arbitrage opportunities. Traditional methods that solve this set of problems mostly rely on statistical methods such as regression. In contrast to the statistical approaches, recent advances in computational intelligence (CI are leading to promising opportunities for solving problems in the financial applications more effectively. In this paper, we present a novel methodology for pairs trading using genetic algorithms (GA. Our results showed that the GA-based models are able to significantly outperform the benchmark and our proposed method is capable of generating robust models to tackle the dynamic characteristics in the financial application studied. Based upon the promising results obtained, we expect this GA-based method to advance the research in computational intelligence for finance and provide an effective solution to pairs trading for investment in practice.
Gys Albertus Marthinus Meiring; Hermanus Carel Myburgh
2015-01-01
In this paper the various driving style analysis solutions are investigated. An in-depth investigation is performed to identify the relevant machine learning and artificial intelligence algorithms utilised in current driver behaviour and driving style analysis systems. This review therefore serves as a trove of information, and will inform the specialist and the student regarding the current state of the art in driver style analysis systems, the application of these systems and the underlying...
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Zhou Hao
2015-06-01
Full Text Available The traditional MUltiple SIgnal Classification (MUSIC algorithm requires significant computational effort and can not be employed for the Direction Of Arrival (DOA estimation of targets in a low-altitude multipath environment. As such, a novel MUSIC approach is proposed on the basis of the algorithm of Adaptive Step Glowworm Swarm Optimization (ASGSO. The virtual spatial smoothing of the matrix formed by each snapshot is used to realize the decorrelation of the multipath signal and the establishment of a fullorder correlation matrix. ASGSO optimizes the function and estimates the elevation of the target. The simulation results suggest that the proposed method can overcome the low altitude multipath effect and estimate the DOA of target readily and precisely without radar effective aperture loss.
Poultangari, Iman; Shahnazi, Reza; Sheikhan, Mansour
2012-09-01
In order to control the pitch angle of blades in wind turbines, commonly the proportional and integral (PI) controller due to its simplicity and industrial usability is employed. The neural networks and evolutionary algorithms are tools that provide a suitable ground to determine the optimal PI gains. In this paper, a radial basis function (RBF) neural network based PI controller is proposed for collective pitch control (CPC) of a 5-MW wind turbine. In order to provide an optimal dataset to train the RBF neural network, particle swarm optimization (PSO) evolutionary algorithm is used. The proposed method does not need the complexities, nonlinearities and uncertainties of the system under control. The simulation results show that the proposed controller has satisfactory performance. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.
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Jie-Sheng Wang
2014-02-01
Full Text Available A mathematical model of electroslag remelting (ESR process is established based on its technical features and dynamic characteristics. A new multivariable self-tuning proportional-integral-derivative (PID controller tuned optimally by an improved particle swarm optimization (IPSO algorithm is proposed to control the two-input/two-output (TITO ESR process. An adaptive chaotic migration mutation operator is used to tackle the particles trapped in the clustering field in order to enhance the diversity of the particles in the population, prevent premature convergence and improve the search efficiency of PSO algorithm. The simulation results show the feasibility and effectiveness of the proposed control method. The new method can overcome dynamic working conditions and coupling features of the system in a wide range, and it has strong robustness and adaptability.
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M. AKBARI
2013-12-01
Full Text Available Energy group structure has a significant effect on the results of multigroup transport calculations. It is known that UO2–PUO2 (MOX is a recently developed fuel which consumes recycled plutonium. For such fuel which contains various resonant nuclides, the selection of energy group structure is more crucial comparing to the UO2 fuels. In this paper, in order to improve the accuracy of the integral results in MOX thermal lattices calculated by WIMSD-5B code, a swarm intelligence method is employed to optimize the energy group structure of WIMS library. In this process, the NJOY code system is used to generate the 69 group cross sections of WIMS code for the specified energy structure. In addition, the multiplication factor and spectral indices are compared against the results of continuous energy MCNP-4C code for evaluating the energy group structure. Calculations performed in four different types of H2O moderated UO2–PuO2 (MOX lattices show that the optimized energy structure obtains more accurate results in comparison with the WIMS original structure.
Prathabrao, M.; Nawawi, Azli; Sidek, Noor Azizah
2017-04-01
Radio Frequency Identification (RFID) system has multiple benefits which can improve the operational efficiency of the organization. The advantages are the ability to record data systematically and quickly, reducing human errors and system errors, update the database automatically and efficiently. It is often more readers (reader) is needed for the installation purposes in RFID system. Thus, it makes the system more complex. As a result, RFID network planning process is needed to ensure the RFID system works perfectly. The planning process is also considered as an optimization process and power adjustment because the coordinates of each RFID reader to be determined. Therefore, algorithms inspired by the environment (Algorithm Inspired by Nature) is often used. In the study, PSO algorithm is used because it has few number of parameters, the simulation time is fast, easy to use and also very practical. However, PSO parameters must be adjusted correctly, for robust and efficient usage of PSO. Failure to do so may result in disruption of performance and results of PSO optimization of the system will be less good. To ensure the efficiency of PSO, this study will examine the effects of two parameters on the performance of PSO Algorithm in RFID tag coverage optimization. The parameters to be studied are the swarm size and iteration number. In addition to that, the study will also recommend the most optimal adjustment for both parameters that is, 200 for the no. iterations and 800 for the no. of swarms. Finally, the results of this study will enable PSO to operate more efficiently in order to optimize RFID network planning system.
A New Swarm Intelligence Approach for Clustering Based on Krill Herd with Elitism Strategy
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Zhi-Yong Li
2015-10-01
Full Text Available As one of the most popular and well-recognized clustering methods, fuzzy C-means (FCM clustering algorithm is the basis of other fuzzy clustering analysis methods in theory and application respects. However, FCM algorithm is essentially a local search optimization algorithm. Therefore, sometimes, it may fail to find the global optimum. For the purpose of getting over the disadvantages of FCM algorithm, a new version of the krill herd (KH algorithm with elitism strategy, called KHE, is proposed to solve the clustering problem. Elitism tragedy has a strong ability of preventing the krill population from degrading. In addition, the well-selected parameters are used in the KHE method instead of originating from nature. Through an array of simulation experiments, the results show that the KHE is indeed a good choice for solving general benchmark problems and fuzzy clustering analyses.
Swarm Intelligence Based Multi-phase OPF For Peak Power Loss Reduction In A Smart Grid
Anwar, Adnan; Mahmood, A. N.
2014-01-01
Recently there has been increasing interest in improving smart grids efficiency using computational intelligence. A key challenge in future smart grid is designing Optimal Power Flow tool to solve important planning problems including optimal DG capacities. Although, a number of OPF tools exists for balanced networks there is a lack of research for unbalanced multi-phase distribution networks. In this paper, a new OPF technique has been proposed for the DG capacity planning of a smart grid. D...
An artificial intelligent algorithm for tumor detection in screening mammogram.
Zheng, L; Chan, A K
2001-07-01
Cancerous tumor mass is one of the major types of breast cancer. When cancerous masses are embedded in and camouflaged by varying densities of parenchymal tissue structures, they are very difficult to be visually detected on mammograms. This paper presents an algorithm that combines several artificial intelligent techniques with the discrete wavelet transform (DWT) for detection of masses in mammograms. The AI techniques include fractal dimension analysis, multiresolution markov random field, dogs-and-rabbits algorithm, and others. The fractal dimension analysis serves as a preprocessor to determine the approximate locations of the regions suspicious for cancer in the mammogram. The dogs-and-rabbits clustering algorithm is used to initiate the segmentation at the LL subband of a three-level DWT decomposition of the mammogram. A tree-type classification strategy is applied at the end to determine whether a given region is suspicious for cancer. We have verified the algorithm with 322 mammograms in the Mammographic Image Analysis Society Database. The verification results show that the proposed algorithm has a sensitivity of 97.3% and the number of false positive per image is 3.92.
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Ketut Bayu Yogha Bintoro
2017-10-01
Full Text Available This research aims to classify which SI algorithms have CAS or non-CAS criteria. The statistical comparative test method with 5 (five characteristic test parameters was used as the proof approach that produces the classification. Based on the hypothesis that has been tested from 15 (fifteen algorithms compared in this study, It was obtained that 8 of 15 (53.33% algorithms has the majority of CAS characteristics, 3 of 15 (20% algorithms has a minority of characteristics of CAS, and 4 out of 15 (26.66% algorithms did not have CAS characteristics. The result can be a reference in understanding the characteristics of SI algorithms in the CAS and vice versa. Penelitian ini bertujuan mengklasifikasikan algoritma SI yang memiliki kriteria CAS ataupun tidak. Metode uji komparasi statistik dengan 5 (lima parameter uji karakteristik CAS digunakan sebagai pendekatan pembuktian yang menghasilkan klasifikasi tersebut. Berdasarkan hipotesis yang telah diuji dan dibahas, dari 15 (lima belas algoritma yang dibandingkan dalam penelitian ini, didapatkan 8 dari 15 (53,33% algoritma memiliki mayoritas karakteristik CAS, 3 dari 15 (20% algoritma memiliki minoritas karakteristik CAS, dan 4 dari 15 (26,66% tidak memiliki karakteristik CAS. Hasil tersebut dapat menjadi referensi teori dalam memahami karakteristik algoritma SI dalam CAS dan sebaliknya.
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Mikhael Vidi Santoso
2017-01-01
Full Text Available Optimization technique is a treatment to increase the value of efficiency and effectiveness of an industrial system and for obtaining an objective optimal of system. Optimization technique, itself, is used in industrial sector a lot enough, particularly in the fields of energy industry, where other industries sector, such as manufacturing, transportation, and telecommunications, including the provision. As user optimization technique within period of quite long time, energy industry facing a wide range of issues, such as climate change, the use of tissues and reliability, the repeated construction of a system, and many more. Optimization technique helps companies in the field of energy through these issues with a better way and faster decisions. Economic dispatch (or ED is one of the main problems towards energy companies. Economic dispatch (ED is essential in control and power system. On this Final Project, the method offered is a method of modification of Particle Swarm Optimization, namely Random Drift Particle Swarm Optimization (RDPSO Algorithm. RDPSO could produce the power 0.15% more efficient and 78% faster to reach the convergent point.
DEFF Research Database (Denmark)
Soleimani, Hamed; Kannan, Govindan
2015-01-01
, customer awareness, and the economical motivations of the organizations. However, designing and planning a closed-loop supply chain is an NP-hard problem, which makes it difficult to achieve acceptable results in a reasonable time. In this paper, we try to cope with this problem by proposing a new......-heuristic algorithms are considered to develop a new elevated hybrid algorithm: the genetic algorithm (GA) and particle swarm optimization (PSO). Analyzing the above-mentioned algorithms' strengths and weaknesses leads us to attempt to improve the GA using some aspects of PSO. Therefore, a new hybrid algorithm...
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Mahsa Ghovvati
2016-03-01
Full Text Available Almost all optimization techniques are restricted by the problems' dimensions and large search spaces. This research focuses on a special hybrid method combining two meta-heuristic techniques, genetic algorithms (GA and particle swarm optimization (PSO that aims at overcoming this issue. This method investigates the potential impact of constraints (or the feasible regions on the search pattern of GA and PSO. The proposed algorithm was applied for parameter estimation of batch fermentation process for alkaline protease production by Bacillus licheniformis in submerged culture. Furthermore, a comparison of proposed hybrid GA/PSO with pure GA and pure PSO was carried out. The results revealed that combination of these two meta-heuristic algorithms speeds up the search (about two-fold faster in comparison to pure algorithms, since it benefits from synergy. Hence, the proposed method can be considered as an applicable method for parameter estimation of biological models in particular for large search space problems. Also, it was concluded that PSO has a slightly better performance and possesses better convergence and computational time than GA.
Mousavi, Maryam; Yap, Hwa Jen; Musa, Siti Nurmaya; Tahriri, Farzad; Md Dawal, Siti Zawiah
2017-01-01
Flexible manufacturing system (FMS) enhances the firm's flexibility and responsiveness to the ever-changing customer demand by providing a fast product diversification capability. Performance of an FMS is highly dependent upon the accuracy of scheduling policy for the components of the system, such as automated guided vehicles (AGVs). An AGV as a mobile robot provides remarkable industrial capabilities for material and goods transportation within a manufacturing facility or a warehouse. Allocating AGVs to tasks, while considering the cost and time of operations, defines the AGV scheduling process. Multi-objective scheduling of AGVs, unlike single objective practices, is a complex and combinatorial process. In the main draw of the research, a mathematical model was developed and integrated with evolutionary algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and hybrid GA-PSO) to optimize the task scheduling of AGVs with the objectives of minimizing makespan and number of AGVs while considering the AGVs' battery charge. Assessment of the numerical examples' scheduling before and after the optimization proved the applicability of all the three algorithms in decreasing the makespan and AGV numbers. The hybrid GA-PSO produced the optimum result and outperformed the other two algorithms, in which the mean of AGVs operation efficiency was found to be 69.4, 74, and 79.8 percent in PSO, GA, and hybrid GA-PSO, respectively. Evaluation and validation of the model was performed by simulation via Flexsim software.
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Maryam Mousavi
Full Text Available Flexible manufacturing system (FMS enhances the firm's flexibility and responsiveness to the ever-changing customer demand by providing a fast product diversification capability. Performance of an FMS is highly dependent upon the accuracy of scheduling policy for the components of the system, such as automated guided vehicles (AGVs. An AGV as a mobile robot provides remarkable industrial capabilities for material and goods transportation within a manufacturing facility or a warehouse. Allocating AGVs to tasks, while considering the cost and time of operations, defines the AGV scheduling process. Multi-objective scheduling of AGVs, unlike single objective practices, is a complex and combinatorial process. In the main draw of the research, a mathematical model was developed and integrated with evolutionary algorithms (genetic algorithm (GA, particle swarm optimization (PSO, and hybrid GA-PSO to optimize the task scheduling of AGVs with the objectives of minimizing makespan and number of AGVs while considering the AGVs' battery charge. Assessment of the numerical examples' scheduling before and after the optimization proved the applicability of all the three algorithms in decreasing the makespan and AGV numbers. The hybrid GA-PSO produced the optimum result and outperformed the other two algorithms, in which the mean of AGVs operation efficiency was found to be 69.4, 74, and 79.8 percent in PSO, GA, and hybrid GA-PSO, respectively. Evaluation and validation of the model was performed by simulation via Flexsim software.
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Ying Zhang
2016-02-01
Full Text Available Due to their special environment, Underwater Wireless Sensor Networks (UWSNs are usually deployed over a large sea area and the nodes are usually floating. This results in a lower beacon node distribution density, a longer time for localization, and more energy consumption. Currently most of the localization algorithms in this field do not pay enough consideration on the mobility of the nodes. In this paper, by analyzing the mobility patterns of water near the seashore, a localization method for UWSNs based on a Mobility Prediction and a Particle Swarm Optimization algorithm (MP-PSO is proposed. In this method, the range-based PSO algorithm is used to locate the beacon nodes, and their velocities can be calculated. The velocity of an unknown node is calculated by using the spatial correlation of underwater object’s mobility, and then their locations can be predicted. The range-based PSO algorithm may cause considerable energy consumption and its computation complexity is a little bit high, nevertheless the number of beacon nodes is relatively smaller, so the calculation for the large number of unknown nodes is succinct, and this method can obviously decrease the energy consumption and time cost of localizing these mobile nodes. The simulation results indicate that this method has higher localization accuracy and better localization coverage rate compared with some other widely used localization methods in this field.
Zhang, Ying; Liang, Jixing; Jiang, Shengming; Chen, Wei
2016-02-06
Due to their special environment, Underwater Wireless Sensor Networks (UWSNs) are usually deployed over a large sea area and the nodes are usually floating. This results in a lower beacon node distribution density, a longer time for localization, and more energy consumption. Currently most of the localization algorithms in this field do not pay enough consideration on the mobility of the nodes. In this paper, by analyzing the mobility patterns of water near the seashore, a localization method for UWSNs based on a Mobility Prediction and a Particle Swarm Optimization algorithm (MP-PSO) is proposed. In this method, the range-based PSO algorithm is used to locate the beacon nodes, and their velocities can be calculated. The velocity of an unknown node is calculated by using the spatial correlation of underwater object's mobility, and then their locations can be predicted. The range-based PSO algorithm may cause considerable energy consumption and its computation complexity is a little bit high, nevertheless the number of beacon nodes is relatively smaller, so the calculation for the large number of unknown nodes is succinct, and this method can obviously decrease the energy consumption and time cost of localizing these mobile nodes. The simulation results indicate that this method has higher localization accuracy and better localization coverage rate compared with some other widely used localization methods in this field.
Luo, T. H.; Liang, S.; Miao, C. B.
2017-12-01
A method of terminal vibration analysis based on Time-varying Glowworm Swarm Optimization algorithm is proposed in order to solve the problem that terminal vibration of the large flexible robot cantilever under heavy load precision.The robot cantilever of the ballastless track is used as the research target and the natural parameters of the flexible cantilever such as the natural frequency, the load impact and the axial deformation is considered. Taking into account the change of the minimum distance between the glowworm individuals, the terminal vibration response and adaptability could meet. According to the Boltzmann selection mechanism, the dynamic parameters in the motion simulation process are determined, while the influence of the natural frequency and the load impact as well as the axial deformation on the terminal vibration is studied. The method is effective and stable, which is of great theoretical basis for the study of vibration control of flexible cantilever terminal.
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Jie-sheng Wang
2014-01-01
Full Text Available For predicting the key technology index of electroslag remelting (ESR process (the melting rate and cone purification coefficient of the consumable electrode, a radial basis function (RBF neural network soft-sensor model optimized by the artificial fish swarm algorithm (AFSA is proposed. Based on the technique characteristics of ESR production process, the auxiliary variables of soft-sensor model are selected. Then the AFSA is adopted to train the RBF neural network prediction model in order to realize the nonlinear mapping between input and output variables. Simulation results show that the model has better generalization and prediction accuracy, which can meet the online soft sensing requirement of ESR process real-time control.
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Kun Zhang
2016-01-01
Full Text Available Due to the fact that the fluctuation of network traffic is affected by various factors, accurate prediction of network traffic is regarded as a challenging task of the time series prediction process. For this purpose, a novel prediction method of network traffic based on QPSO algorithm and fuzzy wavelet neural network is proposed in this paper. Firstly, quantum-behaved particle swarm optimization (QPSO was introduced. Then, the structure and operation algorithms of WFNN are presented. The parameters of fuzzy wavelet neural network were optimized by QPSO algorithm. Finally, the QPSO-FWNN could be used in prediction of network traffic simulation successfully and evaluate the performance of different prediction models such as BP neural network, RBF neural network, fuzzy neural network, and FWNN-GA neural network. Simulation results show that QPSO-FWNN has a better precision and stability in calculation. At the same time, the QPSO-FWNN also has better generalization ability, and it has a broad prospect on application.
Chiang, Tzu-An; Che, Z H; Cui, Zhihua
2014-01-01
This study designed a cross-stage reverse logistics course for defective products so that damaged products generated in downstream partners can be directly returned to upstream partners throughout the stages of a supply chain for rework and maintenance. To solve this reverse supply chain design problem, an optimal cross-stage reverse logistics mathematical model was developed. In addition, we developed a genetic algorithm (GA) and three particle swarm optimization (PSO) algorithms: the inertia weight method (PSOA_IWM), V(Max) method (PSOA_VMM), and constriction factor method (PSOA_CFM), which we employed to find solutions to support this mathematical model. Finally, a real case and five simulative cases with different scopes were used to compare the execution times, convergence times, and objective function values of the four algorithms used to validate the model proposed in this study. Regarding system execution time, the GA consumed more time than the other three PSOs did. Regarding objective function value, the GA, PSOA_IWM, and PSOA_CFM could obtain a lower convergence value than PSOA_VMM could. Finally, PSOA_IWM demonstrated a faster convergence speed than PSOA_VMM, PSOA_CFM, and the GA did.
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Tzu-An Chiang
2014-01-01
Full Text Available This study designed a cross-stage reverse logistics course for defective products so that damaged products generated in downstream partners can be directly returned to upstream partners throughout the stages of a supply chain for rework and maintenance. To solve this reverse supply chain design problem, an optimal cross-stage reverse logistics mathematical model was developed. In addition, we developed a genetic algorithm (GA and three particle swarm optimization (PSO algorithms: the inertia weight method (PSOA_IWM, VMax method (PSOA_VMM, and constriction factor method (PSOA_CFM, which we employed to find solutions to support this mathematical model. Finally, a real case and five simulative cases with different scopes were used to compare the execution times, convergence times, and objective function values of the four algorithms used to validate the model proposed in this study. Regarding system execution time, the GA consumed more time than the other three PSOs did. Regarding objective function value, the GA, PSOA_IWM, and PSOA_CFM could obtain a lower convergence value than PSOA_VMM could. Finally, PSOA_IWM demonstrated a faster convergence speed than PSOA_VMM, PSOA_CFM, and the GA did.
Chiang, Tzu-An; Che, Z. H.
2014-01-01
This study designed a cross-stage reverse logistics course for defective products so that damaged products generated in downstream partners can be directly returned to upstream partners throughout the stages of a supply chain for rework and maintenance. To solve this reverse supply chain design problem, an optimal cross-stage reverse logistics mathematical model was developed. In addition, we developed a genetic algorithm (GA) and three particle swarm optimization (PSO) algorithms: the inertia weight method (PSOA_IWM), VMax method (PSOA_VMM), and constriction factor method (PSOA_CFM), which we employed to find solutions to support this mathematical model. Finally, a real case and five simulative cases with different scopes were used to compare the execution times, convergence times, and objective function values of the four algorithms used to validate the model proposed in this study. Regarding system execution time, the GA consumed more time than the other three PSOs did. Regarding objective function value, the GA, PSOA_IWM, and PSOA_CFM could obtain a lower convergence value than PSOA_VMM could. Finally, PSOA_IWM demonstrated a faster convergence speed than PSOA_VMM, PSOA_CFM, and the GA did. PMID:24772026
1st International Conference on Intelligent Computing and Communication
Satapathy, Suresh; Sanyal, Manas; Bhateja, Vikrant
2017-01-01
The book covers a wide range of topics in Computer Science and Information Technology including swarm intelligence, artificial intelligence, evolutionary algorithms, and bio-inspired algorithms. It is a collection of papers presented at the First International Conference on Intelligent Computing and Communication (ICIC2) 2016. The prime areas of the conference are Intelligent Computing, Intelligent Communication, Bio-informatics, Geo-informatics, Algorithm, Graphics and Image Processing, Graph Labeling, Web Security, Privacy and e-Commerce, Computational Geometry, Service Orient Architecture, and Data Engineering.
A New Logistic Dynamic Particle Swarm Optimization Algorithm Based on Random Topology
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Qingjian Ni
2013-01-01
Full Text Available Population topology of particle swarm optimization (PSO will directly affect the dissemination of optimal information during the evolutionary process and will have a significant impact on the performance of PSO. Classic static population topologies are usually used in PSO, such as fully connected topology, ring topology, star topology, and square topology. In this paper, the performance of PSO with the proposed random topologies is analyzed, and the relationship between population topology and the performance of PSO is also explored from the perspective of graph theory characteristics in population topologies. Further, in a relatively new PSO variant which named logistic dynamic particle optimization, an extensive simulation study is presented to discuss the effectiveness of the random topology and the design strategies of population topology. Finally, the experimental data are analyzed and discussed. And about the design and use of population topology on PSO, some useful conclusions are proposed which can provide a basis for further discussion and research.
A new logistic dynamic particle swarm optimization algorithm based on random topology.
Ni, Qingjian; Deng, Jianming
2013-01-01
Population topology of particle swarm optimization (PSO) will directly affect the dissemination of optimal information during the evolutionary process and will have a significant impact on the performance of PSO. Classic static population topologies are usually used in PSO, such as fully connected topology, ring topology, star topology, and square topology. In this paper, the performance of PSO with the proposed random topologies is analyzed, and the relationship between population topology and the performance of PSO is also explored from the perspective of graph theory characteristics in population topologies. Further, in a relatively new PSO variant which named logistic dynamic particle optimization, an extensive simulation study is presented to discuss the effectiveness of the random topology and the design strategies of population topology. Finally, the experimental data are analyzed and discussed. And about the design and use of population topology on PSO, some useful conclusions are proposed which can provide a basis for further discussion and research.
Design optimization of pin fin geometry using particle swarm optimization algorithm.
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Nawaf Hamadneh
Full Text Available Particle swarm optimization (PSO is employed to investigate the overall performance of a pin fin.The following study will examine the effect of governing parameters on overall thermal/fluid performance associated with different fin geometries, including, rectangular plate fins as well as square, circular, and elliptical pin fins. The idea of entropy generation minimization, EGM is employed to combine the effects of thermal resistance and pressure drop within the heat sink. A general dimensionless expression for the entropy generation rate is obtained by considering a control volume around the pin fin including base plate and applying the conservations equations for mass and energy with the entropy balance. Selected fin geometries are examined for the heat transfer, fluid friction, and the minimum entropy generation rate corresponding to different parameters including axis ratio, aspect ratio, and Reynolds number. The results clearly indicate that the preferred fin profile is very dependent on these parameters.
Development of hybrid artificial intelligent based handover decision algorithm
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A.M. Aibinu
2017-04-01
Full Text Available The possibility of seamless handover remains a mirage despite the plethora of existing handover algorithms. The underlying factor responsible for this has been traced to the Handover decision module in the Handover process. Hence, in this paper, the development of novel hybrid artificial intelligent handover decision algorithm has been developed. The developed model is made up of hybrid of Artificial Neural Network (ANN based prediction model and Fuzzy Logic. On accessing the network, the Received Signal Strength (RSS was acquired over a period of time to form a time series data. The data was then fed to the newly proposed k-step ahead ANN-based RSS prediction system for estimation of prediction model coefficients. The synaptic weights and adaptive coefficients of the trained ANN was then used to compute the k-step ahead ANN based RSS prediction model coefficients. The predicted RSS value was later codified as Fuzzy sets and in conjunction with other measured network parameters were fed into the Fuzzy logic controller in order to finalize handover decision process. The performance of the newly developed k-step ahead ANN based RSS prediction algorithm was evaluated using simulated and real data acquired from available mobile communication networks. Results obtained in both cases shows that the proposed algorithm is capable of predicting ahead the RSS value to about ±0.0002 dB. Also, the cascaded effect of the complete handover decision module was also evaluated. Results obtained show that the newly proposed hybrid approach was able to reduce ping-pong effect associated with other handover techniques.
Selectively-informed particle swarm optimization.
Gao, Yang; Du, Wenbo; Yan, Gang
2015-03-19
Particle swarm optimization (PSO) is a nature-inspired algorithm that has shown outstanding performance in solving many realistic problems. In the original PSO and most of its variants all particles are treated equally, overlooking the impact of structural heterogeneity on individual behavior. Here we employ complex networks to represent the population structure of swarms and propose a selectively-informed PSO (SIPSO), in which the particles choose different learning strategies based on their connections: a densely-connected hub particle gets full information from all of its neighbors while a non-hub particle with few connections can only follow a single yet best-performed neighbor. Extensive numerical experiments on widely-used benchmark functions show that our SIPSO algorithm remarkably outperforms the PSO and its existing variants in success rate, solution quality, and convergence speed. We also explore the evolution process from a microscopic point of view, leading to the discovery of different roles that the particles play in optimization. The hub particles guide the optimization process towards correct directions while the non-hub particles maintain the necessary population diversity, resulting in the optimum overall performance of SIPSO. These findings deepen our understanding of swarm intelligence and may shed light on the underlying mechanism of information exchange in natural swarm and flocking behaviors.
Ghiasi, Mohammad Sadegh; Arjmand, Navid; Boroushaki, Mehrdad; Farahmand, Farzam
2016-03-01
A six-degree-of-freedom musculoskeletal model of the lumbar spine was developed to predict the activity of trunk muscles during light, moderate and heavy lifting tasks in standing posture. The model was formulated into a multi-objective optimization problem, minimizing the sum of the cubed muscle stresses and maximizing the spinal stability index. Two intelligent optimization algorithms, i.e., the vector evaluated particle swarm optimization (VEPSO) and nondominated sorting genetic algorithm (NSGA), were employed to solve the optimization problem. The optimal solution for each task was then found in the way that the corresponding in vivo intradiscal pressure could be reproduced. Results indicated that both algorithms predicted co-activity in the antagonistic abdominal muscles, as well as an increase in the stability index when going from the light to the heavy task. For all of the light, moderate and heavy tasks, the muscles' activities predictions of the VEPSO and the NSGA were generally consistent and in the same order of the in vivo electromyography data. The proposed methodology is thought to provide improved estimations for muscle activities by considering the spinal stability and incorporating the in vivo intradiscal pressure data.
A New Modified Firefly Algorithm
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Medha Gupta
2016-07-01
Full Text Available Nature inspired meta-heuristic algorithms studies the emergent collective intelligence of groups of simple agents. Firefly Algorithm is one of the new such swarm-based metaheuristic algorithm inspired by the flashing behavior of fireflies. The algorithm was first proposed in 2008 and since then has been successfully used for solving various optimization problems. In this work, we intend to propose a new modified version of Firefly algorithm (MoFA and later its performance is compared with the standard firefly algorithm along with various other meta-heuristic algorithms. Numerical studies and results demonstrate that the proposed algorithm is superior to existing algorithms.
Khehra, Baljit Singh; Pharwaha, Amar Partap Singh
2017-04-01
Ductal carcinoma in situ (DCIS) is one type of breast cancer. Clusters of microcalcifications (MCCs) are symptoms of DCIS that are recognized by mammography. Selection of robust features vector is the process of selecting an optimal subset of features from a large number of available features in a given problem domain after the feature extraction and before any classification scheme. Feature selection reduces the feature space that improves the performance of classifier and decreases the computational burden imposed by using many features on classifier. Selection of an optimal subset of features from a large number of available features in a given problem domain is a difficult search problem. For n features, the total numbers of possible subsets of features are 2n. Thus, selection of an optimal subset of features problem belongs to the category of NP-hard problems. In this paper, an attempt is made to find the optimal subset of MCCs features from all possible subsets of features using genetic algorithm (GA), particle swarm optimization (PSO) and biogeography-based optimization (BBO). For simulation, a total of 380 benign and malignant MCCs samples have been selected from mammogram images of DDSM database. A total of 50 features extracted from benign and malignant MCCs samples are used in this study. In these algorithms, fitness function is correct classification rate of classifier. Support vector machine is used as a classifier. From experimental results, it is also observed that the performance of PSO-based and BBO-based algorithms to select an optimal subset of features for classifying MCCs as benign or malignant is better as compared to GA-based algorithm.
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Jing Li
2017-01-01
Full Text Available The goal of this study is to improve thermal comfort and indoor air quality with the adaptive network-based fuzzy inference system (ANFIS model and improved particle swarm optimization (PSO algorithm. A method to optimize air conditioning parameters and installation distance is proposed. The methodology is demonstrated through a prototype case, which corresponds to a typical laboratory in colleges and universities. A laboratory model is established, and simulated flow field information is obtained with the CFD software. Subsequently, the ANFIS model is employed instead of the CFD model to predict indoor flow parameters, and the CFD database is utilized to train ANN input-output “metamodels” for the subsequent optimization. With the improved PSO algorithm and the stratified sequence method, the objective functions are optimized. The functions comprise PMV, PPD, and mean age of air. The optimal installation distance is determined with the hemisphere model. Results show that most of the staff obtain a satisfactory degree of thermal comfort and that the proposed method can significantly reduce the cost of building an experimental device. The proposed methodology can be used to determine appropriate air supply parameters and air conditioner installation position for a pleasant and healthy indoor environment.
Chuang, Li-Yeh; Lane, Hsien-Yuan; Lin, Yu-Da; Lin, Ming-Teng; Yang, Cheng-Hong; Chang, Hsueh-Wei
2014-01-01
Facial emotion perception (FEP) can affect social function. We previously reported that parts of five tested single-nucleotide polymorphisms (SNPs) in the MET and AKT1 genes may individually affect FEP performance. However, the effects of SNP-SNP interactions on FEP performance remain unclear. This study compared patients with high and low FEP performances (n = 89 and 93, respectively). A particle swarm optimization (PSO) algorithm was used to identify the best SNP barcodes (i.e., the SNP combinations and genotypes that revealed the largest differences between the high and low FEP groups). The analyses of individual SNPs showed no significant differences between the high and low FEP groups. However, comparisons of multiple SNP-SNP interactions involving different combinations of two to five SNPs showed that the best PSO-generated SNP barcodes were significantly associated with high FEP score. The analyses of the joint effects of the best SNP barcodes for two to five interacting SNPs also showed that the best SNP barcodes had significantly higher odds ratios (2.119 to 3.138; P < 0.05) compared to other SNP barcodes. In conclusion, the proposed PSO algorithm effectively identifies the best SNP barcodes that have the strongest associations with FEP performance. This study also proposes a computational methodology for analyzing complex SNP-SNP interactions in social cognition domains such as recognition of facial emotion.
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Zeyu Chen
2015-04-01
Full Text Available Plug-in hybrid electric vehicles (PHEVs have been recognized as one of the most promising vehicle categories nowadays due to their low fuel consumption and reduced emissions. Energy management is critical for improving the performance of PHEVs. This paper proposes an energy management approach based on a particle swarm optimization (PSO algorithm. The optimization objective is to minimize total energy cost (summation of oil and electricity from vehicle utilization. A main drawback of optimal strategies is that they can hardly be used in real-time control. In order to solve this problem, a rule-based strategy containing three operation modes is proposed first, and then the PSO algorithm is implemented on four threshold values in the presented rule-based strategy. The proposed strategy has been verified by the US06 driving cycle under the MATLAB/Simulink software environment. Two different driving cycles are adopted to evaluate the generalization ability of the proposed strategy. Simulation results indicate that the proposed PSO-based energy management method can achieve better energy efficiency compared with traditional blended strategies. Online control performance of the proposed approach has been demonstrated through a driver-in-the-loop real-time experiment.
Shidrokh Goudarzi; Wan Haslina Hassan; Mohammad Hossein Anisi; Seyed Ahmad Soleymani; Parvaneh Shabanzadeh
2015-01-01
The vertical handover mechanism is an essential issue in the heterogeneous wireless environments where selection of an efficient network that provides seamless connectivity involves complex scenarios. This study uses two modules that utilize the particle swarm optimization (PSO) algorithm to predict and make an intelligent vertical handover decision. In this paper, we predict the received signal strength indicator parameter using the curve fitting based particle swarm optimization (CF-PSO) an...
A particle swarm-based algorithm for optimization of multi-layered and graded dental ceramics.
Askari, Ehsan; Flores, Paulo; Silva, Filipe
2017-10-04
The thermal residual stresses (TRSs) generated owing to the cooling down from the processing temperature in layered ceramic systems can lead to crack formation as well as influence the bending stress distribution and the strength of the structure. The purpose of this study is to minimize the thermal residual and bending stresses in dental ceramics to enhance their strength as well as to prevent the structure failure. Analytical parametric models are developed to evaluate thermal residual stresses in zirconia-porcelain multi-layered and graded discs and to simulate the piston-on-ring test. To identify optimal designs of zirconia-based dental restorations, a particle swarm optimizer is also developed. The thickness of each interlayer and compositional distribution are referred to as design variables. The effect of layers number constituting the interlayer between two based materials on the performance of graded prosthetic systems is also investigated. The developed methodology is validated against results available in literature and a finite element model constructed in the present study. Three different cases are considered to determine the optimal design of graded prosthesis based on minimizing (a) TRSs; (b) bending stresses; and (c) both TRS and bending stresses. It is demonstrated that each layer thickness and composition profile have important contributions into the resulting stress field and magnitude. Copyright © 2017 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Simon Fong
2015-01-01
Full Text Available Human motion sensing technology gains tremendous popularity nowadays with practical applications such as video surveillance for security, hand signing, and smart-home and gaming. These applications capture human motions in real-time from video sensors, the data patterns are nonstationary and ever changing. While the hardware technology of such motion sensing devices as well as their data collection process become relatively mature, the computational challenge lies in the real-time analysis of these live feeds. In this paper we argue that traditional data mining methods run short of accurately analyzing the human activity patterns from the sensor data stream. The shortcoming is due to the algorithmic design which is not adaptive to the dynamic changes in the dynamic gesture motions. The successor of these algorithms which is known as data stream mining is evaluated versus traditional data mining, through a case of gesture recognition over motion data by using Microsoft Kinect sensors. Three different subjects were asked to read three comic strips and to tell the stories in front of the sensor. The data stream contains coordinates of articulation points and various positions of the parts of the human body corresponding to the actions that the user performs. In particular, a novel technique of feature selection using swarm search and accelerated PSO is proposed for enabling fast preprocessing for inducing an improved classification model in real-time. Superior result is shown in the experiment that runs on this empirical data stream. The contribution of this paper is on a comparative study between using traditional and data stream mining algorithms and incorporation of the novel improved feature selection technique with a scenario where different gesture patterns are to be recognized from streaming sensor data.
A Novel Discrete Fruit Fly Optimization Algorithm for Intelligent Parallel Test sheets Generation
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Wang Fengrui
2015-01-01
Full Text Available Parallel test sheet generation (PTSG is a NP-hard combinational optimization problem, in which test sheet generation algorithm with high quality and efficiency is the core technology. Basic fruit fly optimization algorithm (FOA has the defects of easily relapsing into local optimal and low convergence precision when solving PTSG problem. In this paper, a novel discrete fruit fly optimization algorithm is proposed to solve the PTSG problem, in which a discrete osphesis searching operator based on the problem-specific knowledge is designed to help the FOA escaping from being trapped in local minima. To evaluate the performance of the proposed algorithm, the simulation experiments were conducted using a series of item banks with different scales. The superiority of the proposed algorithm is demonstrated by comparing it with the particle swarm optimization algorithm and differential evolution algorithm.
Cui, Ying; Chen, Qinggang; Li, Yaxiao; Tang, Ling
2017-02-01
Flavonoids exhibit a high affinity for the purified cytosolic NBD (C-terminal nucleotide-binding domain) of P-glycoprotein (P-gp). To explore the affinity of flavonoids for P-gp, quantitative structure-activity relationship (QSAR) models were developed using support vector machines (SVMs). A novel method coupling a modified particle swarm optimization algorithm with random mutation strategy and a genetic algorithm coupled with SVM was proposed to simultaneously optimize the kernel parameters of SVM and determine the subset of optimized features for the first time. Using DRAGON descriptors to represent compounds for QSAR, three subsets (training, prediction and external validation set) derived from the dataset were employed to investigate QSAR. With excluding of the outlier, the correlation coefficient (R(2)) of the whole training set (training and prediction) was 0.924, and the R(2) of the external validation set was 0.941. The root-mean-square error (RMSE) of the whole training set was 0.0588; the RMSE of the cross-validation of the external validation set was 0.0443. The mean Q(2) value of leave-many-out cross-validation was 0.824. With more informations from results of randomization analysis and applicability domain, the proposed model is of good predictive ability, stability.
Safari, Mir Jafar Sadegh; Shirzad, Akbar; Mohammadi, Mirali
2017-08-01
May proposed two dimensionless parameters of transport (η) and mobility (Fs) for self-cleansing design of sewers with deposited bed condition. The relationships between those two parameters were introduced in conditional form for specific ranges of Fs, which makes it difficult to use as a practical tool for sewer design. In this study, using the same experimental data used by May and employing the particle swarm optimization algorithm, a unified equation is recommended based on η and Fs. The developed model is compared with original May relationships as well as corresponding models available in the literature. A large amount of data taken from the literature is used for the models' evaluation. The results demonstrate that the developed model in this study is superior to May and other existing models in the literature. Due to the fact that in May's dimensionless parameters more effective variables in the sediment transport process in sewers with deposited bed condition are considered, it is concluded that the revised May equation proposed in this study is a reliable model for sewer design.
Mousavi, Seyed Mohsen; Niaki, S T A; Bahreininejad, Ardeshir; Musa, Siti Nurmaya
2014-01-01
A multi-item multiperiod inventory control model is developed for known-deterministic variable demands under limited available budget. Assuming the order quantity is more than the shortage quantity in each period, the shortage in combination of backorder and lost sale is considered. The orders are placed in batch sizes and the decision variables are assumed integer. Moreover, all unit discounts for a number of products and incremental quantity discount for some other items are considered. While the objectives are to minimize both the total inventory cost and the required storage space, the model is formulated into a fuzzy multicriteria decision making (FMCDM) framework and is shown to be a mixed integer nonlinear programming type. In order to solve the model, a multiobjective particle swarm optimization (MOPSO) approach is applied. A set of compromise solution including optimum and near optimum ones via MOPSO has been derived for some numerical illustration, where the results are compared with those obtained using a weighting approach. To assess the efficiency of the proposed MOPSO, the model is solved using multi-objective genetic algorithm (MOGA) as well. A large number of numerical examples are generated at the end, where graphical and statistical approaches show more efficiency of MOPSO compared with MOGA.
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Yulong Ying
2015-01-01
Full Text Available In the lifespan of a gas turbine engine, abrupt faults and performance degradation of its gas-path components may happen; however the performance degradation is not easily foreseeable when the level of degradation is small. Gas path analysis (GPA method has been widely applied to monitor gas turbine engine health status as it can easily obtain the magnitudes of the detected component faults. However, when the number of components within engine is large or/and the measurement noise level is high, the smearing effect may be strong and the degraded components may not be recognized. In order to improve diagnostic effect, a nonlinear steady-state model based gas turbine health status estimation approach with improved particle swarm optimization algorithm (PSO-GPA has been proposed in this study. The proposed approach has been tested in ten test cases where the degradation of a model three-shaft marine engine has been analyzed. These case studies have shown that the approach can accurately search and isolate the degraded components and further quantify the degradation for major gas-path components. Compared with the typical GPA method, the approach has shown better measurement noise immunity and diagnostic accuracy.
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Pan Pan
2012-11-01
Full Text Available This paper presents an optimization method for the structural design of horizontal-axis wind turbine (HAWT blades based on the particle swarm optimization algorithm (PSO combined with the finite element method (FEM. The main goal is to create an optimization tool and to demonstrate the potential improvements that could be brought to the structural design of HAWT blades. A multi-criteria constrained optimization design model pursued with respect to minimum mass of the blade is developed. The number and the location of layers in the spar cap and the positions of the shear webs are employed as the design variables, while the strain limit, blade/tower clearance limit and vibration limit are taken into account as the constraint conditions. The optimization of the design of a commercial 1.5 MW HAWT blade is carried out by combining the above method and design model under ultimate (extreme flap-wise load conditions. The optimization results are described and compared with the original design. It shows that the method used in this study is efficient and produces improved designs.
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Min Jin
2014-01-01
Full Text Available There is recently a great deal of interest and excitement in understanding the role of inertia and acceleration in the motion equation of discrete particle swarm optimization (DPSO algorithms. It still remains unknown whether the inertia section should be abandoned and how to select the appropriate acceleration in order for DPSO to show the best convergence performance. Adopting channel assignment as a case study, this paper systematically conducts experimental filtering research on this issue. Compared with other channel assignment schemes, the proposed scheme and the selection of inertia and acceleration are verified to have the advantage to channel assignment in three respects of convergence rate, convergence speed, and the independency of the quality of initial solution. Furthermore, the experimental result implies that DSPO might have the best convergence performance when its motion equation includes an inertia section in a less medium weight, a bigger acceleration coefficient for global-search optimum, and a smaller acceleration coefficient for individual-search optimum.
Energy Technology Data Exchange (ETDEWEB)
Assareh, E.; Behrang, M.A. [Department of Mechanical Engineering, Young Researchers Club, Islamic Azad University, Dezful Branch (Iran, Islamic Republic of); Assari, M.R. [Department of Mechanical Engineering, Engineering Faculty, Jundi Shapour University, Dezful (Iran, Islamic Republic of); Ghanbarzadeh, A. [Department of Mechanical Engineering, Engineering Faculty, Shahid Chamran University, Ahvaz (Iran, Islamic Republic of)
2010-12-15
This paper presents application of PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) techniques to estimate oil demand in Iran, based on socio-economic indicators. The models are developed in two forms (exponential and linear) and applied to forecast oil demand in Iran. PSO-DEM and GA-DEM (PSO and GA demand estimation models) are developed to estimate the future oil demand values based on population, GDP (gross domestic product), import and export data. Oil consumption in Iran from 1981 to 2005 is considered as the case of this study. The available data is partly used for finding the optimal, or near optimal values of the weighting parameters (1981-1999) and partly for testing the models (2000-2005). For the best results of GA, the average relative errors on testing data were 2.83% and 1.72% for GA-DEM{sub exponential} and GA-DEM{sub linear}, respectively. The corresponding values for PSO were 1.40% and 1.36% for PSO-DEM{sub exponential} and PSO-DEM{sub linear}, respectively. Oil demand in Iran is forecasted up to year 2030. (author)
A comparative intelligibility study of single-microphone noise reduction algorithms.
Hu, Yi; Loizou, Philipos C
2007-09-01
The evaluation of intelligibility of noise reduction algorithms is reported. IEEE sentences and consonants were corrupted by four types of noise including babble, car, street and train at two signal-to-noise ratio levels (0 and 5 dB), and then processed by eight speech enhancement methods encompassing four classes of algorithms: spectral subtractive, sub-space, statistical model based and Wiener-type algorithms. The enhanced speech was presented to normal-hearing listeners for identification. With the exception of a single noise condition, no algorithm produced significant improvements in speech intelligibility. Information transmission analysis of the consonant confusion matrices indicated that no algorithm improved significantly the place feature score, significantly, which is critically important for speech recognition. The algorithms which were found in previous studies to perform the best in terms of overall quality, were not the same algorithms that performed the best in terms of speech intelligibility. The subspace algorithm, for instance, was previously found to perform the worst in terms of overall quality, but performed well in the present study in terms of preserving speech intelligibility. Overall, the analysis of consonant confusion matrices suggests that in order for noise reduction algorithms to improve speech intelligibility, they need to improve the place and manner feature scores.
Joon-Woo Lee; Won Kim
2015-01-01
This paper reports the design of a randomly deployed heterogeneous wireless sensor network (HWSN) with two types of nodes: a powerful node and an ordinary node. Powerful nodes, such as Cluster Heads (CHs), communicate directly to the data sink of the network, and ordinary nodes sense the desired information and transmit the processed data to powerful nodes. The heterogeneity of HWSNs improves the networks lifetime and coverage. This paper focuses on the design of a random network among HWSNs....
Particle Swarm Optimization Toolbox
Grant, Michael J.
2010-01-01
The Particle Swarm Optimization Toolbox is a library of evolutionary optimization tools developed in the MATLAB environment. The algorithms contained in the library include a genetic algorithm (GA), a single-objective particle swarm optimizer (SOPSO), and a multi-objective particle swarm optimizer (MOPSO). Development focused on both the SOPSO and MOPSO. A GA was included mainly for comparison purposes, and the particle swarm optimizers appeared to perform better for a wide variety of optimization problems. All algorithms are capable of performing unconstrained and constrained optimization. The particle swarm optimizers are capable of performing single and multi-objective optimization. The SOPSO and MOPSO algorithms are based on swarming theory and bird-flocking patterns to search the trade space for the optimal solution or optimal trade in competing objectives. The MOPSO generates Pareto fronts for objectives that are in competition. A GA, based on Darwin evolutionary theory, is also included in the library. The GA consists of individuals that form a population in the design space. The population mates to form offspring at new locations in the design space. These offspring contain traits from both of the parents. The algorithm is based on this combination of traits from parents to hopefully provide an improved solution than either of the original parents. As the algorithm progresses, individuals that hold these optimal traits will emerge as the optimal solutions. Due to the generic design of all optimization algorithms, each algorithm interfaces with a user-supplied objective function. This function serves as a "black-box" to the optimizers in which the only purpose of this function is to evaluate solutions provided by the optimizers. Hence, the user-supplied function can be numerical simulations, analytical functions, etc., since the specific detail of this function is of no concern to the optimizer. These algorithms were originally developed to support entry
Autonomous distributed LQR/APF control algorithms for CubeSat swarms manoeuvring in eccentric orbits
Palacios, L.; Ceriotti, Matteo; Radice, G.
2013-01-01
Spacecraft formation flying has shown to be promising approach to enhance mission capabilities. Nevertheless, formation flying presents several control challenges which escalate as the numbers of elements in the formation is increased. The objective of this paper is to develop decentralised control algorithms to regulate the station-keeping, reconfiguration and collision avoidance of spacecraft in formation around eccentric reference orbits using the combination of a Linear Quadratic Regulato...
ALE-PSO: An Adaptive Swarm Algorithm to Solve Design Problems of Laminates
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Paolo Vannucci
2009-04-01
Full Text Available This paper presents an adaptive PSO algorithm whose numerical parameters can be updated following a scheduled protocol respecting some known criteria of convergence in order to enhance the chances to reach the global optimum of a hard combinatorial optimization problem, such those encountered in global optimization problems of composite laminates. Some examples concerning hard design problems are provided, showing the effectiveness of the approach.
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Zhang Biao
2016-01-01
Full Text Available In this study, a continuous ant colony optimization algorithm on the basis of probability density function was applied to the inverse problems of one-dimensional coupled radiative and conductive heat transfer. To overcome the slow convergence of the ant colony optimization algorithm for continuous domain problems, a novel hybrid ant colony optimization and particle swarm optimization algorithm was proposed. To illustrate the performances of these algorithms, the thermal conductivity, absorption coefficient and scattering coefficient of the one-dimensional homogeneous semi-transparent medium were retrieved for several test cases. The temperature and radiative heat flux simulated by the finite volume method were served as inputs for the inverse analysis. Through function estimation and parameter estimation, the HAPO algorithm was proved to be effective and robust.
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Jie-Sheng Wang
2015-01-01
Full Text Available For predicting the key technology indicators (concentrate grade and tailings recovery rate of flotation process, a feed-forward neural network (FNN based soft-sensor model optimized by the hybrid algorithm combining particle swarm optimization (PSO algorithm and gravitational search algorithm (GSA is proposed. Although GSA has better optimization capability, it has slow convergence velocity and is easy to fall into local optimum. So in this paper, the velocity vector and position vector of GSA are adjusted by PSO algorithm in order to improve its convergence speed and prediction accuracy. Finally, the proposed hybrid algorithm is adopted to optimize the parameters of FNN soft-sensor model. Simulation results show that the model has better generalization and prediction accuracy for the concentrate grade and tailings recovery rate to meet the online soft-sensor requirements of the real-time control in the flotation process.
2017-05-25
motion,” provides a method that combines situational awareness, elusiveness, mass, speed, mobility, and surprise to physically and cognitively overwhelm...Napoleon’s Great Army at Ulm, provide operational shock and cognitive dissonance to opposing military systems and personnel. In Swarming and the...Scythians, Alexander used similar anti-swarm methods that bottlenose dolphins use to catch swarming fish. In order to catch fish utilizing swarms
Swarm satellite mission scheduling & planning using Hybrid Dynamic Mutation Genetic Algorithm
Zheng, Zixuan; Guo, Jian; Gill, Eberhard
2017-08-01
Space missions have traditionally been controlled by operators from a mission control center. Given the increasing number of satellites for some space missions, generating a command list for multiple satellites can be time-consuming and inefficient. Developing multi-satellite, onboard mission scheduling & planning techniques is, therefore, a key research field for future space mission operations. In this paper, an improved Genetic Algorithm (GA) using a new mutation strategy is proposed as a mission scheduling algorithm. This new mutation strategy, called Hybrid Dynamic Mutation (HDM), combines the advantages of both dynamic mutation strategy and adaptive mutation strategy, overcoming weaknesses such as early convergence and long computing time, which helps standard GA to be more efficient and accurate in dealing with complex missions. HDM-GA shows excellent performance in solving both unconstrained and constrained test functions. The experiments of using HDM-GA to simulate a multi-satellite, mission scheduling problem demonstrates that both the computation time and success rate mission requirements can be met. The results of a comparative test between HDM-GA and three other mutation strategies also show that HDM has outstanding performance in terms of speed and reliability.
Intelligent Emergency Stop Algorithm for a Manipulator Using a New Regression Method
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Mignon Park
2012-05-01
Full Text Available In working environments with large manipulators, accidental collisions can cause severe personal injuries and can seriously damage manipulators, necessitating the development of an emergency stop algorithm to prevent such occurrences. In this paper, we propose an emergency stop system for the efficient and safe operation of a manipulator by applying an intelligent emergency stop algorithm. Our proposed intelligent algorithm considers the direction of motion of the manipulator. In addition, using a new regression method, the algorithm includes a decision step that determines whether a detected object is a collision-causing obstacle or a part of the manipulator. We apply our emergency stop system to a two-link manipulator and assess the performance of our intelligent emergency stop algorithm as compared with other models.
Applied Swarm-based medicine: collecting decision trees for patterns of algorithms analysis.
Panje, Cédric M; Glatzer, Markus; von Rappard, Joscha; Rothermundt, Christian; Hundsberger, Thomas; Zumstein, Valentin; Plasswilm, Ludwig; Putora, Paul Martin
2017-08-16
The objective consensus methodology has recently been applied in consensus finding in several studies on medical decision-making among clinical experts or guidelines. The main advantages of this method are an automated analysis and comparison of treatment algorithms of the participating centers which can be performed anonymously. Based on the experience from completed consensus analyses, the main steps for the successful implementation of the objective consensus methodology were identified and discussed among the main investigators. The following steps for the successful collection and conversion of decision trees were identified and defined in detail: problem definition, population selection, draft input collection, tree conversion, criteria adaptation, problem re-evaluation, results distribution and refinement, tree finalisation, and analysis. This manuscript provides information on the main steps for successful collection of decision trees and summarizes important aspects at each point of the analysis.
Samareh, Hossein; Khoshrou, Seyed Hassan; Shahriar, Kourosh; Ebadzadeh, Mohammad Mehdi; Eslami, Mohammad
2017-09-01
When particle's wave velocity resulting from mining blasts exceeds a certain level, then the intensity of produced vibrations incur damages to the structures around the blasting regions. Development of mathematical models for predicting the peak particle velocity (PPV) based on the properties of the wave emission environment is an appropriate method for better designing of blasting parameters, since the probability of incurred damages can considerably be mitigated by controlling the intensity of vibrations at the building sites. In this research, first out of 11 blasting and geo-mechanical parameters of rock masses, four parameters which had the greatest influence on the vibrational wave velocities were specified using regression analysis. Thereafter, some models were developed for predicting the PPV by nonlinear regression analysis (NLRA) and artificial neural network (ANN) with correlation coefficients of 0.854 and 0.662, respectively. Afterward, the coefficients associated with the parameters in the NLRA model were optimized using optimization particle swarm-genetic algorithm. The values of PPV were estimated for 18 testing dataset in order to evaluate the accuracy of the prediction and performance of the developed models. By calculating statistical indices for the test recorded maps, it was found that the optimized model can predict the PPV with a lower error than the other two models. Furthermore, considering the correlation coefficient (0.75) between the values of the PPV measured and predicted by the optimized nonlinear model, it was found that this model possesses a more desirable performance for predicting the PPV than the other two models.
Lin, Wei-Qi; Jiang, Jian-Hui; Zhou, Yan-Ping; Wu, Hai-Long; Shen, Guo-Li; Yu, Ru-Qin
2007-01-30
Multilayer feedforward neural networks (MLFNNs) are important modeling techniques widely used in QSAR studies for their ability to represent nonlinear relationships between descriptors and activity. However, the problems of overfitting and premature convergence to local optima still pose great challenges in the practice of MLFNNs. To circumvent these problems, a support vector machine (SVM) based training algorithm for MLFNNs has been developed with the incorporation of particle swarm optimization (PSO). The introduction of the SVM based training mechanism imparts the developed algorithm with inherent capacity for combating the overfitting problem. Moreover, with the implementation of PSO for searching the optimal network weights, the SVM based learning algorithm shows relatively high efficiency in converging to the optima. The proposed algorithm has been evaluated using the Hansch data set. Application to QSAR studies of the activity of COX-2 inhibitors is also demonstrated. The results reveal that this technique provides superior performance to backpropagation (BP) and PSO training neural networks.
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Ping Jiang
2015-01-01
Full Text Available To mitigate the increase of anxiety resulting from the depletion of fossil fuels and destruction of the ecosystem, wind power, as the most common renewable energy, is a flourishing industry. Thus, accurate wind speed forecasting is critical for the efficient function of wind farms. However, affected by complicated influence factors in meteorology and volatile physical property, wind speed forecasting is difficult and challenging. Based on previous research efforts, an intelligent hybrid model was proposed in this paper in an attempt to tackle this difficult task. First, wavelet transform was utilized to extract the main components of the original wind speed data while eliminating noise. To make better use of the back-propagation artificial neural network, the initial parameters of the network are substituted with optimized ones, which are achieved by using the artificial fish swarm algorithm (AFSA, and the final combination model is employed to conduct wind speed forecasting. A series of data are collected from four different observation sites to test the validity of the proposed model. Through comprehensive comparison with the traditional models, the experiment results clearly indicate that the proposed hybrid model outperforms the traditional single models.
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Siti Komsiyah
2012-06-01
Full Text Available On electric power system operation, economic planning problem is one variable to take into account due to operational cost efficiency. Economic Dispatch problem of electric power generation is discussed in this study to manage the output division on several units based on the the required load demand, with minimum operating cost yet is able to satisfy equality and inequality constraint of all units and system. In this study the Economic Dispatch problem which has non linear cost function is solved using swarm intelligent method is Gaussian Particle Swarm Optimization (GPSO and Lagrange Multiplier. GPSO is a population-based stochastic algorithms which their moving is inspired by swarm intelligent and probabilities theories. To analize its accuracy, the Economic Dispatch solution by GPSO method is compared with Lagrange Multiplier method. From the test result it is proved that GPSO method gives economic planning calculation better than Lagrange Multiplier does.
Allaoua, Boumediene; Laoufi, Abdellah; Gasbaoui, Brahim; Abdelfatah NASRI; Abdessalam ABDERRAHMANI
2008-01-01
In this paper, an intelligent controller of the DC (Direct current) Motor drive is designed using fuzzy logic-genetic algorithms optimization. First, a controller is designed according to fuzzy rules such that the systems are fundamentally robust. To obtain the globally optimal values, parameters of the fuzzy controller are improved by genetic algorithms optimization model. Computer MATLAB work space demonstrate that the fuzzy controller associated to the genetic algorithms approach became ve...
Dash, Subhransu; Panigrahi, Bijaya
2015-01-01
The book is a collection of high-quality peer-reviewed research papers presented in Proceedings of International Conference on Artificial Intelligence and Evolutionary Algorithms in Engineering Systems (ICAEES 2014) held at Noorul Islam Centre for Higher Education, Kumaracoil, India. These research papers provide the latest developments in the broad area of use of artificial intelligence and evolutionary algorithms in engineering systems. The book discusses wide variety of industrial, engineering and scientific applications of the emerging techniques. It presents invited papers from the inventors/originators of new applications and advanced technologies.
Opposition-Based Adaptive Fireworks Algorithm
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Chibing Gong
2016-07-01
Full Text Available A fireworks algorithm (FWA is a recent swarm intelligence algorithm that is inspired by observing fireworks explosions. An adaptive fireworks algorithm (AFWA proposes additional adaptive amplitudes to improve the performance of the enhanced fireworks algorithm (EFWA. The purpose of this paper is to add opposition-based learning (OBL to AFWA with the goal of further boosting performance and achieving global optimization. Twelve benchmark functions are tested in use of an opposition-based adaptive fireworks algorithm (OAFWA. The final results conclude that OAFWA significantly outperformed EFWA and AFWA in terms of solution accuracy. Additionally, OAFWA was compared with a bat algorithm (BA, differential evolution (DE, self-adapting control parameters in differential evolution (jDE, a firefly algorithm (FA, and a standard particle swarm optimization 2011 (SPSO2011 algorithm. The research results indicate that OAFWA ranks the highest of the six algorithms for both solution accuracy and runtime cost.
Swarmie User Manual: A Rover Used for Multi-agent Swarm Research
Montague, Gilbert
2014-01-01
The ability to create multiple functional yet cost effective robots is crucial for conducting swarming robotics research. The Center Innovation Fund (CIF) swarming robotics project is a collaboration among the KSC Granular Mechanics and Regolith Operations (GMRO) group, the University of New Mexico Biological Computation Lab, and the NASA Ames Intelligent Robotics Group (IRG) that uses rovers, dubbed "Swarmies", as test platforms for genetic search algorithms. This fall, I assisted in the development of the software modules used on the Swarmies and created this guide to provide thorough instructions on how to configure your workspace to operate a Swarmie both in simulation and out in the field.
Improved bat algorithm applied to multilevel image thresholding.
Alihodzic, Adis; Tuba, Milan
2014-01-01
Multilevel image thresholding is a very important image processing technique that is used as a basis for image segmentation and further higher level processing. However, the required computational time for exhaustive search grows exponentially with the number of desired thresholds. Swarm intelligence metaheuristics are well known as successful and efficient optimization methods for intractable problems. In this paper, we adjusted one of the latest swarm intelligence algorithms, the bat algorithm, for the multilevel image thresholding problem. The results of testing on standard benchmark images show that the bat algorithm is comparable with other state-of-the-art algorithms. We improved standard bat algorithm, where our modifications add some elements from the differential evolution and from the artificial bee colony algorithm. Our new proposed improved bat algorithm proved to be better than five other state-of-the-art algorithms, improving quality of results in all cases and significantly improving convergence speed.
Improved Bat Algorithm Applied to Multilevel Image Thresholding
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Adis Alihodzic
2014-01-01
Full Text Available Multilevel image thresholding is a very important image processing technique that is used as a basis for image segmentation and further higher level processing. However, the required computational time for exhaustive search grows exponentially with the number of desired thresholds. Swarm intelligence metaheuristics are well known as successful and efficient optimization methods for intractable problems. In this paper, we adjusted one of the latest swarm intelligence algorithms, the bat algorithm, for the multilevel image thresholding problem. The results of testing on standard benchmark images show that the bat algorithm is comparable with other state-of-the-art algorithms. We improved standard bat algorithm, where our modifications add some elements from the differential evolution and from the artificial bee colony algorithm. Our new proposed improved bat algorithm proved to be better than five other state-of-the-art algorithms, improving quality of results in all cases and significantly improving convergence speed.
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Farahnaz SADOUGHI
2014-03-01
Full Text Available Breast cancer is the most commonly diagnosed cancer and the most common cause of death in women all over the world. Use of computer technology supporting breast cancer diagnosing is now widespread and pervasive across a broad range of medical areas. Early diagnosis of this disease can greatly enhance the chances of long-term survival of breast cancer victims. Artificial Neural Networks (ANN as mainly method play important role in early diagnoses breast cancer. This paper studies Levenberg Marquardet Backpropagation (LMBP neural network and Levenberg Marquardet Backpropagation based Particle Swarm Optimization(LMBP-PSO for the diagnosis of breast cancer. The obtained results show that LMBP and LMBP based PSO system provides higher classification efficiency. But LMBP based PSO needs minimum training and testing time. It helps in developing Medical Decision System (MDS for breast cancer diagnosing. It can also be used as secondary observer in clinical decision making.
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Luckhana Lawtrakul
2009-05-01
Full Text Available The Particle Swarm Optimization (PSO and Support Vector Machines (SVMs approaches are used for predicting the thermodynamic parameters for the 1:1 inclusion complexation of chiral guests with β-cyclodextrin. A PSO is adopted for descriptor selection in the quantitative structure-property relationships (QSPR of a dataset of 74 chiral guests due to its simplicity, speed, and consistency. The modified PSO is then combined with SVMs for its good approximating properties, to generate a QSPR model with the selected features. Linear, polynomial, and Gaussian radial basis functions are used as kernels in SVMs. All models have demonstrated an impressive performance with R2 higher than 0.8.
International Conference on Computational Intelligence 2015
Saha, Sujan
2017-01-01
This volume comprises the proceedings of the International Conference on Computational Intelligence 2015 (ICCI15). This book aims to bring together work from leading academicians, scientists, researchers and research scholars from across the globe on all aspects of computational intelligence. The work is composed mainly of original and unpublished results of conceptual, constructive, empirical, experimental, or theoretical work in all areas of computational intelligence. Specifically, the major topics covered include classical computational intelligence models and artificial intelligence, neural networks and deep learning, evolutionary swarm and particle algorithms, hybrid systems optimization, constraint programming, human-machine interaction, computational intelligence for the web analytics, robotics, computational neurosciences, neurodynamics, bioinspired and biomorphic algorithms, cross disciplinary topics and applications. The contents of this volume will be of use to researchers and professionals alike....
Energy Technology Data Exchange (ETDEWEB)
Siqueira, Newton Norat
2006-12-15
This work shows a new approach to solve availability maximization problems in electromechanical systems, under periodic preventive scheduled tests. This approach uses a new Optimization tool called PSO developed by Kennedy and Eberhart (2001), Particle Swarm Optimization, integrated with probabilistic safety analysis model. Two maintenance optimization problems are solved by the proposed technique, the first one is a hypothetical electromechanical configuration and the second one is a real case from a nuclear power plant (Emergency Diesel Generators). For both problem PSO is compared to a genetic algorithm (GA). In the experiments made, PSO was able to obtain results comparable or even slightly better than those obtained b GA. Therefore, the PSO algorithm is simpler and its convergence is faster, indicating that PSO is a good alternative for solving such kind of problems. (author)
An Intelligent Broadcasting Algorithm for Early Warning Message Dissemination in VANETs
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Ihn-Han Bae
2015-01-01
Full Text Available Vehicular ad hoc network (VANET has gained much attention recently to improve road safety, reduce traffic congestion, and enable efficient traffic management because of its many important applications in transportation. In this paper, an early warning intelligence broadcasting algorithm is proposed, EW-ICAST, to disseminate a safety message for VANETs. The proposed EW-ICAST uses not only the early warning system on the basis of time to collision (TTC but also the intelligent broadcasting algorithm on the basis of fuzzy logic. Thus, the EW-ICAST resolves effectively broadcast storm problem and meets time-critical requirement. The performance of EW-ICAST is evaluated through simulation and compared with that of other alert message dissemination algorithms. From the simulation results, we know that EW-ICAST is superior to Simple, P-persistence, and EDB algorithms.
Research on intelligent algorithm of electro - hydraulic servo control system
Wang, Yannian; Zhao, Yuhui; Liu, Chengtao
2017-09-01
In order to adapt the nonlinear characteristics of the electro-hydraulic servo control system and the influence of complex interference in the industrial field, using a fuzzy PID switching learning algorithm is proposed and a fuzzy PID switching learning controller is designed and applied in the electro-hydraulic servo controller. The designed controller not only combines the advantages of the fuzzy control and PID control, but also introduces the learning algorithm into the switching function, which makes the learning of the three parameters in the switching function can avoid the instability of the system during the switching between the fuzzy control and PID control algorithms. It also makes the switch between these two control algorithm more smoother than that of the conventional fuzzy PID.
ARBITER: Adaptive rate-based intelligent HTTP streaming algorithm
Zahran, Ahmed H.; Sreenan, Cormac J.
2016-01-01
Dynamic Adaptive streaming over HTTP (DASH) is widely used by content providers for video delivery and dominates traffic on cellular networks. The inherent variability in both video bitrate and network bandwidth negatively impacts the user Quality of Experience (QoE), motivating the design of better DASH-compliant adaptation algorithms. In this paper we present ARBITER, a novel streaming adaptation algorithm that explicitly integrates the variations in both video and network dynamics in its a...
Algorithmic governance: Developing a research agenda through the power of collective intelligence
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John Danaher
2017-09-01
Full Text Available We are living in an algorithmic age where mathematics and computer science are coming together in powerful new ways to influence, shape and guide our behaviour and the governance of our societies. As these algorithmic governance structures proliferate, it is vital that we ensure their effectiveness and legitimacy. That is, we need to ensure that they are an effective means for achieving a legitimate policy goal that are also procedurally fair, open and unbiased. But how can we ensure that algorithmic governance structures are both? This article shares the results of a collective intelligence workshop that addressed exactly this question. The workshop brought together a multidisciplinary group of scholars to consider (a barriers to legitimate and effective algorithmic governance and (b the research methods needed to address the nature and impact of specific barriers. An interactive management workshop technique was used to harness the collective intelligence of this multidisciplinary group. This method enabled participants to produce a framework and research agenda for those who are concerned about algorithmic governance. We outline this research agenda below, providing a detailed map of key research themes, questions and methods that our workshop felt ought to be pursued. This builds upon existing work on research agendas for critical algorithm studies in a unique way through the method of collective intelligence.
Performance Review of Harmony Search, Differential Evolution and Particle Swarm Optimization
Mohan Pandey, Hari
2017-08-01
Metaheuristic algorithms are effective in the design of an intelligent system. These algorithms are widely applied to solve complex optimization problems, including image processing, big data analytics, language processing, pattern recognition and others. This paper presents a performance comparison of three meta-heuristic algorithms, namely Harmony Search, Differential Evolution, and Particle Swarm Optimization. These algorithms are originated altogether from different fields of meta-heuristics yet share a common objective. The standard benchmark functions are used for the simulation. Statistical tests are conducted to derive a conclusion on the performance. The key motivation to conduct this research is to categorize the computational capabilities, which might be useful to the researchers.
The intelligent web search, smart algorithms, and big data
Shroff, Gautam
2013-01-01
As we use the Web for social networking, shopping, and news, we leave a personal trail. These days, linger over a Web page selling lamps, and they will turn up at the advertising margins as you move around the Internet, reminding you, tempting you to make that purchase. Search engines such as Google can now look deep into the data on the Web to pull out instances of the words you are looking for. And there are pages that collect and assess information to give you a snapshot ofchanging political opinion. These are just basic examples of the growth of ""Web intelligence"", as increasingly sophis
The Impact of Critical Thinking and Logico-Mathematical Intelligence on Algorithmic Design Skills
Korkmaz, Ozgen
2012-01-01
The present study aims to reveal the impact of students' critical thinking and logico-mathematical intelligence levels of students on their algorithm design skills. This research was a descriptive study and carried out by survey methods. The sample consisted of 45 first-year educational faculty undergraduate students. The data was collected by…
Tang, Li-Juan; Zhou, Yan-Ping; Jiang, Jian-Hui; Zou, Hong-Yan; Wu, Hai-Long; Shen, Guo-Li; Yu, Ru-Qin
2007-01-01
The support vector machine (SVM) has been receiving increasing interest in an area of QSAR study for its ability in function approximation and remarkable generalization performance. However, selection of support vectors and intensive optimization of kernel width of a nonlinear SVM are inclined to get trapped into local optima, leading to an increased risk of underfitting or overfitting. To overcome these problems, a new nonlinear SVM algorithm is proposed using adaptive kernel transform based on a radial basis function network (RBFN) as optimized by particle swarm optimization (PSO). The new algorithm incorporates a nonlinear transform of the original variables to feature space via a RBFN with one input and one hidden layer. Such a transform intrinsically yields a kernel transform of the original variables. A synergetic optimization of all parameters including kernel centers and kernel widths as well as SVM model coefficients using PSO enables the determination of a flexible kernel transform according to the performance of the total model. The implementation of PSO demonstrates a relatively high efficiency in convergence to a desired optimum. Applications of the proposed algorithm to QSAR studies of binding affinity of HIV-1 reverse transcriptase inhibitors and activity of 1-phenylbenzimidazoles reveal that the new algorithm provides superior performance to the backpropagation neural network and a conventional nonlinear SVM, indicating that this algorithm holds great promise in nonlinear SVM learning.
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Chouaib Labiod
2017-01-01
Full Text Available This paper presents torque ripple reduction with speed control of 8/6 Switched Reluctance Motor (SRM by the determination of the optimal parameters of the turn on, turn off angles Theta_(on, Theta_(off, and the supply voltage using Particle Swarm Optimization (PSO algorithm and steady state Genetic Algorithm (ssGA. With SRM model, there is difficulty in the control relapsed into highly non-linear static characteristics. For this, the Finite Elements Method (FEM has been used because it is a powerful tool to get a model closer to reality. The mechanism used in this kind of machine control consists of a speed controller in order to determine current reference which must be produced to get the desired speed, hence, hysteresis controller is used to compare current reference with current measured up to achieve switching signals needed in the inverter. Depending on this control, the intelligent routing algorithms get the fitness equation from torque ripple and speed response so as to give the optimal parameters for better results. Obtained results from the proposed strategy based on metaheuristic methods are compared with the basic case without considering the adjustment of specific parameters. Optimized results found clearly confirmed the ability and the efficiency of the proposed strategy based on metaheuristic methods in improving the performances of the SRM control considering different torque loads.
Particle swarm optimization based space debris surveillance network scheduling
Jiang, Hai; Liu, Jing; Cheng, Hao-Wen; Zhang, Yao
2017-02-01
The increasing number of space debris has created an orbital debris environment that poses increasing impact risks to existing space systems and human space flights. For the safety of in-orbit spacecrafts, we should optimally schedule surveillance tasks for the existing facilities to allocate resources in a manner that most significantly improves the ability to predict and detect events involving affected spacecrafts. This paper analyzes two criteria that mainly affect the performance of a scheduling scheme and introduces an artificial intelligence algorithm into the scheduling of tasks of the space debris surveillance network. A new scheduling algorithm based on the particle swarm optimization algorithm is proposed, which can be implemented in two different ways: individual optimization and joint optimization. Numerical experiments with multiple facilities and objects are conducted based on the proposed algorithm, and simulation results have demonstrated the effectiveness of the proposed algorithm.
An improved clustering algorithm based on reverse learning in intelligent transportation
Qiu, Guoqing; Kou, Qianqian; Niu, Ting
2017-05-01
With the development of artificial intelligence and data mining technology, big data has gradually entered people's field of vision. In the process of dealing with large data, clustering is an important processing method. By introducing the reverse learning method in the clustering process of PAM clustering algorithm, to further improve the limitations of one-time clustering in unsupervised clustering learning, and increase the diversity of clustering clusters, so as to improve the quality of clustering. The algorithm analysis and experimental results show that the algorithm is feasible.
Turgut, Oguz Emrah; Turgut, Mert Sinan; Coban, Mustafa Turhan
2014-01-01
This study explores the thermal design of shell and tube heat exchangers by using Improved Intelligent Tuned Harmony Search (I-ITHS) algorithm. Intelligent Tuned Harmony Search (ITHS) is an upgraded version of harmony search algorithm which has an advantage of deciding intensification and diversification processes by applying proper pitch adjusting strategy. In this study, we aim to improve the search capacity of ITHS algorithm by utilizing chaotic sequences instead of uniformly distributed r...
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Nebojsa Bacanin
2014-01-01
portfolio model with entropy constraint. Firefly algorithm is one of the latest, very successful swarm intelligence algorithm; however, it exhibits some deficiencies when applied to constrained problems. To overcome lack of exploration power during early iterations, we modified the algorithm and tested it on standard portfolio benchmark data sets used in the literature. Our proposed modified firefly algorithm proved to be better than other state-of-the-art algorithms, while introduction of entropy diversity constraint further improved results.
Sue-Ann, Goh; Ponnambalam, S. G.
This paper focuses on the operational issues of a Two-echelon Single-Vendor-Multiple-Buyers Supply chain (TSVMBSC) under vendor managed inventory (VMI) mode of operation. To determine the optimal sales quantity for each buyer in TSVMBC, a mathematical model is formulated. Based on the optimal sales quantity can be obtained and the optimal sales price that will determine the optimal channel profit and contract price between the vendor and buyer. All this parameters depends upon the understanding of the revenue sharing between the vendor and buyers. A Particle Swarm Optimization (PSO) is proposed for this problem. Solutions obtained from PSO is compared with the best known results reported in literature.
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Xing Liu
2016-11-01
Full Text Available A method is proposed for reducing speed ripple of permanent magnet synchronous motors (PMSMs controlled by space vector pulse width modulation (SVPWM. A flux graph and mathematics are used to analyze the speed ripple characteristics of the PMSM. Analysis indicates that the 6P (P refers to pole pairs of the PMSM time harmonic of rotor mechanical speed is the main harmonic component in the SVPWM control PMSM system. To reduce PMSM speed ripple, harmonics are superposed on a SVPWM reference signal. A particle swarm optimization (PSO algorithm is proposed to determine the optimal phase and multiplier coefficient of the superposed harmonics. The results of a Fourier decomposition and an optimized simulation model verified the accuracy of the analysis as well as the effectiveness of the speed ripple reduction methods, respectively.
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P. Mathiyalagan
2013-10-01
Full Text Available As grid is a heterogeneous environment, finding an optimal schedule for the job is always a complex task. In this paper, a hybridization technique using intelligent water drops and Ant colony optimization which are nature-inspired swarm intelligence approaches are used to find the best resource for the job. Intelligent water drops involves in finding out all matching resources for the job requirements and the routing information (optimal path to reach those resources. Ant Colony optimization chooses the best resource among all matching resources for the job. The objective of this approach is to converge to the optimal schedule faster, minimize the make span of the job, improve load balancing of resources and efficient utilization of available resources.
Implementing embedded artificial intelligence rules within algorithmic programming languages
Feyock, Stefan
1988-01-01
Most integrations of artificial intelligence (AI) capabilities with non-AI (usually FORTRAN-based) application programs require the latter to execute separately to run as a subprogram or, at best, as a coroutine, of the AI system. In many cases, this organization is unacceptable; instead, the requirement is for an AI facility that runs in embedded mode; i.e., is called as subprogram by the application program. The design and implementation of a Prolog-based AI capability that can be invoked in embedded mode are described. The significance of this system is twofold: Provision of Prolog-based symbol-manipulation and deduction facilities makes a powerful symbolic reasoning mechanism available to applications programs written in non-AI languages. The power of the deductive and non-procedural descriptive capabilities of Prolog, which allow the user to describe the problem to be solved, rather than the solution, is to a large extent vitiated by the absence of the standard control structures provided by other languages. Embedding invocations of Prolog rule bases in programs written in non-AI languages makes it possible to put Prolog calls inside DO loops and similar control constructs. The resulting merger of non-AI and AI languages thus results in a symbiotic system in which the advantages of both programming systems are retained, and their deficiencies largely remedied.
Yang, Qidong; Zuo, Hongchao; Li, Weidong
2016-01-01
Improving the capability of land-surface process models to simulate soil moisture assists in better understanding the atmosphere-land interaction. In semi-arid regions, due to limited near-surface observational data and large errors in large-scale parameters obtained by the remote sensing method, there exist uncertainties in land surface parameters, which can cause large offsets between the simulated results of land-surface process models and the observational data for the soil moisture. In this study, observational data from the Semi-Arid Climate Observatory and Laboratory (SACOL) station in the semi-arid loess plateau of China were divided into three datasets: summer, autumn, and summer-autumn. By combing the particle swarm optimization (PSO) algorithm and the land-surface process model SHAW (Simultaneous Heat and Water), the soil and vegetation parameters that are related to the soil moisture but difficult to obtain by observations are optimized using three datasets. On this basis, the SHAW model was run with the optimized parameters to simulate the characteristics of the land-surface process in the semi-arid loess plateau. Simultaneously, the default SHAW model was run with the same atmospheric forcing as a comparison test. Simulation results revealed the following: parameters optimized by the particle swarm optimization algorithm in all simulation tests improved simulations of the soil moisture and latent heat flux; differences between simulated results and observational data are clearly reduced, but simulation tests involving the adoption of optimized parameters cannot simultaneously improve the simulation results for the net radiation, sensible heat flux, and soil temperature. Optimized soil and vegetation parameters based on different datasets have the same order of magnitude but are not identical; soil parameters only vary to a small degree, but the variation range of vegetation parameters is large.
International Conference on Frontiers of Intelligent Computing : Theory and Applications
Udgata, Siba; Biswal, Bhabendra
2014-01-01
This volume contains the papers presented at the Second International Conference on Frontiers in Intelligent Computing: Theory and Applications (FICTA-2013) held during 14-16 November 2013 organized by Bhubaneswar Engineering College (BEC), Bhubaneswar, Odisha, India. It contains 63 papers focusing on application of intelligent techniques which includes evolutionary computation techniques like genetic algorithm, particle swarm optimization techniques, teaching-learning based optimization etc for various engineering applications such as data mining, Fuzzy systems, Machine Intelligence and ANN, Web technologies and Multimedia applications and Intelligent computing and Networking etc.
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Marcelo Seido Nagano
2012-10-01
Full Text Available This work aimed to apply genetic algorithms (GA and particle swarm optimization (PSO in cash balance management using Miller-Orr model, which consists in a stochastic model that does not define a single ideal point for cash balance, but an oscillation range between a lower bound, an ideal balance and an upper bound. Thus, this paper proposes the application of GA and PSO to minimize the Total Cost of cash maintenance, obtaining the parameter of the lower bound of the Miller-Orr model, using for this the assumptions presented in literature. Computational experiments were applied in the development and validation of the models. The results indicated that both the GA and PSO are applicable in determining the cash level from the lower limit, with best results of PSO model, which had not yet been applied in this type of problem.O presente trabalho tem por objetivo a aplicação de algoritmos genéticos (AG e particle swarm optimization (PSO no gerenciamento do saldo de caixa, a partir do modelo Miller-Orr, que consiste em um modelo estocástico que não define um único ponto ideal para o saldo de caixa, mas uma faixa de oscilação entre um limite inferior, um saldo ideal e um limite superior. Assim, este trabalho propõe a aplicação de AG e PSO, para minimizar o Custo Total de manutenção do saldo de caixa, obtendo o parâmetro de limite inferior do modelo Miller-Orr, utilizando para isso premissas apresentadas na literatura. São aplicados experimentos computacionais no desenvolvimento e validação dos modelos. Os resultados indicam que tanto AG quanto PSO são aplicáveis na determinação do nível de caixa a partir do limite inferior, com melhores resultados do modelo PSO, que até então não havia sido aplicado neste tipo de problema.
Rahman, Imran; Vasant, Pandian M.; Singh, Balbir Singh Mahinder; Abdullah-Al-Wadud, M.
2014-10-01
Recent researches towards the use of green technologies to reduce pollution and increase penetration of renewable energy sources in the transportation sector are gaining popularity. The development of the smart grid environment focusing on PHEVs may also heal some of the prevailing grid problems by enabling the implementation of Vehicle-to-Grid (V2G) concept. Intelligent energy management is an important issue which has already drawn much attention to researchers. Most of these works require formulation of mathematical models which extensively use computational intelligence-based optimization techniques to solve many technical problems. Higher penetration of PHEVs require adequate charging infrastructure as well as smart charging strategies. We used Gravitational Search Algorithm (GSA) to intelligently allocate energy to the PHEVs considering constraints such as energy price, remaining battery capacity, and remaining charging time.
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Shaolong Chen
2016-01-01
Full Text Available Parameter estimation is an important problem in nonlinear system modeling and control. Through constructing an appropriate fitness function, parameter estimation of system could be converted to a multidimensional parameter optimization problem. As a novel swarm intelligence algorithm, chicken swarm optimization (CSO has attracted much attention owing to its good global convergence and robustness. In this paper, a method based on improved boundary chicken swarm optimization (IBCSO is proposed for parameter estimation of nonlinear systems, demonstrated and tested by Lorenz system and a coupling motor system. Furthermore, we have analyzed the influence of time series on the estimation accuracy. Computer simulation results show it is feasible and with desirable performance for parameter estimation of nonlinear systems.
Modeling and Flocking Consensus Analysis for Large-Scale UAV Swarms
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Li Bing
2013-01-01
Full Text Available Recently, distributed coordination control of the unmanned aerial vehicle (UAV swarms has been a particularly active topic in intelligent system field. In this paper, through understanding the emergent mechanism of the complex system, further research on the flocking and the dynamic characteristic of UAV swarms will be given. Firstly, this paper analyzes the current researches and existent problems of UAV swarms. Afterwards, by the theory of stochastic process and supplemented variables, a differential-integral model is established, converting the system model into Volterra integral equation. The existence and uniqueness of the solution of the system are discussed. Then the flocking control law is given based on artificial potential with system consensus. At last, we analyze the stability of the proposed flocking control algorithm based on the Lyapunov approach and prove that the system in a limited time can converge to the consensus direction of the velocity. Simulation results are provided to verify the conclusion.
Particle Swarm and Ant Colony Approaches in Multiobjective Optimization
Rao, S. S.
2010-10-01
The social behavior of groups of birds, ants, insects and fish has been used to develop evolutionary algorithms known as swarm intelligence techniques for solving optimization problems. This work presents the development of strategies for the application of two of the popular swarm intelligence techniques, namely the particle swarm and ant colony methods, for the solution of multiobjective optimization problems. In a multiobjective optimization problem, the objectives exhibit a conflicting nature and hence no design vector can minimize all the objectives simultaneously. The concept of Pareto-optimal solution is used in finding a compromise solution. A modified cooperative game theory approach, in which each objective is associated with a different player, is used in this work. The applicability and computational efficiencies of the proposed techniques are demonstrated through several illustrative examples involving unconstrained and constrained problems with single and multiple objectives and continuous and mixed design variables. The present methodologies are expected to be useful for the solution of a variety of practical continuous and mixed optimization problems involving single or multiple objectives with or without constraints.
Intelligent Control of Urban Road Networks: Algorithms, Systems and Communications
Smith, Mike
This paper considers control in road networks. Using a simple example based on the well-known Braess network [1] the paper shows that reducing delay for traffic, assuming that the traffic distribution is fixed, may increase delay when travellers change their travel choices in light of changes in control settings and hence delays. It is shown that a similar effect occurs within signal controlled networks. In this case the effect appears at first sight to be much stronger: the overall capacity of a network may be substantially reduced by utilising standard responsive signal control algorithms. In seeking to reduce delays for existing flows, these policies do not allow properly for consequent routeing changes by travellers. Control methods for signal-controlled networks that do take proper account of the reactions of users are suggested; these require further research, development, and careful real-life trials.
Yildirim, Sule; Beachell, Ronald L.; Veflingstad, Henning
2007-01-01
Future space exploration can utilize artificial intelligence as an integral part of next generation space rover technology to make the rovers more autonomous in performing mission objectives. The main advantage of the increased autonomy through a higher degree of intelligence is that it allows for greater utilization of rover resources by reducing the frequency of time consuming communications between rover and earth. In this paper, we propose a space exploration application of our research on a non-symbolic algorithm and concepts model. This model is based on one of the most recent approaches of cognitive science and artificial intelligence research, a parallel distributed processing approach. We use the Mars rovers. Sprit and Opportunity, as a starting point for proposing what rovers in the future could do if the presented model of non-symbolic algorithms and concepts is embedded in a future space rover. The chosen space exploration application for this paper, novel rock detection, is only one of many potential space exploration applications which can be optimized (through reduction of the frequency of rover-earth communications. collection and transmission of only data that is distinctive/novel) through the use of artificial intelligence technology compared to existing approaches.
Intelligent Diagnostic Assistant for Complicated Skin Diseases through C5's Algorithm.
Jeddi, Fatemeh Rangraz; Arabfard, Masoud; Kermany, Zahra Arab
2017-09-01
Intelligent Diagnostic Assistant can be used for complicated diagnosis of skin diseases, which are among the most common causes of disability. The aim of this study was to design and implement a computerized intelligent diagnostic assistant for complicated skin diseases through C5's Algorithm. An applied-developmental study was done in 2015. Knowledge base was developed based on interviews with dermatologists through questionnaires and checklists. Knowledge representation was obtained from the train data in the database using Excel Microsoft Office. Clementine Software and C5's Algorithms were applied to draw the decision tree. Analysis of test accuracy was performed based on rules extracted using inference chains. The rules extracted from the decision tree were entered into the CLIPS programming environment and the intelligent diagnostic assistant was designed then. The rules were defined using forward chaining inference technique and were entered into Clips programming environment as RULE. The accuracy and error rates obtained in the training phase from the decision tree were 99.56% and 0.44%, respectively. The accuracy of the decision tree was 98% and the error was 2% in the test phase. Intelligent diagnostic assistant can be used as a reliable system with high accuracy, sensitivity, specificity, and agreement.
A Hybrid Algorithm Based on ACO and PSO for Capacitated Vehicle Routing Problems
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Yucheng Kao
2012-01-01
Full Text Available The vehicle routing problem (VRP is a well-known combinatorial optimization problem. It has been studied for several decades because finding effective vehicle routes is an important issue of logistic management. This paper proposes a new hybrid algorithm based on two main swarm intelligence (SI approaches, ant colony optimization (ACO and particle swarm optimization (PSO, for solving capacitated vehicle routing problems (CVRPs. In the proposed algorithm, each artificial ant, like a particle in PSO, is allowed to memorize the best solution ever found. After solution construction, only elite ants can update pheromone according to their own best-so-far solutions. Moreover, a pheromone disturbance method is embedded into the ACO framework to overcome the problem of pheromone stagnation. Two sets of benchmark problems were selected to test the performance of the proposed algorithm. The computational results show that the proposed algorithm performs well in comparison with existing swarm intelligence approaches.
Energy Technology Data Exchange (ETDEWEB)
Stone, D. K. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
2017-06-30
In April of 2016, the Lawrence Livermore National Laboratory External Dosimetry Program underwent a Department of Energy Laboratory Accreditation Program (DOELAP) on-site assessment. The assessment reported a concern that the study performed in 2013 Angular Dependence Study Panasonic UD-802 and UD-810 Dosimeters LLNL Artificial Intelligence Algorithm was incomplete. Only the responses at ±60° and 0° were evaluated and independent data from dosimeters was not used to evaluate the algorithm. Additionally, other configurations of LLNL dosimeters were not considered in this study. This includes nuclear accident dosimeters (NAD) which are placed in the wells surrounding the TLD in the dosimeter holder.
Research on intelligent recommendation algorithm of e-commerce based on association rules
Shen, Jiajie; Cheng, Xianyi
2017-09-01
As the commodities of e-commerce are more and more rich, more and more consumers are willing to choose online shopping, because of these rich varieties of commodity information, customers will often appear aesthetic fatigue. Therefore, we need a recommendation algorithm according to the recent behavior of customers including browsing and consuming to predicate and intelligently recommend goods which the customers need, thus to improve the satisfaction of customers and to increase the profit of e-commerce. This paper first discusses recommendation algorithm, then improves Apriori. Finally, using R language realizes a recommendation algorithm of commodities. The result shows that this algorithm provides a certain decision-making role for customers to buy commodities.
A novel particle swarm optimization based on population category
Wang, Jingying; Qu, Jianhua
2017-10-01
This paper raised a novel particle swarm optimization algorithm based on population category. Traditional particle swarm optimization algorithm is easily to trap in local optimum. In order to avoid standard algorithm appearing premature convergence, this novel algorithm use population category strategy to find new directions for particles. At last, computational results show that the new method is effective and has a high-performance.
Ismail, Ahmad Muhaimin; Mohamad, Mohd Saberi; Abdul Majid, Hairudin; Abas, Khairul Hamimah; Deris, Safaai; Zaki, Nazar; Mohd Hashim, Siti Zaiton; Ibrahim, Zuwairie; Remli, Muhammad Akmal
2017-09-23
Mathematical modelling is fundamental to understand the dynamic behavior and regulation of the biochemical metabolisms and pathways that are found in biological systems. Pathways are used to describe complex processes that involve many parameters. It is important to have an accurate and complete set of parameters that describe the characteristics of a given model. However, measuring these parameters is typically difficult and even impossible in some cases. Furthermore, the experimental data are often incomplete and also suffer from experimental noise. These shortcomings make it challenging to identify the best-fit parameters that can represent the actual biological processes involved in biological systems. Computational approaches are required to estimate these parameters. The estimation is converted into multimodal optimization problems that require a global optimization algorithm that can avoid local solutions. These local solutions can lead to a bad fit when calibrating with a model. Although the model itself can potentially match a set of experimental data, a high-performance estimation algorithm is required to improve the quality of the solutions. This paper describes an improved hybrid of particle swarm optimization and the gravitational search algorithm (IPSOGSA) to improve the efficiency of a global optimum (the best set of kinetic parameter values) search. The findings suggest that the proposed algorithm is capable of narrowing down the search space by exploiting the feasible solution areas. Hence, the proposed algorithm is able to achieve a near-optimal set of parameters at a fast convergence speed. The proposed algorithm was tested and evaluated based on two aspartate pathways that were obtained from the BioModels Database. The results show that the proposed algorithm outperformed other standard optimization algorithms in terms of accuracy and near-optimal kinetic parameter estimation. Nevertheless, the proposed algorithm is only expected to work well in
Celik, Yuksel; Ulker, Erkan
2013-01-01
Marriage in honey bees optimization (MBO) is a metaheuristic optimization algorithm developed by inspiration of the mating and fertilization process of honey bees and is a kind of swarm intelligence optimizations. In this study we propose improved marriage in honey bees optimization (IMBO) by adding Levy flight algorithm for queen mating flight and neighboring for worker drone improving. The IMBO algorithm's performance and its success are tested on the well-known six unconstrained test functions and compared with other metaheuristic optimization algorithms.
Seifbarghy, Mehdi; Kalani, Masoud Mirzaei; Hemmati, Mojtaba
2016-11-01
This paper formulates a two-echelon single-producer multi-buyer supply chain model, while a single product is produced and transported to the buyers by the producer. The producer and the buyers apply vendor-managed inventory mode of operation. It is assumed that the producer applies economic production quantity policy, which implies a constant production rate at the producer. The operational parameters of each buyer are sales quantity, sales price and production rate. Channel profit of the supply chain and contract price between the producer and each buyer is determined based on the values of the operational parameters. Since the model belongs to nonlinear integer programs, we use a discrete particle swarm optimization algorithm (DPSO) to solve the addressed problem; however, the performance of the DPSO is compared utilizing two well-known heuristics, namely genetic algorithm and simulated annealing. A number of examples are provided to verify the model and assess the performance of the proposed heuristics. Experimental results indicate that DPSO outperforms the rival heuristics, with respect to some comparison metrics.
Sharkey, Amanda J. C.
2007-09-01
Swarm Robotics (SR) is closely related to Swarm Intelligence, and both were initially inspired by studies of social insects. Their guiding principles are based on their biological inspiration and take the form of an emphasis on decentralized local control and communication. Earlier studies went a step further in emphasizing the use of simple reactive robots that only communicate indirectly through the environment. More recently SR studies have moved beyond these constraints to explore the use of non-reactive robots that communicate directly, and that can learn and represent their environment. There is no clear agreement in the literature about how far such extensions of the original principles could go. Should there be any limitations on the individual abilities of the robots used in SR studies? Should knowledge of the capabilities of social insects lead to constraints on the capabilities of individual robots in SR studies? There is a lack of explicit discussion of such questions, and researchers have adopted a variety of constraints for a variety of reasons. A simple taxonomy of swarm robotics is presented here with the aim of addressing and clarifying these questions. The taxonomy distinguishes subareas of SR based on the emphases and justifications for minimalism and individual simplicity.
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Chinmaya P. Mohanty
2017-04-01
Full Text Available Although significant research has gone into the field of electrical discharge machining (EDM, analysis related to the machining efficiency of the process with different electrodes has not been adequately made. Copper and brass are frequently used as electrode materials but graphite can be used as a potential electrode material due to its high melting point temperature and good electrical conductivity. In view of this, the present work attempts to compare the machinability of copper, graphite and brass electrodes while machining Inconel 718 super alloy. Taguchi’s L27 orthogonal array has been employed to collect data for the study and analyze effect of machining parameters on performance measures. The important performance measures selected for this study are material removal rate, tool wear rate, surface roughness and radial overcut. Machining parameters considered for analysis are open circuit voltage, discharge current, pulse-on-time, duty factor, flushing pressure and electrode material. From the experimental analysis, it is observed that electrode material, discharge current and pulse-on-time are the important parameters for all the performance measures. Utility concept has been implemented to transform a multiple performance characteristics into an equivalent performance characteristic. Non-linear regression analysis is carried out to develop a model relating process parameters and overall utility index. Finally, the quantum behaved particle swarm optimization (QPSO and particle swarm optimization (PSO algorithms have been used to compare the optimal level of cutting parameters. Results demonstrate the elegance of QPSO in terms of convergence and computational effort. The optimal parametric setting obtained through both the approaches is validated by conducting confirmation experiments.
Improving Vector Evaluated Particle Swarm Optimisation by incorporating nondominated solutions.
Lim, Kian Sheng; Ibrahim, Zuwairie; Buyamin, Salinda; Ahmad, Anita; Naim, Faradila; Ghazali, Kamarul Hawari; Mokhtar, Norrima
2013-01-01
The Vector Evaluated Particle Swarm Optimisation algorithm is widely used to solve multiobjective optimisation problems. This algorithm optimises one objective using a swarm of particles where their movements are guided by the best solution found by another swarm. However, the best solution of a swarm is only updated when a newly generated solution has better fitness than the best solution at the objective function optimised by that swarm, yielding poor solutions for the multiobjective optimisation problems. Thus, an improved Vector Evaluated Particle Swarm Optimisation algorithm is introduced by incorporating the nondominated solutions as the guidance for a swarm rather than using the best solution from another swarm. In this paper, the performance of improved Vector Evaluated Particle Swarm Optimisation algorithm is investigated using performance measures such as the number of nondominated solutions found, the generational distance, the spread, and the hypervolume. The results suggest that the improved Vector Evaluated Particle Swarm Optimisation algorithm has impressive performance compared with the conventional Vector Evaluated Particle Swarm Optimisation algorithm.
Time Optimal Reachability Analysis Using Swarm Verification
DEFF Research Database (Denmark)
Zhang, Zhengkui; Nielsen, Brian; Larsen, Kim Guldstrand
2016-01-01
and planning problems, response time optimization etc. We propose swarm verification to accelerate time optimal reachability using the real-time model-checker Uppaal. In swarm verification, a large number of model checker instances execute in parallel on a computer cluster using different, typically randomized...... search strategies. We develop four swarm algorithms and evaluate them with four models in terms scalability, and time- and memory consumption. Three of these cooperate by exchanging costs of intermediate solutions to prune the search using a branch-and-bound approach. Our results show that swarm...
Bioinspired Intelligent Algorithm and Its Applications for Mobile Robot Control: A Survey
Directory of Open Access Journals (Sweden)
Jianjun Ni
2016-01-01
Full Text Available Bioinspired intelligent algorithm (BIA is a kind of intelligent computing method, which is with a more lifelike biological working mechanism than other types. BIAs have made significant progress in both understanding of the neuroscience and biological systems and applying to various fields. Mobile robot control is one of the main application fields of BIAs which has attracted more and more attention, because mobile robots can be used widely and general artificial intelligent algorithms meet a development bottleneck in this field, such as complex computing and the dependence on high-precision sensors. This paper presents a survey of recent research in BIAs, which focuses on the research in the realization of various BIAs based on different working mechanisms and the applications for mobile robot control, to help in understanding BIAs comprehensively and clearly. The survey has four primary parts: a classification of BIAs from the biomimetic mechanism, a summary of several typical BIAs from different levels, an overview of current applications of BIAs in mobile robot control, and a description of some possible future directions for research.
Directory of Open Access Journals (Sweden)
Andreas König
2009-11-01
Full Text Available The emergence of novel sensing elements, computing nodes, wireless communication and integration technology provides unprecedented possibilities for the design and application of intelligent systems. Each new application system must be designed from scratch, employing sophisticated methods ranging from conventional signal processing to computational intelligence. Currently, a significant part of this overall algorithmic chain of the computational system model still has to be assembled manually by experienced designers in a time and labor consuming process. In this research work, this challenge is picked up and a methodology and algorithms for automated design of intelligent integrated and resource-aware multi-sensor systems employing multi-objective evolutionary computation are introduced. The proposed methodology tackles the challenge of rapid-prototyping of such systems under realization constraints and, additionally, includes features of system instance specific self-correction for sustained operation of a large volume and in a dynamically changing environment. The extension of these concepts to the reconfigurable hardware platform renders so called self-x sensor systems, which stands, e.g., for self-monitoring, -calibrating, -trimming, and -repairing/-healing systems. Selected experimental results prove the applicability and effectiveness of our proposed methodology and emerging tool. By our approach, competitive results were achieved with regard to classification accuracy, flexibility, and design speed under additional design constraints.
Bioinspired Intelligent Algorithm and Its Applications for Mobile Robot Control: A Survey.
Ni, Jianjun; Wu, Liuying; Fan, Xinnan; Yang, Simon X
2016-01-01
Bioinspired intelligent algorithm (BIA) is a kind of intelligent computing method, which is with a more lifelike biological working mechanism than other types. BIAs have made significant progress in both understanding of the neuroscience and biological systems and applying to various fields. Mobile robot control is one of the main application fields of BIAs which has attracted more and more attention, because mobile robots can be used widely and general artificial intelligent algorithms meet a development bottleneck in this field, such as complex computing and the dependence on high-precision sensors. This paper presents a survey of recent research in BIAs, which focuses on the research in the realization of various BIAs based on different working mechanisms and the applications for mobile robot control, to help in understanding BIAs comprehensively and clearly. The survey has four primary parts: a classification of BIAs from the biomimetic mechanism, a summary of several typical BIAs from different levels, an overview of current applications of BIAs in mobile robot control, and a description of some possible future directions for research.
Incorporating the Avoidance Behavior to the Standard Particle Swarm Optimization 2011
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ALTINOZ, O. T.
2015-05-01
Full Text Available Inspired from social and cognitive behaviors of animals living as swarms; particle swarm optimization (PSO provides a simple but very powerful tool for researchers who are dealing with collective intelligence. The algorithm depends on modeling the very basic random behavior (i.e. exploration capability of individuals in addition to their tendency to revisit positions of good memories (cognitive behavior and tendency to keep an eye on and follow the majority of swarm members (social behavior. The balance among these three major behaviors is the key of success of the algorithm. On the other hand, there are other social and cognitive phenomena, which might be useful for improvement of the algorithm. In this paper, we particularly investigate avoidance from the bad behavior. We propose modifications about modeling the Standard PSO 2011 formulation, and we test performance of our proposals at each step via benchmark functions, and compare the results of the proposed algorithms with well-known algorithms. Our results show that incorporation of Social Avoidance behavior into SPSO11 improves the performance. It is also shown that in case the Social Avoidance behavior is applied in an adaptive manner at the very first iterations of the algorithm, there might be further improvements.
Generic, scalable and decentralized fault detection for robot swarms
Christensen, Anders Lyhne; Timmis, Jon
2017-01-01
Robot swarms are large-scale multirobot systems with decentralized control which means that each robot acts based only on local perception and on local coordination with neighboring robots. The decentralized approach to control confers number of potential benefits. In particular, inherent scalability and robustness are often highlighted as key distinguishing features of robot swarms compared with systems that rely on traditional approaches to multirobot coordination. It has, however, been shown that swarm robotics systems are not always fault tolerant. To realize the robustness potential of robot swarms, it is thus essential to give systems the capacity to actively detect and accommodate faults. In this paper, we present a generic fault-detection system for robot swarms. We show how robots with limited and imperfect sensing capabilities are able to observe and classify the behavior of one another. In order to achieve this, the underlying classifier is an immune system-inspired algorithm that learns to distinguish between normal behavior and abnormal behavior online. Through a series of experiments, we systematically assess the performance of our approach in a detailed simulation environment. In particular, we analyze our system’s capacity to correctly detect robots with faults, false positive rates, performance in a foraging task in which each robot exhibits a composite behavior, and performance under perturbations of the task environment. Results show that our generic fault-detection system is robust, that it is able to detect faults in a timely manner, and that it achieves a low false positive rate. The developed fault-detection system has the potential to enable long-term autonomy for robust multirobot systems, thus increasing the usefulness of robots for a diverse repertoire of upcoming applications in the area of distributed intelligent automation. PMID:28806756
Generic, scalable and decentralized fault detection for robot swarms.
Tarapore, Danesh; Christensen, Anders Lyhne; Timmis, Jon
2017-01-01
Robot swarms are large-scale multirobot systems with decentralized control which means that each robot acts based only on local perception and on local coordination with neighboring robots. The decentralized approach to control confers number of potential benefits. In particular, inherent scalability and robustness are often highlighted as key distinguishing features of robot swarms compared with systems that rely on traditional approaches to multirobot coordination. It has, however, been shown that swarm robotics systems are not always fault tolerant. To realize the robustness potential of robot swarms, it is thus essential to give systems the capacity to actively detect and accommodate faults. In this paper, we present a generic fault-detection system for robot swarms. We show how robots with limited and imperfect sensing capabilities are able to observe and classify the behavior of one another. In order to achieve this, the underlying classifier is an immune system-inspired algorithm that learns to distinguish between normal behavior and abnormal behavior online. Through a series of experiments, we systematically assess the performance of our approach in a detailed simulation environment. In particular, we analyze our system's capacity to correctly detect robots with faults, false positive rates, performance in a foraging task in which each robot exhibits a composite behavior, and performance under perturbations of the task environment. Results show that our generic fault-detection system is robust, that it is able to detect faults in a timely manner, and that it achieves a low false positive rate. The developed fault-detection system has the potential to enable long-term autonomy for robust multirobot systems, thus increasing the usefulness of robots for a diverse repertoire of upcoming applications in the area of distributed intelligent automation.
Ortuño, Francisco M; Valenzuela, Olga; Pomares, Hector; Rojas, Fernando; Florido, Javier P; Urquiza, Jose M; Rojas, Ignacio
2013-01-07
Multiple sequence alignments (MSAs) have become one of the most studied approaches in bioinformatics to perform other outstanding tasks such as structure prediction, biological function analysis or next-generation sequencing. However, current MSA algorithms do not always provide consistent solutions, since alignments become increasingly difficult when dealing with low similarity sequences. As widely known, these algorithms directly depend on specific features of the sequences, causing relevant influence on the alignment accuracy. Many MSA tools have been recently designed but it is not possible to know in advance which one is the most suitable for a particular set of sequences. In this work, we analyze some of the most used algorithms presented in the bibliography and their dependences on several features. A novel intelligent algorithm based on least square support vector machine is then developed to predict how accurate each alignment could be, depending on its analyzed features. This algorithm is performed with a dataset of 2180 MSAs. The proposed system first estimates the accuracy of possible alignments. The most promising methodologies are then selected in order to align each set of sequences. Since only one selected algorithm is run, the computational time is not excessively increased.
POLICE OFFICE MODEL IMPROVEMENT FOR SECURITY OF SWARM ROBOTIC SYSTEMS
I. A. Zikratov; A. V. Gurtov; T. V. Zikratova; Kozlova, E. V.
2014-01-01
This paper focuses on aspects of information security for group of mobile robotic systems with swarm intellect. The ways for hidden attacks realization by the opposing party on swarm algorithm are discussed. We have fulfilled numerical modeling of potentially destructive information influence on the ant shortest path algorithm. We have demonstrated the consequences of attacks on the ant algorithm with different concentration in a swarm of subversive robots. Approaches are suggested for inform...
Recent Advances in Intelligent Engineering Systems
Klempous, Ryszard; Araujo, Carmen
2012-01-01
This volume is a collection of 19 chapters on intelligent engineering systems written by respectable experts of the fields. The book consists of three parts. The first part is devoted to the foundational aspects of computational intelligence. It consists of 8 chapters that include studies in genetic algorithms, fuzzy logic connectives, enhanced intelligence in product models, nature-inspired optimization technologies, particle swarm optimization, evolution algorithms, model complexity of neural networks, and fitness landscape analysis. The second part contains contributions to intelligent computation in networks, presented in 5 chapters. The covered subjects include the application of self-organizing maps for early detection of denial of service attacks, combating security threats via immunity and adaptability in cognitive radio networks, novel modifications in WSN network design for improved SNR and reliability, a conceptual framework for the design of audio based cognitive infocommunication channels, and a ...
Advances in chaos theory and intelligent control
Vaidyanathan, Sundarapandian
2016-01-01
The book reports on the latest advances in and applications of chaos theory and intelligent control. Written by eminent scientists and active researchers and using a clear, matter-of-fact style, it covers advanced theories, methods, and applications in a variety of research areas, and explains key concepts in modeling, analysis, and control of chaotic and hyperchaotic systems. Topics include fractional chaotic systems, chaos control, chaos synchronization, memristors, jerk circuits, chaotic systems with hidden attractors, mechanical and biological chaos, and circuit realization of chaotic systems. The book further covers fuzzy logic controllers, evolutionary algorithms, swarm intelligence, and petri nets among other topics. Not only does it provide the readers with chaos fundamentals and intelligent control-based algorithms; it also discusses key applications of chaos as well as multidisciplinary solutions developed via intelligent control. The book is a timely and comprehensive reference guide for graduate s...
An efficient algorithm for function optimization: modified stem cells algorithm
Taherdangkoo, Mohammad; Paziresh, Mahsa; Yazdi, Mehran; Bagheri, Mohammad
2013-03-01
In this paper, we propose an optimization algorithm based on the intelligent behavior of stem cell swarms in reproduction and self-organization. Optimization algorithms, such as the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, Ant Colony Optimization (ACO) algorithm and Artificial Bee Colony (ABC) algorithm, can give solutions to linear and non-linear problems near to the optimum for many applications; however, in some case, they can suffer from becoming trapped in local optima. The Stem Cells Algorithm (SCA) is an optimization algorithm inspired by the natural behavior of stem cells in evolving themselves into new and improved cells. The SCA avoids the local optima problem successfully. In this paper, we have made small changes in the implementation of this algorithm to obtain improved performance over previous versions. Using a series of benchmark functions, we assess the performance of the proposed algorithm and compare it with that of the other aforementioned optimization algorithms. The obtained results prove the superiority of the Modified Stem Cells Algorithm (MSCA).
Directory of Open Access Journals (Sweden)
Ronghui Zhang
2017-05-01
Full Text Available Focusing on safety, comfort and with an overall aim of the comprehensive improvement of a vision-based intelligent vehicle, a novel Advanced Emergency Braking System (AEBS is proposed based on Nonlinear Model Predictive Algorithm. Considering the nonlinearities of vehicle dynamics, a vision-based longitudinal vehicle dynamics model is established. On account of the nonlinear coupling characteristics of the driver, surroundings, and vehicle itself, a hierarchical control structure is proposed to decouple and coordinate the system. To avoid or reduce the collision risk between the intelligent vehicle and collision objects, a coordinated cost function of tracking safety, comfort, and fuel economy is formulated. Based on the terminal constraints of stable tracking, a multi-objective optimization controller is proposed using the theory of non-linear model predictive control. To quickly and precisely track control target in a finite time, an electronic brake controller for AEBS is designed based on the Nonsingular Fast Terminal Sliding Mode (NFTSM control theory. To validate the performance and advantages of the proposed algorithm, simulations are implemented. According to the simulation results, the proposed algorithm has better integrated performance in reducing the collision risk and improving the driving comfort and fuel economy of the smart car compared with the existing single AEBS.
Intelligent computing systems emerging application areas
Virvou, Maria; Jain, Lakhmi
2016-01-01
This book at hand explores emerging scientific and technological areas in which Intelligent Computing Systems provide efficient solutions and, thus, may play a role in the years to come. It demonstrates how Intelligent Computing Systems make use of computational methodologies that mimic nature-inspired processes to address real world problems of high complexity for which exact mathematical solutions, based on physical and statistical modelling, are intractable. Common intelligent computational methodologies are presented including artificial neural networks, evolutionary computation, genetic algorithms, artificial immune systems, fuzzy logic, swarm intelligence, artificial life, virtual worlds and hybrid methodologies based on combinations of the previous. The book will be useful to researchers, practitioners and graduate students dealing with mathematically-intractable problems. It is intended for both the expert/researcher in the field of Intelligent Computing Systems, as well as for the general reader in t...
Sheikhan, Mansour; Abbasnezhad Arabi, Mahdi; Gharavian, Davood
2015-10-01
Artificial neural networks are efficient models in pattern recognition applications, but their performance is dependent on employing suitable structure and connection weights. This study used a hybrid method for obtaining the optimal weight set and architecture of a recurrent neural emotion classifier based on gravitational search algorithm (GSA) and its binary version (BGSA), respectively. By considering the features of speech signal that were related to prosody, voice quality, and spectrum, a rich feature set was constructed. To select more efficient features, a fast feature selection method was employed. The performance of the proposed hybrid GSA-BGSA method was compared with similar hybrid methods based on particle swarm optimisation (PSO) algorithm and its binary version, PSO and discrete firefly algorithm, and hybrid of error back-propagation and genetic algorithm that were used for optimisation. Experimental tests on Berlin emotional database demonstrated the superior performance of the proposed method using a lighter network structure.
Directory of Open Access Journals (Sweden)
Yan Xiang
2017-01-01
Full Text Available Extreme hydrological events induced by typhoons in reservoir areas have presented severe challenges to the safe operation of hydraulic structures. Based on analysis of the seepage characteristics of an earth rock dam, a novel seepage safety monitoring model was constructed in this study. The nonlinear influence processes of the antecedent reservoir water level and rainfall were assumed to follow normal distributions. The particle swarm optimization (PSO algorithm was used to optimize the model parameters so as to raise the fitting accuracy. In addition, a mutation factor was introduced to simulate the sudden increase in the piezometric level induced by short-duration heavy rainfall and the possible historical extreme reservoir water level during a typhoon. In order to verify the efficacy of this model, the earth rock dam of the Siminghu Reservoir was used as an example. The piezometric level at the SW1-2 measuring point during Typhoon Fitow in 2013 was fitted with the present model, and a corresponding theoretical expression was established. Comparison of fitting results of the piezometric level obtained from the present statistical model and traditional statistical model with monitored values during the typhoon shows that the present model has a higher fitting accuracy and can simulate the uprush feature of the seepage pressure during the typhoon perfectly.
Jia, F.; Lichti, D.
2017-09-01
The optimal network design problem has been well addressed in geodesy and photogrammetry but has not received the same attention for terrestrial laser scanner (TLS) networks. The goal of this research is to develop a complete design system that can automatically provide an optimal plan for high-accuracy, large-volume scanning networks. The aim in this paper is to use three heuristic optimization methods, simulated annealing (SA), genetic algorithm (GA) and particle swarm optimization (PSO), to solve the first-order design (FOD) problem for a small-volume indoor network and make a comparison of their performances. The room is simplified as discretized wall segments and possible viewpoints. Each possible viewpoint is evaluated with a score table representing the wall segments visible from each viewpoint based on scanning geometry constraints. The goal is to find a minimum number of viewpoints that can obtain complete coverage of all wall segments with a minimal sum of incidence angles. The different methods have been implemented and compared in terms of the quality of the solutions, runtime and repeatability. The experiment environment was simulated from a room located on University of Calgary campus where multiple scans are required due to occlusions from interior walls. The results obtained in this research show that PSO and GA provide similar solutions while SA doesn't guarantee an optimal solution within limited iterations. Overall, GA is considered as the best choice for this problem based on its capability of providing an optimal solution and fewer parameters to tune.
Ahirwal, M K; Kumar, Anil; Singh, G K
2013-01-01
This paper explores the migration of adaptive filtering with swarm intelligence/evolutionary techniques employed in the field of electroencephalogram/event-related potential noise cancellation or extraction. A new approach is proposed in the form of controlled search space to stabilize the randomness of swarm intelligence techniques especially for the EEG signal. Swarm-based algorithms such as Particles Swarm Optimization, Artificial Bee Colony, and Cuckoo Optimization Algorithm with their variants are implemented to design optimized adaptive noise canceler. The proposed controlled search space technique is tested on each of the swarm intelligence techniques and is found to be more accurate and powerful. Adaptive noise canceler with traditional algorithms such as least-mean-square, normalized least-mean-square, and recursive least-mean-square algorithms are also implemented to compare the results. ERP signals such as simulated visual evoked potential, real visual evoked potential, and real sensorimotor evoked potential are used, due to their physiological importance in various EEG studies. Average computational time and shape measures of evolutionary techniques are observed 8.21E-01 sec and 1.73E-01, respectively. Though, traditional algorithms take negligible time consumption, but are unable to offer good shape preservation of ERP, noticed as average computational time and shape measure difference, 1.41E-02 sec and 2.60E+00, respectively.
A Clustering Approach Using Cooperative Artificial Bee Colony Algorithm
Directory of Open Access Journals (Sweden)
Wenping Zou
2010-01-01
Full Text Available Artificial Bee Colony (ABC is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. This paper presents an extended ABC algorithm, namely, the Cooperative Article Bee Colony (CABC, which significantly improves the original ABC in solving complex optimization problems. Clustering is a popular data analysis and data mining technique; therefore, the CABC could be used for solving clustering problems. In this work, first the CABC algorithm is used for optimizing six widely used benchmark functions and the comparative results produced by ABC, Particle Swarm Optimization (PSO, and its cooperative version (CPSO are studied. Second, the CABC algorithm is used for data clustering on several benchmark data sets. The performance of CABC algorithm is compared with PSO, CPSO, and ABC algorithms on clustering problems. The simulation results show that the proposed CABC outperforms the other three algorithms in terms of accuracy, robustness, and convergence speed.
Design of intelligent locks based on the triple KeeLoq algorithm
Directory of Open Access Journals (Sweden)
Huibin Chen
2016-04-01
Full Text Available KeeLoq algorithm with high security was usually used in wireless codec. Its security lack is indicated in this article according to the detailed rationale and the introduction of previous attack researches. Taking examples from Triple Data Encryption Standard algorithm, the triple KeeLoq codec algorithm was first proposed. Experimental results showed that the algorithm would not reduce powerful rolling effect and in consideration of limited computing power of embedded microcontroller three 64-bit keys were suitable to increase the crack difficulties and further improved its security. The method was applied to intelligent door access system for experimental verification. 16F690 extended Bluetooth or WiFi interface was employed to design the lock system on door. Key application was constructed on Android platform. The wireless communication between the lock on door and Android key application employed triple KeeLoq algorithm to ensure the higher security. Due to flexibility and multiformity (an Android key application with various keys of software-based keys, the solution owned overwhelmed advantages of low cost, high security, humanity, and green environmental protection.
Knee Joint Optimization Design of Intelligent Bionic Leg Based on Genetic Algorithm
Directory of Open Access Journals (Sweden)
Hualong Xie
2014-09-01
Full Text Available Intelligent bionic leg (IBL is an advanced prosthesis which can maximum functionally simulate and approach the motion trajectory of human leg. Knee joint is the most important bone of human leg and its bionic design has great significance to prosthesis performance. The structural components of IBL are introduced and virtual prototype is given. The advantages of 4-bar knee joint are analyzed and are adopted in IBL design. The kinematics model of 4-bar knee joint is established. The objective function, constraint condition, parameters selection and setting of genetic algorithm are discussed in detail. Based on genetic algorithm, the optimization design of IBL knee joint is done. The optimization results indicate that the 4-bar mechanism can achieve better anthropomorphic characteristics of human knee joint.
Zhang, Xu; Wang, Yujie; Liu, Chang; Chen, Zonghai
2018-02-01
An accurate battery pack state of health (SOH) estimation is important to characterize the dynamic responses of battery pack and ensure the battery work with safety and reliability. However, the different performances in battery discharge/charge characteristics and working conditions in battery pack make the battery pack SOH estimation difficult. In this paper, the battery pack SOH is defined as the change of battery pack maximum energy storage. It contains all the cells' information including battery capacity, the relationship between state of charge (SOC) and open circuit voltage (OCV), and battery inconsistency. To predict the battery pack SOH, the method of particle swarm optimization-genetic algorithm is applied in battery pack model parameters identification. Based on the results, a particle filter is employed in battery SOC and OCV estimation to avoid the noise influence occurring in battery terminal voltage measurement and current drift. Moreover, a recursive least square method is used to update cells' capacity. Finally, the proposed method is verified by the profiles of New European Driving Cycle and dynamic test profiles. The experimental results indicate that the proposed method can estimate the battery states with high accuracy for actual operation. In addition, the factors affecting the change of SOH is analyzed.
Intelligent 3D packing using a grouping algorithm for automotive container engineering
Directory of Open Access Journals (Sweden)
Youn-Kyoung Joung
2014-04-01
Full Text Available Storing, and the loading and unloading of materials at production sites in the manufacturing sector for mass production is a critical problem that affects various aspects: the layout of the factory, line-side space, logistics, workers’ work paths and ease of work, automatic procurement of components, and transfer and supply. Traditionally, the nesting problem has been an issue to improve the efficiency of raw materials; further, research into mainly 2D optimization has progressed. Also, recently, research into the expanded usage of 3D models to implement packing optimization has been actively carried out. Nevertheless, packing algorithms using 3D models are not widely used in practice, due to the large decrease in efficiency, owing to the complexity and excessive computational time. In this paper, the problem of efficiently loading and unloading freeform 3D objects into a given container has been solved, by considering the 3D form, ease of loading and unloading, and packing density. For this reason, a Group Packing Approach for workers has been developed, by using analyzed truck packing work patterns and Group Technology, which is to enhance the efficiency of storage in the manufacturing sector. Also, an algorithm for 3D packing has been developed, and implemented in a commercial 3D CAD modeling system. The 3D packing method consists of a grouping algorithm, a sequencing algorithm, an orientating algorithm, and a loading algorithm. These algorithms concern the respective aspects: the packing order, orientation decisions of parts, collision checking among parts and processing, position decisions of parts, efficiency verification, and loading and unloading simulation. Storage optimization and examination of the ease of loading and unloading are possible, and various kinds of engineering analysis, such as work performance analysis, are facilitated through the intelligent 3D packing method developed in this paper, by using the results of the 3D
behaved particle swarm optimization (QPSO)
African Journals Online (AJOL)
Administrator
2011-06-13
Jun 13, 2011 ... fermentation process, and consequently, it increased the yield of fermentation. Key words: Soft-sensing model, quantum-behaved particle swarm optimization algorithm, neural network. INTRODUCTION. In industrial production through fermentation, the main effect variables include physical variables (the ...
Salehi, Mojtaba; Bahreininejad, Ardeshir
2011-08-01
Optimization of process planning is considered as the key technology for computer-aided process planning which is a rather complex and difficult procedure. A good process plan of a part is built up based on two elements: (1) the optimized sequence of the operations of the part; and (2) the optimized selection of the machine, cutting tool and Tool Access Direction (TAD) for each operation. In the present work, the process planning is divided into preliminary planning, and secondary/detailed planning. In the preliminary stage, based on the analysis of order and clustering constraints as a compulsive constraint aggregation in operation sequencing and using an intelligent searching strategy, the feasible sequences are generated. Then, in the detailed planning stage, using the genetic algorithm which prunes the initial feasible sequences, the optimized operation sequence and the optimized selection of the machine, cutting tool and TAD for each operation based on optimization constraints as an additive constraint aggregation are obtained. The main contribution of this work is the optimization of sequence of the operations of the part, and optimization of machine selection, cutting tool and TAD for each operation using the intelligent search and genetic algorithm simultaneously.
Landau Theory of Adaptive Integration in Computational Intelligence
Plewczynski, Dariusz
2010-01-01
Computational Intelligence (CI) is a sub-branch of Artificial Intelligence paradigm focusing on the study of adaptive mechanisms to enable or facilitate intelligent behavior in complex and changing environments. There are several paradigms of CI [like artificial neural networks, evolutionary computations, swarm intelligence, artificial immune systems, fuzzy systems and many others], each of these has its origins in biological systems [biological neural systems, natural Darwinian evolution, social behavior, immune system, interactions of organisms with their environment]. Most of those paradigms evolved into separate machine learning (ML) techniques, where probabilistic methods are used complementary with CI techniques in order to effectively combine elements of learning, adaptation, evolution and Fuzzy logic to create heuristic algorithms that are, in some sense, intelligent. The current trend is to develop consensus techniques, since no single machine learning algorithms is superior to others in all possible...
Particle swarm optimization with random keys applied to the nuclear reactor reload problem
Energy Technology Data Exchange (ETDEWEB)
Meneses, Anderson Alvarenga de Moura [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil). Coordenacao dos Programas de Pos-graduacao de Engenharia (COPPE). Programa de Engenharia Nuclear; Fundacao Educacional de Macae (FUNEMAC), RJ (Brazil). Faculdade Professor Miguel Angelo da Silva Santos; Machado, Marcelo Dornellas; Medeiros, Jose Antonio Carlos Canedo; Schirru, Roberto [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil). Coordenacao dos Programas de Pos-graduacao de Engenharia (COPPE). Programa de Engenharia Nuclear]. E-mails: ameneses@con.ufrj.br; marcelo@lmp.ufrj.br; canedo@lmp.ufrj.br; schirru@lmp.ufrj.br
2007-07-01
In 1995, Kennedy and Eberhart presented the Particle Swarm Optimization (PSO), an Artificial Intelligence metaheuristic technique to optimize non-linear continuous functions. The concept of Swarm Intelligence is based on the socials aspects of intelligence, it means, the ability of individuals to learn with their own experience in a group as well as to take advantage of the performance of other individuals. Some PSO models for discrete search spaces have been developed for combinatorial optimization, although none of them presented satisfactory results to optimize a combinatorial problem as the nuclear reactor fuel reloading problem (NRFRP). In this sense, we developed the Particle Swarm Optimization with Random Keys (PSORK) in previous research to solve Combinatorial Problems. Experiences demonstrated that PSORK performed comparable to or better than other techniques. Thus, PSORK metaheuristic is being applied in optimization studies of the NRFRP for Angra 1 Nuclear Power Plant. Results will be compared with Genetic Algorithms and the manual method provided by a specialist. In this experience, the problem is being modeled for an eight-core symmetry and three-dimensional geometry, aiming at the minimization of the Nuclear Enthalpy Power Peaking Factor as well as the maximization of the cycle length. (author)
Robust and intelligent algorithms for TDOA localization in distributed sensor networks
Fang, Jiaqi; He, Zhiyi; Jian, Cui
2017-10-01
Passive source localization utilizing time difference of arrival (TDOA) has widely application in radar, navigation, surveillance, wireless communication, distributed sensor network, etc. This paper presents two robust algorithms named Modified Taylor-seriesmethod (MTS) and Modified Newton (MNT) method. The proposed algorithms are the improvement of the Taylor-series (TS) and Newton (NT) methods for solving the convergent problem which is critical in the iterative methods. The key component of the proposed algorithms is to produce a new modified Hessian matrix intelligently using the Regularization theory which can turn the ill-posed Hessian matrix into a well-conditioned matrix. The regularization parameter which controls the properties of the regularized solution can be automatically determined by the L-curve method. With this procedure, the proposed methods are robust to make the iteration convergence with a bad initial. Simulation results show that the proposed methods improve the convergent probability and have better capability to distinguish the local minimums from the global solutions compared with the TS and NT methods. The proposed methods give superiorperformances of the location accuracy comparing with the closed-form algorithms at large measurement noises.
Zhao, Wei-hu; Zhao, Jing; Zhao, Shang-hong; Li, Yong-jun; Wang, Xiang; Dong, Yi; Dong, Chen
2013-08-01
Optical satellite communication with the advantages of broadband, large capacity and low power consuming broke the bottleneck of the traditional microwave satellite communication. The formation of the Space-based Information System with the technology of high performance optical inter-satellite communication and the realization of global seamless coverage and mobile terminal accessing are the necessary trend of the development of optical satellite communication. Considering the resources, missions and restraints of Data Relay Satellite Optical Communication System, a model of optical communication resources scheduling is established and a scheduling algorithm based on artificial intelligent optimization is put forwarded. According to the multi-relay-satellite, multi-user-satellite, multi-optical-antenna and multi-mission with several priority weights, the resources are scheduled reasonable by the operation: "Ascertain Current Mission Scheduling Time" and "Refresh Latter Mission Time-Window". The priority weight is considered as the parameter of the fitness function and the scheduling project is optimized by the Genetic Algorithm. The simulation scenarios including 3 relay satellites with 6 optical antennas, 12 user satellites and 30 missions, the simulation result reveals that the algorithm obtain satisfactory results in both efficiency and performance and resources scheduling model and the optimization algorithm are suitable in multi-relay-satellite, multi-user-satellite, and multi-optical-antenna recourses scheduling problem.
New Dandelion Algorithm Optimizes Extreme Learning Machine for Biomedical Classification Problems
Directory of Open Access Journals (Sweden)
Xiguang Li
2017-01-01
Full Text Available Inspired by the behavior of dandelion sowing, a new novel swarm intelligence algorithm, namely, dandelion algorithm (DA, is proposed for global optimization of complex functions in this paper. In DA, the dandelion population will be divided into two subpopulations, and different subpopulations will undergo different sowing behaviors. Moreover, another sowing method is designed to jump out of local optimum. In order to demonstrate the validation of DA, we compare the proposed algorithm with other existing algorithms, including bat algorithm, particle swarm optimization, and enhanced fireworks algorithm. Simulations show that the proposed algorithm seems much superior to other algorithms. At the same time, the proposed algorithm can be applied to optimize extreme learning machine (ELM for biomedical classification problems, and the effect is considerable. At last, we use different fusion methods to form different fusion classifiers, and the fusion classifiers can achieve higher accuracy and better stability to some extent.
New Dandelion Algorithm Optimizes Extreme Learning Machine for Biomedical Classification Problems
Li, Xiguang; Zhao, Liang; Gong, Changqing; Liu, Xiaojing
2017-01-01
Inspired by the behavior of dandelion sowing, a new novel swarm intelligence algorithm, namely, dandelion algorithm (DA), is proposed for global optimization of complex functions in this paper. In DA, the dandelion population will be divided into two subpopulations, and different subpopulations will undergo different sowing behaviors. Moreover, another sowing method is designed to jump out of local optimum. In order to demonstrate the validation of DA, we compare the proposed algorithm with other existing algorithms, including bat algorithm, particle swarm optimization, and enhanced fireworks algorithm. Simulations show that the proposed algorithm seems much superior to other algorithms. At the same time, the proposed algorithm can be applied to optimize extreme learning machine (ELM) for biomedical classification problems, and the effect is considerable. At last, we use different fusion methods to form different fusion classifiers, and the fusion classifiers can achieve higher accuracy and better stability to some extent. PMID:29085425
Modeling of Biological Intelligence for SCM System Optimization
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Shengyong Chen
2012-01-01
Full Text Available This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms.
Modeling of biological intelligence for SCM system optimization.
Chen, Shengyong; Zheng, Yujun; Cattani, Carlo; Wang, Wanliang
2012-01-01
This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms.
7th International Symposium on Intelligent Distributed Computing
Jung, Jason; Badica, Costin
2014-01-01
This book represents the combined peer-reviewed proceedings of the Seventh International Symposium on Intelligent Distributed Computing - IDC-2013, of the Second Workshop on Agents for Clouds - A4C-2013, of the Fifth International Workshop on Multi-Agent Systems Technology and Semantics - MASTS-2013, and of the International Workshop on Intelligent Robots - iR-2013. All the events were held in Prague, Czech Republic during September 4-6, 2013. The 41 contributions published in this book address many topics related to theory and applications of intelligent distributed computing and multi-agent systems, including: agent-based data processing, ambient intelligence, bio-informatics, collaborative systems, cryptography and security, distributed algorithms, grid and cloud computing, information extraction, intelligent robotics, knowledge management, linked data, mobile agents, ontologies, pervasive computing, self-organizing systems, peer-to-peer computing, social networks and trust, and swarm intelligence. .
Intelligent and nature inspired optimization methods in medicine
DEFF Research Database (Denmark)
Marinakis, Yannis; Marinaki, Magdalene; Dounias, Georgios
2009-01-01
, decrease noise and improve speed by the elimination of irrelevant or redundant features. The present paper deals with the optimization of nearest neighbour classifiers via intelligent and nature inspired algorithms for a very significant medical problem, the Pap smear cell classification problem....... The algorithms used include tabu search, genetic algorithms, particle swarm optimization and ant colony optimization. The proposed complete algorithmic scheme is tested on two sets of data. The first consists of 917 images of Pap smear cells and the second set consists of 500 images, classified carefully...
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Kai-Wei Chiang
2010-10-01
Full Text Available Mobile mapping systems have been widely applied for acquiring spatial information in applications such as spatial information systems and 3D city models. Nowadays the most common technologies used for positioning and orientation of a mobile mapping system include a Global Positioning System (GPS as the major positioning sensor and an Inertial Navigation System (INS as the major orientation sensor. In the classical approach, the limitations of the Kalman Filter (KF method and the overall price of multi-sensor systems have limited the popularization of most land-based mobile mapping applications. Although intelligent sensor positioning and orientation schemes consisting of Multi-layer Feed-forward Neural Networks (MFNNs, one of the most famous Artificial Neural Networks (ANNs, and KF/smoothers, have been proposed in order to enhance the performance of low cost Micro Electro Mechanical System (MEMS INS/GPS integrated systems, the automation of the MFNN applied has not proven as easy as initially expected. Therefore, this study not only addresses the problems of insufficient automation in the conventional methodology that has been applied in MFNN-KF/smoother algorithms for INS/GPS integrated systems proposed in previous studies, but also exploits and analyzes the idea of developing alternative intelligent sensor positioning and orientation schemes that integrate various sensors in more automatic ways. The proposed schemes are implemented using one of the most famous constructive neural networks––the Cascade Correlation Neural Network (CCNNs––to overcome the limitations of conventional techniques based on KF/smoother algorithms as well as previously developed MFNN-smoother schemes. The CCNNs applied also have the advantage of a more flexible topology compared to MFNNs. Based on the experimental data utilized the preliminary results presented in this article illustrate the effectiveness of the proposed schemes compared to smoother algorithms
Chiang, Kai-Wei; Chang, Hsiu-Wen
2010-01-01
Mobile mapping systems have been widely applied for acquiring spatial information in applications such as spatial information systems and 3D city models. Nowadays the most common technologies used for positioning and orientation of a mobile mapping system include a Global Positioning System (GPS) as the major positioning sensor and an Inertial Navigation System (INS) as the major orientation sensor. In the classical approach, the limitations of the Kalman Filter (KF) method and the overall price of multi-sensor systems have limited the popularization of most land-based mobile mapping applications. Although intelligent sensor positioning and orientation schemes consisting of Multi-layer Feed-forward Neural Networks (MFNNs), one of the most famous Artificial Neural Networks (ANNs), and KF/smoothers, have been proposed in order to enhance the performance of low cost Micro Electro Mechanical System (MEMS) INS/GPS integrated systems, the automation of the MFNN applied has not proven as easy as initially expected. Therefore, this study not only addresses the problems of insufficient automation in the conventional methodology that has been applied in MFNN-KF/smoother algorithms for INS/GPS integrated systems proposed in previous studies, but also exploits and analyzes the idea of developing alternative intelligent sensor positioning and orientation schemes that integrate various sensors in more automatic ways. The proposed schemes are implemented using one of the most famous constructive neural networks--the Cascade Correlation Neural Network (CCNNs)--to overcome the limitations of conventional techniques based on KF/smoother algorithms as well as previously developed MFNN-smoother schemes. The CCNNs applied also have the advantage of a more flexible topology compared to MFNNs. Based on the experimental data utilized the preliminary results presented in this article illustrate the effectiveness of the proposed schemes compared to smoother algorithms as well as the MFNN
Merging the fields of swarm robotics and new media: Perceiving swarm robotics as new media
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Monika O. Ivanova
2014-06-01
Full Text Available The aim of this paper is to provide evidence that swarm robotic systems can be perceived as new media objects. A thorough description of the five principles of new media proposed by Lev Manovich in “The Language of New Media” is presented. This is complemented by a state of the art on swarm robotics with an in-depth comparison of the characteristics of both fields. Also presented are examples of swarm robotics used in new media installations in order to illustrate the cutting-edge applications of robotics and artificial intelligence achieved through the unity of bothfields. The hypothesis of this research is that a novel point of view would be introduced by examining the field of swarm robotics through the scope of new media, which would benefit thework of both new media and swarm robotic researchers.
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Deep Agnani
Full Text Available P-glycoprotein, a human multidrug resistance transporter, has been extensively studied due to its importance to human health and disease. In order to understand transport kinetics via P-gp, confluent cell monolayers overexpressing P-gp are widely used. The purpose of this study is to obtain the mass action elementary rate constants for P-gp's transport and to functionally characterize members of P-gp's network, i.e., other transporters that transport P-gp substrates in hMDR1-MDCKII confluent cell monolayers and are essential to the net substrate flux. Transport of a range of concentrations of amprenavir, loperamide, quinidine and digoxin across the confluent monolayer of cells was measured in both directions, apical to basolateral and basolateral to apical. We developed a global optimization algorithm using the Particle Swarm method that can simultaneously fit all datasets to yield accurate and exhaustive fits of these elementary rate constants. The statistical sensitivity of the fitted values was determined by using 24 identical replicate fits, yielding simple averages and standard deviations for all of the kinetic parameters, including the efflux active P-gp surface density. Digoxin required additional basolateral and apical transporters, while loperamide required just a basolateral tranporter. The data were better fit by assuming bidirectional transporters, rather than active importers, suggesting that they are not MRP or active OATP transporters. The P-gp efflux rate constants for quinidine and digoxin were about 3-fold smaller than reported ATP hydrolysis rate constants from P-gp proteoliposomes. This suggests a roughly 3∶1 stoichiometry between ATP hydrolysis and P-gp transport for these two drugs. The fitted values of the elementary rate constants for these P-gp substrates support the hypotheses that the selective pressures on P-gp are to maintain a broad substrate range and to keep xenobiotics out of the cytosol, but not out of the
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Xiujuan Zhao
Full Text Available The correct location of earthquake emergency shelters and their allocation to residents can effectively reduce the number of casualties by providing safe havens and efficient evacuation routes during the chaotic period of the unfolding disaster. However, diverse and strict constraints and the discrete feasible domain of the required models make the problem of shelter location and allocation more difficult. A number of models have been developed to solve this problem, but there are still large differences between the models and the actual situation because the characteristics of the evacuees and the construction costs of the shelters have been excessively simplified. We report here the development of a multi-objective model for the allocation of residents to earthquake shelters by considering these factors using the Chaoyang district, Beijing, China as a case study. The two objectives of this model were to minimize the total weighted evacuation time from residential areas to a specified shelter and to minimize the total area of all the shelters. The two constraints were the shelter capacity and the service radius. Three scenarios were considered to estimate the number of people who would need to be evacuated. The particle swarm optimization algorithm was first modified by applying the von Neumann structure in former loops and global structure in later loops, and then used to solve this problem. The results show that increasing the shelter area can result in a large decrease in the total weighted evacuation time from scheme 1 to scheme 9 in scenario A, from scheme 1 to scheme 9 in scenario B, from scheme 1 to scheme 19 in scenario C. If the funding were not a limitation, then the final schemes of each scenario are the best solutions, otherwise the earlier schemes are more reasonable. The modified model proved to be useful for the optimization of shelter allocation, and the result can be used as a scientific reference for planning shelters in the
Adaptation and hybridization in computational intelligence
Jr, Iztok
2015-01-01
This carefully edited book takes a walk through recent advances in adaptation and hybridization in the Computational Intelligence (CI) domain. It consists of ten chapters that are divided into three parts. The first part illustrates background information and provides some theoretical foundation tackling the CI domain, the second part deals with the adaptation in CI algorithms, while the third part focuses on the hybridization in CI. This book can serve as an ideal reference for researchers and students of computer science, electrical and civil engineering, economy, and natural sciences that are confronted with solving the optimization, modeling and simulation problems. It covers the recent advances in CI that encompass Nature-inspired algorithms, like Artificial Neural networks, Evolutionary Algorithms and Swarm Intelligence –based algorithms.
Swarm Robot Systems Based on the Evolution of Personality Traits
Jr., Sidney Nascimento GIVIGI; SCHWARTZ, Howard M.
2007-01-01
Game theory may be very useful in modeling and analyzing swarms of robots. Using game theory in conjunction with traits of personalities, we achieve intelligent swarm robots. Traits of personality are characteristics of each robot that define the robots' behaviours. The environment is represented as a game and due to the evolution of the traits through a learning process, we show how the robots may react intelligently to changes in the environment. A proof of convergence f...
Game Algorithm for Resource Allocation Based on Intelligent Gradient in HetNet
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Fang Ye
2017-02-01
Full Text Available In order to improve system performance such as throughput, heterogeneous network (HetNet has become an effective solution in Long Term Evolution-Advanced (LET-A. However, co-channel interference leads to degradation of the HetNet throughput, because femtocells are always arranged to share the spectrum with the macro base station. In this paper, in view of the serious cross-layer interference in double layer HetNet, the Stackelberg game model is adopted to analyze the resource allocation methods of the network. Unlike the traditional system models only focusing on macro base station performance improvement, we take into account the overall system performance and build a revenue function with convexity. System utility functions are defined as the average throughput, which does not adopt frequency spectrum trading method, so as to avoid excessive signaling overhead. Due to the value scope of continuous Nash equilibrium of the built game model, the gradient iterative algorithm is introduced to reduce the computational complexity. As for the solution of Nash equilibrium, one kind of gradient iterative algorithm is proposed, which is able to intelligently choose adjustment factors. The Nash equilibrium can be quickly solved; meanwhile, the step of presetting adjustment factors is avoided according to network parameters in traditional linear iterative model. Simulation results show that the proposed algorithm enhances the overall performance of the system.
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Oguz Emrah Turgut
2014-12-01
Full Text Available This study explores the thermal design of shell and tube heat exchangers by using Improved Intelligent Tuned Harmony Search (I-ITHS algorithm. Intelligent Tuned Harmony Search (ITHS is an upgraded version of harmony search algorithm which has an advantage of deciding intensification and diversification processes by applying proper pitch adjusting strategy. In this study, we aim to improve the search capacity of ITHS algorithm by utilizing chaotic sequences instead of uniformly distributed random numbers and applying alternative search strategies inspired by Artificial Bee Colony algorithm and Opposition Based Learning on promising areas (best solutions. Design variables including baffle spacing, shell diameter, tube outer diameter and number of tube passes are used to minimize total cost of heat exchanger that incorporates capital investment and the sum of discounted annual energy expenditures related to pumping and heat exchanger area. Results show that I-ITHS can be utilized in optimizing shell and tube heat exchangers.
A new hybrid teaching–learning particle swarm optimization ...
Indian Academy of Sciences (India)
This paper proposes a novel hybrid teaching–learning particle swarm optimization (HTLPSO) algorithm, which merges two established nature-inspired algorithms, namely, optimization based on teaching–learning (TLBO) and particle swarm optimization (PSO). The HTLPSO merges the best half of population obtained after ...
Toushmalani, Reza
2013-01-01
The purpose of this study was to compare the performance of two methods for gravity inversion of a fault. First method [Particle swarm optimization (PSO)] is a heuristic global optimization method and also an optimization algorithm, which is based on swarm intelligence. It comes from the research on the bird and fish flock movement behavior. Second method [The Levenberg-Marquardt algorithm (LM)] is an approximation to the Newton method used also for training ANNs. In this paper first we discussed the gravity field of a fault, then describes the algorithms of PSO and LM And presents application of Levenberg-Marquardt algorithm, and a particle swarm algorithm in solving inverse problem of a fault. Most importantly the parameters for the algorithms are given for the individual tests. Inverse solution reveals that fault model parameters are agree quite well with the known results. A more agreement has been found between the predicted model anomaly and the observed gravity anomaly in PSO method rather than LM method.
DeMAID/GA USER'S GUIDE Design Manager's Aid for Intelligent Decomposition with a Genetic Algorithm
Rogers, James L.
1996-01-01
Many companies are looking for new tools and techniques to aid a design manager in making decisions that can reduce the time and cost of a design cycle. One tool that is available to aid in this decision making process is the Design Manager's Aid for Intelligent Decomposition (DeMAID). Since the initial release of DEMAID in 1989, numerous enhancements have been added to aid the design manager in saving both cost and time in a design cycle. The key enhancement is a genetic algorithm (GA) and the enhanced version is called DeMAID/GA. The GA orders the sequence of design processes to minimize the cost and time to converge to a solution. These enhancements as well as the existing features of the original version of DEMAID are described. Two sample problems are used to show how these enhancements can be applied to improve the design cycle. This report serves as a user's guide for DeMAID/GA.
Bacanin, Nebojsa; Tuba, Milan
2014-01-01
Portfolio optimization (selection) problem is an important and hard optimization problem that, with the addition of necessary realistic constraints, becomes computationally intractable. Nature-inspired metaheuristics are appropriate for solving such problems; however, literature review shows that there are very few applications of nature-inspired metaheuristics to portfolio optimization problem. This is especially true for swarm intelligence algorithms which represent the newer branch of nature-inspired algorithms. No application of any swarm intelligence metaheuristics to cardinality constrained mean-variance (CCMV) portfolio problem with entropy constraint was found in the literature. This paper introduces modified firefly algorithm (FA) for the CCMV portfolio model with entropy constraint. Firefly algorithm is one of the latest, very successful swarm intelligence algorithm; however, it exhibits some deficiencies when applied to constrained problems. To overcome lack of exploration power during early iterations, we modified the algorithm and tested it on standard portfolio benchmark data sets used in the literature. Our proposed modified firefly algorithm proved to be better than other state-of-the-art algorithms, while introduction of entropy diversity constraint further improved results.
Sunal, Cynthia Szymanski; Karr, Charles L.; Sunal, Dennis W.
2003-01-01
Students' conceptions of three major artificial intelligence concepts used in the modeling of systems in science, fuzzy logic, neural networks, and genetic algorithms were investigated before and after a higher education science course. Students initially explored their prior ideas related to the three concepts through active tasks. Then,…
Jin, Xiuliang; Li, Zhenhai; Yang, Guijun; Yang, Hao; Feng, Haikuan; Xu, Xingang; Wang, Jihua; Li, Xinchuan; Luo, Juhua
2017-04-01
Timely and accurate estimation of winter wheat yield at a regional scale is crucial for national food policy and security assessments. Near-infrared reflectance is not sensitive to the leaf area index (LAI) and biomass of winter wheat at medium to high canopy cover (CC), and most of the vegetation indices displayed saturation phenomenon. However, LAI and biomass at medium to high CC can be efficiently estimated using imaging data from radar with stronger penetration, such as RADARSAT-2. This study had the following three objectives: (i) to combine vegetation indices based on our previous studies for estimating CC and biomass for winter wheat using HJ-1A/B and RADARSAT-2 imaging data; (ii) to combine HJ-1A/B and RADARSAT-2 imaging data with the AquaCrop model using the particle swarm optimization (PSO) algorithm to estimate winter wheat yield; and (iii) to compare the results from the assimilation of HJ-1A/B + RADARSAT-2 imaging data, HJ-1A/B imaging data, and RADARSAT-2 imaging data into the AquaCrop model using the PSO algorithm. Remote sensing data and concurrent LAI, biomass, and yield of sample fields were acquired in Yangling District, Shaanxi, China, during the 2014 winter wheat growing season. The PSO optimization algorithm was used to integrate the AquaCrop model and remote sensing data for yield estimation. The modified triangular vegetation index 2 (MTVI2) × radar vegetation index (RVI) and the enhanced vegetation index (EVI) × RVI had good relationships with CC and biomass, respectively. The results indicated that the predicted and measured yield (R2 = 0.31 and RMSE = 0.94 ton/ha) had agreement when the estimated CC from the HJ-1A/B and RADARSAT-2 data was used as the dynamic input variable for the AquaCrop model. When the estimated biomass from the HJ-1A/B and RADARSAT-2 data was used as the dynamic input variable for the AquaCrop model, the predicted yield showed agreement with the measured yield (R2 = 0.42 and RMSE = 0.81 ton/ha). These results show
Sternberg, Robert J
2012-09-01
Intelligence is the ability to learn from past experience and, in general, to adapt to, shape, and select environments. Aspects of intelligence are measured by standardized tests of intelligence. Average raw (number-correct) scores on such tests vary across the life span and also across generations, as well as across ethnic and socioeconomic groups. Intelligence can be understood in part in terms of the biology of the brain-especially with regard to the functioning in the prefrontal cortex. Measured values correlate with brain size, at least within humans. The heritability coefficient (ratio of genetic to phenotypic variation) is between 0.4 and 0.8. But genes always express themselves through environment. Heritability varies as a function of a number of factors, including socioeconomic status and range of environments. Racial-group differences in measured intelligence have been reported, but race is a socially constructed rather than biological variable. As a result, these differences are difficult to interpret. Different cultures have different conceptions of the nature of intelligence, and also require different skills in order to express intelligence in the environment. WIREs Cogn Sci 2012 doi: 10.1002/wcs.1193 For further resources related to this article, please visit the WIREs website. Copyright © 2012 John Wiley & Sons, Ltd.
A self-learning particle swarm optimizer for global optimization problems.
Li, Changhe; Yang, Shengxiang; Nguyen, Trung Thanh
2012-06-01
Particle swarm optimization (PSO) has been shown as an effective tool for solving global optimization problems. So far, most PSO algorithms use a single learning pattern for all particles, which means that all particles in a swarm use the same strategy. This monotonic learning pattern may cause the lack of intelligence for a particular particle, which makes it unable to deal with different complex situations. This paper presents a novel algorithm, called self-learning particle swarm optimizer (SLPSO), for global optimization problems. In SLPSO, each particle has a set of four strategies to cope with different situations in the search space. The cooperation of the four strategies is implemented by an adaptive learning framework at the individual level, which can enable a particle to choose the optimal strategy according to its own local fitness landscape. The experimental study on a set of 45 test functions and two real-world problems show that SLPSO has a superior performance in comparison with several other peer algorithms.
Luo, Yugong; Chen, Tao; Li, Keqiang
2015-12-01
The paper presents a novel active distance control strategy for intelligent hybrid electric vehicles (IHEV) with the purpose of guaranteeing an optimal performance in view of the driving functions, optimum safety, fuel economy and ride comfort. Considering the complexity of driving situations, the objects of safety and ride comfort are decoupled from that of fuel economy, and a hierarchical control architecture is adopted to improve the real-time performance and the adaptability. The hierarchical control structure consists of four layers: active distance control object determination, comprehensive driving and braking torque calculation, comprehensive torque distribution and torque coordination. The safety distance control and the emergency stop algorithms are designed to achieve the safety and ride comfort goals. The optimal rule-based energy management algorithm of the hybrid electric system is developed to improve the fuel economy. The torque coordination control strategy is proposed to regulate engine torque, motor torque and hydraulic braking torque to improve the ride comfort. This strategy is verified by simulation and experiment using a forward simulation platform and a prototype vehicle. The results show that the novel control strategy can achieve the integrated and coordinated control of its multiple subsystems, which guarantees top performance of the driving functions and optimum safety, fuel economy and ride comfort.
Study on Immune Relevant Vector Machine Based Intelligent Fault Detection and Diagnosis Algorithm
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Zhong-hua Miao
2013-01-01
Full Text Available An immune relevant vector machine (IRVM based intelligent classification method is proposed by combining the random real-valued negative selection (RRNS algorithm and the relevant vector machine (RVM algorithm. The method proposed is aimed to handle the training problem of missing or incomplete fault sampling data and is inspired by the “self/nonself” recognition principle in the artificial immune systems. The detectors, generated by the RRNS, are treated as the “nonself” training samples and used to train the RVM model together with the “self” training samples. After the training succeeds, the “nonself” detection model, which requires only the “self” training samples, is obtained for the fault detection and diagnosis. It provides a general way solving the problems of this type and can be applied for both fault detection and fault diagnosis. The standard Fisher's Iris flower dataset is used to experimentally testify the proposed method, and the results are compared with those from the support vector data description (SVDD method. Experimental results have shown the validity and practicability of the proposed method.
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Montri Inthachot
2016-01-01
Full Text Available This study investigated the use of Artificial Neural Network (ANN and Genetic Algorithm (GA for prediction of Thailand’s SET50 index trend. ANN is a widely accepted machine learning method that uses past data to predict future trend, while GA is an algorithm that can find better subsets of input variables for importing into ANN, hence enabling more accurate prediction by its efficient feature selection. The imported data were chosen technical indicators highly regarded by stock analysts, each represented by 4 input variables that were based on past time spans of 4 different lengths: 3-, 5-, 10-, and 15-day spans before the day of prediction. This import undertaking generated a big set of diverse input variables with an exponentially higher number of possible subsets that GA culled down to a manageable number of more effective ones. SET50 index data of the past 6 years, from 2009 to 2014, were used to evaluate this hybrid intelligence prediction accuracy, and the hybrid’s prediction results were found to be more accurate than those made by a method using only one input variable for one fixed length of past time span.
Inthachot, Montri; Boonjing, Veera; Intakosum, Sarun
2016-01-01
This study investigated the use of Artificial Neural Network (ANN) and Genetic Algorithm (GA) for prediction of Thailand's SET50 index trend. ANN is a widely accepted machine learning method that uses past data to predict future trend, while GA is an algorithm that can find better subsets of input variables for importing into ANN, hence enabling more accurate prediction by its efficient feature selection. The imported data were chosen technical indicators highly regarded by stock analysts, each represented by 4 input variables that were based on past time spans of 4 different lengths: 3-, 5-, 10-, and 15-day spans before the day of prediction. This import undertaking generated a big set of diverse input variables with an exponentially higher number of possible subsets that GA culled down to a manageable number of more effective ones. SET50 index data of the past 6 years, from 2009 to 2014, were used to evaluate this hybrid intelligence prediction accuracy, and the hybrid's prediction results were found to be more accurate than those made by a method using only one input variable for one fixed length of past time span.
Liu, Yaolin; Wang, Hua; Ji, Yingli; Liu, Zhongqiu; Zhao, Xiang
2012-01-01
Comprehensive land-use planning (CLUP) at the county level in China must include land-use zoning. This is specifically stipulated by the China Land Management Law and aims to achieve strict control on the usages of land. The land-use zoning problem is treated as a multi-objective optimization problem (MOOP) in this article, which is different from the traditional treatment. A particle swarm optimization (PSO) based model is applied to the problem and is developed to maximize the attribute differences between land-use zones, the spatial compactness, the degree of spatial harmony and the ecological benefits of the land-use zones. This is subject to some constraints such as: the quantity limitations for varying land-use zones, regulations assigning land units to a certain land-use zone, and the stipulation of a minimum parcel area in a land-use zoning map. In addition, a crossover and mutation operator from a genetic algorithm is adopted to avoid the prematurity of PSO. The results obtained for Yicheng, a county in central China, using different objective weighting schemes, are compared and suggest that: (1) the fundamental demand for attribute difference between land-use zones leads to a mass of fragmentary land-use zones; (2) the spatial pattern of land-use zones is remarkably optimized when a weight is given to the sub-objectives of spatial compactness and the degree of spatial harmony, simultaneously, with a reduction of attribute difference between land-use zones; (3) when a weight is given to the sub-objective of ecological benefits of the land-use zones, the ecological benefits get a slight increase also at the expense of a reduction in attribute difference between land-use zones; (4) the pursuit of spatial harmony or spatial compactness may have a negative effect on each other; (5) an increase in the ecological benefits may improve the spatial compactness and spatial harmony of the land-use zones; (6) adjusting the weights assigned to each sub-objective can
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Xiang Zhao
2012-08-01
Full Text Available Comprehensive land-use planning (CLUP at the county level in China must include land-use zoning. This is specifically stipulated by the China Land Management Law and aims to achieve strict control on the usages of land. The land-use zoning problem is treated as a multi-objective optimization problem (MOOP in this article, which is different from the traditional treatment. A particle swarm optimization (PSO based model is applied to the problem and is developed to maximize the attribute differences between land-use zones, the spatial compactness, the degree of spatial harmony and the ecological benefits of the land-use zones. This is subject to some constraints such as: the quantity limitations for varying land-use zones, regulations assigning land units to a certain land-use zone, and the stipulation of a minimum parcel area in a land-use zoning map. In addition, a crossover and mutation operator from a genetic algorithm is adopted to avoid the prematurity of PSO. The results obtained for Yicheng, a county in central China, using different objective weighting schemes, are compared and suggest that: (1 the fundamental demand for attribute difference between land-use zones leads to a mass of fragmentary land-use zones; (2 the spatial pattern of land-use zones is remarkably optimized when a weight is given to the sub-objectives of spatial compactness and the degree of spatial harmony, simultaneously, with a reduction of attribute difference between land-use zones; (3 when a weight is given to the sub-objective of ecological benefits of the land-use zones, the ecological benefits get a slight increase also at the expense of a reduction in attribute difference between land-use zones; (4 the pursuit of spatial harmony or spatial compactness may have a negative effect on each other; (5 an increase in the ecological benefits may improve the spatial compactness and spatial harmony of the land-use zones; (6 adjusting the weights assigned to each sub
Liu, Yaolin; Wang, Hua; Ji, Yingli; Liu, Zhongqiu; Zhao, Xiang
2012-08-01
Comprehensive land-use planning (CLUP) at the county level in China must include land-use zoning. This is specifically stipulated by the China Land Management Law and aims to achieve strict control on the usages of land. The land-use zoning problem is treated as a multi-objective optimization problem (MOOP) in this article, which is different from the traditional treatment. A particle swarm optimization (PSO) based model is applied to the problem and is developed to maximize the attribute differences between land-use zones, the spatial compactness, the degree of spatial harmony and the ecological benefits of the land-use zones. This is subject to some constraints such as: the quantity limitations for varying land-use zones, regulations assigning land units to a certain land-use zone, and the stipulation of a minimum parcel area in a land-use zoning map. In addition, a crossover and mutation operator from a genetic algorithm is adopted to avoid the prematurity of PSO. The results obtained for Yicheng, a county in central China, using different objective weighting schemes, are compared and suggest that: (1) the fundamental demand for attribute difference between land-use zones leads to a mass of fragmentary land-use zones; (2) the spatial pattern of land-use zones is remarkably optimized when a weight is given to the sub-objectives of spatial compactness and the degree of spatial harmony, simultaneously, with a reduction of attribute difference between land-use zones; (3) when a weight is given to the sub-objective of ecological benefits of the land-use zones, the ecological benefits get a slight increase also at the expense of a reduction in attribute difference between land-use zones; (4) the pursuit of spatial harmony or spatial compactness may have a negative effect on each other; (5) an increase in the ecological benefits may improve the spatial compactness and spatial harmony of the land-use zones; (6) adjusting the weights assigned to each sub-objective can
Sternberg, Robert J.
2012-01-01
Intelligence is the ability to learn from experience and to adapt to, shape, and select environments. Intelligence as measured by (raw scores on) conventional standardized tests varies across the lifespan, and also across generations. Intelligence can be understood in part in terms of the biology of the brain—especially with regard to the functioning in the prefrontal cortex—and also correlates with brain size, at least within humans. Studies of the effects of genes and environment suggest that the heritability coefficient (ratio of genetic to phenotypic variation) is between .4 and .8, although heritability varies as a function of socioeconomic status and other factors. Racial differences in measured intelligence have been observed, but race is a socially constructed rather than biological variable, so such differences are difficult to interpret. PMID:22577301
Sternberg, Robert J
2012-03-01
Intelligence is the ability to learn from experience and to adapt to, shape, and select environments. Intelligence as measured by (raw scores on) conventional standardized tests varies across the lifespan, and also across generations. Intelligence can be understood in part in terms of the biology of the brain-especially with regard to the functioning in the prefrontal cortex-and also correlates with brain size, at least within humans. Studies of the effects of genes and environment suggest that the heritability coefficient (ratio of genetic to phenotypic variation) is between .4 and .8, although heritability varies as a function of socioeconomic status and other factors. Racial differences in measured intelligence have been observed, but race is a socially constructed rather than biological variable, so such differences are difficult to interpret.
A swarm optimized neural network system for classification of microcalcification in mammograms.
Dheeba, J; Selvi, S Tamil
2012-10-01
Early detection of microcalcification clusters in breast tissue will significantly increase the survival rate of the patients. Radiologists use mammography for breast cancer diagnosis at early stage. It is a very challenging and difficult task for radiologists to correctly classify the abnormal regions in the breast tissue, because mammograms are noisy images. To improve the accuracy rate of detection of breast cancer, a novel intelligent computer aided classifier is used, which detects the presence of microcalcification clusters. In this paper, an innovative approach for detection of microcalcification in digital mammograms using Swarm Optimization Neural Network (SONN) is used. Prior to classification Laws texture features are extracted from the image to capture descriptive texture information. These features are used to extract texture energy measures from the Region of Interest (ROI) containing microcalcification (MC). A feedforward neural network is used for detection of abnormal regions in breast tissue is optimally designed using Particle Swarm Optimization algorithm. The proposed intelligent classifier is evaluated based on the MIAS database where 51 malignant, 63 benign and 208 normal images are utilized. The approach has also been tested on 216 real time clinical images having abnormalities which showed that the results are statistically significant. With the proposed methodology, the area under the ROC curve (A ( z )) reached 0.9761 for MIAS database and 0.9138 for real clinical images. The classification results prove that the proposed swarm optimally tuned neural network highly contribute to computer-aided diagnosis of breast cancer.
A Sustainable City Planning Algorithm Based on TLBO and Local Search
Zhang, Ke; Lin, Li; Huang, Xuanxuan; Liu, Yiming; Zhang, Yonggang
2017-09-01
Nowadays, how to design a city with more sustainable features has become a center problem in the field of social development, meanwhile it has provided a broad stage for the application of artificial intelligence theories and methods. Because the design of sustainable city is essentially a constraint optimization problem, the swarm intelligence algorithm of extensive research has become a natural candidate for solving the problem. TLBO (Teaching-Learning-Based Optimization) algorithm is a new swarm intelligence algorithm. Its inspiration comes from the “teaching” and “learning” behavior of teaching class in the life. The evolution of the population is realized by simulating the “teaching” of the teacher and the student “learning” from each other, with features of less parameters, efficient, simple thinking, easy to achieve and so on. It has been successfully applied to scheduling, planning, configuration and other fields, which achieved a good effect and has been paid more and more attention by artificial intelligence researchers. Based on the classical TLBO algorithm, we propose a TLBO_LS algorithm combined with local search. We design and implement the random generation algorithm and evaluation model of urban planning problem. The experiments on the small and medium-sized random generation problem showed that our proposed algorithm has obvious advantages over DE algorithm and classical TLBO algorithm in terms of convergence speed and solution quality.
Classification of remote sensed data using Artificial Bee Colony algorithm
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J. Jayanth
2015-06-01
Full Text Available The present study employs the traditional swarm intelligence technique in the classification of satellite data since the traditional statistical classification technique shows limited success in classifying remote sensing data. The traditional statistical classifiers examine only the spectral variance ignoring the spatial distribution of the pixels corresponding to the land cover classes and correlation between various bands. The Artificial Bee Colony (ABC algorithm based upon swarm intelligence which is used to characterise spatial variations within imagery as a means of extracting information forms the basis of object recognition and classification in several domains avoiding the issues related to band correlation. The results indicate that ABC algorithm shows an improvement of 5% overall classification accuracy at 6 classes over the traditional Maximum Likelihood Classifier (MLC and Artificial Neural Network (ANN and 3% against support vector machine.
International Conference on Frontiers of Intelligent Computing : Theory and Applications
Udgata, Siba; Biswal, Bhabendra
2013-01-01
The volume contains the papers presented at FICTA 2012: International Conference on Frontiers in Intelligent Computing: Theory and Applications held on December 22-23, 2012 in Bhubaneswar engineering College, Bhubaneswar, Odissa, India. It contains 86 papers contributed by authors from the globe. These research papers mainly focused on application of intelligent techniques which includes evolutionary computation techniques like genetic algorithm, particle swarm optimization techniques, teaching-learning based optimization etc for various engineering applications such as data mining, image processing, cloud computing, networking etc.
iTop-Q: an Intelligent Tool for Top-down Proteomics Quantitation Using DYAMOND Algorithm.
Chang, Hui-Yin; Chen, Ching-Tai; Ko, Chu-Ling; Chen, Yi-Ju; Chen, Yu-Ju; Hsu, Wen-Lian; Juo, Chiun-Gung; Sung, Ting-Yi
2017-12-19
Top-down proteomics using liquid chromatogram coupled with mass spectrometry has been increasingly applied for analyzing intact proteins to study genetic variation, alternative splicing, and post-translational modifications (PTMs) of the proteins (proteoforms). However, only a few tools have been developed for charge state deconvolution, monoisotopic/average molecular weight determination and quantitation of proteoforms from LC-MS1 spectra. Though Decon2LS and MASH Suite Pro have been available to provide intraspectrum charge state deconvolution and quantitation, manual processing is still required to quantify proteoforms across multiple MS1 spectra. An automated tool for interspectrum quantitation is a pressing need. Thus, in this paper, we present a user-friendly tool, called iTop-Q (intelligent Top-down Proteomics Quantitation), that automatically performs large-scale proteoform quantitation based on interspectrum abundance in top-down proteomics. Instead of utilizing single spectrum for proteoform quantitation, iTop-Q constructs extracted ion chromatograms (XICs) of possible proteoform peaks across adjacent MS1 spectra to calculate abundances for accurate quantitation. Notably, iTop-Q is implemented with a newly proposed algorithm, called DYAMOND, using dynamic programming for charge state deconvolution. In addition, iTop-Q performs proteoform alignment to support quantitation analysis across replicates/samples. The performance evaluations on an in-house standard data set and a public large-scale yeast lysate data set show that iTop-Q achieves highly accurate quantitation, more consistent quantitation than using intraspectrum quantitation. Furthermore, the DYAMOND algorithm is suitable for high charge state deconvolution and can distinguish shared peaks in coeluting proteoforms. iTop-Q is publicly available for download at http://ms.iis.sinica.edu.tw/COmics/Software_iTop-Q .
Application of an Intelligent Fuzzy Regression Algorithm in Road Freight Transportation Modeling
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Pooya Najaf
2013-07-01
Full Text Available Road freight transportation between provinces of a country has an important effect on the traffic flow of intercity transportation networks. Therefore, an accurate estimation of the road freight transportation for provinces of a country is so crucial to improve the rural traffic operation in a large scale management. Accordingly, the focused case study database in this research is the information related to Iran’s provinces in the year 2008. Correlation between road freight transportation with variables such as transport cost and distance, population, average household income and Gross Domestic Product (GDP of each province is calculated. Results clarify that the population is the most effective factor in the prediction of provinces’ transported freight. Linear Regression Model (LRM is calibrated based on the population variable, and afterwards Fuzzy Regression Algorithm (FRA is generated on the basis of the LRM. The proposed FRA is an intelligent modified algorithm with an accurate prediction and fitting ability. This methodology can be significantly useful in macro-level planning problems where decreasing prediction error values is one of the most important concerns for decision makers. In addition, Back-Propagation Neural Network (BPNN is developed to evaluate the prediction capability of the models and to be compared with FRA. According to the final results, the modified FRA estimates road freight transportation values more accurately than the BPNN and LRM. Finally, in order to predict the road freight transportation values, the reliability of the calibrated models is analyzed using the information of the year 2009. Results show higher reliability for the proposed modified FRA.
Software Engineering and Swarm-Based Systems
Hinchey, Michael G.; Sterritt, Roy; Pena, Joaquin; Rouff, Christopher A.
2006-01-01
We discuss two software engineering aspects in the development of complex swarm-based systems. NASA researchers have been investigating various possible concept missions that would greatly advance future space exploration capabilities. The concept mission that we have focused on exploits the principles of autonomic computing as well as being based on the use of intelligent swarms, whereby a (potentially large) number of similar spacecraft collaborate to achieve mission goals. The intent is that such systems not only can be sent to explore remote and harsh environments but also are endowed with greater degrees of protection and longevity to achieve mission goals.
Avadhani, P; Abraham, Ajith
2012-01-01
This volume contains the papers presented at INDIA-2012: International conference on Information system Design and Intelligent Applications held on January 5-7, 2012 in Vishakhapatnam, India. This conference was organized by Computer Society of India (CSI), Vishakhapatnam chapter well supported by Vishakhapatnam Steel, RINL, Govt of India. It contains 108 papers contributed by authors from six different countries across four continents. These research papers mainly focused on intelligent applications and various system design issues. The papers cover a wide range of topics of computer science and information technology discipline ranging from image processing, data base application, data mining, grid and cloud computing, bioinformatics among many others. The various intelligent tools like swarm intelligence, artificial intelligence, evolutionary algorithms, bio-inspired algorithms have been applied in different papers for solving various challenging IT related problems.
AntStar: Enhancing Optimization Problems by Integrating an Ant System and A⁎ Algorithm
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Mohammed Faisal
2016-01-01
Full Text Available Recently, nature-inspired techniques have become valuable to many intelligent systems in different fields of technology and science. Among these techniques, Ant Systems (AS have become a valuable technique for intelligent systems in different fields. AS is a computational system inspired by the foraging behavior of ants and intended to solve practical optimization problems. In this paper, we introduce the AntStar algorithm, which is swarm intelligence based. AntStar enhances the optimization and performance of an AS by integrating the AS and A⁎ algorithm. Applying the AntStar algorithm to the single-source shortest-path problem has been done to ensure the efficiency of the proposed AntStar algorithm. The experimental result of the proposed algorithm illustrated the robustness and accuracy of the AntStar algorithm.
Intelligent control a hybrid approach based on fuzzy logic, neural networks and genetic algorithms
Siddique, Nazmul
2014-01-01
Intelligent Control considers non-traditional modelling and control approaches to nonlinear systems. Fuzzy logic, neural networks and evolutionary computing techniques are the main tools used. The book presents a modular switching fuzzy logic controller where a PD-type fuzzy controller is executed first followed by a PI-type fuzzy controller thus improving the performance of the controller compared with a PID-type fuzzy controller. The advantage of the switching-type fuzzy controller is that it uses one rule-base thus minimises the rule-base during execution. A single rule-base is developed by merging the membership functions for change of error of the PD-type controller and sum of error of the PI-type controller. Membership functions are then optimized using evolutionary algorithms. Since the two fuzzy controllers were executed in series, necessary further tuning of the differential and integral scaling factors of the controller is then performed. Neural-network-based tuning for the scaling parameters of t...
a Novel 3d Intelligent Fuzzy Algorithm Based on Minkowski-Clustering
Toori, S.; Esmaeily, A.
2017-09-01
Assessing and monitoring the state of the earth surface is a key requirement for global change research. In this paper, we propose a new consensus fuzzy clustering algorithm that is based on the Minkowski distance. This research concentrates on Tehran's vegetation mass and its changes during 29 years using remote sensing technology. The main purpose of this research is to evaluate the changes in vegetation mass using a new process by combination of intelligent NDVI fuzzy clustering and Minkowski distance operation. The dataset includes the images of Landsat8 and Landsat TM, from 1989 to 2016. For each year three images of three continuous days were used to identify vegetation impact and recovery. The result was a 3D NDVI image, with one dimension for each day NDVI. The next step was the classification procedure which is a complicated process of categorizing pixels into a finite number of separate classes, based on their data values. If a pixel satisfies a certain set of standards, the pixel is allocated to the class that corresponds to those criteria. This method is less sensitive to noise and can integrate solutions from multiple samples of data or attributes for processing data in the processing industry. The result was a fuzzy one dimensional image. This image was also computed for the next 28 years. The classification was done in both specified urban and natural park areas of Tehran. Experiments showed that our method worked better in classifying image pixels in comparison with the standard classification methods.
HCCI Intelligent Rapid Modeling by Artificial Neural Network and Genetic Algorithm
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AbdoulAhad Validi
2012-01-01
Full Text Available A Dynamic model of Homogeneous Charge Compression Ignition (HCCI, based on chemical kinetics principles and artificial intelligence, is developed. The model can rapidly predict the combustion probability, thermochemistry properties, and exact timing of the Start of Combustion (SOC. A realization function is developed on the basis of the Sandia National Laboratory chemical kinetics model, and GRI3.0 methane chemical mechanism. The inlet conditions are optimized by Genetic Algorithm (GA, so that combustion initiates and SOC timing posits in the desired crank angle. The best SOC timing to achieve higher performance and efficiency in HCCI engines is between 5 and 15 degrees crank angle (CAD after top dead center (TDC. To achieve this SOC timing, in the first case, the inlet temperature and equivalence ratio are optimized simultaneously and in the second case, compression ratio is optimized by GA. The model’s results are validated with previous works. The SOC timing can be predicted in less than 0.01 second and the CPU time savings are encouraging. This model can successfully be used for real engine control applications.
A REVIEW OF SWARMING UNMANNED AERIAL VEHICLES
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CORNEA Mihai
2016-11-01
Full Text Available This paper in if fact an overview of state of the art in mobile multi-robot systems as an initial part of our research in implementing a system based on swarm robotics concepts to be used in natural disaster search and rescue missions. The system is to be composed of a group of drones that can detect survivor mobile cell signals and exhibit some other features as well. This paper surveys the swarm robotics research landscape to provide a theoretical background to the implementation and help determine the techniques available to create the system. The Particle swarm optimization (PSO and Glowworm swarm optimization (GSO algorithms are briefly described and there is also insight into Bird flocking behavior and the model behind it
Distributed pheromone-based swarming control of unmanned air and ground vehicles for RSTA
Sauter, John A.; Mathews, Robert S.; Yinger, Andrew; Robinson, Joshua S.; Moody, John; Riddle, Stephanie
2008-04-01
The use of unmanned vehicles in Reconnaissance, Surveillance, and Target Acquisition (RSTA) applications has received considerable attention recently. Cooperating land and air vehicles can support multiple sensor modalities providing pervasive and ubiquitous broad area sensor coverage. However coordination of multiple air and land vehicles serving different mission objectives in a dynamic and complex environment is a challenging problem. Swarm intelligence algorithms, inspired by the mechanisms used in natural systems to coordinate the activities of many entities provide a promising alternative to traditional command and control approaches. This paper describes recent advances in a fully distributed digital pheromone algorithm that has demonstrated its effectiveness in managing the complexity of swarming unmanned systems. The results of a recent demonstration at NASA's Wallops Island of multiple Aerosonde Unmanned Air Vehicles (UAVs) and Pioneer Unmanned Ground Vehicles (UGVs) cooperating in a coordinated RSTA application are discussed. The vehicles were autonomously controlled by the onboard digital pheromone responding to the needs of the automatic target recognition algorithms. UAVs and UGVs controlled by the same pheromone algorithm self-organized to perform total area surveillance, automatic target detection, sensor cueing, and automatic target recognition with no central processing or control and minimal operator input. Complete autonomy adds several safety and fault tolerance requirements which were integrated into the basic pheromone framework. The adaptive algorithms demonstrated the ability to handle some unplanned hardware failures during the demonstration without any human intervention. The paper describes lessons learned and the next steps for this promising technology.
Improved cuckoo search with particle swarm optimization for ...
Indian Academy of Sciences (India)
Content based image retrieval (CBIR); image compression; partial recurrent neural network (PRNN); particle swarm optimization (PSO); HAARwavelet; Cuckoo Search ... are NP hard, a hybrid Particle Swarm Optimization (PSO) – Cuckoo Search algorithm (CS) is proposed to optimize the learning rate of the neural network.
Real-time intelligent pattern recognition algorithm for surface EMG signals
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Jahed Mehran
2007-12-01
Full Text Available Abstract Background Electromyography (EMG is the study of muscle function through the inquiry of electrical signals that the muscles emanate. EMG signals collected from the surface of the skin (Surface Electromyogram: sEMG can be used in different applications such as recognizing musculoskeletal neural based patterns intercepted for hand prosthesis movements. Current systems designed for controlling the prosthetic hands either have limited functions or can only be used to perform simple movements or use excessive amount of electrodes in order to achieve acceptable results. In an attempt to overcome these problems we have proposed an intelligent system to recognize hand movements and have provided a user assessment routine to evaluate the correctness of executed movements. Methods We propose to use an intelligent approach based on adaptive neuro-fuzzy inference system (ANFIS integrated with a real-time learning scheme to identify hand motion commands. For this purpose and to consider the effect of user evaluation on recognizing hand movements, vision feedback is applied to increase the capability of our system. By using this scheme the user may assess the correctness of the performed hand movement. In this work a hybrid method for training fuzzy system, consisting of back-propagation (BP and least mean square (LMS is utilized. Also in order to optimize the number of fuzzy rules, a subtractive clustering algorithm has been developed. To design an effective system, we consider a conventional scheme of EMG pattern recognition system. To design this system we propose to use two different sets of EMG features, namely time domain (TD and time-frequency representation (TFR. Also in order to decrease the undesirable effects of the dimension of these feature sets, principle component analysis (PCA is utilized. Results In this study, the myoelectric signals considered for classification consists of six unique hand movements. Features chosen for EMG signal
Particle Swarm Optimization With Interswarm Interactive Learning Strategy.
Qin, Quande; Cheng, Shi; Zhang, Qingyu; Li, Li; Shi, Yuhui
2016-10-01
The learning strategy in the canonical particle swarm optimization (PSO) algorithm is often blamed for being the primary reason for loss of diversity. Population diversity maintenance is crucial for preventing particles from being stuck into local optima. In this paper, we present an improved PSO algorithm with an interswarm interactive learning strategy (IILPSO) by overcoming the drawbacks of the canonical PSO algorithm's learning strategy. IILPSO is inspired by the phenomenon in human society that the interactive learning behavior takes place among different groups. Particles in IILPSO are divided into two swarms. The interswarm interactive learning (IIL) behavior is triggered when the best particle's fitness value of both the swarms does not improve for a certain number of iterations. According to the best particle's fitness value of each swarm, the softmax method and roulette method are used to determine the roles of the two swarms as the learning swarm and the learned swarm. In addition, the velocity mutation operator and global best vibration strategy are used to improve the algorithm's global search capability. The IIL strategy is applied to PSO with global star and local ring structures, which are termed as IILPSO-G and IILPSO-L algorithm, respectively. Numerical experiments are conducted to compare the proposed algorithms with eight popular PSO variants. From the experimental results, IILPSO demonstrates the good performance in terms of solution accuracy, convergence speed, and reliability. Finally, the variations of the population diversity in the entire search process provide an explanation why IILPSO performs effectively.
Chaotic particle swarm optimization with mutation for classification.
Assarzadeh, Zahra; Naghsh-Nilchi, Ahmad Reza
2015-01-01
In this paper, a chaotic particle swarm optimization with mutation-based classifier particle swarm optimization is proposed to classify patterns of different classes in the feature space. The introduced mutation operators and chaotic sequences allows us to overcome the problem of early convergence into a local minima associated with particle swarm optimization algorithms. That is, the mutation operator sharpens the convergence and it tunes the best possible solution. Furthermore, to remove the irrelevant data and reduce the dimensionality of medical datasets, a feature selection approach using binary version of the proposed particle swarm optimization is introduced. In order to demonstrate the effectiveness of our proposed classifier, mutation-based classifier particle swarm optimization, it is checked out with three sets of data classifications namely, Wisconsin diagnostic breast cancer, Wisconsin breast cancer and heart-statlog, with different feature vector dimensions. The proposed algorithm is compared with different classifier algorithms including k-nearest neighbor, as a conventional classifier, particle swarm-classifier, genetic algorithm, and Imperialist competitive algorithm-classifier, as more sophisticated ones. The performance of each classifier was evaluated by calculating the accuracy, sensitivity, specificity and Matthews's correlation coefficient. The experimental results show that the mutation-based classifier particle swarm optimization unequivocally performs better than all the compared algorithms.
Research on UAV Intelligent Obstacle Avoidance Technology During Inspection of Transmission Line
Wei, Chuanhu; Zhang, Fei; Yin, Chaoyuan; Liu, Yue; Liu, Liang; Li, Zongyu; Wang, Wanguo
Autonomous obstacle avoidance of unmanned aerial vehicle (hereinafter referred to as UAV) in electric power line inspection process has important significance for operation safety and economy for UAV intelligent inspection system of transmission line as main content of UAV intelligent inspection system on transmission line. In the paper, principles of UAV inspection obstacle avoidance technology of transmission line are introduced. UAV inspection obstacle avoidance technology based on particle swarm global optimization algorithm is proposed after common obstacle avoidance technologies are studied. Stimulation comparison is implemented with traditional UAV inspection obstacle avoidance technology which adopts artificial potential field method. Results show that UAV inspection strategy of particle swarm optimization algorithm, adopted in the paper, is prominently better than UAV inspection strategy of artificial potential field method in the aspects of obstacle avoidance effect and the ability of returning to preset inspection track after passing through the obstacle. An effective method is provided for UAV inspection obstacle avoidance of transmission line.
Convergence Analysis of a Class of Computational Intelligence Approaches
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Junfeng Chen
2013-01-01
Full Text Available Computational intelligence approaches is a relatively new interdisciplinary field of research with many promising application areas. Although the computational intelligence approaches have gained huge popularity, it is difficult to analyze the convergence. In this paper, a computational model is built up for a class of computational intelligence approaches represented by the canonical forms of generic algorithms, ant colony optimization, and particle swarm optimization in order to describe the common features of these algorithms. And then, two quantification indices, that is, the variation rate and the progress rate, are defined, respectively, to indicate the variety and the optimality of the solution sets generated in the search process of the model. Moreover, we give four types of probabilistic convergence for the solution set updating sequences, and their relations are discussed. Finally, the sufficient conditions are derived for the almost sure weak convergence and the almost sure strong convergence of the model by introducing the martingale theory into the Markov chain analysis.
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Shidrokh Goudarzi
2015-01-01
Full Text Available The vertical handover mechanism is an essential issue in the heterogeneous wireless environments where selection of an efficient network that provides seamless connectivity involves complex scenarios. This study uses two modules that utilize the particle swarm optimization (PSO algorithm to predict and make an intelligent vertical handover decision. In this paper, we predict the received signal strength indicator parameter using the curve fitting based particle swarm optimization (CF-PSO and the RBF neural networks. The results of the proposed methodology compare the predictive capabilities in terms of coefficient determination (R2 and mean square error (MSE based on the validation dataset. The results show that the effect of the model based on the CF-PSO is better than that of the model based on the RBF neural network in predicting the received signal strength indicator situation. In addition, we present a novel network selection algorithm to select the best candidate access point among the various access technologies based on the PSO. Simulation results indicate that using CF-PSO algorithm can decrease the number of unnecessary handovers and prevent the “Ping-Pong” effect. Moreover, it is demonstrated that the multiobjective particle swarm optimization based method finds an optimal network selection in a heterogeneous wireless environment.
Parameter identification of robot manipulators: a heuristic particle swarm search approach.
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Danping Yan
Full Text Available Parameter identification of robot manipulators is an indispensable pivotal process of achieving accurate dynamic robot models. Since these kinetic models are highly nonlinear, it is not easy to tackle the matter of identifying their parameters. To solve the difficulty effectively, we herewith present an intelligent approach, namely, a heuristic particle swarm optimization (PSO algorithm, which we call the elitist learning strategy (ELS and proportional integral derivative (PID controller hybridized PSO approach (ELPIDSO. A specified PID controller is designed to improve particles' local and global positions information together with ELS. Parameter identification of robot manipulators is conducted for performance evaluation of our proposed approach. Experimental results clearly indicate the following findings: Compared with standard PSO (SPSO algorithm, ELPIDSO has improved a lot. It not only enhances the diversity of the swarm, but also features better search effectiveness and efficiency in solving practical optimization problems. Accordingly, ELPIDSO is superior to least squares (LS method, genetic algorithm (GA, and SPSO algorithm in estimating the parameters of the kinetic models of robot manipulators.
Parameter identification of robot manipulators: a heuristic particle swarm search approach.
Yan, Danping; Lu, Yongzhong; Levy, David
2015-01-01
Parameter identification of robot manipulators is an indispensable pivotal process of achieving accurate dynamic robot models. Since these kinetic models are highly nonlinear, it is not easy to tackle the matter of identifying their parameters. To solve the difficulty effectively, we herewith present an intelligent approach, namely, a heuristic particle swarm optimization (PSO) algorithm, which we call the elitist learning strategy (ELS) and proportional integral derivative (PID) controller hybridized PSO approach (ELPIDSO). A specified PID controller is designed to improve particles' local and global positions information together with ELS. Parameter identification of robot manipulators is conducted for performance evaluation of our proposed approach. Experimental results clearly indicate the following findings: Compared with standard PSO (SPSO) algorithm, ELPIDSO has improved a lot. It not only enhances the diversity of the swarm, but also features better search effectiveness and efficiency in solving practical optimization problems. Accordingly, ELPIDSO is superior to least squares (LS) method, genetic algorithm (GA), and SPSO algorithm in estimating the parameters of the kinetic models of robot manipulators.
Formation Control of Robotic Swarm Using Bounded Artificial Forces
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Long Qin
2013-01-01
Full Text Available Formation control of multirobot systems has drawn significant attention in the recent years. This paper presents a potential field control algorithm, navigating a swarm of robots into a predefined 2D shape while avoiding intermember collisions. The algorithm applies in both stationary and moving targets formation. We define the bounded artificial forces in the form of exponential functions, so that the behavior of the swarm drove by the forces can be adjusted via selecting proper control parameters. The theoretical analysis of the swarm behavior proves the stability and convergence properties of the algorithm. We further make certain modifications upon the forces to improve the robustness of the swarm behavior in the presence of realistic implementation considerations. The considerations include obstacle avoidance, local minima, and deformation of the shape. Finally, detailed simulation results validate the efficiency of the proposed algorithm, and the direction of possible futrue work is discussed in the conclusions.
Gene expression in Pseudomonas aeruginosa swarming motility
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Déziel Eric
2010-10-01
Full Text Available Abstract Background The bacterium Pseudomonas aeruginosa is capable of three types of motilities: swimming, twitching and swarming. The latter is characterized by a fast and coordinated group movement over a semi-solid surface resulting from intercellular interactions and morphological differentiation. A striking feature of swarming motility is the complex fractal-like patterns displayed by migrating bacteria while they move away from their inoculation point. This type of group behaviour is still poorly understood and its characterization provides important information on bacterial structured communities such as biofilms. Using GeneChip® Affymetrix microarrays, we obtained the transcriptomic profiles of both bacterial populations located at the tip of migrating tendrils and swarm center of swarming colonies and compared these profiles to that of a bacterial control population grown on the same media but solidified to not allow swarming motility. Results Microarray raw data were corrected for background noise with the RMA algorithm and quantile normalized. Differentially expressed genes between the three conditions were selected using a threshold of 1.5 log2-fold, which gave a total of 378 selected genes (6.3% of the predicted open reading frames of strain PA14. Major shifts in gene expression patterns are observed in each growth conditions, highlighting the presence of distinct bacterial subpopulations within a swarming colony (tendril tips vs. swarm center. Unexpectedly, microarrays expression data reveal that a minority of genes are up-regulated in tendril tip populations. Among them, we found energy metabolism, ribosomal protein and transport of small molecules related genes. On the other hand, many well-known virulence factors genes were globally repressed in tendril tip cells. Swarm center cells are distinct and appear to be under oxidative and copper stress responses. Conclusions Results reported in this study show that, as opposed to
A Game Theoretic Approach to Swarm Robotics
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S. N. Givigi
2006-01-01
Full Text Available In this article, we discuss some techniques for achieving swarm intelligent robots through the use of traits of personality. Traits of personality are characteristics of each robot that, altogether, define the robot's behaviours. We discuss the use of evolutionary psychology to select a set of traits of personality that will evolve due to a learning process based on reinforcement learning. The use of Game Theory is introduced, and some simulations showing its potential are reported.
Properties of a Formal Method to Model Emergence in Swarm-Based Systems
Rouff, Christopher; Vanderbilt, Amy; Truszkowski, Walt; Rash, James; Hinchey, Mike
2004-01-01
Future space missions will require cooperation between multiple satellites and/or rovers. Developers are proposing intelligent autonomous swarms for these missions, but swarm-based systems are difficult or impossible to test with current techniques. This viewgraph presentation examines the use of formal methods in testing swarm-based systems. The potential usefulness of formal methods in modeling the ANTS asteroid encounter mission is also examined.
Novelty-driven Particle Swarm Optimization
DEFF Research Database (Denmark)
Galvao, Diana; Lehman, Joel Anthony; Urbano, Paulo
2015-01-01
Particle Swarm Optimization (PSO) is a well-known population-based optimization algorithm. Most often it is applied to optimize objective-based fitness functions that reward progress towards a desired objective or behavior. As a result, search increasingly focuses on higher-fitness areas. However...
Swarm controlled emergence for ant clustering
DEFF Research Database (Denmark)
Scheidler, Alexander; Merkle, Daniel; Middendorf, Martin
2013-01-01
implications: The particular finding, that certain behaviours of control agents can lead to stronger clustering, can help to design improved clustering algorithms by using heterogeneous swarms of agents. Originality/value: In general, the control of (unwanted) emergent effects in artificial systems...
Business and Social Behaviour Intelligence Analysis Using PSO
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Vinay S Bhaskar
2014-06-01
Full Text Available The goal of this paper is to elaborate swarm intelligence for business intelligence decision making and the business rules management improvement. The paper introduces the decision making model which is based on the application of Artiﬁcial Neural Networks (ANNs and Particle Swarm Optimization (PSO algorithm. Essentially the business spatial data illustrate the group behaviors. The swarm optimization, which is highly influenced by the behavior of creature, performs in group. The Spatial data is defined as data that is represented by 2D or 3D images. SQL Server supports only 2D images till now. As we know that location is an essential part of any organizational data as well as business data: enterprises maintain customer address lists, own property, ship goods from and to warehouses, manage transport flows among their workforce, and perform many other activities. By means to say a lot of spatial data is used and processed by enterprises, organizations and other bodies in order to make the things more visible and self-descriptive. From the experiments, we found that PSO is can facilitate the intelligence in social and business behaviour
Oommen, T.; Baise, L. G.; Gens, R.; Prakash, A.; Gupta, R. P.
2009-12-01
bands by neighborhood correlation image analysis using an artificial intelligence algorithm called support vector machine to remotely identify and document liquefaction failures across a region; and assess the reliability and accuracy of the thermal remote sensing approach in documenting regional liquefaction failures. Finally, we present the applicability of the satellite data analyzed and appropriateness of a multisensor and multispectral approach for documenting liquefaction related failures.
Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance
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Nabil M. Hewahi
2017-07-01
Full Text Available The robots science has been developed over the past few years, where robots have become used to accomplish difficult, repetitive or accurate tasks, which are very hard for humans to carry out. In this paper, we propose an algorithm to control the motion of a swarm of robots and make them able to avoid obstacles. The proposed solution is based on forming the robots in circular fashion. A group set of robots consists of multiple groups of robots, each group of robots consists of robots forming a circular shape and each group set is a circular form of robots. The proposed algorithm is concerned with first locating the randomly generated robots in groups and secondly with the swarm robot motion and finally with the swarm obstacle avoidance and swarm reorganization after crossing the obstacle. The proposed algorithm has been simulated with five different obstacles with various numbers of randomly generated robots. The results show that the swarm in the circular form can deal with the obstacles very effectively by passing the obstacles smoothly. The proposed algorithm has been compared with flocking algorithm and it is shown that the circular formation algorithm does not need extensive computation after obstacle avoidance whereas the flocking algorithm needs extensive computation. In addition, the circular formation algorithm maintains every robot in its group after avoiding the obstacles whereas with flocking algorithm does not.
Ghanem, Bernard
2013-01-01
This paper proposes the problem of modeling video sequences of dynamic swarms (DSs). We define a DS as a large layout of stochastically repetitive spatial configurations of dynamic objects (swarm elements) whose motions exhibit local spatiotemporal interdependency and stationarity, i.e., the motions are similar in any small spatiotemporal neighborhood. Examples of DS abound in nature, e.g., herds of animals and flocks of birds. To capture the local spatiotemporal properties of the DS, we present a probabilistic model that learns both the spatial layout of swarm elements (based on low-level image segmentation) and their joint dynamics that are modeled as linear transformations. To this end, a spatiotemporal neighborhood is associated with each swarm element, in which local stationarity is enforced both spatially and temporally. We assume that the prior on the swarm dynamics is distributed according to an MRF in both space and time. Embedding this model in a MAP framework, we iterate between learning the spatial layout of the swarm and its dynamics. We learn the swarm transformations using ICM, which iterates between estimating these transformations and updating their distribution in the spatiotemporal neighborhoods. We demonstrate the validity of our method by conducting experiments on real and synthetic video sequences. Real sequences of birds, geese, robot swarms, and pedestrians evaluate the applicability of our model to real world data. © 2012 Elsevier Inc. All rights reserved.
2010-05-05
employed biomimicry to model a swarm of UAS as a colony of ants, where each UAS dynamically updates a global memory map, allowing pheromone-like...matter of design, DSE-R-0808 employed biomimicry to model a swarm of UAS as a colony of ants, where each UAS dynamically updates a global memory map
A review on economic emission dispatch problems using quantum computational intelligence
Mahdi, Fahad Parvez; Vasant, Pandian; Kallimani, Vish; Abdullah-Al-Wadud, M.
2016-11-01
Economic emission dispatch (EED) problems are one of the most crucial problems in power systems. Growing energy demand, limitation of natural resources and global warming make this topic into the center of discussion and research. This paper reviews the use of Quantum Computational Intelligence (QCI) in solving Economic Emission Dispatch problems. QCI techniques like Quantum Genetic Algorithm (QGA) and Quantum Particle Swarm Optimization (QPSO) algorithm are discussed here. This paper will encourage the researcher to use more QCI based algorithm to get better optimal result for solving EED problems.
Swarms of UAVs and fighter aircraft
Energy Technology Data Exchange (ETDEWEB)
Trahan, M.W.; Wagner, J.S.; Stantz, K.M.; Gray, P.C.; Robinett, R.
1998-11-01
This paper describes a method of modeling swarms of UAVs and/or fighter aircraft using particle simulation concepts. Recent investigations into the use of genetic algorithms to design neural networks for the control of autonomous vehicles (i.e., robots) led to the examination of methods of simulating large collections of robots. This paper describes the successful implementation of a model of swarm dynamics using particle simulation concepts. Several examples of the complex behaviors achieved in a target/interceptor scenario are presented.
Algorithm for Public Electric Transport Schedule Control for Intelligent Embedded Devices
Alps, Ivars; Potapov, Andrey; Gorobetz, Mikhail; Levchenkov, Anatoly
2010-01-01
In this paper authors present heuristics algorithm for precise schedule fulfilment in city traffic conditions taking in account traffic lights. The algorithm is proposed for programmable controller. PLC is proposed to be installed in electric vehicle to control its motion speed and signals of traffic lights. Algorithm is tested using real controller connected to virtual devices and real functional models of real tram devices. Results of experiments show high precision of public transport schedule fulfilment using proposed algorithm.
Directory of Open Access Journals (Sweden)
Bingbing Liang
2016-11-01
Full Text Available This article addresses the flight control problem of air-breathing hypersonic vehicles and proposes a novel intelligent algorithm optimized control method. To achieve the climbing, cruising and descending flight control of the air-breathing hypersonic vehicle, an engineering-oriented flight control system based on a Proportional Integral Derivative (PID method is designed for the hypersonic vehicle, which including the height loop, the pitch angle loop and the velocity loop. Moreover, as a variant of nature-inspired algorithm, modified shuffled frog leaping algorithm is presented to optimize the flight control parameters and is characterized by better exploration and exploitation than the standard shuffled frog leaping algorithm. A nonlinear model of air-breathing hypersonic vehicle is used to verify the dynamic characteristics achieved by the intelligent flight control system. Simulation results demonstrate that the proposed swarm intelligence optimized PID controllers are effective in achieving better flight trajectory and velocity control performance than the traditional controllers.
Computational intelligence techniques in bioinformatics.
Hassanien, Aboul Ella; Al-Shammari, Eiman Tamah; Ghali, Neveen I
2013-12-01
Computational intelligence (CI) is a well-established paradigm with current systems having many of the characteristics of biological computers and capable of performing a variety of tasks that are difficult to do using conventional techniques. It is a methodology involving adaptive mechanisms and/or an ability to learn that facilitate intelligent behavior in complex and changing environments, such that the system is perceived to possess one or more attributes of reason, such as generalization, discovery, association and abstraction. The objective of this article is to present to the CI and bioinformatics research communities some of the state-of-the-art in CI applications to bioinformatics and motivate research in new trend-setting directions. In this article, we present an overview of the CI techniques in bioinformatics. We will show how CI techniques including neural networks, restricted Boltzmann machine, deep belief network, fuzzy logic, rough sets, evolutionary algorithms (EA), genetic algorithms (GA), swarm intelligence, artificial immune systems and support vector machines, could be successfully employed to tackle various problems such as gene expression clustering and classification, protein sequence classification, gene selection, DNA fragment assembly, multiple sequence alignment, and protein function prediction and its structure. We discuss some representative methods to provide inspiring examples to illustrate how CI can be utilized to address these problems and how bioinformatics data can be characterized by CI. Challenges to be addressed and future directions of research are also presented and an extensive bibliography is included. Copyright © 2013 Elsevier Ltd. All rights reserved.
Exergoeconomic optimization of a thermal power plant using particle swarm optimization
Directory of Open Access Journals (Sweden)
Groniewsky Axel
2013-01-01
Full Text Available The basic concept in applying numerical optimization methods for power plants optimization problems is to combine a State of the art search algorithm with a powerful, power plant simulation program to optimize the energy conversion system from both economic and thermodynamic viewpoints. Improving the energy conversion system by optimizing the design and operation and studying interactions among plant components requires the investigation of a large number of possible design and operational alternatives. State of the art search algorithms can assist in the development of cost-effective power plant concepts. The aim of this paper is to present how nature-inspired swarm intelligence (especially PSO can be applied in the field of power plant optimization and how to find solutions for the problems arising and also to apply exergoeconomic optimization technics for thermal power plants.
APPLICATION OF A PARTICLE SWARM OPTIMIZATION IN AN OPTIMAL POWER FLOW
Directory of Open Access Journals (Sweden)
D. Ben Attous
2010-12-01
Full Text Available In this paper an efficient and Particle Swarm Optimization (PSO has been presented for solving the economic dispatch problem. The objective is to minimize the total generation fuel and keep the power outputs of generators; bus voltages and transformer tap setting in their secure limits. The conventional load flow and incorporation of the proposed method using PSO has been examined and tested for standard IEEE 30 bus system. The PSO method is demonstrated and compared with conventional OPF method (NR, Quasi Newton, and the intelligence heuristic algorithms such ac genetic algorithm, evolutionary programming. From simulation results it has been found that PSO method is highly competitive for its better general convergence performance.
APPLICATION OF A PARTICLE SWARM OPTIMIZATION IN AN OPTIMAL POWER FLOW
Directory of Open Access Journals (Sweden)
D. Ben Attous
2015-08-01
Full Text Available In this paper an efficient and Particle Swarm Optimization (PSO has been presented for solving the economic dispatch problem. The objective is to minimize the total generation fuel and keep the power outputs of generators; bus voltages and transformer tap setting in their secure limits. The conventional load flow and incorporation of the proposed method using PSO has been examined and tested for standard IEEE 30 bus system. The PSO method is demonstrated and compared with conventional OPF method (NR, Quasi Newton, and the intelligence heuristic algorithms such ac genetic algorithm, evolutionary programming.From simulation results it has been found that PSO method is highly competitive for its better general convergence performance.
From Swarm Intelligence to Swarm Malice: An Appeal
Stephan Weichert
2016-01-01
In social networks, controversy, provocation, and incitement mutate quickly to an explosive mixture. Journalists, who aim for a factual moderation, are often highly frustrated to meet the criticism of trolls and haters. The essay addresses the following questions: How can newsrooms cope with the massively growing feedback from users? What responsibility carries the media and the civil society in designing a constructive net debate culture? And are there alternatives to foster an open-minded p...
From Swarm Intelligence to Swarm Malice: An Appeal
Directory of Open Access Journals (Sweden)
Stephan Weichert
2016-03-01
Full Text Available In social networks, controversy, provocation, and incitement mutate quickly to an explosive mixture. Journalists, who aim for a factual moderation, are often highly frustrated to meet the criticism of trolls and haters. The essay addresses the following questions: How can newsrooms cope with the massively growing feedback from users? What responsibility carries the media and the civil society in designing a constructive net debate culture? And are there alternatives to foster an open-minded public discourse?
Particle swarm optimization based optimal bidding strategy in an ...
African Journals Online (AJOL)
user
compared with the Genetic Algorithm (GA) approach. Test results indicate that the proposed algorithm outperforms the Genetic. Algorithm approach with respect to total profit and convergence time. Keywords: Electricity Market, Market Clearing Price (MCP), Optimal bidding strategy, Particle Swarm Optimization (PSO).
Artificial Intelligence and Economic Theories
Marwala, Tshilidzi; Hurwitz, Evan
2017-01-01
The advent of artificial intelligence has changed many disciplines such as engineering, social science and economics. Artificial intelligence is a computational technique which is inspired by natural intelligence such as the swarming of birds, the working of the brain and the pathfinding of the ants. These techniques have impact on economic theories. This book studies the impact of artificial intelligence on economic theories, a subject that has not been extensively studied. The theories that...
Double Flight-Modes Particle Swarm Optimization
Directory of Open Access Journals (Sweden)
Wang Yong
2013-01-01
Full Text Available Getting inspiration from the real birds in flight, we propose a new particle swarm optimization algorithm that we call the double flight modes particle swarm optimization (DMPSO in this paper. In the DMPSO, each bird (particle can use both rotational flight mode and nonrotational flight mode to fly, while it is searching for food in its search space. There is a King in the swarm of birds, and the King controls each bird’s flight behavior in accordance with certain rules all the time. Experiments were conducted on benchmark functions such as Schwefel, Rastrigin, Ackley, Step, Griewank, and Sphere. The experimental results show that the DMPSO not only has marked advantage of global convergence property but also can effectively avoid the premature convergence problem and has good performance in solving the complex and high-dimensional optimization problems.
Directory of Open Access Journals (Sweden)
Neeraj Kanwar
2015-01-01
Full Text Available This paper addresses a new methodology for the simultaneous optimal allocation of DSTATCOM and DG in radial distribution systems to maximize power loss reduction while maintaining better node voltage profiles under multilevel load profile. Cat Swarm Optimization (CSO is one of the recently developed powerful swarm intelligence-based optimization techniques that mimics the natural behavior of cats but usually suffers from poor convergence and accuracy while subjected to large dimension problem. Therefore, an Improved CSO (ICSO technique is proposed to efficiently solve the problem where the seeking mode of CSO is modified to enhance its exploitation potential. In addition, the problem search space is virtually squeezed by suggesting an intelligent search approach which smartly scans the problem search space. Further, the effect of network reconfiguration has also been investigated after optimally placing DSTATCOMs and DGs in the distribution network. The suggested measures enhance the convergence and accuracy of the algorithm without loss of diversity. The proposed method is investigated on 69-bus test distribution system and the application results are very promising for the operation of smart distribution systems.
From hybrid swarms to swarms of hybrids
The introgression of modern humans (Homo sapiens) with Neanderthals 40,000 YBP after a half-million years of separation, may have led to the best example of a hybrid swarm on earth. Modern trade and transportation in support of the human hybrids has continued to introduce additional species, genotyp...
Physics-based approach to chemical source localization using mobile robotic swarms
Zarzhitsky, Dimitri
2008-07-01
Recently, distributed computation has assumed a dominant role in the fields of artificial intelligence and robotics. To improve system performance, engineers are combining multiple cooperating robots into cohesive collectives called swarms. This thesis illustrates the application of basic principles of physicomimetics, or physics-based design, to swarm robotic systems. Such principles include decentralized control, short-range sensing and low power consumption. We show how the application of these principles to robotic swarms results in highly scalable, robust, and adaptive multi-robot systems. The emergence of these valuable properties can be predicted with the help of well-developed theoretical methods. In this research effort, we have designed and constructed a distributed physicomimetics system for locating sources of airborne chemical plumes. This task, called chemical plume tracing (CPT), is receiving a great deal of attention due to persistent homeland security threats. For this thesis, we have created a novel CPT algorithm called fluxotaxis that is based on theoretical principles of fluid dynamics. Analytically, we show that fluxotaxis combines the essence, as well as the strengths, of the two most popular biologically-inspired CPT methods-- chemotaxis and anemotaxis. The chemotaxis strategy consists of navigating in the direction of the chemical density gradient within the plume, while the anemotaxis approach is based on an upwind traversal of the chemical cloud. Rigorous and extensive experimental evaluations have been performed in simulated chemical plume environments. Using a suite of performance metrics that capture the salient aspects of swarm-specific behavior, we have been able to evaluate and compare the three CPT algorithms. We demonstrate the improved performance of our fluxotaxis approach over both chemotaxis and anemotaxis in these realistic simulation environments, which include obstacles. To test our understanding of CPT on actual hardware
MAGNAS - Magnetic Nanoprobe SWARM
DEFF Research Database (Denmark)
Lubberstedt, H.; Koebel, D.; Hansen, Flemming
2005-01-01
This paper presents the Magnetic Nano-Probe Swarm mission utilising a constellation of several swarms of nano-satellites in order to acquire simultaneous measurements of the geomagnetic field resolving the local field gradients. The space segment comprises of up to 4 S/C swarms each consisting...... of up to 6 nano-satellites (Nano-Probes) and 1 mother spacecraft (MSC) to be launched with a single launcher in polar low Earth orbits. The Nano-Probes. equipped with magnetometer payloads operate in the vicinity of the MSCs. The MSCs will eject the NPs after acquisition of the initial orbits. provide...
POLICE OFFICE MODEL IMPROVEMENT FOR SECURITY OF SWARM ROBOTIC SYSTEMS
Directory of Open Access Journals (Sweden)
I. A. Zikratov
2014-09-01
Full Text Available This paper focuses on aspects of information security for group of mobile robotic systems with swarm intellect. The ways for hidden attacks realization by the opposing party on swarm algorithm are discussed. We have fulfilled numerical modeling of potentially destructive information influence on the ant shortest path algorithm. We have demonstrated the consequences of attacks on the ant algorithm with different concentration in a swarm of subversive robots. Approaches are suggested for information security mechanisms in swarm robotic systems, based on the principles of centralized security management for mobile agents. We have developed the method of forming a self-organizing information security management system for robotic agents in swarm groups implementing POM (Police Office Model – a security model based on police offices, to provide information security in multi-agent systems. The method is based on the usage of police station network in the graph nodes, which have functions of identification and authentication of agents, identifying subversive robots by both their formal characteristics and their behavior in the swarm. We have suggested a list of software and hardware components for police stations, consisting of: communication channels between the robots in police office, nodes register, a database of robotic agents, a database of encryption and decryption module. We have suggested the variants of logic for the mechanism of information security in swarm systems with different temporary diagrams of data communication between police stations. We present comparative analysis of implementation of protected swarm systems depending on the functioning logic of police offices, integrated in swarm system. It is shown that the security model saves the ability to operate in noisy environments, when the duration of the interference is comparable to the time necessary for the agent to overcome the path between police stations.
Directory of Open Access Journals (Sweden)
Huanqing Cui
2017-03-01
Full Text Available Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors’ memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm.
Cui, Huanqing; Shu, Minglei; Song, Min; Wang, Yinglong
2017-03-01
Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors' memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm.
Paasche, H.; Tronicke, J.
2012-04-01
In many near surface geophysical applications multiple tomographic data sets are routinely acquired to explore subsurface structures and parameters. Linking the model generation process of multi-method geophysical data sets can significantly reduce ambiguities in geophysical data analysis and model interpretation. Most geophysical inversion approaches rely on local search optimization methods used to find an optimal model in the vicinity of a user-given starting model. The final solution may critically depend on the initial model. Alternatively, global optimization (GO) methods have been used to invert geophysical data. They explore the solution space in more detail and determine the optimal model independently from the starting model. Additionally, they can be used to find sets of optimal models allowing a further analysis of model parameter uncertainties. Here we employ particle swarm optimization (PSO) to realize the global optimization of tomographic data. PSO is an emergent methods based on swarm intelligence characterized by fast and robust convergence towards optimal solutions. The fundamental principle of PSO is inspired by nature, since the algorithm mimics the behavior of a flock of birds searching food in a search space. In PSO, a number of particles cruise a multi-dimensional solution space striving to find optimal model solutions explaining the acquired data. The particles communicate their positions and success and direct their movement according to the position of the currently most successful particle of the swarm. The success of a particle, i.e. the quality of the currently found model by a particle, must be uniquely quantifiable to identify the swarm leader. When jointly inverting disparate data sets, the optimization solution has to satisfy multiple optimization objectives, at least one for each data set. Unique determination of the most successful particle currently leading the swarm is not possible. Instead, only statements about the Pareto
The Swarm Magnetometry Package
DEFF Research Database (Denmark)
Merayo, José M.G.; Jørgensen, John Leif; Friis-Christensen, Eigil
2008-01-01
The Swarm mission under the ESA's Living Planet Programme is planned for launch in 2010 and consists of a constellation of three satellites at LEO. The prime objective of Swarm is to measure the geomagnetic field with unprecedented accuracy in space and time. The magnetometry package consists of ...... of an extremely accurate and stable vector magnetometer, which is co-mounted in an optical bench together with a start tracker system to ensure mechanical stability of the measurements....
Directory of Open Access Journals (Sweden)
Xiaomin Xu
2015-11-01
Full Text Available The uncertainty and regularity of wind power generation are caused by wind resources’ intermittent and randomness. Such volatility brings severe challenges to the wind power grid. The requirements for ultrashort-term and short-term wind power forecasting with high prediction accuracy of the model used, have great significance for reducing the phenomenon of abandoned wind power , optimizing the conventional power generation plan, adjusting the maintenance schedule and developing real-time monitoring systems. Therefore, accurate forecasting of wind power generation is important in electric load forecasting. The echo state network (ESN is a new recurrent neural network composed of input, hidden layer and output layers. It can approximate well the nonlinear system and achieves great results in nonlinear chaotic time series forecasting. Besides, the ESN is simpler and less computationally demanding than the traditional neural network training, which provides more accurate training results. Aiming at addressing the disadvantages of standard ESN, this paper has made some improvements. Combined with the complementary advantages of particle swarm optimization and tabu search, the generalization of ESN is improved. To verify the validity and applicability of this method, case studies of multitime scale forecasting of wind power output are carried out to reconstruct the chaotic time series of the actual wind power generation data in a certain region to predict wind power generation. Meanwhile, the influence of seasonal factors on wind power is taken into consideration. Compared with the classical ESN and the conventional Back Propagation (BP neural network, the results verify the superiority of the proposed method.
Performance comparison of some evolutionary algorithms on job shop scheduling problems
Mishra, S. K.; Rao, C. S. P.
2016-09-01
Job Shop Scheduling as a state space search problem belonging to NP-hard category due to its complexity and combinational explosion of states. Several naturally inspire evolutionary methods have been developed to solve Job Shop Scheduling Problems. In this paper the evolutionary methods namely Particles Swarm Optimization, Artificial Intelligence, Invasive Weed Optimization, Bacterial Foraging Optimization, Music Based Harmony Search Algorithms are applied and find tuned to model and solve Job Shop Scheduling Problems. To compare about 250 Bench Mark instances have been used to evaluate the performance of these algorithms. The capabilities of each these algorithms in solving Job Shop Scheduling Problems are outlined.
Application of the artificial bee colony algorithm for solving the set covering problem.
Crawford, Broderick; Soto, Ricardo; Cuesta, Rodrigo; Paredes, Fernando
2014-01-01
The set covering problem is a formal model for many practical optimization problems. In the set covering problem the goal is to choose a subset of the columns of minimal cost that covers every row. Here, we present a novel application of the artificial bee colony algorithm to solve the non-unicost set covering problem. The artificial bee colony algorithm is a recent swarm metaheuristic technique based on the intelligent foraging behavior of honey bees. Experimental results show that our artificial bee colony algorithm is competitive in terms of solution quality with other recent metaheuristic approaches for the set covering problem.
Directory of Open Access Journals (Sweden)
Yuksel Celik
2013-01-01
Full Text Available Marriage in honey bees optimization (MBO is a metaheuristic optimization algorithm developed by inspiration of the mating and fertilization process of honey bees and is a kind of swarm intelligence optimizations. In this study we propose improved marriage in honey bees optimization (IMBO by adding Levy flight algorithm for queen mating flight and neighboring for worker drone improving. The IMBO algorithm’s performance and its success are tested on the well-known six unconstrained test functions and compared with other metaheuristic optimization algorithms.
Swarm Optimization-Based Magnetometer Calibration for Personal Handheld Devices
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Naser El-Sheimy
2012-09-01
Full Text Available Inertial Navigation Systems (INS consist of accelerometers, gyroscopes and a processor that generates position and orientation solutions by integrating the specific forces and rotation rates. In addition to the accelerometers and gyroscopes, magnetometers can be used to derive the user heading based on Earth’s magnetic field. Unfortunately, the measurements of the magnetic field obtained with low cost sensors are usually corrupted by several errors, including manufacturing defects and external electro-magnetic fields. Consequently, proper calibration of the magnetometer is required to achieve high accuracy heading measurements. In this paper, a Particle Swarm Optimization (PSO-based calibration algorithm is presented to estimate the values of the bias and scale factor of low cost magnetometers. The main advantage of this technique is the use of the artificial intelligence which does not need any error modeling or awareness of the nonlinearity. Furthermore, the proposed algorithm can help in the development of Pedestrian Navigation Devices (PNDs when combined with inertial sensors and GPS/Wi-Fi for indoor navigation and Location Based Services (LBS applications.
Particle swarm optimization of a neural network model in a ...
Indian Academy of Sciences (India)
sets of cutting conditions and noting the root mean square (RMS) value of spindle motor current as well as ... A multi- objective optimization of hard turning using neural network modelling and swarm intelligence ... being used in this study), and these activated values in turn become the starting signals for the next adjacent ...
Adaptive Parameters for a Modified Comprehensive Learning Particle Swarm Optimizer
Directory of Open Access Journals (Sweden)
Yu-Jun Zheng
2012-01-01
Full Text Available Particle swarm optimization (PSO is a stochastic optimization method sensitive to parameter settings. The paper presents a modification on the comprehensive learning particle swarm optimizer (CLPSO, which is one of the best performing PSO algorithms. The proposed method introduces a self-adaptive mechanism that dynamically changes the values of key parameters including inertia weight and acceleration coefficient based on evolutionary information of individual particles and the swarm during the search. Numerical experiments demonstrate that our approach with adaptive parameters can provide comparable improvement in performance of solving global optimization problems.
Algorithme intelligent d'optimisation d'un design structurel de grande envergure
Dominique, Stephane
The implementation of an automated decision support system in the field of design and structural optimisation can give a significant advantage to any industry working on mechanical designs. Indeed, by providing solution ideas to a designer or by upgrading existing design solutions while the designer is not at work, the system may reduce the project cycle time, or allow more time to produce a better design. This thesis presents a new approach to automate a design process based on Case-Based Reasoning (CBR), in combination with a new genetic algorithm named Genetic Algorithm with Territorial core Evolution (GATE). This approach was developed in order to reduce the operating cost of the process. However, as the system implementation cost is quite expensive, the approach is better suited for large scale design problem, and particularly for design problems that the designer plans to solve for many different specification sets. First, the CBR process uses a databank filled with every known solution to similar design problems. Then, the closest solutions to the current problem in term of specifications are selected. After this, during the adaptation phase, an artificial neural network (ANN) interpolates amongst known solutions to produce an additional solution to the current problem using the current specifications as inputs. Each solution produced and selected by the CBR is then used to initialize the population of an island of the genetic algorithm. The algorithm will optimise the solution further during the refinement phase. Using progressive refinement, the algorithm starts using only the most important variables for the problem. Then, as the optimisation progress, the remaining variables are gradually introduced, layer by layer. The genetic algorithm that is used is a new algorithm specifically created during this thesis to solve optimisation problems from the field of mechanical device structural design. The algorithm is named GATE, and is essentially a real number
Analysis of the Emergence in Swarm Model Based on Largest Lyapunov Exponent
Directory of Open Access Journals (Sweden)
Yu Wu
2011-01-01
Full Text Available Emergent behaviors of collective intelligence systems, exemplified by swarm model, have attracted broad interests in recent years. However, current research mostly stops at observational interpretations and qualitative descriptions of emergent phenomena and is essentially short of quantitative analysis and evaluation. In this paper, we conduct a quantitative study on the emergence of swarm model by using chaos analysis of complex dynamic systems. This helps to achieve a more exact understanding of emergent phenomena. In particular, we evaluate the emergent behaviors of swarm model quantitatively by using the chaos and stability analysis of swarm model based on largest Lyapunov exponent. It is concluded that swarm model is at the edge of chaos when emergence occurs, and whether chaotic or stable at the beginning, swarm model will converge to stability with the elapse of time along with interactions among agents.