Asteroid Rendezvous Mission Design Using Multiobjective Particle Swarm Optimization

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Ya-zhong Luo

2014-01-01

Full Text Available A new preliminary trajectory design method for asteroid rendezvous mission using multiobjective optimization techniques is proposed. This method can overcome the disadvantages of the widely employed Pork-Chop method. The multiobjective integrated launch window and multi-impulse transfer trajectory design model is formulated, which employes minimum-fuel cost and minimum-time transfer as two objective functions. The multiobjective particle swarm optimization (MOPSO is employed to locate the Pareto solution. The optimization results of two different asteroid mission designs show that the proposed approach can effectively and efficiently demonstrate the relations among the mission characteristic parameters such as launch time, transfer time, propellant cost, and number of maneuvers, which will provide very useful reference for practical asteroid mission design. Compared with the PCP method, the proposed approach is demonstrated to be able to provide much more easily used results, obtain better propellant-optimal solutions, and have much better efficiency. The MOPSO shows a very competitive performance with respect to the NSGA-II and the SPEA-II; besides a proposed boundary constraint optimization strategy is testified to be able to improve its performance.

Thermodynamic design of Stirling engine using multi-objective particle swarm optimization algorithm

International Nuclear Information System (INIS)

Duan, Chen; Wang, Xinggang; Shu, Shuiming; Jing, Changwei; Chang, Huawei

2014-01-01

Highlights: • An improved thermodynamic model taking into account irreversibility parameter was developed. • A multi-objective optimization method for designing Stirling engine was investigated. • Multi-objective particle swarm optimization algorithm was adopted in the area of Stirling engine for the first time. - Abstract: In the recent years, the interest in Stirling engine has remarkably increased due to its ability to use any heat source from outside including solar energy, fossil fuels and biomass. A large number of studies have been done on Stirling cycle analysis. In the present study, a mathematical model based on thermodynamic analysis of Stirling engine considering regenerative losses and internal irreversibilities has been developed. Power output, thermal efficiency and the cycle irreversibility parameter of Stirling engine are optimized simultaneously using Particle Swarm Optimization (PSO) algorithm, which is more effective than traditional genetic algorithms. In this optimization problem, some important parameters of Stirling engine are considered as decision variables, such as temperatures of the working fluid both in the high temperature isothermal process and in the low temperature isothermal process, dead volume ratios of each heat exchanger, volumes of each working spaces, effectiveness of the regenerator, and the system charge pressure. The Pareto optimal frontier is obtained and the final design solution has been selected by Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP). Results show that the proposed multi-objective optimization approach can significantly outperform traditional single objective approaches

Multi-objective parallel particle swarm optimization for day-ahead Vehicle-to-Grid scheduling

DEFF Research Database (Denmark)

Soares, Joao; Vale, Zita; Canizes, Bruno

2013-01-01

This paper presents a methodology for multi-objective day-ahead energy resource scheduling for smart grids considering intensive use of distributed generation and Vehicle-To-Grid (V2G). The main focus is the application of weighted Pareto to a multi-objective parallel particle swarm approach aiming...... to solve the dual-objective V2G scheduling: minimizing total operation costs and maximizing V2G income. A realistic mathematical formulation, considering the network constraints and V2G charging and discharging efficiencies is presented and parallel computing is applied to the Pareto weights. AC power flow...

A modified multi-objective particle swarm optimization approach and its application to the design of a deepwater composite riser

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Zheng, Y.; Chen, J.

2017-09-01

A modified multi-objective particle swarm optimization method is proposed for obtaining Pareto-optimal solutions effectively. Different from traditional multi-objective particle swarm optimization methods, Kriging meta-models and the trapezoid index are introduced and integrated with the traditional one. Kriging meta-models are built to match expensive or black-box functions. By applying Kriging meta-models, function evaluation numbers are decreased and the boundary Pareto-optimal solutions are identified rapidly. For bi-objective optimization problems, the trapezoid index is calculated as the sum of the trapezoid's area formed by the Pareto-optimal solutions and one objective axis. It can serve as a measure whether the Pareto-optimal solutions converge to the Pareto front. Illustrative examples indicate that to obtain Pareto-optimal solutions, the method proposed needs fewer function evaluations than the traditional multi-objective particle swarm optimization method and the non-dominated sorting genetic algorithm II method, and both the accuracy and the computational efficiency are improved. The proposed method is also applied to the design of a deepwater composite riser example in which the structural performances are calculated by numerical analysis. The design aim was to enhance the tension strength and minimize the cost. Under the buckling constraint, the optimal trade-off of tensile strength and material volume is obtained. The results demonstrated that the proposed method can effectively deal with multi-objective optimizations with black-box functions.

A Hybrid Multiobjective Discrete Particle Swarm Optimization Algorithm for a SLA-Aware Service Composition Problem

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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.

Multi-Objective Bidding Strategy for Genco Using Non-Dominated Sorting Particle Swarm Optimization

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Saksinchai, Apinat; Boonchuay, Chanwit; Ongsakul, Weerakorn

2010-06-01

This paper proposes a multi-objective bidding strategy for a generation company (GenCo) in uniform price spot market using non-dominated sorting particle swarm optimization (NSPSO). Instead of using a tradeoff technique, NSPSO is introduced to solve the multi-objective strategic bidding problem considering expected profit maximization and risk (profit variation) minimization. Monte Carlo simulation is employed to simulate rivals' bidding behavior. Test results indicate that the proposed approach can provide the efficient non-dominated solution front effectively. In addition, it can be used as a decision making tool for a GenCo compromising between expected profit and price risk in spot market.

Improving Vector Evaluated Particle Swarm Optimisation by incorporating nondominated solutions.

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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.

Design for Sustainability of Industrial Symbiosis based on Emergy and Multi-objective Particle Swarm Optimization

DEFF Research Database (Denmark)

Ren, Jingzheng; Liang, Hanwei; Dong, Liang

2016-01-01

approach for supporting decision-making in the design for the sustainability with the implementation of industrial symbiosis in chemical complex. Through incorporating the emergy theory, the model is formulated as a multi-objective approach that can optimize both the economic benefit and sustainable...... performance of the integrated industrial system. A set of emergy based evaluation index are designed. Multi-objective Particle Swarm Algorithm is proposed to solve the model, and the decision-makers are allowed to choose the suitable solutions form the Pareto solutions. An illustrative case has been studied...

Improving Vector Evaluated Particle Swarm Optimisation by Incorporating Nondominated Solutions

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Kian Sheng Lim

2013-01-01

Full Text Available 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.

A multi-objective particle swarm optimization for production-distribution planning in supply chain network

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Alireza Pourrousta

2012-04-01

Full Text Available Integrated supply chain includes different components of order, production and distribution and it plays an important role on reducing the cost of manufacturing system. In this paper, an integrated supply chain in a form of multi-objective decision-making problem is presented. The proposed model of this paper considers different parameters with uncertainty using trapezoid numbers. We first implement a ranking method to covert the fuzzy model into a crisp one and using multi-objective particle swarm optimization, we solve the resulted model. The results are compared with the performance of NSGA-II for some randomly generated problems and the preliminary results indicate that the proposed model of the paper performs better than the alternative method.

Multiobjective Reliable Cloud Storage with Its Particle Swarm Optimization Algorithm

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Xiyang Liu

2016-01-01

Full Text Available Information abounds in all fields of the real life, which is often recorded as digital data in computer systems and treated as a kind of increasingly important resource. Its increasing volume growth causes great difficulties in both storage and analysis. The massive data storage in cloud environments has significant impacts on the quality of service (QoS of the systems, which is becoming an increasingly challenging problem. In this paper, we propose a multiobjective optimization model for the reliable data storage in clouds through considering both cost and reliability of the storage service simultaneously. In the proposed model, the total cost is analyzed to be composed of storage space occupation cost, data migration cost, and communication cost. According to the analysis of the storage process, the transmission reliability, equipment stability, and software reliability are taken into account in the storage reliability evaluation. To solve the proposed multiobjective model, a Constrained Multiobjective Particle Swarm Optimization (CMPSO algorithm is designed. At last, experiments are designed to validate the proposed model and its solution PSO algorithm. In the experiments, the proposed model is tested in cooperation with 3 storage strategies. Experimental results show that the proposed model is positive and effective. The experimental results also demonstrate that the proposed model can perform much better in alliance with proper file splitting methods.

An Adaptive Multiobjective Particle Swarm Optimization Based on Multiple Adaptive Methods.

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Han, Honggui; Lu, Wei; Qiao, Junfei

2017-09-01

Multiobjective particle swarm optimization (MOPSO) algorithms have attracted much attention for their promising performance in solving multiobjective optimization problems (MOPs). In this paper, an adaptive MOPSO (AMOPSO) algorithm, based on a hybrid framework of the solution distribution entropy and population spacing (SP) information, is developed to improve the search performance in terms of convergent speed and precision. First, an adaptive global best (gBest) selection mechanism, based on the solution distribution entropy, is introduced to analyze the evolutionary tendency and balance the diversity and convergence of nondominated solutions in the archive. Second, an adaptive flight parameter adjustment mechanism, using the population SP information, is proposed to obtain the distribution of particles with suitable diversity and convergence, which can balance the global exploration and local exploitation abilities of the particles. Third, based on the gBest selection mechanism and the adaptive flight parameter mechanism, this proposed AMOPSO algorithm not only has high accuracy, but also attain a set of optimal solutions with better diversity. Finally, the performance of the proposed AMOPSO algorithm is validated and compared with other five state-of-the-art algorithms on a number of benchmark problems and water distribution system. The experimental results validate the effectiveness of the proposed AMOPSO algorithm, as well as demonstrate that AMOPSO outperforms other MOPSO algorithms in solving MOPs.

Multi-objective particle swarm and genetic algorithm for the optimization of the LANSCE linac operation

International Nuclear Information System (INIS)

Pang, X.; Rybarcyk, L.J.

2014-01-01

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

Multi-objective particle swarm and genetic algorithm for the optimization of the LANSCE linac operation

Energy Technology Data Exchange (ETDEWEB)

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.

Particle Swarm Optimization Toolbox

Science.gov (United States)

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

Power System Stabilizer Design Based on a Particle Swarm Optimization Multiobjective Function Implemented Under Graphical Interface

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Ghouraf Djamel Eddine

2016-05-01

Full Text Available Power system stability considered a necessary condition for normal functioning of an electrical network. The role of regulation and control systems is to ensure that stability by determining the essential elements that influence it. This paper proposes a Particle Swarm Optimization (PSO based multiobjective function to tuning optimal parameters of Power System Stabilizer (PSS; this later is used as auxiliary to generator excitation system in order to damp electro mechanicals oscillations of the rotor and consequently improve Power system stability. The computer simulation results obtained by developed graphical user interface (GUI have proved the efficiency of PSS optimized by a Particle Swarm Optimization, in comparison with a conventional PSS, showing stable   system   responses   almost   insensitive   to   large parameter variations.Our present study was performed using a GUI realized under MATLAB in our work.

Multi-Objective Particle Swarm Optimization Approach for Cost-Based Feature Selection in Classification.

Science.gov (United States)

Zhang, Yong; Gong, Dun-Wei; Cheng, Jian

2017-01-01

Feature selection is an important data-preprocessing technique in classification problems such as bioinformatics and signal processing. Generally, there are some situations where a user is interested in not only maximizing the classification performance but also minimizing the cost that may be associated with features. This kind of problem is called cost-based feature selection. However, most existing feature selection approaches treat this task as a single-objective optimization problem. This paper presents the first study of multi-objective particle swarm optimization (PSO) for cost-based feature selection problems. The task of this paper is to generate a Pareto front of nondominated solutions, that is, feature subsets, to meet different requirements of decision-makers in real-world applications. In order to enhance the search capability of the proposed algorithm, a probability-based encoding technology and an effective hybrid operator, together with the ideas of the crowding distance, the external archive, and the Pareto domination relationship, are applied to PSO. The proposed PSO-based multi-objective feature selection algorithm is compared with several multi-objective feature selection algorithms on five benchmark datasets. Experimental results show that the proposed algorithm can automatically evolve a set of nondominated solutions, and it is a highly competitive feature selection method for solving cost-based feature selection problems.

Multi-Objective Optimization of Squeeze Casting Process using Genetic Algorithm and Particle Swarm Optimization

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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.

Set-Based Discrete Particle Swarm Optimization Based on Decomposition for Permutation-Based Multiobjective Combinatorial Optimization Problems.

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Yu, Xue; Chen, Wei-Neng; Gu, Tianlong; Zhang, Huaxiang; Yuan, Huaqiang; Kwong, Sam; Zhang, Jun

2017-08-07

This paper studies a specific class of multiobjective combinatorial optimization problems (MOCOPs), namely the permutation-based MOCOPs. Many commonly seen MOCOPs, e.g., multiobjective traveling salesman problem (MOTSP), multiobjective project scheduling problem (MOPSP), belong to this problem class and they can be very different. However, as the permutation-based MOCOPs share the inherent similarity that the structure of their search space is usually in the shape of a permutation tree, this paper proposes a generic multiobjective set-based particle swarm optimization methodology based on decomposition, termed MS-PSO/D. In order to coordinate with the property of permutation-based MOCOPs, MS-PSO/D utilizes an element-based representation and a constructive approach. Through this, feasible solutions under constraints can be generated step by step following the permutation-tree-shaped structure. And problem-related heuristic information is introduced in the constructive approach for efficiency. In order to address the multiobjective optimization issues, the decomposition strategy is employed, in which the problem is converted into multiple single-objective subproblems according to a set of weight vectors. Besides, a flexible mechanism for diversity control is provided in MS-PSO/D. Extensive experiments have been conducted to study MS-PSO/D on two permutation-based MOCOPs, namely the MOTSP and the MOPSP. Experimental results validate that the proposed methodology is promising.

Design for sustainability of industrial symbiosis based on emergy and multi-objective particle swarm optimization.

Science.gov (United States)

Ren, Jingzheng; Liang, Hanwei; Dong, Liang; Sun, Lu; Gao, Zhiqiu

2016-08-15

Industrial symbiosis provides novel and practical pathway to the design for the sustainability. Decision support tool for its verification is necessary for practitioners and policy makers, while to date, quantitative research is limited. The objective of this work is to present an innovative approach for supporting decision-making in the design for the sustainability with the implementation of industrial symbiosis in chemical complex. Through incorporating the emergy theory, the model is formulated as a multi-objective approach that can optimize both the economic benefit and sustainable performance of the integrated industrial system. A set of emergy based evaluation index are designed. Multi-objective Particle Swarm Algorithm is proposed to solve the model, and the decision-makers are allowed to choose the suitable solutions form the Pareto solutions. An illustrative case has been studied by the proposed method, a few of compromises between high profitability and high sustainability can be obtained for the decision-makers/stakeholders to make decision. Copyright © 2016 Elsevier B.V. All rights reserved.

Prototype Generation Using Multiobjective Particle Swarm Optimization for Nearest Neighbor Classification.

Science.gov (United States)

Hu, Weiwei; Tan, Ying

2016-12-01

The nearest neighbor (NN) classifier suffers from high time complexity when classifying a test instance since the need of searching the whole training set. Prototype generation is a widely used approach to reduce the classification time, which generates a small set of prototypes to classify a test instance instead of using the whole training set. In this paper, particle swarm optimization is applied to prototype generation and two novel methods for improving the classification performance are presented: 1) a fitness function named error rank and 2) the multiobjective (MO) optimization strategy. Error rank is proposed to enhance the generation ability of the NN classifier, which takes the ranks of misclassified instances into consideration when designing the fitness function. The MO optimization strategy pursues the performance on multiple subsets of data simultaneously, in order to keep the classifier from overfitting the training set. Experimental results over 31 UCI data sets and 59 additional data sets show that the proposed algorithm outperforms nearly 30 existing prototype generation algorithms.

Cancer microarray data feature selection using multi-objective binary particle swarm optimization algorithm

Science.gov (United States)

Annavarapu, Chandra Sekhara Rao; Dara, Suresh; Banka, Haider

2016-01-01

Cancer investigations in microarray data play a major role in cancer analysis and the treatment. Cancer microarray data consists of complex gene expressed patterns of cancer. In this article, a Multi-Objective Binary Particle Swarm Optimization (MOBPSO) algorithm is proposed for analyzing cancer gene expression data. Due to its high dimensionality, a fast heuristic based pre-processing technique is employed to reduce some of the crude domain features from the initial feature set. Since these pre-processed and reduced features are still high dimensional, the proposed MOBPSO algorithm is used for finding further feature subsets. The objective functions are suitably modeled by optimizing two conflicting objectives i.e., cardinality of feature subsets and distinctive capability of those selected subsets. As these two objective functions are conflicting in nature, they are more suitable for multi-objective modeling. The experiments are carried out on benchmark gene expression datasets, i.e., Colon, Lymphoma and Leukaemia available in literature. The performance of the selected feature subsets with their classification accuracy and validated using 10 fold cross validation techniques. A detailed comparative study is also made to show the betterment or competitiveness of the proposed algorithm. PMID:27822174

Optimal Allocation of Generalized Power Sources in Distribution Network Based on Multi-Objective Particle Swarm Optimization Algorithm

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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.

Optimal configuration of power grid sources based on optimal particle swarm algorithm

Science.gov (United States)

Wen, Yuanhua

2018-04-01

In order to optimize the distribution problem of power grid sources, an optimized particle swarm optimization algorithm is proposed. First, the concept of multi-objective optimization and the Pareto solution set are enumerated. Then, the performance of the classical genetic algorithm, the classical particle swarm optimization algorithm and the improved particle swarm optimization algorithm are analyzed. The three algorithms are simulated respectively. Compared with the test results of each algorithm, the superiority of the algorithm in convergence and optimization performance is proved, which lays the foundation for subsequent micro-grid power optimization configuration solution.

Multi-Objective Sustainable Operation of the Three Gorges Cascaded Hydropower System Using Multi-Swarm Comprehensive Learning Particle Swarm Optimization

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Xiang Yu

2016-06-01

Full Text Available Optimal operation of hydropower reservoir systems often needs to optimize multiple conflicting objectives simultaneously. The conflicting objectives result in a Pareto front, which is a set of non-dominated solutions. Non-dominated solutions cannot outperform each other on all the objectives. An optimization framework based on the multi-swarm comprehensive learning particle swarm optimization algorithm is proposed to solve the multi-objective operation of hydropower reservoir systems. Through adopting search techniques such as decomposition, mutation and differential evolution, the algorithm tries to derive multiple non-dominated solutions reasonably distributed over the true Pareto front in one single run, thereby facilitating determining the final tradeoff. The long-term sustainable planning of the Three Gorges cascaded hydropower system consisting of the Three Gorges Dam and Gezhouba Dam located on the Yangtze River in China is studied. Two conflicting objectives, i.e., maximizing hydropower generation and minimizing deviation from the outflow lower target to realize the system’s economic, environmental and social benefits during the drought season, are optimized simultaneously. Experimental results demonstrate that the optimization framework helps to robustly derive multiple feasible non-dominated solutions with satisfactory convergence, diversity and extremity in one single run for the case studied.

Multi-Objective Climb Path Optimization for Aircraft/Engine Integration Using Particle Swarm Optimization

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Aristeidis Antonakis

2017-04-01

Full Text Available In this article, a new multi-objective approach to the aircraft climb path optimization problem, based on the Particle Swarm Optimization algorithm, is introduced to be used for aircraft–engine integration studies. This considers a combination of a simulation with a traditional Energy approach, which incorporates, among others, the use of a proposed path-tracking scheme for guidance in the Altitude–Mach plane. The adoption of population-based solver serves to simplify case setup, allowing for direct interfaces between the optimizer and aircraft/engine performance codes. A two-level optimization scheme is employed and is shown to improve search performance compared to the basic PSO algorithm. The effectiveness of the proposed methodology is demonstrated in a hypothetic engine upgrade scenario for the F-4 aircraft considering the replacement of the aircraft’s J79 engine with the EJ200; a clear advantage of the EJ200-equipped configuration is unveiled, resulting, on average, in 15% faster climbs with 20% less fuel.

Optimal design of permanent magnet flux switching generator for wind applications via artificial neural network and multi-objective particle swarm optimization hybrid approach

International Nuclear Information System (INIS)

Meo, Santolo; Zohoori, Alireza; Vahedi, Abolfazl

2016-01-01

Highlights: • A new optimal design of flux switching permanent magnet generator is developed. • A prototype is employed to validate numerical data used for optimization. • A novel hybrid multi-objective particle swarm optimization approach is proposed. • Optimization targets are weight, cost, voltage and its total harmonic distortion. • The hybrid approach preference is proved compared with other optimization methods. - Abstract: In this paper a new hybrid approach obtained combining a multi-objective particle swarm optimization and artificial neural network is proposed for the design optimization of a direct-drive permanent magnet flux switching generators for low power wind applications. The targets of the proposed multi-objective optimization are to reduce the costs and weight of the machine while maximizing the amplitude of the induced voltage as well as minimizing its total harmonic distortion. The permanent magnet width, the stator and rotor tooth width, the rotor teeth number and stator pole number of the machine define the search space for the optimization problem. Four supervised artificial neural networks are designed for modeling the complex relationships among the weight, the cost, the amplitude and the total harmonic distortion of the output voltage respect to the quantities of the search space. Finite element analysis is adopted to generate training dataset for the artificial neural networks. Finite element analysis based model is verified by experimental results with a 1.5 kW permanent magnet flux switching generator prototype suitable for renewable energy applications, having 6/19 stator poles/rotor teeth. Finally the effectiveness of the proposed hybrid procedure is compared with the results given by conventional multi-objective optimization algorithms. The obtained results show the soundness of the proposed multi objective optimization technique and its feasibility to be adopted as suitable methodology for optimal design of permanent

Implementing of the multi-objective particle swarm optimizer and fuzzy decision-maker in exergetic, exergoeconomic and environmental optimization of a benchmark cogeneration system

International Nuclear Information System (INIS)

Sayyaadi, Hoseyn; Babaie, Meisam; Farmani, Mohammad Reza

2011-01-01

Multi-objective optimization for design of a benchmark cogeneration system namely as the CGAM cogeneration system is performed. In optimization approach, Exergetic, Exergoeconomic and Environmental objectives are considered, simultaneously. In this regard, the set of Pareto optimal solutions known as the Pareto frontier is obtained using the MOPSO (multi-objective particle swarm optimizer). The exergetic efficiency as an exergetic objective is maximized while the unit cost of the system product and the cost of the environmental impact respectively as exergoeconomic and environmental objectives are minimized. Economic model which is utilized in the exergoeconomic analysis is built based on both simple model (used in original researches of the CGAM system) and the comprehensive modeling namely as TTR (total revenue requirement) method (used in sophisticated exergoeconomic analysis). Finally, a final optimal solution from optimal set of the Pareto frontier is selected using a fuzzy decision-making process based on the Bellman-Zadeh approach and results are compared with corresponding results obtained in a traditional decision-making process. Further, results are compared with the corresponding performance of the base case CGAM system and optimal designs of previous works and discussed. -- Highlights: → A multi-objective optimization approach has been implemented in optimization of a benchmark cogeneration system. → Objective functions based on the environmental impact evaluation, thermodynamic and economic analysis are obtained and optimized. → Particle swarm optimizer implemented and its robustness is compared with NSGA-II. → A final optimal configuration is found using various decision-making approaches. → Results compared with previous works in the field.

Nonlinear dynamics optimization with particle swarm and genetic algorithms for SPEAR3 emittance upgrade

International Nuclear Information System (INIS)

Huang, Xiaobiao; Safranek, James

2014-01-01

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

Nonlinear dynamics optimization with particle swarm and genetic algorithms for SPEAR3 emittance upgrade

Energy Technology Data Exchange (ETDEWEB)

Huang, Xiaobiao, E-mail: xiahuang@slac.stanford.edu; 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.

Multi-objective flexible job-shop scheduling problem using modified discrete particle swarm optimization.

Science.gov (United States)

Huang, Song; Tian, Na; Wang, Yan; Ji, Zhicheng

2016-01-01

Taking resource allocation into account, flexible job shop problem (FJSP) is a class of complex scheduling problem in manufacturing system. In order to utilize the machine resources rationally, multi-objective particle swarm optimization (MOPSO) integrating with variable neighborhood search is introduced to address FJSP efficiently. Firstly, the assignment rules (AL) and dispatching rules (DR) are provided to initialize the population. And then special discrete operators are designed to produce new individuals and earliest completion machine (ECM) is adopted in the disturbance operator to escape the optima. Secondly, personal-best archives (cognitive memories) and global-best archive (social memory), which are updated by the predefined non-dominated archive update strategy, are simultaneously designed to preserve non-dominated individuals and select personal-best positions and the global-best position. Finally, three neighborhoods are provided to search the neighborhoods of global-best archive for enhancing local search ability. The proposed algorithm is evaluated by using Kacem instances and Brdata instances, and a comparison with other approaches shows the effectiveness of the proposed algorithm for FJSP.

Optimal Tuning of Decentralized PI Controller of Nonlinear Multivariable Process Using Archival Based Multiobjective Particle Swarm Optimization

Directory of Open Access Journals (Sweden)

R. Kotteeswaran

2014-01-01

Full Text Available A Multiobjective Particle Swarm Optimization (MOPSO algorithm is proposed to fine-tune the baseline PI controller parameters of Alstom gasifier. The existing baseline PI controller is not able to meet the performance requirements of Alstom gasifier for sinusoidal pressure disturbance at 0% load. This is considered the major drawback of controller design. A best optimal solution for Alstom gasifier is obtained from a set of nondominated solutions using MOPSO algorithm. Performance of gasifier is investigated at all load conditions. The controller with optimized controller parameters meets all the performance requirements at 0%, 50%, and 100% load conditions. The investigations are also extended for variations in coal quality, which shows an improved stability of the gasifier over a wide range of coal quality variations.

Particle Swarm Optimization to the U-tube steam generator in the nuclear power plant

International Nuclear Information System (INIS)

Ibrahim, Wesam Zakaria

2014-01-01

Highlights: • We establish stability mathematical model of steam generator and reactor core. • We propose a new Particle Swarm Optimization algorithm. • The algorithm can overcome premature phenomenon and has a high search precision. • Optimal weight of steam generator is 15.1% less than the original. • Sensitivity analysis and optimal design provide reference for steam generator design. - Abstract: This paper, proposed an improved Particle Swarm Optimization approach for optimize a U-tube steam generator mathematical model. The UTSG is one of the most important component related to safety of most of the pressurized water reactor. The purpose of this article is to present an approach to optimization in which every target is considered as a separate objective to be optimized. Multi-objective optimization is a powerful tool for resolving conflicting objectives in engineering design and numerous other fields. One approach to solve multi-objective optimization problems is the non-dominated sorting Particle Swarm Optimization. PSO was applied in regarding the choice of the time intervals for the periodic testing of the model of the steam generator

Particle Swarm Optimization to the U-tube steam generator in the nuclear power plant

Energy Technology Data Exchange (ETDEWEB)

Ibrahim, Wesam Zakaria, E-mail: mimi9_m@yahoo.com

2014-12-15

Highlights: • We establish stability mathematical model of steam generator and reactor core. • We propose a new Particle Swarm Optimization algorithm. • The algorithm can overcome premature phenomenon and has a high search precision. • Optimal weight of steam generator is 15.1% less than the original. • Sensitivity analysis and optimal design provide reference for steam generator design. - Abstract: This paper, proposed an improved Particle Swarm Optimization approach for optimize a U-tube steam generator mathematical model. The UTSG is one of the most important component related to safety of most of the pressurized water reactor. The purpose of this article is to present an approach to optimization in which every target is considered as a separate objective to be optimized. Multi-objective optimization is a powerful tool for resolving conflicting objectives in engineering design and numerous other fields. One approach to solve multi-objective optimization problems is the non-dominated sorting Particle Swarm Optimization. PSO was applied in regarding the choice of the time intervals for the periodic testing of the model of the steam generator.

Enhanced Multi-Objective Energy Optimization by a Signaling Method

OpenAIRE

Soares, João; Borges, Nuno; Vale, Zita; Oliveira, P.B.

2016-01-01

In this paper three metaheuristics are used to solve a smart grid multi-objective energy management problem with conflictive design: how to maximize profits and minimize carbon dioxide (CO2) emissions, and the results compared. The metaheuristics implemented are: weighted particle swarm optimization (W-PSO), multi-objective particle swarm optimization (MOPSO) and non-dominated sorting genetic algorithm II (NSGA-II). The performance of these methods with the use of multi-dimensi...

An Adaptive Multi-Objective Particle Swarm Optimization Algorithm for Multi-Robot Path Planning

Directory of Open Access Journals (Sweden)

Nizar Hadi Abbas

2016-07-01

Full Text Available This paper discusses an optimal path planning algorithm based on an Adaptive Multi-Objective Particle Swarm Optimization Algorithm (AMOPSO for two case studies. First case, single robot wants to reach a goal in the static environment that contain two obstacles and two danger source. The second one, is improving the ability for five robots to reach the shortest way. The proposed algorithm solves the optimization problems for the first case by finding the minimum distance from initial to goal position and also ensuring that the generated path has a maximum distance from the danger zones. And for the second case, finding the shortest path for every robot and without any collision between them with the shortest time. In order to evaluate the proposed algorithm in term of finding the best solution, six benchmark test functions are used to make a comparison between AMOPSO and the standard MOPSO. The results show that the AMOPSO has a better ability to get away from local optimums with a quickest convergence than the MOPSO. The simulation results using Matlab 2014a, indicate that this methodology is extremely valuable for every robot in multi-robot framework to discover its own particular proper pa‌th from the start to the destination position with minimum distance and time.

Portfolio Optimization Using Particle Swarms with Stripes

Directory of Open Access Journals (Sweden)

Mario Villalobos Arias

2011-04-01

Full Text Available In this paper it is consider the Portfolio Optimization Problem developed by Markowitz [11]. The basic assumption is that the investor tries to maximize his/her profit and at the same time, wants to minimize the risk. This problem is usually solved using a scalarization approach (with one objective. Here it is solved it as a bi-objective  optimization problem. It uses a new version of the algorithm of Particle Swarm Optimization for Multi-Objective Problems to which it implemented a method of the stripes to improve dispersion.

Particle swarm optimization for programming deep brain stimulation arrays.

Science.gov (United States)

Peña, Edgar; Zhang, Simeng; Deyo, Steve; Xiao, YiZi; Johnson, Matthew D

2017-02-01

Deep brain stimulation (DBS) therapy relies on both precise neurosurgical targeting and systematic optimization of stimulation settings to achieve beneficial clinical outcomes. One recent advance to improve targeting is the development of DBS arrays (DBSAs) with electrodes segmented both along and around the DBS lead. However, increasing the number of independent electrodes creates the logistical challenge of optimizing stimulation parameters efficiently. Solving such complex problems with multiple solutions and objectives is well known to occur in biology, in which complex collective behaviors emerge out of swarms of individual organisms engaged in learning through social interactions. Here, we developed a particle swarm optimization (PSO) algorithm to program DBSAs using a swarm of individual particles representing electrode configurations and stimulation amplitudes. Using a finite element model of motor thalamic DBS, we demonstrate how the PSO algorithm can efficiently optimize a multi-objective function that maximizes predictions of axonal activation in regions of interest (ROI, cerebellar-receiving area of motor thalamus), minimizes predictions of axonal activation in regions of avoidance (ROA, somatosensory thalamus), and minimizes power consumption. The algorithm solved the multi-objective problem by producing a Pareto front. ROI and ROA activation predictions were consistent across swarms (<1% median discrepancy in axon activation). The algorithm was able to accommodate for (1) lead displacement (1 mm) with relatively small ROI (⩽9.2%) and ROA (⩽1%) activation changes, irrespective of shift direction; (2) reduction in maximum per-electrode current (by 50% and 80%) with ROI activation decreasing by 5.6% and 16%, respectively; and (3) disabling electrodes (n  =  3 and 12) with ROI activation reduction by 1.8% and 14%, respectively. Additionally, comparison between PSO predictions and multi-compartment axon model simulations showed discrepancies

Particle Swarm Optimization for Programming Deep Brain Stimulation Arrays

Science.gov (United States)

Peña, Edgar; Zhang, Simeng; Deyo, Steve; Xiao, YiZi; Johnson, Matthew D.

2017-01-01

Objective Deep brain stimulation (DBS) therapy relies on both precise neurosurgical targeting and systematic optimization of stimulation settings to achieve beneficial clinical outcomes. One recent advance to improve targeting is the development of DBS arrays (DBSAs) with electrodes segmented both along and around the DBS lead. However, increasing the number of independent electrodes creates the logistical challenge of optimizing stimulation parameters efficiently. Approach Solving such complex problems with multiple solutions and objectives is well known to occur in biology, in which complex collective behaviors emerge out of swarms of individual organisms engaged in learning through social interactions. Here, we developed a particle swarm optimization (PSO) algorithm to program DBSAs using a swarm of individual particles representing electrode configurations and stimulation amplitudes. Using a finite element model of motor thalamic DBS, we demonstrate how the PSO algorithm can efficiently optimize a multi-objective function that maximizes predictions of axonal activation in regions of interest (ROI, cerebellar-receiving area of motor thalamus), minimizes predictions of axonal activation in regions of avoidance (ROA, somatosensory thalamus), and minimizes power consumption. Main Results The algorithm solved the multi-objective problem by producing a Pareto front. ROI and ROA activation predictions were consistent across swarms (<1% median discrepancy in axon activation). The algorithm was able to accommodate for (1) lead displacement (1 mm) with relatively small ROI (≤9.2%) and ROA (≤1%) activation changes, irrespective of shift direction; (2) reduction in maximum per-electrode current (by 50% and 80%) with ROI activation decreasing by 5.6% and 16%, respectively; and (3) disabling electrodes (n=3 and 12) with ROI activation reduction by 1.8% and 14%, respectively. Additionally, comparison between PSO predictions and multi-compartment axon model simulations

Particle Swarm Optimization

Science.gov (United States)

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.

Quantum Behaved Particle Swarm Optimization Algorithm Based on Artificial Fish Swarm

OpenAIRE

Yumin, Dong; Li, Zhao

2014-01-01

Quantum behaved particle swarm algorithm is a new intelligent optimization algorithm; the algorithm has less parameters and is easily implemented. In view of the existing quantum behaved particle swarm optimization algorithm for the premature convergence problem, put forward a quantum particle swarm optimization algorithm based on artificial fish swarm. The new algorithm based on quantum behaved particle swarm algorithm, introducing the swarm and following activities, meanwhile using the a...

Design optimization of shell-and-tube heat exchangers using single objective and multiobjective particle swarm optimization

International Nuclear Information System (INIS)

Elsays, Mostafa A.; Naguib Aly, M; Badawi, Alya A.

2010-01-01

The Particle Swarm Optimization (PSO) algorithm is used to optimize the design of shell-and-tube heat exchangers and determine the optimal feasible solutions so as to eliminate trial-and-error during the design process. The design formulation takes into account the area and the total annual cost of heat exchangers as two objective functions together with operating as well as geometrical constraints. The Nonlinear Constrained Single Objective Particle Swarm Optimization (NCSOPSO) algorithm is used to minimize and find the optimal feasible solution for each of the nonlinear constrained objective functions alone, respectively. Then, a novel Nonlinear Constrained Mult-objective Particle Swarm Optimization (NCMOPSO) algorithm is used to minimize and find the Pareto optimal solutions for both of the nonlinear constrained objective functions together. The experimental results show that the two algorithms are very efficient, fast and can find the accurate optimal feasible solutions of the shell and tube heat exchangers design optimization problem. (orig.)

Swarm intelligence for multi-objective optimization of synthesis gas production

Science.gov (United States)

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.

A hybrid multi-objective evolutionary algorithm approach for ...

Indian Academy of Sciences (India)

V K MANUPATI

for handling sequence- and machine-dependent set-up times ... algorithm has been compared to that of multi-objective particle swarm optimization (MOPSO) and conventional ..... position and cognitive learning factor are considered for.

Transport of Particle Swarms Through Fractures

Science.gov (United States)

Boomsma, E.; Pyrak-Nolte, L. J.

2011-12-01

The transport of engineered micro- and nano-scale particles through fractured rock is often assumed to occur as dispersions or emulsions. Another potential transport mechanism is the release of particle swarms from natural or industrial processes where small liquid drops, containing thousands to millions of colloidal-size particles, are released over time from seepage or leaks. Swarms have higher velocities than any individual colloid because the interactions among the particles maintain the cohesiveness of the swarm as it falls under gravity. Thus particle swarms give rise to the possibility that engineered particles may be transported farther and faster in fractures than predicted by traditional dispersion models. In this study, the effect of fractures on colloidal swarm cohesiveness and evolution was studied as a swarm falls under gravity and interacts with fracture walls. Transparent acrylic was used to fabricate synthetic fracture samples with either (1) a uniform aperture or (2) a converging aperture followed by a uniform aperture (funnel-shaped). The samples consisted of two blocks that measured 100 x 100 x 50 mm. The separation between these blocks determined the aperture (0.5 mm to 50 mm). During experiments, a fracture was fully submerged in water and swarms were released into it. The swarms consisted of dilute suspensions of either 25 micron soda-lime glass beads (2% by mass) or 3 micron polystyrene fluorescent beads (1% by mass) with an initial volume of 5μL. The swarms were illuminated with a green (525 nm) LED array and imaged optically with a CCD camera. In the uniform aperture fracture, the speed of the swarm prior to bifurcation increased with aperture up to a maximum at a fracture width of approximately 10 mm. For apertures greater than ~15 mm, the velocity was essentially constant with fracture width (but less than at 10 mm). This peak suggests that two competing mechanisms affect swarm velocity in fractures. The wall provides both drag, which

Symbiosis-Based Alternative Learning Multi-Swarm Particle Swarm Optimization.

Science.gov (United States)

Niu, Ben; Huang, Huali; Tan, Lijing; Duan, Qiqi

2017-01-01

Inspired by the ideas from the mutual cooperation of symbiosis in natural ecosystem, this paper proposes a new variant of PSO, named Symbiosis-based Alternative Learning Multi-swarm Particle Swarm Optimization (SALMPSO). A learning probability to select one exemplar out of the center positions, the local best position, and the historical best position including the experience of internal and external multiple swarms, is used to keep the diversity of the population. Two different levels of social interaction within and between multiple swarms are proposed. In the search process, particles not only exchange social experience with others that are from their own sub-swarms, but also are influenced by the experience of particles from other fellow sub-swarms. According to the different exemplars and learning strategy, this model is instantiated as four variants of SALMPSO and a set of 15 test functions are conducted to compare with some variants of PSO including 10, 30 and 50 dimensions, respectively. Experimental results demonstrate that the alternative learning strategy in each SALMPSO version can exhibit better performance in terms of the convergence speed and optimal values on most multimodal functions in our simulation.

Design of SCADA water resource management control center by a bi-objective redundancy allocation problem and particle swarm optimization

International Nuclear Information System (INIS)

Dolatshahi-Zand, Ali; Khalili-Damghani, Kaveh

2015-01-01

SCADA is an essential system to control critical facilities in big cities. SCADA is utilized in several sectors such as water resource management, power plants, electricity distribution centers, traffic control centers, and gas deputy. The failure of SCADA results in crisis. Hence, the design of SCADA system in order to serve a high reliability considering limited budget and other constraints is essential. In this paper, a bi-objective redundancy allocation problem (RAP) is proposed to design Tehran's SCADA water resource management control center. Reliability maximization and cost minimization are concurrently considered. Since the proposed RAP is a non-linear multi-objective mathematical programming so the exact methods cannot efficiently handle it. A multi-objective particle swarm optimization (MOPSO) algorithm is designed to solve it. Several features such as dynamic parameter tuning, efficient constraint handling and Pareto gridding are inserted in proposed MOPSO. The results of proposed MOPSO are compared with an efficient ε-constraint method. Several non-dominated designs of SCADA system are generated using both methods. Comparison metrics based on accuracy and diversity of Pareto front are calculated for both methods. The proposed MOPSO algorithm reports better performance. Finally, in order to choose the practical design, the TOPSIS algorithm is used to prune the Pareto front. - Highlights: • Multi-objective redundancy allocation problem (MORAP) is proposed to design SCADA system. • Multi-objective particle swarm optimization (MOPSO) is proposed to solve MORAP. • Efficient epsilon-constraint method is adapted to solve MORAP. • Non-dominated solutions are generated on Pareto front of MORAP by both methods. • Several multi-objective metrics are calculated to compare the performance of methods

Distribution Network Expansion Planning Based on Multi-objective PSO Algorithm

DEFF Research Database (Denmark)

Zhang, Chunyu; Ding, Yi; Wu, Qiuwei

2013-01-01

This paper presents a novel approach for electrical distribution network expansion planning using multi-objective particle swarm optimization (PSO). The optimization objectives are: investment and operation cost, energy losses cost, and power congestion cost. A two-phase multi-objective PSO...... algorithm was proposed to solve this optimization problem, which can accelerate the convergence and guarantee the diversity of Pareto-optimal front set as well. The feasibility and effectiveness of both the proposed multi-objective planning approach and the improved multi-objective PSO have been verified...

Particle swarm genetic algorithm and its application

International Nuclear Information System (INIS)

Liu Chengxiang; Yan Changxiang; Wang Jianjun; Liu Zhenhai

2012-01-01

To solve the problems of slow convergence speed and tendency to fall into the local optimum of the standard particle swarm optimization while dealing with nonlinear constraint optimization problem, a particle swarm genetic algorithm is designed. The proposed algorithm adopts feasibility principle handles constraint conditions and avoids the difficulty of penalty function method in selecting punishment factor, generates initial feasible group randomly, which accelerates particle swarm convergence speed, and introduces genetic algorithm crossover and mutation strategy to avoid particle swarm falls into the local optimum Through the optimization calculation of the typical test functions, the results show that particle swarm genetic algorithm has better optimized performance. The algorithm is applied in nuclear power plant optimization, and the optimization results are significantly. (authors)

The influence of swarm deformation on the velocity behavior of falling swarms of particles

Science.gov (United States)

Mitchell, C. A.; Pyrak-Nolte, L. J.; Nitsche, L.

2017-12-01

Cohesive particle swarms have been shown to exhibit enhanced sedimentation in fractures for an optimal range of fracture apertures. Within this range, swarms travel farther and faster than a disperse (particulate) solution. This study aims to uncover the physics underlying the enhanced sedimentation. Swarm behavior at low Reynolds number in a quiescent unbounded fluid and between smooth rigid planar boundaries is investigated numerically using direct-summation, particle-mesh (PM) and particle-particle particle-mesh (P3M) methods - based upon mutually interacting viscous point forces (Stokeslet fields). Wall effects are treated with a least-squares boundary singularity method. Sub-structural effects beyond pseudo-liquid behavior (i.e., particle-scale interactions) are approximated by the P3M method much more efficiently than with direct summation. The model parameters are selected from particle swarm experiments to enable comparison. From the simulations, if the initial swarm geometry at release is unaffected by the fracture aperture, no enhanced transport occurs. The swarm velocity as a function of apertures increases monotonically until it asymptotes to the swarm velocity in an open tank. However, if the fracture aperture affects the initial swarm geometry, the swarm velocity no longer exhibits a monotonic behavior. When swarms are released between two parallel smooth walls with very small apertures, the swarm is forced to reorganize and quickly deform, which results in dramatically reduced swarm velocities. At large apertures, the swarm evolution is similar to that of a swarm in open tank and quickly flattens into a slow speed torus. In the optimal aperture range, the swarm maintains a cohesive unit behaving similarly to a falling sphere. Swarms falling in apertures less than or greater than the optimal aperture range, experience a level of anisotropy that considerably decreases velocities. Unraveling the physics that drives swarm behavior in fractured porous

Particle Swarm Optimization with Double Learning Patterns.

Science.gov (United States)

Shen, Yuanxia; Wei, Linna; Zeng, Chuanhua; Chen, Jian

2016-01-01

Particle Swarm Optimization (PSO) is an effective tool in solving optimization problems. However, PSO usually suffers from the premature convergence due to the quick losing of the swarm diversity. In this paper, we first analyze the motion behavior of the swarm based on the probability characteristic of learning parameters. Then a PSO with double learning patterns (PSO-DLP) is developed, which employs the master swarm and the slave swarm with different learning patterns to achieve a trade-off between the convergence speed and the swarm diversity. The particles in the master swarm and the slave swarm are encouraged to explore search for keeping the swarm diversity and to learn from the global best particle for refining a promising solution, respectively. When the evolutionary states of two swarms interact, an interaction mechanism is enabled. This mechanism can help the slave swarm in jumping out of the local optima and improve the convergence precision of the master swarm. The proposed PSO-DLP is evaluated on 20 benchmark functions, including rotated multimodal and complex shifted problems. The simulation results and statistical analysis show that PSO-DLP obtains a promising performance and outperforms eight PSO variants.

Particle Swarm Optimization with Double Learning Patterns

Science.gov (United States)

Shen, Yuanxia; Wei, Linna; Zeng, Chuanhua; Chen, Jian

2016-01-01

Particle Swarm Optimization (PSO) is an effective tool in solving optimization problems. However, PSO usually suffers from the premature convergence due to the quick losing of the swarm diversity. In this paper, we first analyze the motion behavior of the swarm based on the probability characteristic of learning parameters. Then a PSO with double learning patterns (PSO-DLP) is developed, which employs the master swarm and the slave swarm with different learning patterns to achieve a trade-off between the convergence speed and the swarm diversity. The particles in the master swarm and the slave swarm are encouraged to explore search for keeping the swarm diversity and to learn from the global best particle for refining a promising solution, respectively. When the evolutionary states of two swarms interact, an interaction mechanism is enabled. This mechanism can help the slave swarm in jumping out of the local optima and improve the convergence precision of the master swarm. The proposed PSO-DLP is evaluated on 20 benchmark functions, including rotated multimodal and complex shifted problems. The simulation results and statistical analysis show that PSO-DLP obtains a promising performance and outperforms eight PSO variants. PMID:26858747

Simulation Study of Swarm Intelligence Based on Life Evolution Behavior

Directory of Open Access Journals (Sweden)

Yanmin Liu

2015-01-01

Full Text Available Swarm intelligence (SI is a new evolutionary computation technology, and its performance efficacy is usually affected by each individual behavior in the swarm. According to the genetic and sociological theory, the life evolution behavior process is influenced by the external and internal factors, so the mechanisms of external and internal environment change must be analyzed and explored. Therefore, in this paper, we used the thought of the famous American genetic biologist Morgan, “life = DNA + environment + interaction of environment + gene,” to propose the mutation and crossover operation of DNA fragments by the environmental change to improve the performance efficiency of intelligence algorithms. Additionally, PSO is a random swarm intelligence algorithm with the genetic and sociological property, so we embed the improved mutation and crossover operation to particle swarm optimization (PSO and designed DNA-PSO algorithm to optimize single and multiobjective optimization problems. Simulation experiments in single and multiobjective optimization problems show that the proposed strategies can effectively improve the performance of swarm intelligence.

Bifurcating Particle Swarms in Smooth-Walled Fractures

Science.gov (United States)

Pyrak-Nolte, L. J.; Sun, H.

2010-12-01

Particle swarms can occur naturally or from industrial processes where small liquid drops containing thousands to millions of micron-size to colloidal-size particles are released over time from seepage or leaks into fractured rock. The behavior of these particle swarms as they fall under gravity are affected by particle interactions as well as interactions with the walls of the fractures. In this paper, we present experimental results on the effect of fractures on the cohesiveness of the swarm and the formation of bifurcation structures as they fall under gravity and interact with the fracture walls. A transparent cubic sample (100 mm x 100 mm x 100 mm) containing a synthetic fracture with uniform aperture distributions was optically imaged to quantify the effect of confinement within fractures on particle swarm formation, swarm velocity, and swarm geometry. A fracture with a uniform aperture distribution was fabricated from two polished rectangular prisms of acrylic. A series of experiments were performed to determine how swarm movement and geometry are affected as the walls of the fracture are brought closer together from 50 mm to 1 mm. During the experiments, the fracture was fully saturated with water. We created the swarms using two different particle sizes in dilute suspension (~ 1.0% by mass). The particles were 3 micron diameter fluorescent polymer beads and 25 micron diameter soda-lime glass beads. Experiments were performed using swarms that ranged in size from 5 µl to 60 µl. The swarm behavior was imaged using an optical fluorescent imaging system composed of a CCD camera illuminated by a 100 mW diode-pumped doubled YAG laser. As a swarm falls in an open-tank of water, it forms a torroidal shape that is stable as long as no ambient or background currents exist in the water tank. When a swarm is released into a fracture with an aperture less than 5 mm, the swarm forms the torroidal shape but it is distorted because of the presence of the walls. The

Multiobjective Design of Turbo Injection Mode for Axial Flux Motor in Plastic Injection Molding Machine by Particle Swarm Optimization

Directory of Open Access Journals (Sweden)

Jian-Long Kuo

2015-01-01

Full Text Available This paper proposes a turbo injection mode (TIM for an axial flux motor to apply onto injection molding machine. Since the injection molding machine requires different speed and force parameters setting when finishing a complete injection process. The interleaved winding structure in the motor provides two different injection levels to provide enough injection forces. Two wye-wye windings are designed to switch two control modes conveniently. Wye-wye configuration is used to switch two force levels for the motor. When only one set of wye-winding is energized, field weakening function is achieved. Both of the torque and speed increase under field weakening operation. To achieve two control objectives for torque and speed of the motor, fuzzy based multiple performance characteristics index (MPCI with particle swarm optimization (PSO is used to find out the multiobjective optimal design solution. Both of the torque and speed are expected to be maximal at the same time. Three control factors are selected as studied factors: winding diameter, winding type, and air-gap. Experimental results show that both of the torque and speed increase under the optimal condition. This will provide enough large torque and speed to perform the turbo injection mode in injection process for the injection molding machine.

Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization.

Directory of Open Access Journals (Sweden)

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.

Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization.

Science.gov (United States)

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.

Particle swarm optimization: an alternative in marine propeller optimization?

Science.gov (United States)

Vesting, F.; Bensow, R. E.

2018-01-01

This article deals with improving and evaluating the performance of two evolutionary algorithm approaches for automated engineering design optimization. Here a marine propeller design with constraints on cavitation nuisance is the intended application. For this purpose, the particle swarm optimization (PSO) algorithm is adapted for multi-objective optimization and constraint handling for use in propeller design. Three PSO algorithms are developed and tested for the optimization of four commercial propeller designs for different ship types. The results are evaluated by interrogating the generation medians and the Pareto front development. The same propellers are also optimized utilizing the well established NSGA-II genetic algorithm to provide benchmark results. The authors' PSO algorithms deliver comparable results to NSGA-II, but converge earlier and enhance the solution in terms of constraints violation.

Particle ''swarm'' dynamics in triboelectric systems

International Nuclear Information System (INIS)

Vinay, Stephen J.; Jhon, Myung S.

2001-01-01

Using state-of-the-art flow/particle visualization and animation techniques, the time-dependent statistical distributions of charged-particle ''swarms'' exposed to external fields (both electrostatic and flow) are examined. We found that interparticle interaction and drag forces mainly influenced swarm dispersion in a Lagrangian reference frame, whereas the average particle trajectory was affected primarily by the external electric and flow fields

Chaotic particle swarm optimization with mutation for classification.

Science.gov (United States)

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.

Chaotic Particle Swarm Optimization with Mutation for Classification

Science.gov (United States)

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. PMID:25709937

Particle swarm optimisation classical and quantum perspectives

CERN Document Server

Sun, Jun; Wu, Xiao-Jun

2016-01-01

IntroductionOptimisation Problems and Optimisation MethodsRandom Search TechniquesMetaheuristic MethodsSwarm IntelligenceParticle Swarm OptimisationOverviewMotivationsPSO Algorithm: Basic Concepts and the ProcedureParadigm: How to Use PSO to Solve Optimisation ProblemsSome Harder Examples Some Variants of Particle Swarm Optimisation Why Does the PSO Algorithm Need to Be Improved? Inertia and Constriction-Acceleration Techniques for PSOLocal Best ModelProbabilistic AlgorithmsOther Variants of PSO Quantum-Behaved Particle Swarm Optimisation OverviewMotivation: From Classical Dynamics to Quantum MechanicsQuantum Model: Fundamentals of QPSOQPSO AlgorithmSome Essential ApplicationsSome Variants of QPSOSummary Advanced Topics Behaviour Analysis of Individual ParticlesConvergence Analysis of the AlgorithmTime Complexity and Rate of ConvergenceParameter Selection and PerformanceSummaryIndustrial Applications Inverse Problems for Partial Differential EquationsInverse Problems for Non-Linear Dynamical SystemsOptimal De...

Application of particle swarm optimization in gas turbine engine fuel controller gain tuning

Science.gov (United States)

Montazeri-Gh, M.; Jafari, S.; Ilkhani, M. R.

2012-02-01

This article presents the application of particle swarm optimization (PSO) for gain tuning of the gas turbine engine (GTE) fuel controller. For this purpose, the structure of a fuel controller is firstly designed based on the GTE control requirements and constraints. The controller gains are then tuned by PSO where the tuning process is formulated as an engineering optimization problem. In this study, the response time during engine acceleration and deceleration as well as the engine fuel consumption are considered as the objective functions. A computer simulation is also developed to evaluate the objective values for a single spool GTE. The GTE model employed for the simulation is a Wiener model, the parameters of which are extracted from experimental tests. In addition, the effect of neighbour acceleration on PSO results is studied. The results show that the neighbour acceleration factor has a considerable effect on the convergence rate of the PSO process. The PSO results are also compared with the results obtained through a genetic algorithm (GA) to show the relative merits of PSO. Moreover, the PSO results are compared with the results obtained from the dynamic programming (DP) method in order to illustrate the ability of proposed method in finding the global optimal solution. Furthermore, the objective function is also defined in multi-objective manner and the multi-objective particle swarm optimization (MOPSO) is applied to find the Pareto-front for the problem. Finally, the results obtained from the simulation of the optimized controller confirm the effectiveness of the proposed approach to design an optimal fuel controller resulting in an improved GTE performance as well as protection against the physical limitations.

Discrete particle swarm optimization to solve multi-objective limited-wait hybrid flow shop scheduling problem

Science.gov (United States)

Santosa, B.; Siswanto, N.; Fiqihesa

2018-04-01

This paper proposes a discrete Particle Swam Optimization (PSO) to solve limited-wait hybrid flowshop scheduing problem with multi objectives. Flow shop schedulimg represents the condition when several machines are arranged in series and each job must be processed at each machine with same sequence. The objective functions are minimizing completion time (makespan), total tardiness time, and total machine idle time. Flow shop scheduling model always grows to cope with the real production system accurately. Since flow shop scheduling is a NP-Hard problem then the most suitable method to solve is metaheuristics. One of metaheuristics algorithm is Particle Swarm Optimization (PSO), an algorithm which is based on the behavior of a swarm. Originally, PSO was intended to solve continuous optimization problems. Since flow shop scheduling is a discrete optimization problem, then, we need to modify PSO to fit the problem. The modification is done by using probability transition matrix mechanism. While to handle multi objectives problem, we use Pareto Optimal (MPSO). The results of MPSO is better than the PSO because the MPSO solution set produced higher probability to find the optimal solution. Besides the MPSO solution set is closer to the optimal solution

Transport of Particle Swarms Through Variable Aperture Fractures

Science.gov (United States)

Boomsma, E.; Pyrak-Nolte, L. J.

2012-12-01

Particle transport through fractured rock is a key concern with the increased use of micro- and nano-size particles in consumer products as well as from other activities in the sub- and near surface (e.g. mining, industrial waste, hydraulic fracturing, etc.). While particle transport is often studied as the transport of emulsions or dispersions, particles may also enter the subsurface from leaks or seepage that lead to particle swarms. Swarms are drop-like collections of millions of colloidal-sized particles that exhibit a number of unique characteristics when compared to dispersions and emulsions. Any contaminant or engineered particle that forms a swarm can be transported farther, faster, and more cohesively in fractures than would be expected from a traditional dispersion model. In this study, the effects of several variable aperture fractures on colloidal swarm cohesiveness and evolution were studied as a swarm fell under gravity and interacted with the fracture walls. Transparent acrylic was used to fabricate synthetic fracture samples with (1) a uniform aperture, (2) a converging region followed by a uniform region (funnel shaped), (3) a uniform region followed by a diverging region (inverted funnel), and (4) a cast of a an induced fracture from a carbonate rock. All of the samples consisted of two blocks that measured 100 x 100 x 50 mm. The minimum separation between these blocks determined the nominal aperture (0.5 mm to 20 mm). During experiments a fracture was fully submerged in water and swarms were released into it. The swarms consisted of a dilute suspension of 3 micron polystyrene fluorescent beads (1% by mass) with an initial volume of 5μL. The swarms were illuminated with a green (525 nm) LED array and imaged optically with a CCD camera. The variation in fracture aperture controlled swarm behavior. Diverging apertures caused a sudden loss of confinement that resulted in a rapid change in the swarm's shape as well as a sharp increase in its velocity

Improved quantum-behaved particle swarm optimization with local search strategy

Directory of Open Access Journals (Sweden)

Maolong Xi

2017-03-01

Full Text Available Quantum-behaved particle swarm optimization, which was motivated by analysis of particle swarm optimization and quantum system, has shown compared performance in finding the optimal solutions for many optimization problems to other evolutionary algorithms. To address the problem of premature, a local search strategy is proposed to improve the performance of quantum-behaved particle swarm optimization. In proposed local search strategy, a super particle is presented which is a collection body of randomly selected particles’ dimension information in the swarm. The selected probability of particles in swarm is different and determined by their fitness values. To minimization problems, the fitness value of one particle is smaller; the selected probability is more and will contribute more information in constructing the super particle. In addition, in order to investigate the influence on algorithm performance with different local search space, four methods of computing the local search radius are applied in local search strategy and propose four variants of local search quantum-behaved particle swarm optimization. Empirical studies on a suite of well-known benchmark functions are undertaken in order to make an overall performance comparison among the proposed methods and other quantum-behaved particle swarm optimization. The simulation results show that the proposed quantum-behaved particle swarm optimization variants have better advantages over the original quantum-behaved particle swarm optimization.

Mobility Aware Energy Efficient Clustering for MANET: A Bio-Inspired Approach with Particle Swarm Optimization

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Naghma Khatoon

2017-01-01

Full Text Available Mobility awareness and energy efficiency are two indispensable optimization problems in mobile ad hoc networks (MANETs where nodes move unpredictably in any direction with restricted battery life, resulting in frequent change in topology. These constraints are widely studied to increase the lifetime of such networks. This paper focuses on the problems of mobility as well as energy efficiency to develop a clustering algorithm inspired by multiagent stochastic parallel search technique of particle swarm optimization. The election of cluster heads takes care of mobility and remaining energy as well as the degree of connectivity for selecting nodes to serve as cluster heads for longer duration of time. The cluster formation is presented by taking multiobjective fitness function using particle swarm optimization. The proposed work is experimented extensively in the NS-2 network simulator and compared with the other existing algorithms. The results show the effectiveness of our proposed algorithm in terms of network lifetime, average number of clusters formed, average number of reclustering required, energy consumption, and packet delivery ratio.

Optimization of Multipurpose Reservoir Operation with Application Particle Swarm Optimization Algorithm

Directory of Open Access Journals (Sweden)

Elahe Fallah Mehdipour

2012-12-01

Full Text Available Optimal operation of multipurpose reservoirs is one of the complex and sometimes nonlinear problems in the field of multi-objective optimization. Evolutionary algorithms are optimization tools that search decision space using simulation of natural biological evolution and present a set of points as the optimum solutions of problem. In this research, application of multi-objective particle swarm optimization (MOPSO in optimal operation of Bazoft reservoir with different objectives, including generating hydropower energy, supplying downstream demands (drinking, industry and agriculture, recreation and flood control have been considered. In this regard, solution sets of the MOPSO algorithm in bi-combination of objectives and compromise programming (CP using different weighting and power coefficients have been first compared that the MOPSO algorithm in all combinations of objectives is more capable than the CP to find solution with appropriate distribution and these solutions have dominated the CP solutions. Then, ending points of solution set from the MOPSO algorithm and nonlinear programming (NLP results have been compared. Results showed that the MOPSO algorithm with 0.3 percent difference from the NLP results has more capability to present optimum solutions in the ending points of solution set.

Simultaneous Perturbation Particle Swarm Optimization and Its FPGA Implementation

OpenAIRE

Maeda, Yutaka; Matsushita, Naoto

2009-01-01

In this paper, we presented hardware implementation of the particle swarm optimization algorithm which is combination of the ordinary particle swarm optimization and the simultaneous perturbation method. FPGA is used to realize the system. This algorithm utilizes local information of objective function effectively without lack of advantage of the original particle swarm optimization. Moreover, the FPGA implementation gives higher operation speed effectively using parallelism of the particle s...

Multi-objective Reactive Power Optimization Based on Improved Particle Swarm Algorithm

Science.gov (United States)

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.

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......, in problems with many local optima, such focus often leads to premature convergence that precludes reaching the intended objective. To remedy this problem in certain types of domains, this paper introduces Novelty-driven Particle Swarm Optimization (NdPSO), which is motivated by the novelty search algorithm...

Markerless human motion tracking using hierarchical multi-swarm cooperative particle swarm optimization.

Science.gov (United States)

Saini, Sanjay; Zakaria, Nordin; Rambli, Dayang Rohaya Awang; Sulaiman, Suziah

2015-01-01

The high-dimensional search space involved in markerless full-body articulated human motion tracking from multiple-views video sequences has led to a number of solutions based on metaheuristics, the most recent form of which is Particle Swarm Optimization (PSO). However, the classical PSO suffers from premature convergence and it is trapped easily into local optima, significantly affecting the tracking accuracy. To overcome these drawbacks, we have developed a method for the problem based on Hierarchical Multi-Swarm Cooperative Particle Swarm Optimization (H-MCPSO). The tracking problem is formulated as a non-linear 34-dimensional function optimization problem where the fitness function quantifies the difference between the observed image and a projection of the model configuration. Both the silhouette and edge likelihoods are used in the fitness function. Experiments using Brown and HumanEva-II dataset demonstrated that H-MCPSO performance is better than two leading alternative approaches-Annealed Particle Filter (APF) and Hierarchical Particle Swarm Optimization (HPSO). Further, the proposed tracking method is capable of automatic initialization and self-recovery from temporary tracking failures. Comprehensive experimental results are presented to support the claims.

Particle Swarm Optimization With Interswarm Interactive Learning Strategy.

Science.gov (United States)

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.

Particle Swarm Transport through Immiscible Fluid Layers in a Fracture

Science.gov (United States)

Teasdale, N. D.; Boomsma, E.; Pyrak-Nolte, L. J.

2011-12-01

Immiscible fluids occur either naturally (e.g. oil & water) or from anthropogenic processes (e.g. liquid CO2 & water) in the subsurface and complicate the transport of natural or engineered micro- or nano-scale particles. In this study, we examined the effect of immiscible fluids on the formation and evolution of particle swarms in a fracture. A particle swarm is a collection of colloidal-size particles in a dilute suspension that exhibits cohesive behavior. Swarms fall under gravity with a velocity that is greater than the settling velocity of a single particle. Thus a particle swarm of colloidal contaminants can potentially travel farther and faster in a fracture than expected for a dispersion or emulsion of colloidal particles. We investigated the formation, evolution, and break-up of colloidal swarms under gravity in a uniform aperture fracture as hydrophobic/hydrophyllic particle swarms move across an oil-water interface. A uniform aperture fracture was fabricated from two transparent acrylic rectangular prisms (100 mm x 50 mm x 100 mm) that are separated by 1, 2.5, 5, 10 or 50 mm. The fracture was placed, vertically, inside a glass tank containing a layer of pure silicone oil (polydimethylsiloxane) on distilled water. Along the length of the fracture, 30 mm was filled with oil and 70 mm with water. Experiments were conducted using silicone oils with viscosities of 5, 10, 100, or 1000 cSt. Particle swarms (5 μl) were comprised of a 1% concentration (by mass) of 25 micron glass beads (hydrophilic) suspended in a water drop, or a 1% concentration (by mass) of 3 micron polystyrene fluorescent beads (hydrophobic) suspended in a water drop. The swarm behavior was imaged using an optical fluorescent imaging system composed of a CCD camera and by green (525 nm) LED arrays for illumination. Swarms were spherical and remained coherent as they fell through the oil because of the immiscibility of oil and water. However, as a swarm approached the oil-water interface, it

Automatic Multi-Level Thresholding Segmentation Based on Multi-Objective Optimization

Directory of Open Access Journals (Sweden)

L. DJEROU,

2012-01-01

Full Text Available In this paper, we present a new multi-level image thresholding technique, called Automatic Threshold based on Multi-objective Optimization "ATMO" that combines the flexibility of multi-objective fitness functions with the power of a Binary Particle Swarm Optimization algorithm "BPSO", for searching the "optimum" number of the thresholds and simultaneously the optimal thresholds of three criteria: the between-class variances criterion, the minimum error criterion and the entropy criterion. Some examples of test images are presented to compare our segmentation method, based on the multi-objective optimization approach with Otsu’s, Kapur’s and Kittler’s methods. Our experimental results show that the thresholding method based on multi-objective optimization is more efficient than the classical Otsu’s, Kapur’s and Kittler’s methods.

Particle Swarms in Fractures: Open Versus Partially Closed Systems

Science.gov (United States)

Boomsma, E.; Pyrak-Nolte, L. J.

2014-12-01

In the field, fractures may be isolated or connected to fluid reservoirs anywhere along the perimeter of a fracture. These boundaries affect fluid circulation, flow paths and communication with external reservoirs. The transport of drop like collections of colloidal-sized particles (particle swarms) in open and partially closed systems was studied. A uniform aperture synthetic fracture was constructed using two blocks (100 x 100 x 50 mm) of transparent acrylic placed parallel to each other. The fracture was fully submerged a tank filled with 100cSt silicone oil. Fracture apertures were varied from 5-80 mm. Partially closed systems were created by sealing the sides of the fracture with plastic film. The four boundary conditions study were: (Case 1) open, (Case 2) closed on the sides, (Case 3) closed on the bottom, and (Case 4) closed on both the sides and bottom of the fracture. A 15 μL dilute suspension of soda-lime glass particles in oil (2% by mass) were released into the fracture. Particle swarms were illuminated using a green (525 nm) LED array and imaged with a CCD camera. The presence of the additional boundaries modified the speed of the particle swarms (see figure). In Case 1, enhanced swarm transport was observed for a range of apertures, traveling faster than either very small or very large apertures. In Case 2, swarm velocities were enhanced over a larger range of fracture apertures than in any of the other cases. Case 3 shifted the enhanced transport regime to lower apertures and also reduced swarm speed when compared to Case 2. Finally, Case 4 eliminated the enhanced transport regime entirely. Communication between the fluid in the fracture and an external fluid reservoir resulted in enhanced swarm transport in Cases 1-3. The non-rigid nature of a swarm enables drag from the fracture walls to modify the swarm geometry. The particles composing a swarm reorganize in response to the fracture, elongating the swarm and maintaining its density. Unlike a

Modified Chaos Particle Swarm Optimization-Based Optimized Operation Model for Stand-Alone CCHP Microgrid

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Fei Wang

2017-07-01

Full Text Available The optimized dispatch of different distributed generations (DGs in stand-alone microgrid (MG is of great significance to the operation’s reliability and economy, especially for energy crisis and environmental pollution. Based on controllable load (CL and combined cooling-heating-power (CCHP model of micro-gas turbine (MT, a multi-objective optimization model with relevant constraints to optimize the generation cost, load cut compensation and environmental benefit is proposed in this paper. The MG studied in this paper consists of photovoltaic (PV, wind turbine (WT, fuel cell (FC, diesel engine (DE, MT and energy storage (ES. Four typical scenarios were designed according to different day types (work day or weekend and weather conditions (sunny or rainy in view of the uncertainty of renewable energy in variable situations and load fluctuation. A modified dispatch strategy for CCHP is presented to further improve the operation economy without reducing the consumers’ comfort feeling. Chaotic optimization and elite retention strategy are introduced into basic particle swarm optimization (PSO to propose modified chaos particle swarm optimization (MCPSO whose search capability and convergence speed are improved greatly. Simulation results validate the correctness of the proposed model and the effectiveness of MCPSO algorithm in the optimized operation application of stand-alone MG.

A Parallel Particle Swarm Optimizer

National Research Council Canada - National Science Library

Schutte, J. F; Fregly, B .J; Haftka, R. T; George, A. D

2003-01-01

.... Motivated by a computationally demanding biomechanical system identification problem, we introduce a parallel implementation of a stochastic population based global optimizer, the Particle Swarm...

A Swarm Optimization Genetic Algorithm Based on Quantum-Behaved Particle Swarm Optimization.

Science.gov (United States)

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.

Modelling and Pareto optimization of mechanical properties of friction stir welded AA7075/AA5083 butt joints using neural network and particle swarm algorithm

International Nuclear Information System (INIS)

Shojaeefard, Mohammad Hasan; Behnagh, Reza Abdi; Akbari, Mostafa; Givi, Mohammad Kazem Besharati; Farhani, Foad

2013-01-01

Highlights: ► Defect-free friction stir welds have been produced for AA5083-O/AA7075-O. ► Back-propagation was sufficient for predicting hardness and tensile strength. ► A hybrid multi-objective algorithm is proposed to deal with this MOP. ► Multi-objective particle swarm optimization was used to find the Pareto solutions. ► TOPSIS is used to rank the given alternatives of the Pareto solutions. -- Abstract: Friction Stir Welding (FSW) has been successfully used to weld similar and dissimilar cast and wrought aluminium alloys, especially for aircraft aluminium alloys, that generally present with low weldability by the traditional fusion welding process. This paper focuses on the microstructural and mechanical properties of the Friction Stir Welding (FSW) of AA7075-O to AA5083-O aluminium alloys. Weld microstructures, hardness and tensile properties were evaluated in as-welded condition. Tensile tests indicated that mechanical properties of the joint were better than in the base metals. An Artificial Neural Network (ANN) model was developed to simulate the correlation between the Friction Stir Welding parameters and mechanical properties. Performance of the ANN model was excellent and the model was employed to predict the ultimate tensile strength and hardness of butt joint of AA7075–AA5083 as functions of weld and rotational speeds. The multi-objective particle swarm optimization was used to obtain the Pareto-optimal set. Finally, the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) was applied to determine the best compromised solution.

Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization.

Science.gov (United States)

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.

Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization

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.

The infrared spectral transmittance of Aspergillus niger spore aggregated particle swarm

Science.gov (United States)

Zhao, Xinying; Hu, Yihua; Gu, Youlin; Li, Le

2015-10-01

Microorganism aggregated particle swarm, which is quite an important composition of complex media environment, can be developed as a new kind of infrared functional materials. Current researches mainly focus on the optical properties of single microorganism particle. As for the swarm, especially the microorganism aggregated particle swarm, a more accurate simulation model should be proposed to calculate its extinction effect. At the same time, certain parameters deserve to be discussed, which helps to better develop the microorganism aggregated particle swarm as a new kind of infrared functional materials. In this paper, take Aspergillus Niger spore as an example. On the one hand, a new calculation model is established. Firstly, the cluster-cluster aggregation (CCA) model is used to simulate the structure of Aspergillus Niger spore aggregated particle. Secondly, the single scattering extinction parameters for Aspergillus Niger spore aggregated particle are calculated by using the discrete dipole approximation (DDA) method. Thirdly, the transmittance of Aspergillus Niger spore aggregated particle swarm is simulated by using Monte Carlo method. On the other hand, based on the model proposed above, what influences can wavelength causes has been studied, including the spectral distribution of scattering intensity of Aspergillus Niger spore aggregated particle and the infrared spectral transmittance of the aggregated particle swarm within the range of 8-14μm incident infrared wavelengths. Numerical results indicate that the scattering intensity of Aspergillus Niger spore aggregated particle reduces with the increase of incident wavelengths at each scattering angle. Scattering energy mainly concentrates on the scattering angle between 0-40°, forward scattering has an obvious effect. In addition, the infrared transmittance of Aspergillus Niger spore aggregated particle swarm goes up with the increase of incident wavelengths. However, some turning points of the trend are

Study of particle swarm optimization particle trajectories

CSIR Research Space (South Africa)

Van den Bergh, F

2006-01-01

Full Text Available . These theoretical studies concentrate mainly on simplified PSO systems. This paper overviews current theoretical studies, and extend these studies to investigate particle trajectories for general swarms to include the influence of the inertia term. The paper also...

Optimization of a Conical Corrugated Antenna Using Multiobjective Heuristics for Radio-Astronomy Applications

Directory of Open Access Journals (Sweden)

S. López-Ruiz

2016-01-01

Full Text Available This paper presents the design of a tree sections corrugated horn antenna with a modified linear profile, using NURBS, suitable for radio-astronomy applications. The operating band ranges from 4.5 to 8.8 GHz. The aperture efficiency is higher than 84% and the return losses are greater than 20 dB in the whole bandwidth. The antenna optimization has been carried out with multiobjective versions of an evolutionary algorithm (EA and a particle swarm optimization (PSO algorithm. We show that both techniques provide good antenna design, but the experience carried out shows that the results of the evolutionary algorithm outperform the particle swarm results.

A new global particle swarm optimization for the economic emission dispatch with or without transmission losses

International Nuclear Information System (INIS)

Zou, Dexuan; Li, Steven; Li, Zongyan; Kong, Xiangyong

2017-01-01

Highlights: • A new global particle swarm optimization (NGPSO) is proposed. • NGPSO has strong convergence and desirable accuracy. • NGPSO is used to handle the economic emission dispatch with or without transmission losses. • The equality constraint can be satisfied by solving a quadratic equation. • The inequality constraints can be satisfied by using penalty function method. - Abstract: A new global particle swarm optimization (NGPSO) algorithm is proposed to solve the economic emission dispatch (EED) problems in this paper. NGPSO is different from the traditional particle swarm optimization (PSO) algorithm in two aspects. First, NGPSO uses a new position updating equation which relies on the global best particle to guide the searching activities of all particles. Second, it uses the randomization based on the uniform distribution to slightly disturb the flight trajectories of particles during the late evolutionary process. The two steps enable NGPSO to effectively execute a number of global searches, and thus they increase the chance of exploring promising solution space, and reduce the probabilities of getting trapped into local optima for all particles. On the other hand, the two objective functions of EED are normalized separately according to all candidate solutions, and then they are incorporated into one single objective function. The transformation steps are very helpful in eliminating the difference caused by the different dimensions of the two functions, and thus they strike a balance between the fuel cost and emission. In addition, a simple and common penalty function method is employed to facilitate the satisfactions of EED’s constraints. Based on these improvements in PSO, objective functions and constraints handling, high-quality solutions can be obtained for EED problems. Five examples are chosen to testify the performance of three improved PSOs on solving EED problems with or without transmission losses. Experimental results show that

A Constructive Data Classification Version of the Particle Swarm Optimization Algorithm

Directory of Open Access Journals (Sweden)

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.

A Novel Particle Swarm Optimization Algorithm for Global Optimization.

Science.gov (United States)

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.

Self-regulating and self-evolving particle swarm optimizer

Science.gov (United States)

Wang, Hui-Min; Qiao, Zhao-Wei; Xia, Chang-Liang; Li, Liang-Yu

2015-01-01

In this article, a novel self-regulating and self-evolving particle swarm optimizer (SSPSO) is proposed. Learning from the idea of direction reversal, self-regulating behaviour is a modified position update rule for particles, according to which the algorithm improves the best position to accelerate convergence in situations where the traditional update rule does not work. Borrowing the idea of mutation from evolutionary computation, self-evolving behaviour acts on the current best particle in the swarm to prevent the algorithm from prematurely converging. The performance of SSPSO and four other improved particle swarm optimizers is numerically evaluated by unimodal, multimodal and rotated multimodal benchmark functions. The effectiveness of SSPSO in solving real-world problems is shown by the magnetic optimization of a Halbach-based permanent magnet machine. The results show that SSPSO has good convergence performance and high reliability, and is well matched to actual problems.

behaved particle swarm optimization (QPSO)

African Journals Online (AJOL)

Administrator

2011-06-13

Jun 13, 2011 ... experiment results of L-glutamic acid fermentation process showed that our ... Key words: Soft-sensing model, quantum-behaved particle swarm optimization ... information about such biochemical variables is, in most practical ...

Fractional order Darwinian particle swarm optimization applications and evaluation of an evolutionary algorithm

CERN Document Server

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

A Synchronous-Asynchronous Particle Swarm Optimisation Algorithm

Science.gov (United States)

Ab Aziz, Nor Azlina; Mubin, Marizan; Mohamad, Mohd Saberi; Ab Aziz, Kamarulzaman

2014-01-01

In the original particle swarm optimisation (PSO) algorithm, the particles' velocities and positions are updated after the whole swarm performance is evaluated. This algorithm is also known as synchronous PSO (S-PSO). The strength of this update method is in the exploitation of the information. Asynchronous update PSO (A-PSO) has been proposed as an alternative to S-PSO. A particle in A-PSO updates its velocity and position as soon as its own performance has been evaluated. Hence, particles are updated using partial information, leading to stronger exploration. In this paper, we attempt to improve PSO by merging both update methods to utilise the strengths of both methods. The proposed synchronous-asynchronous PSO (SA-PSO) algorithm divides the particles into smaller groups. The best member of a group and the swarm's best are chosen to lead the search. Members within a group are updated synchronously, while the groups themselves are asynchronously updated. Five well-known unimodal functions, four multimodal functions, and a real world optimisation problem are used to study the performance of SA-PSO, which is compared with the performances of S-PSO and A-PSO. The results are statistically analysed and show that the proposed SA-PSO has performed consistently well. PMID:25121109

A Distributed Particle Swarm Optimization Zlgorithmfor Flexible Job-hop Scheduling Problem

Directory of Open Access Journals (Sweden)

LIU Sheng--hui

2017-06-01

Full Text Available According to the characteristics of the Flexible job shop scheduling problem the minimum makespan as measures we proposed a distributed particle swarm optimization algorithm aiming to solve flexible job shop scheduling problem. The algorithm adopts the method of distributed ideas to solve problems and we are established for two multi agent particle swarm optimization model in this algorithm it can solve the traditional particle swarm optimization algorithm when making decisions in real time according to the emergencies. Finally some benthmark problems were experimented and the results are compared with the traditional algorithm. Experimental results proved that the developed distributed PSO is enough effective and efficient to solve the FJSP and it also verified the reasonableness of the multi}gent particle swarm optimization model.

Solving multiobjective optimal reactive power dispatch using modified NSGA-II

Energy Technology Data Exchange (ETDEWEB)

Jeyadevi, S.; Baskar, S.; Babulal, C.K.; Willjuice Iruthayarajan, M. [Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, Madurai, Tamilnadu 625 015 (India)

2011-02-15

This paper addresses an application of modified NSGA-II (MNSGA-II) by incorporating controlled elitism and dynamic crowding distance (DCD) strategies in NSGA-II to multiobjective optimal reactive power dispatch (ORPD) problem by minimizing real power loss and maximizing the system voltage stability. To validate the Pareto-front obtained using MNSGA-II, reference Pareto-front is generated using multiple runs of single objective optimization with weighted sum of objectives. For simulation purposes, IEEE 30 and IEEE 118 bus test systems are considered. The performance of MNSGA-II, NSGA-II and multiobjective particle swarm optimization (MOPSO) approaches are compared with respect to multiobjective performance measures. TOPSIS technique is applied on obtained non-dominated solutions to determine best compromise solution (BCS). Karush-Kuhn-Tucker (KKT) conditions are also applied on the obtained non-dominated solutions to substantiate a claim on optimality. Simulation results are quite promising and the MNSGA-II performs better than NSGA-II in maintaining diversity and authenticates its potential to solve multiobjective ORPD effectively. (author)

Estimation of Valve Stiction Using Particle Swarm Optimization

Directory of Open Access Journals (Sweden)

S. Sivagamasundari

2011-06-01

Full Text Available This paper presents a procedure for quantifying valve stiction in control loops based on particle swarm optimization. Measurements of the Process Variable (PV and Controller Output (OP are used to estimate the parameters of a Hammerstein system, consisting of connection of a non linear control valve stiction model and a linear process model. The parameters of the Hammerstein model are estimated using particle swarm optimization, from the input-output data by minimizing the error between the true model output and the identified model output. Using particle swarm optimization, Hammerstein models with known nonlinear structure and unknown parameters can be identified. A cost-effective optimization technique is adopted to find the best valve stiction models representing a more realistic valve behavior in the oscillating loop. Simulation and practical laboratory control system results are included, which demonstrates the effectiveness and robustness of the identification scheme.

A Novel Distributed Quantum-Behaved Particle Swarm Optimization

Directory of Open Access Journals (Sweden)

Yangyang Li

2017-01-01

Full Text Available Quantum-behaved particle swarm optimization (QPSO is an improved version of particle swarm optimization (PSO and has shown superior performance on many optimization problems. But for now, it may not always satisfy the situations. Nowadays, problems become larger and more complex, and most serial optimization algorithms cannot deal with the problem or need plenty of computing cost. Fortunately, as an effective model in dealing with problems with big data which need huge computation, MapReduce has been widely used in many areas. In this paper, we implement QPSO on MapReduce model and propose MapReduce quantum-behaved particle swarm optimization (MRQPSO which achieves parallel and distributed QPSO. Comparisons are made between MRQPSO and QPSO on some test problems and nonlinear equation systems. The results show that MRQPSO could complete computing task with less time. Meanwhile, from the view of optimization performance, MRQPSO outperforms QPSO in many cases.

Anti-predatory particle swarm optimization: Solution to nonconvex economic dispatch problems

Energy Technology Data Exchange (ETDEWEB)

Selvakumar, A. Immanuel [Department of Electrical and Electronics Engineering, Karunya Institute of Technology and Sciences, Coimbatore 641114, Tamilnadu (India); Thanushkodi, K. [Department of Electronics and Instrumentation Engineering, Government College of Technology, Coimbatore 641013, Tamilnadu (India)

2008-01-15

This paper proposes a new particle swarm optimization (PSO) strategy namely, anti-predatory particle swarm optimization (APSO) to solve nonconvex economic dispatch problems. In the classical PSO, the movement of a particle (bird) is governed by three behaviors: inertial, cognitive and social. The cognitive and social behaviors are the components of the foraging activity, which help the swarm of birds to locate food. Another activity that is observed in birds is the anti-predatory nature, which helps the swarm to escape from the predators. In this work, the anti-predatory activity is modeled and embedded in the classical PSO to form APSO. This inclusion enhances the exploration capability of the swarm. To validate the proposed APSO model, it is applied to two test systems having nonconvex solution spaces. Satisfactory results are obtained when compared with previous approaches. (author)

Extending Particle Swarm Optimisers with Self-Organized Criticality

DEFF Research Database (Denmark)

Løvbjerg, Morten; Krink, Thiemo

2002-01-01

Particle swarm optimisers (PSOs) show potential in function optimisation, but still have room for improvement. Self-organized criticality (SOC) can help control the PSO and add diversity. Extending the PSO with SOC seems promising reaching faster convergence and better solutions.......Particle swarm optimisers (PSOs) show potential in function optimisation, but still have room for improvement. Self-organized criticality (SOC) can help control the PSO and add diversity. Extending the PSO with SOC seems promising reaching faster convergence and better solutions....

Chaotically encoded particle swarm optimization algorithm and its applications

International Nuclear Information System (INIS)

Alatas, Bilal; Akin, Erhan

2009-01-01

This paper proposes a novel particle swarm optimization (PSO) algorithm, chaotically encoded particle swarm optimization algorithm (CENPSOA), based on the notion of chaos numbers that have been recently proposed for a novel meaning to numbers. In this paper, various chaos arithmetic and evaluation measures that can be used in CENPSOA have been described. Furthermore, CENPSOA has been designed to be effectively utilized in data mining applications.

Cultural-based particle swarm for dynamic optimisation problems

Science.gov (United States)

Daneshyari, Moayed; Yen, Gary G.

2012-07-01

Many practical optimisation problems are with the existence of uncertainties, among which a significant number belong to the dynamic optimisation problem (DOP) category in which the fitness function changes through time. In this study, we propose the cultural-based particle swarm optimisation (PSO) to solve DOP problems. A cultural framework is adopted incorporating the required information from the PSO into five sections of the belief space, namely situational, temporal, domain, normative and spatial knowledge. The stored information will be adopted to detect the changes in the environment and assists response to the change through a diversity-based repulsion among particles and migration among swarms in the population space, and also helps in selecting the leading particles in three different levels, personal, swarm and global levels. Comparison of the proposed heuristics over several difficult dynamic benchmark problems demonstrates the better or equal performance with respect to most of other selected state-of-the-art dynamic PSO heuristics.

An efficient scenario-based and fuzzy self-adaptive learning particle swarm optimization approach for dynamic economic emission dispatch considering load and wind power uncertainties

International Nuclear Information System (INIS)

Bahmani-Firouzi, Bahman; Farjah, Ebrahim; Azizipanah-Abarghooee, Rasoul

2013-01-01

Renewable energy resources such as wind power plants are playing an ever-increasing role in power generation. This paper extends the dynamic economic emission dispatch problem by incorporating wind power plant. This problem is a multi-objective optimization approach in which total electrical power generation costs and combustion emissions are simultaneously minimized over a short-term time span. A stochastic approach based on scenarios is suggested to model the uncertainty associated with hourly load and wind power forecasts. A roulette wheel technique on the basis of probability distribution functions of load and wind power is implemented to generate scenarios. As a result, the stochastic nature of the suggested problem is emancipated by decomposing it into a set of equivalent deterministic problem. An improved multi-objective particle swarm optimization algorithm is applied to obtain the best expected solutions for the proposed stochastic programming framework. To enhance the overall performance and effectiveness of the particle swarm optimization, a fuzzy adaptive technique, θ-search and self-adaptive learning strategy for velocity updating are used to tune the inertia weight factor and to escape from local optima, respectively. The suggested algorithm goes through the search space in the polar coordinates instead of the Cartesian one; whereby the feasible space is more compact. In order to evaluate the efficiency and feasibility of the suggested framework, it is applied to two test systems with small and large scale characteristics. - Highlights: ► Formulates multi-objective DEED problem under a stochastic programming framework. ► Considers uncertainties related to forecasted values of load demand and wind power. ► Proposes an interactive fuzzy satisfying method based on the novel FSALPSO. ► Presents a new self-adaptive learning strategy to improve original PSO algorithm

A dynamic inertia weight particle swarm optimization algorithm

International Nuclear Information System (INIS)

Jiao Bin; Lian Zhigang; Gu Xingsheng

2008-01-01

Particle swarm optimization (PSO) algorithm has been developing rapidly and has been applied widely since it was introduced, as it is easily understood and realized. This paper presents an improved particle swarm optimization algorithm (IPSO) to improve the performance of standard PSO, which uses the dynamic inertia weight that decreases according to iterative generation increasing. It is tested with a set of 6 benchmark functions with 30, 50 and 150 different dimensions and compared with standard PSO. Experimental results indicate that the IPSO improves the search performance on the benchmark functions significantly

Optimal PMU Placement By Improved Particle Swarm Optimization

DEFF Research Database (Denmark)

Rather, Zakir Hussain; Liu, Leo; Chen, Zhe

2013-01-01

This paper presents an improved method of binary particle swarm optimization (IBPSO) technique for optimal phasor measurement unit (PMU) placement in a power network for complete system observability. Various effective improvements have been proposed to enhance the efficiency and convergence rate...... of conventional particle swarm optimization method. The proposed method of IBPSO ensures optimal PMU placement with and without consideration of zero injection measurements. The proposed method has been applied to standard test systems like 17 bus, IEEE 24-bus, IEEE 30-bus, New England 39-bus, IEEE 57-bus system...

An Orthogonal Multi-Swarm Cooperative PSO Algorithm with a Particle Trajectory Knowledge Base

Directory of Open Access Journals (Sweden)

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.

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Directory of Open Access Journals (Sweden)

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.

Chaotic particle swarm optimization for economic dispatch considering the generator constraints

International Nuclear Information System (INIS)

Cai, Jiejin; Ma, Xiaoqian; Li, Lixiang; Haipeng, Peng

2007-01-01

Chaotic particle swarm optimization (CPSO) methods are optimization approaches based on the proposed particle swarm optimization (PSO) with adaptive inertia weight factor (AIWF) and chaotic local search (CLS). In this paper, two CPSO methods based on the logistic equation and the Tent equation are presented to solve economic dispatch (ED) problems with generator constraints and applied in two power system cases. Compared with the traditional PSO method, the convergence iterative numbers of the CPSO methods are reduced, and the solutions generation costs decrease around 5 $/h in the six unit system and 24 $/h in the 15 unit system. The simulation results show that the CPSO methods have good convergence property. The generation costs of the CPSO methods are lower than those of the traditional particle swarm optimization algorithm, and hence, CPSO methods can result in great economic effect. For economic dispatch problems, the CPSO methods are more feasible and more effective alternative approaches than the traditional particle swarm optimization algorithm

Intelligent discrete particle swarm optimization for multiprocessor task scheduling problem

Directory of Open Access Journals (Sweden)

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.

Novel Particle Swarm Optimization and Its Application in Calibrating the Underwater Transponder Coordinates

OpenAIRE

Zheping Yan; Chao Deng; Benyin Li; Jiajia Zhou

2014-01-01

A novel improved particle swarm algorithm named competition particle swarm optimization (CPSO) is proposed to calibrate the Underwater Transponder coordinates. To improve the performance of the algorithm, TVAC algorithm is introduced into CPSO to present an extension competition particle swarm optimization (ECPSO). The proposed method is tested with a set of 10 standard optimization benchmark problems and the results are compared with those obtained through existing PSO algorithms, basic par...

An Improved Quantum-Behaved Particle Swarm Optimization Algorithm with Elitist Breeding for Unconstrained Optimization.

Science.gov (United States)

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.

A Diversity-Guided Particle Swarm Optimizer - the ARPSO

DEFF Research Database (Denmark)

Vesterstrøm, Jacob Svaneborg; Riget, Jacques

2002-01-01

The particle swarm optimization (PSO) algorithm is a new population based search strat- egy, which has exhibited good performance on well-known numerical test problems. How- ever, on strongly multi-modal test problems the PSO tends to suffer from premature convergence. This is due to a decrease...... that the ARPSO prevents premature convergence to a high degree, but still keeps a rapid convergence like the basic PSO. Thus, it clearly outperforms the basic PSO as well as the implemented GA in multi-modal optimization. Keywords Particle Swarm Optimization, Diversity-Guided Search 1 Introduction The PSO model...

Implementasi Algoritma Particle Swarm untuk Menyelesaikan Sistem Persamaan Nonlinear

Directory of Open Access Journals (Sweden)

Ardiana Rosita

2012-09-01

Full Text Available Penyelesaian sistem persamaan nonlinear merupakan salah satu permasalahan yang sulit pada komputasi numerik dan berbagai aplikasi teknik. Beberapa metode telah dikembangkan untuk menyelesaikan sistem persamaan ini dan metode Newton merupakan metode yang paling sering digunakan. Namun metode ini memerlukan perkiraan solusi awal dan memilih perkiraan solusi awal yang baik untuk sebagian besar sistem persamaan nonlinear tidaklah mudah. Pada makalah ini, algoritma Particle Swarm yang diusulkan oleh Jaberipour dan kawan-kawan[1] diimplementasikan. Algoritma ini merupakan pengembangan dari algoritma Particle Swarm Optimization (PSO. Algoritma ini meyelesaikan sistem persamaan nonlinear yang sebelumnya telah diubah menjadi permasalahan optimasi. Uji coba dilakukan terhadap beberapa fungsi dan sistem persamaan nonlinear untuk menguji kinerja dan efisiensi algoritma. Berdasarkan hasil uji coba, beberapa fungsi dan sistem persamaan nonlinear telah konvergen pada iterasi ke 10 sampai 20 dan terdapat fungsi yang konvergen pada iterasi ke 200. Selain itu, solusi yang dihasilkan algoritma Particle Swarm mendekati solusi eksak.

Performance of multiobjective computational intelligence algorithms for the routing and wavelength assignment problem

Directory of Open Access Journals (Sweden)

Jorge Patiño

2016-01-01

Full Text Available This paper presents an evaluation performance of computational intelligence algorithms based on the multiobjective theory for the solution of the Routing and Wavelength Assignment problem (RWA in optical networks. The study evaluates the Firefly Algorithm, the Differential Evolutionary Algorithm, the Simulated Annealing Algorithm and two versions of the Particle Swarm Optimization algorithm. The paper provides a description of the multiobjective algorithms; then, an evaluation based on the performance provided by the multiobjective algorithms versus mono-objective approaches when dealing with different traffic loads, different numberof wavelengths and wavelength conversion process over the NSFNet topology is presented. Simulation results show that monoobjective algorithms properly solve the RWA problem for low values of data traffic and low number of wavelengths. However, the multiobjective approaches adapt better to online traffic when the number of wavelengths available in the network increases as well as when wavelength conversion is implemented in the nodes.

Hybrid Multi-objective Forecasting of Solar Photovoltaic Output Using Kalman Filter based Interval Type-2 Fuzzy Logic System

DEFF Research Database (Denmark)

Hassan, Saima; Ahmadieh Khanesar, Mojtaba; Hajizadeh, Amin

2017-01-01

Learning of fuzzy parameters for system modeling using evolutionary algorithms is an interesting topic. In this paper, two optimal design and tuning of Interval type-2 fuzzy logic system are proposed using hybrid learning algorithms. The consequent parameters of the interval type-2 fuzzy logic...... system in both the hybrid algorithms are tuned using Kalman filter. Whereas the antecedent parameters of the system in the first hybrid algorithm is optimized using the multi-objective particle swarm optimization (MOPSO) and using the multi-objective evolutionary algorithm Based on Decomposition (MOEA...

Multiobjective Robust Design of the Double Wishbone Suspension System Based on Particle Swarm Optimization

Directory of Open Access Journals (Sweden)

Xianfu Cheng

2014-01-01

Full Text Available The performance of the suspension system is one of the most important factors in the vehicle design. For the double wishbone suspension system, the conventional deterministic optimization does not consider any deviations of design parameters, so design sensitivity analysis and robust optimization design are proposed. In this study, the design parameters of the robust optimization are the positions of the key points, and the random factors are the uncertainties in manufacturing. A simplified model of the double wishbone suspension is established by software ADAMS. The sensitivity analysis is utilized to determine main design variables. Then, the simulation experiment is arranged and the Latin hypercube design is adopted to find the initial points. The Kriging model is employed for fitting the mean and variance of the quality characteristics according to the simulation results. Further, a particle swarm optimization method based on simple PSO is applied and the tradeoff between the mean and deviation of performance is made to solve the robust optimization problem of the double wishbone suspension system.

Multiobjective Robust Design of the Double Wishbone Suspension System Based on Particle Swarm Optimization

Science.gov (United States)

Lin, Yuqun

2014-01-01

The performance of the suspension system is one of the most important factors in the vehicle design. For the double wishbone suspension system, the conventional deterministic optimization does not consider any deviations of design parameters, so design sensitivity analysis and robust optimization design are proposed. In this study, the design parameters of the robust optimization are the positions of the key points, and the random factors are the uncertainties in manufacturing. A simplified model of the double wishbone suspension is established by software ADAMS. The sensitivity analysis is utilized to determine main design variables. Then, the simulation experiment is arranged and the Latin hypercube design is adopted to find the initial points. The Kriging model is employed for fitting the mean and variance of the quality characteristics according to the simulation results. Further, a particle swarm optimization method based on simple PSO is applied and the tradeoff between the mean and deviation of performance is made to solve the robust optimization problem of the double wishbone suspension system. PMID:24683334

Multiobjective robust design of the double wishbone suspension system based on particle swarm optimization.

Science.gov (United States)

Cheng, Xianfu; Lin, Yuqun

2014-01-01

The performance of the suspension system is one of the most important factors in the vehicle design. For the double wishbone suspension system, the conventional deterministic optimization does not consider any deviations of design parameters, so design sensitivity analysis and robust optimization design are proposed. In this study, the design parameters of the robust optimization are the positions of the key points, and the random factors are the uncertainties in manufacturing. A simplified model of the double wishbone suspension is established by software ADAMS. The sensitivity analysis is utilized to determine main design variables. Then, the simulation experiment is arranged and the Latin hypercube design is adopted to find the initial points. The Kriging model is employed for fitting the mean and variance of the quality characteristics according to the simulation results. Further, a particle swarm optimization method based on simple PSO is applied and the tradeoff between the mean and deviation of performance is made to solve the robust optimization problem of the double wishbone suspension system.

Energy Management Strategy in Consideration of Battery Health for PHEV via Stochastic Control and Particle Swarm Optimization Algorithm

Directory of Open Access Journals (Sweden)

Yuying Wang

2017-11-01

Full Text Available This paper presents an energy management strategy for plug-in hybrid electric vehicles (PHEVs that not only tries to minimize the energy consumption, but also considers the battery health. First, a battery model that can be applied to energy management optimization is given. In this model, battery health damage can be estimated in the different states of charge (SOC and temperature of the battery pack. Then, because of the inevitability that limiting the battery health degradation will increase energy consumption, a Pareto energy management optimization problem is formed. This multi-objective optimal control problem is solved numerically by using stochastic dynamic programming (SDP and particle swarm optimization (PSO for satisfying the vehicle power demand and considering the tradeoff between energy consumption and battery health at the same time. The optimization solution is obtained offline by utilizing real historical traffic data and formed as mappings on the system operating states so as to implement online in the actual driving conditions. Finally, the simulation results carried out on the GT-SUITE-based PHEV test platform are illustrated to demonstrate that the proposed multi-objective optimal control strategy would effectively yield benefits.

Light-Controlled Swarming and Assembly of Colloidal Particles

Directory of Open Access Journals (Sweden)

Jianhua Zhang

2018-02-01

Full Text Available Swarms and assemblies are ubiquitous in nature and they can perform complex collective behaviors and cooperative functions that they cannot accomplish individually. In response to light, some colloidal particles (CPs, including light active and passive CPs, can mimic their counterparts in nature and organize into complex structures that exhibit collective functions with remote controllability and high temporospatial precision. In this review, we firstly analyze the structural characteristics of swarms and assemblies of CPs and point out that light-controlled swarming and assembly of CPs are generally achieved by constructing light-responsive interactions between CPs. Then, we summarize in detail the recent advances in light-controlled swarming and assembly of CPs based on the interactions arisen from optical forces, photochemical reactions, photothermal effects, and photoisomerizations, as well as their potential applications. In the end, we also envision some challenges and future prospects of light-controlled swarming and assembly of CPs. With the increasing innovations in mechanisms and control strategies with easy operation, low cost, and arbitrary applicability, light-controlled swarming and assembly of CPs may be employed to manufacture programmable materials and reconfigurable robots for cooperative grasping, collective cargo transportation, and micro- and nanoengineering.

Research on Multiple Particle Swarm Algorithm Based on Analysis of Scientific Materials

Directory of Open Access Journals (Sweden)

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.

Multi-objective unit commitment with wind penetration and emission concerns under stochastic and fuzzy uncertainties

International Nuclear Information System (INIS)

Wang, Bo; Wang, Shuming; Zhou, Xianzhong; Watada, Junzo

2016-01-01

Recent years have witnessed the ever increasing renewable penetration in power generation systems, which entails modern unit commitment problems with modelling and computation burdens. This study aims to simulate the impacts of manifold uncertainties on system operation with emission concerns. First, probability theory and fuzzy set theory are applied to jointly represent the uncertainties such as wind generation, load fluctuation and unit outage that interleaved in unit commitment problems. Second, a Value-at-Risk-based multi-objective approach is developed as a bridge of existing stochastic and robust unit commitment optimizations, which not only captures the inherent conflict between operation cost and supply reliability, but also provides easy-to-adjust robustness against worst-case scenarios. Third, a multi-objective algorithm that integrates fuzzy simulation and particle swarm optimization is developed to achieve approximate Pareto-optimal solutions. The research effectiveness is exemplified by two case studies: The comparison between test systems with and without generation uncertainty demonstrates that this study is practicable and can suggest operational insights of generation mix systems. The sensitivity analysis on Value-at-Risk proves that our method can achieve adequate tradeoff between performance optimality and robustness, thus help system operators in making informed decisions. Finally, the model and algorithm comparisons also justify the superiority of this research. - Highlights: • Probability theory and fuzzy set theory are used to describe different uncertainties. • A Value-at-Risk-based multi-objective unit commitment model is proposed. • An improved multi-objective particle swarm optimization algorithm is developed. • The model achieves adequate trade-off between performance optimality and robustness. • The algorithm can obtain convergent and diversified Pareto fronts.

PARTICLE SWARM OPTIMIZATION BASED OF THE MAXIMUM ...

African Journals Online (AJOL)

2010-06-30

Jun 30, 2010 ... Keywords: Particle Swarm Optimization (PSO), photovoltaic system, MPOP, ... systems from one hand and because of the instantaneous change of ..... Because of the P-V characteristics this heuristic method is used to seek ...

On the premature convergence of particle swarm optimization

DEFF Research Database (Denmark)

Larsen, Rie B.; Jouffroy, Jerome; Lassen, Benny

2016-01-01

This paper discusses convergence issues of the basic particle swarm optimization algorithm for different pa- rameters. For the one-dimensional case, it is shown that, for a specific range of parameters, the particles will converge prematurely, i.e. away from the actual minimum of the objective...

Particle swarm optimization for automatic creation of complex graphic characters

International Nuclear Information System (INIS)

Fister, Iztok; Perc, Matjaž; Ljubič, Karin; Kamal, Salahuddin M.; Iglesias, Andres; Fister, Iztok

2015-01-01

Nature-inspired algorithms are a very promising tool for solving the hardest problems in computer sciences and mathematics. These algorithms are typically inspired by the fascinating behavior at display in biological systems, such as bee swarms or fish schools. So far, these algorithms have been applied in many practical applications. In this paper, we present a simple particle swarm optimization, which allows automatic creation of complex two-dimensional graphic characters. The method involves constructing the base characters, optimizing the modifications of the base characters with the particle swarm optimization algorithm, and finally generating the graphic characters from the solution. We demonstrate the effectiveness of our approach with the creation of simple snowman, but we also outline in detail how more complex characters can be created

Aerodynamic multi-objective integrated optimization based on principal component analysis

Directory of Open Access Journals (Sweden)

Jiangtao HUANG

2017-08-01

Full Text Available Based on improved multi-objective particle swarm optimization (MOPSO algorithm with principal component analysis (PCA methodology, an efficient high-dimension multi-objective optimization method is proposed, which, as the purpose of this paper, aims to improve the convergence of Pareto front in multi-objective optimization design. The mathematical efficiency, the physical reasonableness and the reliability in dealing with redundant objectives of PCA are verified by typical DTLZ5 test function and multi-objective correlation analysis of supercritical airfoil, and the proposed method is integrated into aircraft multi-disciplinary design (AMDEsign platform, which contains aerodynamics, stealth and structure weight analysis and optimization module. Then the proposed method is used for the multi-point integrated aerodynamic optimization of a wide-body passenger aircraft, in which the redundant objectives identified by PCA are transformed to optimization constraints, and several design methods are compared. The design results illustrate that the strategy used in this paper is sufficient and multi-point design requirements of the passenger aircraft are reached. The visualization level of non-dominant Pareto set is improved by effectively reducing the dimension without losing the primary feature of the problem.

Reserve-Constrained Multiarea Environmental/Economic Dispatch Using Enhanced Particle Swarm Optimization

OpenAIRE

Wang, Lingfeng; Singh, Chanan

2007-01-01

Source: Swarm Intelligence: Focus on Ant and Particle Swarm Optimization, Book edited by: Felix T. S. Chan and Manoj Kumar Tiwari, ISBN 978-3-902613-09-7, pp. 532, December 2007, Itech Education and Publishing, Vienna, Austria

Energy group structure determination using particle swarm optimization

International Nuclear Information System (INIS)

Yi, Ce; Sjoden, Glenn

2013-01-01

Highlights: ► Particle swarm optimization is applied to determine broad group structure. ► A graph representation of the broad group structure problem is introduced. ► The approach is tested on a fuel-pin model. - Abstract: Multi-group theory is widely applied for the energy domain discretization when solving the Linear Boltzmann Equation. To reduce the computational cost, fine group cross libraries are often down-sampled into broad group cross section libraries. Cross section data collapsing generally involves two steps: Firstly, the broad group structure has to be determined; secondly, a weighting scheme is used to evaluate the broad cross section library based on the fine group cross section data and the broad group structure. A common scheme is to average the fine group cross section weighted by the fine group flux. Cross section collapsing techniques have been intensively researched. However, most studies use a pre-determined group structure, open based on experience, to divide the neutron energy spectrum into thermal, epi-thermal, fast, etc. energy range. In this paper, a swarm intelligence algorithm, particle swarm optimization (PSO), is applied to optimize the broad group structure. A graph representation of the broad group structure determination problem is introduced. And the swarm intelligence algorithm is used to solve the graph model. The effectiveness of the approach is demonstrated using a fuel-pin model

Application of evolutionary algorithms for multi-objective optimization in VLSI and embedded systems

CERN Document Server

2015-01-01

This book describes how evolutionary algorithms (EA), including genetic algorithms (GA) and particle swarm optimization (PSO) can be utilized for solving multi-objective optimization problems in the area of embedded and VLSI system design. Many complex engineering optimization problems can be modelled as multi-objective formulations. This book provides an introduction to multi-objective optimization using meta-heuristic algorithms, GA and PSO, and how they can be applied to problems like hardware/software partitioning in embedded systems, circuit partitioning in VLSI, design of operational amplifiers in analog VLSI, design space exploration in high-level synthesis, delay fault testing in VLSI testing, and scheduling in heterogeneous distributed systems. It is shown how, in each case, the various aspects of the EA, namely its representation, and operators like crossover, mutation, etc. can be separately formulated to solve these problems. This book is intended for design engineers and researchers in the field ...

A dynamic global and local combined particle swarm optimization algorithm

International Nuclear Information System (INIS)

Jiao Bin; Lian Zhigang; Chen Qunxian

2009-01-01

Particle swarm optimization (PSO) algorithm has been developing rapidly and many results have been reported. PSO algorithm has shown some important advantages by providing high speed of convergence in specific problems, but it has a tendency to get stuck in a near optimal solution and one may find it difficult to improve solution accuracy by fine tuning. This paper presents a dynamic global and local combined particle swarm optimization (DGLCPSO) algorithm to improve the performance of original PSO, in which all particles dynamically share the best information of the local particle, global particle and group particles. It is tested with a set of eight benchmark functions with different dimensions and compared with original PSO. Experimental results indicate that the DGLCPSO algorithm improves the search performance on the benchmark functions significantly, and shows the effectiveness of the algorithm to solve optimization problems.

Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms.

Science.gov (United States)

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.

Multi-Objective Optimization of Start-up Strategy for Pumped Storage Units

Directory of Open Access Journals (Sweden)

Jinjiao Hou

2018-05-01

Full Text Available This paper proposes a multi-objective optimization method for the start-up strategy of pumped storage units (PSU for the first time. In the multi-objective optimization method, the speed rise time and the overshoot during the process of the start-up are taken as the objectives. A precise simulation platform is built for simulating the transient process of start-up, and for calculating the objectives based on the process. The Multi-objective Particle Swarm Optimization algorithm (MOPSO is adopted to optimize the widely applied start-up strategies based on one-stage direct guide vane control (DGVC, and two-stage DGVC. Based on the Pareto Front obtained, a multi-objective decision-making method based on the relative objective proximity is used to sort the solutions in the Pareto Front. Start-up strategy optimization for a PSU of a pumped storage power station in Jiangxi Province in China is conducted in experiments. The results show that: (1 compared with the single objective optimization, the proposed multi-objective optimization of start-up strategy not only greatly shortens the speed rise time and the speed overshoot, but also makes the speed curve quickly stabilize; (2 multi-objective optimization of strategy based on two-stage DGVC achieves better solution for a quick and smooth start-up of PSU than that of the strategy based on one-stage DGVC.

An Improved Multiobjective PSO for the Scheduling Problem of Panel Block Construction

Directory of Open Access Journals (Sweden)

Zhi Yang

2016-01-01

Full Text Available Uncertainty is common in ship construction. However, few studies have focused on scheduling problems under uncertainty in shipbuilding. This paper formulates the scheduling problem of panel block construction as a multiobjective fuzzy flow shop scheduling problem (FSSP with a fuzzy processing time, a fuzzy due date, and the just-in-time (JIT concept. An improved multiobjective particle swarm optimization called MOPSO-M is developed to solve the scheduling problem. MOPSO-M utilizes a ranked-order-value rule to convert the continuous position of particles into the discrete permutations of jobs, and an available mapping is employed to obtain the precedence-based permutation of the jobs. In addition, to improve the performance of MOPSO-M, archive maintenance is combined with global best position selection, and mutation and a velocity constriction mechanism are introduced into the algorithm. The feasibility and effectiveness of MOPSO-M are assessed in comparison with general MOPSO and nondominated sorting genetic algorithm-II (NSGA-II.

A Hybrid Chaos-Particle Swarm Optimization Algorithm for the Vehicle Routing Problem with Time Window

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.

PREDIKSI KEMUNGKINAN BPREDIKSI BANJIR SUNGAI CITARUM DENGAN LOGIKA FUZZY HASIL ALGORITMA PARTICLE SWARM OPTIMIZATION

Directory of Open Access Journals (Sweden)

Phitsa Mauliana

2016-09-01

Full Text Available Abstract The purpose of this paper is the prediction of the possibility of flooding using fuzzy logic results of data processing algorithms using particle swarm optimization (PSO. Flooding is the water level exceeds the normal stream. Usually on the face of water and erratic rainfall cause people cannot predict the occurrence of floods. It required an effort to predict the flood in order to minimize losses resulting from flooding. Particle swarm optimization algorithm can solve a system of nonlinear equations for predicting flooding is a non-linear data processing. Particle swarm optimization algorithm and sample used was rainfall and water level, the result is a flood prediction accuracy of 73% based on the resulting confusion matrix calculations. Implementation of fuzzy logic can help predict the likelihood of flooding around the Citarum River. Keywords: Prediction, Flood, Particle Swarm Optimization, Fuzzy Logic.

A Novel Chaotic Particle Swarm Optimization Algorithm for Parking Space Guidance

Directory of Open Access Journals (Sweden)

Na Dong

2016-01-01

Full Text Available An evolutionary approach of parking space guidance based upon a novel Chaotic Particle Swarm Optimization (CPSO algorithm is proposed. In the newly proposed CPSO algorithm, the chaotic dynamics is combined into the position updating rules of Particle Swarm Optimization to improve the diversity of solutions and to avoid being trapped in the local optima. This novel approach, that combines the strengths of Particle Swarm Optimization and chaotic dynamics, is then applied into the route optimization (RO problem of parking lots, which is an important issue in the management systems of large-scale parking lots. It is used to find out the optimized paths between any source and destination nodes in the route network. Route optimization problems based on real parking lots are introduced for analyzing and the effectiveness and practicability of this novel optimization algorithm for parking space guidance have been verified through the application results.

Improved particle swarm optimization combined with chaos

International Nuclear Information System (INIS)

Liu Bo; Wang Ling; Jin Yihui; Tang Fang; Huang Dexian

2005-01-01

As a novel optimization technique, chaos has gained much attention and some applications during the past decade. For a given energy or cost function, by following chaotic ergodic orbits, a chaotic dynamic system may eventually reach the global optimum or its good approximation with high probability. To enhance the performance of particle swarm optimization (PSO), which is an evolutionary computation technique through individual improvement plus population cooperation and competition, hybrid particle swarm optimization algorithm is proposed by incorporating chaos. Firstly, adaptive inertia weight factor (AIWF) is introduced in PSO to efficiently balance the exploration and exploitation abilities. Secondly, PSO with AIWF and chaos are hybridized to form a chaotic PSO (CPSO), which reasonably combines the population-based evolutionary searching ability of PSO and chaotic searching behavior. Simulation results and comparisons with the standard PSO and several meta-heuristics show that the CPSO can effectively enhance the searching efficiency and greatly improve the searching quality

A Particle Swarm Optimization Algorithm with Variable Random Functions and Mutation

Institute of Scientific and Technical Information of China (English)

ZHOU Xiao-Jun; YANG Chun-Hua; GUI Wei-Hua; DONG Tian-Xue

2014-01-01

The convergence analysis of the standard particle swarm optimization (PSO) has shown that the changing of random functions, personal best and group best has the potential to improve the performance of the PSO. In this paper, a novel strategy with variable random functions and polynomial mutation is introduced into the PSO, which is called particle swarm optimization algorithm with variable random functions and mutation (PSO-RM). Random functions are adjusted with the density of the population so as to manipulate the weight of cognition part and social part. Mutation is executed on both personal best particle and group best particle to explore new areas. Experiment results have demonstrated the effectiveness of the strategy.

Parallel and Cooperative Particle Swarm Optimizer for Multimodal Problems

Directory of Open Access Journals (Sweden)

Geng Zhang

2015-01-01

Full Text Available Although the original particle swarm optimizer (PSO method and its related variant methods show some effectiveness for solving optimization problems, it may easily get trapped into local optimum especially when solving complex multimodal problems. Aiming to solve this issue, this paper puts forward a novel method called parallel and cooperative particle swarm optimizer (PCPSO. In case that the interacting of the elements in D-dimensional function vector X=[x1,x2,…,xd,…,xD] is independent, cooperative particle swarm optimizer (CPSO is used. Based on this, the PCPSO is presented to solve real problems. Since the dimension cannot be split into several lower dimensional search spaces in real problems because of the interacting of the elements, PCPSO exploits the cooperation of two parallel CPSO algorithms by orthogonal experimental design (OED learning. Firstly, the CPSO algorithm is used to generate two locally optimal vectors separately; then the OED is used to learn the merits of these two vectors and creates a better combination of them to generate further search. Experimental studies on a set of test functions show that PCPSO exhibits better robustness and converges much closer to the global optimum than several other peer algorithms.

Parallel Global Optimization with the Particle Swarm Algorithm (Preprint)

National Research Council Canada - National Science Library

Schutte, J. F; Reinbolt, J. A; Fregly, B. J; Haftka, R. T; George, A. D

2004-01-01

.... To obtain enhanced computational throughput and global search capability, we detail the coarse-grained parallelization of an increasingly popular global search method, the Particle Swarm Optimization (PSO) algorithm...

Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization

Science.gov (United States)

Abdulameer, Mohammed Hasan; Othman, Zulaiha Ali

2014-01-01

Existing face recognition methods utilize particle swarm optimizer (PSO) and opposition based particle swarm optimizer (OPSO) to optimize the parameters of SVM. However, the utilization of random values in the velocity calculation decreases the performance of these techniques; that is, during the velocity computation, we normally use random values for the acceleration coefficients and this creates randomness in the solution. To address this problem, an adaptive acceleration particle swarm optimization (AAPSO) technique is proposed. To evaluate our proposed method, we employ both face and iris recognition based on AAPSO with SVM (AAPSO-SVM). In the face and iris recognition systems, performance is evaluated using two human face databases, YALE and CASIA, and the UBiris dataset. In this method, we initially perform feature extraction and then recognition on the extracted features. In the recognition process, the extracted features are used for SVM training and testing. During the training and testing, the SVM parameters are optimized with the AAPSO technique, and in AAPSO, the acceleration coefficients are computed using the particle fitness values. The parameters in SVM, which are optimized by AAPSO, perform efficiently for both face and iris recognition. A comparative analysis between our proposed AAPSO-SVM and the PSO-SVM technique is presented. PMID:24790584

Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization

Directory of Open Access Journals (Sweden)

Mohammed Hasan Abdulameer

2014-01-01

Full Text Available Existing face recognition methods utilize particle swarm optimizer (PSO and opposition based particle swarm optimizer (OPSO to optimize the parameters of SVM. However, the utilization of random values in the velocity calculation decreases the performance of these techniques; that is, during the velocity computation, we normally use random values for the acceleration coefficients and this creates randomness in the solution. To address this problem, an adaptive acceleration particle swarm optimization (AAPSO technique is proposed. To evaluate our proposed method, we employ both face and iris recognition based on AAPSO with SVM (AAPSO-SVM. In the face and iris recognition systems, performance is evaluated using two human face databases, YALE and CASIA, and the UBiris dataset. In this method, we initially perform feature extraction and then recognition on the extracted features. In the recognition process, the extracted features are used for SVM training and testing. During the training and testing, the SVM parameters are optimized with the AAPSO technique, and in AAPSO, the acceleration coefficients are computed using the particle fitness values. The parameters in SVM, which are optimized by AAPSO, perform efficiently for both face and iris recognition. A comparative analysis between our proposed AAPSO-SVM and the PSO-SVM technique is presented.

Particle swarm optimization using multi-information characteristics of all personal-best information.

Science.gov (United States)

Huang, Song; Tian, Na; Wang, Yan; Ji, Zhicheng

2016-01-01

Convergence stagnation is the chief difficulty to solve hard optimization problems for most particle swarm optimization variants. To address this issue, a novel particle swarm optimization using multi-information characteristics of all personal-best information is developed in our research. In the modified algorithm, two positions are defined by personal-best positions and an improved cognition term with three positions of all personal-best information is used in velocity update equation to enhance the search capability. This strategy could make particles fly to a better direction by discovering useful information from all the personal-best positions. The validity of the proposed algorithm is assessed on twenty benchmark problems including unimodal, multimodal, rotated and shifted functions, and the results are compared with that obtained by some published variants of particle swarm optimization in the literature. Computational results demonstrate that the proposed algorithm finds several global optimum and high-quality solutions in most case with a fast convergence speed.

Genetic particle swarm parallel algorithm analysis of optimization arrangement on mistuned blades

Science.gov (United States)

Zhao, Tianyu; Yuan, Huiqun; Yang, Wenjun; Sun, Huagang

2017-12-01

This article introduces a method of mistuned parameter identification which consists of static frequency testing of blades, dichotomy and finite element analysis. A lumped parameter model of an engine bladed-disc system is then set up. A bladed arrangement optimization method, namely the genetic particle swarm optimization algorithm, is presented. It consists of a discrete particle swarm optimization and a genetic algorithm. From this, the local and global search ability is introduced. CUDA-based co-evolution particle swarm optimization, using a graphics processing unit, is presented and its performance is analysed. The results show that using optimization results can reduce the amplitude and localization of the forced vibration response of a bladed-disc system, while optimization based on the CUDA framework can improve the computing speed. This method could provide support for engineering applications in terms of effectiveness and efficiency.

Application of particle swarm optimization algorithm in the heating system planning problem.

Science.gov (United States)

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.

Studies of fuel loading pattern optimization for a typical pressurized water reactor (PWR) using improved pivot particle swarm method

International Nuclear Information System (INIS)

Liu, Shichang; Cai, Jiejin

2012-01-01

Highlights: ► The mathematical model of loading pattern problems for PWR has been established. ► IPPSO was integrated with ‘donjon’ and ‘dragon’ into fuel arrangement optimizing code. ► The novel method showed highly efficiency for the LP problems. ► The core effective multiplication factor increases by about 10% in simulation cases. ► The power peaking factor decreases by about 0.6% in simulation cases. -- Abstract: An in-core fuel reload design tool using the improved pivot particle swarm method was developed for the loading pattern optimization problems in a typical PWR, such as Daya Bay Nuclear Power Plant. The discrete, multi-objective improved pivot particle swarm optimization, was integrated with the in-core physics calculation code ‘donjon’ based on finite element method, and assemblies’ group constant calculation code ‘dragon’, composing the optimization code for fuel arrangement. The codes of both ‘donjon’ and ‘dragon’ were programmed by Institute of Nuclear Engineering of Polytechnique Montréal, Canada. This optimization code was aiming to maximize the core effective multiplication factor (Keff), while keeping the local power peaking factor (Ppf) lower than a predetermined value to maintain fuel integrity. At last, the code was applied to the first cycle loading of Daya Bay Nuclear Power Plant. The result showed that, compared with the reference loading pattern design, the core effective multiplication factor increased by 9.6%, while the power peaking factor decreased by 0.6%, meeting the safety requirement.

Multi-objective Design Method for Hybrid Active Power Filter

Science.gov (United States)

Yu, Jingrong; Deng, Limin; Liu, Maoyun; Qiu, Zhifeng

2017-10-01

In this paper, a multi-objective optimal design for transformerless hybrid active power filter (HAPF) is proposed. The interactions between the active and passive circuits is analyzed, and by taking the interactions into consideration, a three-dimensional objective problem comprising of performance, efficiency and cost of HAPF system is formulated. To deal with the multiple constraints and the strong coupling characteristics of the optimization model, a novel constraint processing mechanism based on distance measurement and adaptive penalty function is presented. In order to improve the diversity of optimal solution and the local searching ability of the particle swarm optimization (PSO) algorithm, a chaotic mutation operator based on multistage neighborhood is proposed. The simulation results show that the optimums near the ordinate origin of the three-dimension space make better tradeoff among the performance, efficiency and cost of HAPF, and the experimental results of transformerless HAPF verify the effectiveness of the method for multi-objective optimization and design.

Particle swarm optimization - Genetic algorithm (PSOGA) on linear transportation problem

Science.gov (United States)

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.

Optimization of a Conical Corrugated Antenna Using Multiobjective Heuristics for Radio-Astronomy Applications

OpenAIRE

López-Ruiz, S.; Sánchez Montero, R.; Tercero-Martínez, F.; López-Espí, P. L.; López-Fernandez, J. A.

2016-01-01

This paper presents the design of a tree sections corrugated horn antenna with a modified linear profile, using NURBS, suitable for radio-astronomy applications. The operating band ranges from 4.5 to 8.8 GHz. The aperture efficiency is higher than 84% and the return losses are greater than 20 dB in the whole bandwidth. The antenna optimization has been carried out with multiobjective versions of an evolutionary algorithm (EA) and a particle swarm optimization (PSO) algorithm. We show that bot...

Inverse problem for particle size distributions of atmospheric aerosols using stochastic particle swarm optimization

International Nuclear Information System (INIS)

Yuan Yuan; Yi Hongliang; Shuai Yong; Wang Fuqiang; Tan Heping

2010-01-01

As a part of resolving optical properties in atmosphere radiative transfer calculations, this paper focuses on obtaining aerosol optical thicknesses (AOTs) in the visible and near infrared wave band through indirect method by gleaning the values of aerosol particle size distribution parameters. Although various inverse techniques have been applied to obtain values for these parameters, we choose a stochastic particle swarm optimization (SPSO) algorithm to perform an inverse calculation. Computational performances of different inverse methods are investigated and the influence of swarm size on the inverse problem of computation particles is examined. Next, computational efficiencies of various particle size distributions and the influences of the measured errors on computational accuracy are compared. Finally, we recover particle size distributions for atmospheric aerosols over Beijing using the measured AOT data (at wavelengths λ=0.400, 0.690, 0.870, and 1.020 μm) obtained from AERONET at different times and then calculate other AOT values for this band based on the inverse results. With calculations agreeing with measured data, the SPSO algorithm shows good practicability.

Penerapan Metode Particle Swarm Optimization Pada Optimasi Prediksi Pemasaran Langsung

Directory of Open Access Journals (Sweden)

Yuni Eka Achyani

2018-04-01

Full Text Available Abstrak Dalam persaingan ketat saat ini, promosi yang baik dapat memberikan kredibilitas untuk produk baru. Promosi perlu mendapat perhatian lebih dan serius, karena dalam kehidupan sehari-hari timbul produk unggulan, jika tidak mengetahuinya, kemungkinan produk yang ditawarkan kepada konsumen kurang ditanggapi oleh pasar, oleh karena itu perusahaan harus mengupayakan produknya, meyakinkan dan mempengaruhi konsumen untuk menciptakan permintaan akan produk ini. Langkah yang bisa dilakukan oleh perusahaan untuk melakukannya adalah dengan melakukan pemasaran langsung. Peningkatan akurasi prediksi pemasaran langsung dapat dilakukan dengan cara melakukan seleksi terhadap atribut, karena seleksi atribut mengurangi dimensi dari data sehingga operasi algoritma data mining dapat berjalan lebih efektif dan lebih cepat. Dalam penelitian ini akan digunakan metode support vector machine dan akan dilakukan seleksi atribut dengan menggunakan particle swarm optimization untuk prediksi pemasaran langsung. Setelah dilakukan pengujian maka hasil yang didapat adalah support vector machine menghasilkan nilai akurasi sebesar 88,71 %, nilai precision 89,47% dan nilai AUC sebesar 0,896. Kemudian dilakukan seleksi atribut dengan menggunakan particle swarm optimization dimana atribut yang semula berjumlah 16 variabel prediktor terpilih 12 atribut yang digunakan. Hasil menunjukkan nilai akurasi yang lebih tinggi yaitu sebesar 89,38%, nilai precision 89,89% dan nilai AUC sebesar 0,909 dengan nilai akurasi klasifikasi sangat baik (excellent clasiffication. Sehingga dicapai peningkatan akurasi sebesar 0,67 %, dan peningkatan AUC sebesar 0,013. Kata Kunci: Particle Swarm Optimization, Pemasaran Langsung, Seleksi Atribut Abstract In the current intense competition a good promotion can provide credibility for a new product. Promotion needs to get more attention and serious, because in everyday life arise a prime product, if not find out, the possibility of products offered to

Multi-Objective Optimization for Energy Performance Improvement of Residential Buildings: A Comparative Study

Directory of Open Access Journals (Sweden)

Kangji Li

2017-02-01

Full Text Available Numerous conflicting criteria exist in building design optimization, such as energy consumption, greenhouse gas emission and indoor thermal performance. Different simulation-based optimization strategies and various optimization algorithms have been developed. A few of them are analyzed and compared in solving building design problems. This paper presents an efficient optimization framework to facilitate optimization designs with the aid of commercial simulation software and MATLAB. The performances of three optimization strategies, including the proposed approach, GenOpt method and artificial neural network (ANN method, are investigated using a case study of a simple building energy model. Results show that the proposed optimization framework has competitive performances compared with the GenOpt method. Further, in another practical case, four popular multi-objective algorithms, e.g., the non-dominated sorting genetic algorithm (NSGA-II, multi-objective particle swarm optimization (MOPSO, the multi-objective genetic algorithm (MOGA and multi-objective differential evolution (MODE, are realized using the propose optimization framework and compared with three criteria. Results indicate that MODE achieves close-to-optimal solutions with the best diversity and execution time. An uncompetitive result is achieved by the MOPSO in this case study.

Steady-state configuration and tension calculations of marine cables under complex currents via separated particle swarm optimization

Science.gov (United States)

Xu, Xue-song

2014-12-01

Under complex currents, the motion governing equations of marine cables are complex and nonlinear, and the calculations of cable configuration and tension become difficult compared with those under the uniform or simple currents. To obtain the numerical results, the usual Newton-Raphson iteration is often adopted, but its stability depends on the initial guessed solution to the governing equations. To improve the stability of numerical calculation, this paper proposed separated the particle swarm optimization, in which the variables are separated into several groups, and the dimension of search space is reduced to facilitate the particle swarm optimization. Via the separated particle swarm optimization, these governing nonlinear equations can be solved successfully with any initial solution, and the process of numerical calculation is very stable. For the calculations of cable configuration and tension of marine cables under complex currents, the proposed separated swarm particle optimization is more effective than the other particle swarm optimizations.

Four-dimensional parameter estimation of plane waves using swarming intelligence

International Nuclear Information System (INIS)

Zaman Fawad; Munir Fahad; Khan Zafar Ullah; Qureshi Ijaz Mansoor

2014-01-01

This paper proposes an efficient approach for four-dimensional (4D) parameter estimation of plane waves impinging on a 2-L shape array. The 4D parameters include amplitude, frequency and the two-dimensional (2D) direction of arrival, namely, azimuth and elevation angles. The proposed approach is based on memetic computation, in which the global optimizer, particle swarm optimization is hybridized with a rapid local search technique, pattern search. For this purpose, a new multi-objective fitness function is used. This fitness function is the combination of mean square error and the correlation between the normalized desired and estimated vectors. The proposed hybrid scheme is not only compared with individual performances of particle swarm optimization and pattern search, but also with the performance of the hybrid genetic algorithm and that of the traditional approach. A large number of Monte—Carlo simulations are carried out to validate the performance of the proposed scheme. It gives promising results in terms of estimation accuracy, convergence rate, proximity effect and robustness against noise. (interdisciplinary physics and related areas of science and technology)

A Local and Global Search Combined Particle Swarm Optimization Algorithm and Its Convergence Analysis

Directory of Open Access Journals (Sweden)

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.

Multi-objective swarm intelligence theoretical advances and applications

CERN Document Server

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.       

Energy-Efficient Scheduling Problem Using an Effective Hybrid Multi-Objective Evolutionary Algorithm

Directory of Open Access Journals (Sweden)

Lvjiang Yin

2016-12-01

Full Text Available Nowadays, manufacturing enterprises face the challenge of just-in-time (JIT production and energy saving. Therefore, study of JIT production and energy consumption is necessary and important in manufacturing sectors. Moreover, energy saving can be attained by the operational method and turn off/on idle machine method, which also increases the complexity of problem solving. Thus, most researchers still focus on small scale problems with one objective: a single machine environment. However, the scheduling problem is a multi-objective optimization problem in real applications. In this paper, a single machine scheduling model with controllable processing and sequence dependence setup times is developed for minimizing the total earliness/tardiness (E/T, cost, and energy consumption simultaneously. An effective multi-objective evolutionary algorithm called local multi-objective evolutionary algorithm (LMOEA is presented to tackle this multi-objective scheduling problem. To accommodate the characteristic of the problem, a new solution representation is proposed, which can convert discrete combinational problems into continuous problems. Additionally, a multiple local search strategy with self-adaptive mechanism is introduced into the proposed algorithm to enhance the exploitation ability. The performance of the proposed algorithm is evaluated by instances with comparison to other multi-objective meta-heuristics such as Nondominated Sorting Genetic Algorithm II (NSGA-II, Strength Pareto Evolutionary Algorithm 2 (SPEA2, Multiobjective Particle Swarm Optimization (OMOPSO, and Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D. Experimental results demonstrate that the proposed LMOEA algorithm outperforms its counterparts for this kind of scheduling problems.

Color Feature-Based Object Tracking through Particle Swarm Optimization with Improved Inertia Weight.

Science.gov (United States)

Guo, Siqiu; Zhang, Tao; Song, Yulong; Qian, Feng

2018-04-23

This paper presents a particle swarm tracking algorithm with improved inertia weight based on color features. The weighted color histogram is used as the target feature to reduce the contribution of target edge pixels in the target feature, which makes the algorithm insensitive to the target non-rigid deformation, scale variation, and rotation. Meanwhile, the influence of partial obstruction on the description of target features is reduced. The particle swarm optimization algorithm can complete the multi-peak search, which can cope well with the object occlusion tracking problem. This means that the target is located precisely where the similarity function appears multi-peak. When the particle swarm optimization algorithm is applied to the object tracking, the inertia weight adjustment mechanism has some limitations. This paper presents an improved method. The concept of particle maturity is introduced to improve the inertia weight adjustment mechanism, which could adjust the inertia weight in time according to the different states of each particle in each generation. Experimental results show that our algorithm achieves state-of-the-art performance in a wide range of scenarios.

A Review of Particle Swarm Optimization

Science.gov (United States)

Jain, N. K.; Nangia, Uma; Jain, Jyoti

2018-03-01

This paper presents an overview of the research progress in Particle Swarm Optimization (PSO) during 1995-2017. Fifty two papers have been reviewed. They have been categorized into nine categories based on various aspects. This technique has attracted many researchers because of its simplicity which led to many improvements and modifications of the basic PSO. Some researchers carried out the hybridization of PSO with other evolutionary techniques. This paper discusses the progress of PSO, its improvements, modifications and applications.

Optimum Design of a Five-Phase Permanent Magnet Synchronous Motor for Underwater Vehicles by use of Particle Swarm Optimization

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S. Asghar Gholamian

2012-12-01

Full Text Available Permanent magnet synchronous motors are efficient motors which have widespread applications in electric industry due to their noticeable features. One of the interesting applications of such motors is in underwater vehicles. In these cases, reaching to minimum volume and high torque of the motor are the major concern. Design optimization can enhance their merits considerably, thus reduce volume and improve performance of motors. In this paper, a new method for optimum design of a five-phase surface-mounted permanent magnet synchronous motor is presented to achieve minimum loss and magnet volume with an increased torque. A multi-objective optimization is performed in search for optimum dimensions of the motor and its permanent magnets using particle swarm optimization. The design optimization results in a motor with great improvement regarding the original motor. Finally, finite element analysis is utilized to validate the accuracy of the design.

QoE Guarantee Scheme Based on Cooperative Cognitive Cloud and Opportunistic Weight Particle Swarm

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Weihang Shi

2015-01-01

Full Text Available It is well known that the Internet application of cloud services may be affected by the inefficiency of cloud computing and inaccurate evaluation of quality of experience (QoE seriously. In our paper, based on construction algorithms of cooperative cognitive cloud platform and optimization algorithm of opportunities weight particle swarm clustering, the QoE guarantee mechanism was proposed. The mechanism, through the sending users of requests and the cognitive neighbor users’ cooperation, combined the cooperation of subcloud platforms and constructed the optimal cloud platform with the different service. At the same time, the particle swarm optimization algorithm could be enhanced dynamically according to all kinds of opportunity request weight, which could optimize the cooperative cognitive cloud platform. Finally, the QoE guarantee scheme was proposed with the opportunity weight particle swarm optimization algorithm and collaborative cognitive cloud platform. The experimental results show that the proposed mechanism compared is superior to the QoE guarantee scheme based on cooperative cloud and QoE guarantee scheme based on particle swarm optimization, compared with optimization fitness and high cloud computing service execution efficiency and high throughput performance advantages.

A novel neutron energy spectrum unfolding code using particle swarm optimization

International Nuclear Information System (INIS)

Shahabinejad, H.; Sohrabpour, M.

2017-01-01

A novel neutron Spectrum Deconvolution using Particle Swarm Optimization (SDPSO) code has been developed to unfold the neutron spectrum from a pulse height distribution and a response matrix. The Particle Swarm Optimization (PSO) imitates the bird flocks social behavior to solve complex optimization problems. The results of the SDPSO code have been compared with those of the standard spectra and recently published Two-steps Genetic Algorithm Spectrum Unfolding (TGASU) code. The TGASU code have been previously compared with the other codes such as MAXED, GRAVEL, FERDOR and GAMCD and shown to be more accurate than the previous codes. The results of the SDPSO code have been demonstrated to match well with those of the TGASU code for both under determined and over-determined problems. In addition the SDPSO has been shown to be nearly two times faster than the TGASU code. - Highlights: • Introducing a novel method for neutron spectrum unfolding. • Implementation of a particle swarm optimization code for neutron unfolding. • Comparing results of the PSO code with those of recently published TGASU code. • Match results of the PSO code with those of TGASU code. • Greater convergence rate of implemented PSO code than TGASU code.

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.

A new inertia weight control strategy for particle swarm optimization

Science.gov (United States)

Zhu, Xianming; Wang, Hongbo

2018-04-01

Particle Swarm Optimization is a member of swarm intelligence algorithms, which is inspired by the behavior of bird flocks. The inertia weight, one of the most important parameters of PSO, is crucial for PSO, for it balances the performance of exploration and exploitation of the algorithm. This paper proposes a new inertia weight control strategy and PSO with this new strategy is tested by four benchmark functions. The results shows that the new strategy provides the PSO with better performance.

Auto-Clustering using Particle Swarm Optimization and Bacterial Foraging

DEFF Research Database (Denmark)

Rutkowski Olesen, Jakob; Cordero, Jorge; Zeng, Yifeng

2009-01-01

by using simplistic collaboration. Inspired by the advances in clustering using particle swarm optimization, we suggest further improvements. Moreover, we gathered standard benchmark datasets and compared our new approach against the standard K-means algorithm, obtaining promising results. Our hybrid...

Robust design of broadband EUV multilayer beam splitters based on particle swarm optimization

International Nuclear Information System (INIS)

Jiang, Hui; Michette, Alan G.

2013-01-01

A robust design idea for broadband EUV multilayer beam splitters is introduced that achieves the aim of decreasing the influence of layer thickness errors on optical performances. Such beam splitters can be used in interferometry to determine the quality of EUVL masks by comparing with a reference multilayer. In the optimization, particle swarm techniques were used for the first time in such designs. Compared to conventional genetic algorithms, particle swarm optimization has stronger ergodicity, simpler processing and faster convergence

Optimization of fuel core loading pattern design in a VVER nuclear power reactors using Particle Swarm Optimization (PSO)

International Nuclear Information System (INIS)

Babazadeh, Davood; Boroushaki, Mehrdad; Lucas, Caro

2009-01-01

The two main goals in core fuel loading pattern design optimization are maximizing the core effective multiplication factor (K eff ) in order to extract the maximum energy, and keeping the local power peaking factor (P q ) lower than a predetermined value to maintain fuel integrity. In this research, a new strategy based on Particle Swarm Optimization (PSO) algorithm has been developed to optimize the fuel core loading pattern in a typical VVER. The PSO algorithm presents a simple social model by inspiration from bird collective behavior in finding food. A modified version of PSO algorithm for discrete variables has been developed and implemented successfully for the multi-objective optimization of fuel loading pattern design with constraints of keeping P q lower than a predetermined value and maximizing K eff . This strategy has been accomplished using WIMSD and CITATION calculation codes. Simulation results show that this algorithm can help in the acquisition of a new pattern without contravention of the constraints.

A Novel Adaptive Particle Swarm Optimization Algorithm with Foraging Behavior in Optimization Design

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Liu Yan

2018-01-01

Full Text Available The method of repeated trial and proofreading is generally used to the convention reducer design, but these methods is low efficiency and the size of the reducer is often large. Aiming the problems, this paper presents an adaptive particle swarm optimization algorithm with foraging behavior, in this method, the bacterial foraging process is introduced into the adaptive particle swarm optimization algorithm, which can provide the function of particle chemotaxis, swarming, reproduction, elimination and dispersal, to improve the ability of local search and avoid premature behavior. By test verification through typical function and the application of the optimization design in the structure of the reducer with discrete and continuous variables, the results are shown that the new algorithm has the advantages of good reliability, strong searching ability and high accuracy. It can be used in engineering design, and has a strong applicability.

Particle Swarm Optimization Based of the Maximum Photovoltaic ...

African Journals Online (AJOL)

Photovoltaic electricity is seen as an important source of renewable energy. The photovoltaic array is an unstable source of power since the peak power point depends on the temperature and the irradiation level. A maximum peak power point tracking is then necessary for maximum efficiency. In this work, a Particle Swarm ...

Camera Network Coverage Improving by Particle Swarm Optimization

NARCIS (Netherlands)

Xu, Y.C.; Lei, B.; Hendriks, E.A.

2011-01-01

This paper studies how to improve the field of view (FOV) coverage of a camera network. We focus on a special but practical scenario where the cameras are randomly scattered in a wide area and each camera may adjust its orientation but cannot move in any direction. We propose a particle swarm

Particle Swarm Imaging (PSIM). A swarming algorithm for the reporting of robust, optimal measurement uncertainties

International Nuclear Information System (INIS)

Parvin, Dan; Clarke, Sean

2015-01-01

Particle Swarm Imaging (PSIM) overcomes some of the challenges associated with the accurate declaration of measurement uncertainties of radionuclide inventories within waste items when the distribution of activity is unknown. Implementation requires minimal equipment, making use of gamma‑ray measurements taken from different locations around the waste item, using only a single electrically cooled HRGS gamma‑ray detector for objects up to a UK ISO freight container in size. The PSIM technique is a computational method that iteratively ‘homes‑in’ on the true location of activity concentrations in waste items. PSIM differs from conventional assay techniques by allowing only viable solutions - that is those that could actually give rise to the measured data - to be considered. Thus PSIM avoids the drawback of conventional analyses, namely, the adoption of unrealistic assumptions about the activity distribution that inevitably leads to the declaration of pessimistic (and in some cases optimistic) activity estimates and uncertainties. PSIM applies an optimisation technique based upon ‘particle swarming’ methods to determine a set of candidate solutions within a ‘search space’ defined by the interior volume of a waste item. The positions and activities of the swarm are used in conjunction with a mathematical model to simulate the measurement response for the current swarm location. The swarm is iteratively updated (with modified positions and activities) until a match with sufficient quality is obtained between the simulated and actual measurement data. This process is repeated to build up a distribution of candidate solutions, which is subsequently analysed to calculate a measurement result and uncertainty along with a visual image of the activity distribution. The application of ‘swarming’ computational methods to non‑destructive assay (NDA) measurements is considered novel and this paper is intended to introduce the PSIM concept and provide

Refining design of superconducting magnets synchronous with winding using particle swarm optimization

International Nuclear Information System (INIS)

Du, J.J.; Wu, W.; Mei, E.M.; Yuan, P.; Ma, L.Z.; Dong, Z.W.

2013-01-01

Highlights: ► A method of synchronous optimization design of superconducting magnets is proposed. ► We get a refining design of a main magnet on Lanzhou Penning Trap by the method. ► We expounds the necessity of tracking optimizing of coils for magnets. ► Particle swarm optimization shows effectiveness in magnet optimization. ► The expected homogeneity of the magnet improves considerably. -- Abstract: A methodology of synchronous optimization design of magnets under construction according to original design scheme is put forward in this paper, and it has been successfully used for refining design of a superconducting magnet on Lanzhou Penning Trap (LPT). This paper expounds the necessity of tracking optimization of magnet coil in the process of traditional manufacturing, and optimization design of magnet coils by particle swarm optimization is proposed. Particle swarm optimization is turned out to be an effective design method for magnet optimization. The expected homogeneity of the magnet is improved to 200 ppm from 1150 ppm through the refining optimizing, which provides important guarantee for required homogeneity of the whole magnet

Adaptive feature selection using v-shaped binary particle swarm optimization.

Science.gov (United States)

Teng, Xuyang; Dong, Hongbin; Zhou, Xiurong

2017-01-01

Feature selection is an important preprocessing method in machine learning and data mining. This process can be used not only to reduce the amount of data to be analyzed but also to build models with stronger interpretability based on fewer features. Traditional feature selection methods evaluate the dependency and redundancy of features separately, which leads to a lack of measurement of their combined effect. Moreover, a greedy search considers only the optimization of the current round and thus cannot be a global search. To evaluate the combined effect of different subsets in the entire feature space, an adaptive feature selection method based on V-shaped binary particle swarm optimization is proposed. In this method, the fitness function is constructed using the correlation information entropy. Feature subsets are regarded as individuals in a population, and the feature space is searched using V-shaped binary particle swarm optimization. The above procedure overcomes the hard constraint on the number of features, enables the combined evaluation of each subset as a whole, and improves the search ability of conventional binary particle swarm optimization. The proposed algorithm is an adaptive method with respect to the number of feature subsets. The experimental results show the advantages of optimizing the feature subsets using the V-shaped transfer function and confirm the effectiveness and efficiency of the feature subsets obtained under different classifiers.

Artificial Bee Colony Algorithm Based on K-Means Clustering for Multiobjective Optimal Power Flow Problem

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Liling Sun

2015-01-01

Full Text Available An improved multiobjective ABC algorithm based on K-means clustering, called CMOABC, is proposed. To fasten the convergence rate of the canonical MOABC, the way of information communication in the employed bees’ phase is modified. For keeping the population diversity, the multiswarm technology based on K-means clustering is employed to decompose the population into many clusters. Due to each subcomponent evolving separately, after every specific iteration, the population will be reclustered to facilitate information exchange among different clusters. Application of the new CMOABC on several multiobjective benchmark functions shows a marked improvement in performance over the fast nondominated sorting genetic algorithm (NSGA-II, the multiobjective particle swarm optimizer (MOPSO, and the multiobjective ABC (MOABC. Finally, the CMOABC is applied to solve the real-world optimal power flow (OPF problem that considers the cost, loss, and emission impacts as the objective functions. The 30-bus IEEE test system is presented to illustrate the application of the proposed algorithm. The simulation results demonstrate that, compared to NSGA-II, MOPSO, and MOABC, the proposed CMOABC is superior for solving OPF problem, in terms of optimization accuracy.

Improved Quantum Particle Swarm Optimization for Mangroves Classification

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Zhehuang Huang

2016-01-01

Full Text Available Quantum particle swarm optimization (QPSO is a population based optimization algorithm inspired by social behavior of bird flocking which combines the ideas of quantum computing. For many optimization problems, traditional QPSO algorithm can produce high-quality solution within a reasonable computation time and relatively stable convergence characteristics. But QPSO algorithm also showed some unsatisfactory issues in practical applications, such as premature convergence and poor ability in global optimization. To solve these problems, an improved quantum particle swarm optimization algorithm is proposed and implemented in this paper. There are three main works in this paper. Firstly, an improved QPSO algorithm is introduced which can enhance decision making ability of the model. Secondly, we introduce synergetic neural network model to mangroves classification for the first time which can better handle fuzzy matching of remote sensing image. Finally, the improved QPSO algorithm is used to realize the optimization of network parameter. The experiments on mangroves classification showed that the improved algorithm has more powerful global exploration ability and faster convergence speed.

A Nonlinear Multiobjective Bilevel Model for Minimum Cost Network Flow Problem in a Large-Scale Construction Project

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Jiuping Xu

2012-01-01

Full Text Available The aim of this study is to deal with a minimum cost network flow problem (MCNFP in a large-scale construction project using a nonlinear multiobjective bilevel model with birandom variables. The main target of the upper level is to minimize both direct and transportation time costs. The target of the lower level is to minimize transportation costs. After an analysis of the birandom variables, an expectation multiobjective bilevel programming model with chance constraints is formulated to incorporate decision makers’ preferences. To solve the identified special conditions, an equivalent crisp model is proposed with an additional multiobjective bilevel particle swarm optimization (MOBLPSO developed to solve the model. The Shuibuya Hydropower Project is used as a real-world example to verify the proposed approach. Results and analysis are presented to highlight the performances of the MOBLPSO, which is very effective and efficient compared to a genetic algorithm and a simulated annealing algorithm.

A Parallel Particle Swarm Optimization Algorithm Accelerated by Asynchronous Evaluations

Science.gov (United States)

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.

Parameter estimation for chaotic systems with a Drift Particle Swarm Optimization method

International Nuclear Information System (INIS)

Sun Jun; Zhao Ji; Wu Xiaojun; Fang Wei; Cai Yujie; Xu Wenbo

2010-01-01

Inspired by the motion of electrons in metal conductors in an electric field, we propose a variant of Particle Swarm Optimization (PSO), called Drift Particle Swarm Optimization (DPSO) algorithm, and apply it in estimating the unknown parameters of chaotic dynamic systems. The principle and procedure of DPSO are presented, and the algorithm is used to identify Lorenz system and Chen system. The experiment results show that for the given parameter configurations, DPSO can identify the parameters of the systems accurately and effectively, and it may be a promising tool for chaotic system identification as well as other numerical optimization problems in physics.

Numerical Simulation of a Tumor Growth Dynamics Model Using Particle Swarm Optimization.

Science.gov (United States)

Wang, Zhijun; Wang, Qing

Tumor cell growth models involve high-dimensional parameter spaces that require computationally tractable methods to solve. To address a proposed tumor growth dynamics mathematical model, an instance of the particle swarm optimization method was implemented to speed up the search process in the multi-dimensional parameter space to find optimal parameter values that fit experimental data from mice cancel cells. The fitness function, which measures the difference between calculated results and experimental data, was minimized in the numerical simulation process. The results and search efficiency of the particle swarm optimization method were compared to those from other evolutional methods such as genetic algorithms.

Automatized Parameterization of DFTB Using Particle Swarm Optimization.

Science.gov (United States)

Chou, Chien-Pin; Nishimura, Yoshifumi; Fan, Chin-Chai; Mazur, Grzegorz; Irle, Stephan; Witek, Henryk A

2016-01-12

We present a novel density-functional tight-binding (DFTB) parametrization toolkit developed to optimize the parameters of various DFTB models in a fully automatized fashion. The main features of the algorithm, based on the particle swarm optimization technique, are discussed, and a number of initial pilot applications of the developed methodology to molecular and solid systems are presented.

Optimasi Bobot Jaringan Syaraf Tiruan Mengunakan Particle Swarm Optimization

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Harry Ganda Nugraha

2014-01-01

Abstract Forecasting problem is common problem that easily found in decision making process. The popular tool to handle that problem is artificial neural network. Artificial neural network have been widely use because its ability to forecast nonlinear time series data. The learning method that have been widely use to train artificial neural network weight is backpropagation. Otherwise backpropagation learning process sometimes find problem such as over fiting so it can’t generalized the problem. Particle swarm optimization method had been proposed to train artificial neural network weigth. Mean square error, mean absolute percentage error, normalized mean square error, prediction of change in direction, average relative variance had been use to measures the model performance. Indonesia inflation time series data had been use to analyzed the model. The proposed method show that hybrid system could handle the time series forecasting problem as good as backpropagation artificial neural network   Keywords—artificial neural network, particle swarm optimization, prediction of change in direction, average relative variance.

Initiative Optimization Operation Strategy and Multi-objective Energy Management Method for Combined Cooling Heating and Power

Institute of Scientific and Technical Information of China (English)

Feng Zhao; Chenghui Zhang; Bo Sun

2016-01-01

This paper proposed an initiative optimization operation strategy and multi-objective energy management method for combined cooling heating and power(CCHP) with storage systems.Initially,the initiative optimization operation strategy of CCHP system in the cooling season,the heating season and the transition season was formulated.The energy management of CCHP system was optimized by the multi-objective optimization model with maximum daily energy efficiency,minimum daily carbon emissions and minimum daily operation cost based on the proposed initiative optimization operation strategy.Furthermore,the pareto optimal solution set was solved by using the niche particle swarm multi-objective optimization algorithm.Ultimately,the most satisfactory energy management scheme was obtained by using the technique for order preference by similarity to ideal solution(TOPSIS) method.A case study of CCHP system used in a hospital in the north of China validated the effectiveness of this method.The results showed that the satisfactory energy management scheme of CCHP system was obtained based on this initiative optimization operation strategy and multi-objective energy management method.The CCHP system has achieved better energy efficiency,environmental protection and economic benefits.

Optimization of Fuel Consumption and Emissions for Auxiliary Power Unit Based on Multi-Objective Optimization Model

Directory of Open Access Journals (Sweden)

Yongpeng Shen

2016-02-01

Full Text Available Auxiliary power units (APUs are widely used for electric power generation in various types of electric vehicles, improvements in fuel economy and emissions of these vehicles directly depend on the operating point of the APUs. In order to balance the conflicting goals of fuel consumption and emissions reduction in the process of operating point choice, the APU operating point optimization problem is formulated as a constrained multi-objective optimization problem (CMOP firstly. The four competing objectives of this CMOP are fuel-electricity conversion cost, hydrocarbon (HC emissions, carbon monoxide (CO emissions and nitric oxide (NO x emissions. Then, the multi-objective particle swarm optimization (MOPSO algorithm and weighted metric decision making method are employed to solve the APU operating point multi-objective optimization model. Finally, bench experiments under New European driving cycle (NEDC, Federal test procedure (FTP and high way fuel economy test (HWFET driving cycles show that, compared with the results of the traditional fuel consumption single-objective optimization approach, the proposed multi-objective optimization approach shows significant improvements in emissions performance, at the expense of a slight drop in fuel efficiency.

Joint global optimization of tomographic data based on particle swarm optimization and decision theory

Science.gov (United States)

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

Resolution of the stochastic strategy spatial prisoner's dilemma by means of particle swarm optimization.

Directory of Open Access Journals (Sweden)

Jianlei Zhang

Full Text Available We study the evolution of cooperation among selfish individuals in the stochastic strategy spatial prisoner's dilemma game. We equip players with the particle swarm optimization technique, and find that it may lead to highly cooperative states even if the temptations to defect are strong. The concept of particle swarm optimization was originally introduced within a simple model of social dynamics that can describe the formation of a swarm, i.e., analogous to a swarm of bees searching for a food source. Essentially, particle swarm optimization foresees changes in the velocity profile of each player, such that the best locations are targeted and eventually occupied. In our case, each player keeps track of the highest payoff attained within a local topological neighborhood and its individual highest payoff. Thus, players make use of their own memory that keeps score of the most profitable strategy in previous actions, as well as use of the knowledge gained by the swarm as a whole, to find the best available strategy for themselves and the society. Following extensive simulations of this setup, we find a significant increase in the level of cooperation for a wide range of parameters, and also a full resolution of the prisoner's dilemma. We also demonstrate extreme efficiency of the optimization algorithm when dealing with environments that strongly favor the proliferation of defection, which in turn suggests that swarming could be an important phenomenon by means of which cooperation can be sustained even under highly unfavorable conditions. We thus present an alternative way of understanding the evolution of cooperative behavior and its ubiquitous presence in nature, and we hope that this study will be inspirational for future efforts aimed in this direction.

The Improvement of Particle Swarm Optimization: a Case Study of Optimal Operation in Goupitan Reservoir

Science.gov (United States)

Li, Haichen; Qin, Tao; Wang, Weiping; Lei, Xiaohui; Wu, Wenhui

2018-02-01

Due to the weakness in holding diversity and reaching global optimum, the standard particle swarm optimization has not performed well in reservoir optimal operation. To solve this problem, this paper introduces downhill simplex method to work together with the standard particle swarm optimization. The application of this approach in Goupitan reservoir optimal operation proves that the improved method had better accuracy and higher reliability with small investment.

A New DG Multiobjective Optimization Method Based on an Improved Evolutionary Algorithm

Directory of Open Access Journals (Sweden)

Wanxing Sheng

2013-01-01

Full Text Available A distribution generation (DG multiobjective optimization method based on an improved Pareto evolutionary algorithm is investigated in this paper. The improved Pareto evolutionary algorithm, which introduces a penalty factor in the objective function constraints, uses an adaptive crossover and a mutation operator in the evolutionary process and combines a simulated annealing iterative process. The proposed algorithm is utilized to the optimize DG injection models to maximize DG utilization while minimizing system loss and environmental pollution. A revised IEEE 33-bus system with multiple DG units was used to test the multiobjective optimization algorithm in a distribution power system. The proposed algorithm was implemented and compared with the strength Pareto evolutionary algorithm 2 (SPEA2, a particle swarm optimization (PSO algorithm, and nondominated sorting genetic algorithm II (NGSA-II. The comparison of the results demonstrates the validity and practicality of utilizing DG units in terms of economic dispatch and optimal operation in a distribution power system.

Brain response pattern identification of fMRI data using a particle swarm optimization-based approach.

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Ma, Xinpei; Chou, Chun-An; Sayama, Hiroki; Chaovalitwongse, Wanpracha Art

2016-09-01

Many neuroscience studies have been devoted to understand brain neural responses correlating to cognition using functional magnetic resonance imaging (fMRI). In contrast to univariate analysis to identify response patterns, it is shown that multi-voxel pattern analysis (MVPA) of fMRI data becomes a relatively effective approach using machine learning techniques in the recent literature. MVPA can be considered as a multi-objective pattern classification problem with the aim to optimize response patterns, in which informative voxels interacting with each other are selected, achieving high classification accuracy associated with cognitive stimulus conditions. To solve the problem, we propose a feature interaction detection framework, integrating hierarchical heterogeneous particle swarm optimization and support vector machines, for voxel selection in MVPA. In the proposed approach, we first select the most informative voxels and then identify a response pattern based on the connectivity of the selected voxels. The effectiveness of the proposed approach was examined for the Haxby's dataset of object-level representations. The computational results demonstrated higher classification accuracy by the extracted response patterns, compared to state-of-the-art feature selection algorithms, such as forward selection and backward selection.

EEG Channel Selection Using Particle Swarm Optimization for the Classification of Auditory Event-Related Potentials

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Alejandro Gonzalez

2014-01-01

Full Text Available Brain-machine interfaces (BMI rely on the accurate classification of event-related potentials (ERPs and their performance greatly depends on the appropriate selection of classifier parameters and features from dense-array electroencephalography (EEG signals. Moreover, in order to achieve a portable and more compact BMI for practical applications, it is also desirable to use a system capable of accurate classification using information from as few EEG channels as possible. In the present work, we propose a method for classifying P300 ERPs using a combination of Fisher Discriminant Analysis (FDA and a multiobjective hybrid real-binary Particle Swarm Optimization (MHPSO algorithm. Specifically, the algorithm searches for the set of EEG channels and classifier parameters that simultaneously maximize the classification accuracy and minimize the number of used channels. The performance of the method is assessed through offline analyses on datasets of auditory ERPs from sound discrimination experiments. The proposed method achieved a higher classification accuracy than that achieved by traditional methods while also using fewer channels. It was also found that the number of channels used for classification can be significantly reduced without greatly compromising the classification accuracy.

Comparative Analysis of Particle Swarm and Differential Evolution via Tuning on Ultrasmall Titanium Oxide Nanoclusters

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Inclan, Eric; Lassester, Jack; Geohegan, David; Yoon, Mina

Optimization algorithms (OA) coupled with numerical methods enable researchers to identify and study (meta) stable nanoclusters without the control restrictions of empirical methods. An algorithm's performance is governed by two factors: (1) its compatibility with an objective function, (2) the dimension of a design space, which increases with cluster size. Although researchers often tune an algorithm's user-defined parameters (UDP), tuning is not guaranteed to improve performance. In this research, Particle Swarm (PSO) and Differential Evolution (DE), are compared by tuning their UDP in a multi-objective optimization environment (MOE). Combined with a Kolmogorov Smirnov test for statistical significance, the MOE enables the study of the Pareto Front (PF), made of the UDP settings that trade-off between best performance in energy minimization (``effectiveness'') based on force-field potential energy, and best convergence rate (``efficiency''). By studying the PF, this research finds that UDP values frequently suggested in the literature do not provide best effectiveness for these methods. Additionally, monotonic convergence is found to significantly improve efficiency without sacrificing effectiveness for very small systems, suggesting better compatibility. Work is supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division.

Application of Dynamic Mutated Particle Swarm Optimization Algorithm to Design Water Distribution Networks

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Kazem Mohammadi- Aghdam

2015-10-01

Full Text Available This paper proposes the application of a new version of the heuristic particle swarm optimization (PSO method for designing water distribution networks (WDNs. The optimization problem of looped water distribution networks is recognized as an NP-hard combinatorial problem which cannot be easily solved using traditional mathematical optimization techniques. In this paper, the concept of dynamic swarm size is considered in an attempt to increase the convergence speed of the original PSO algorithm. In this strategy, the size of the swarm is dynamically changed according to the iteration number of the algorithm. Furthermore, a novel mutation approach is introduced to increase the diversification property of the PSO and to help the algorithm to avoid trapping in local optima. The new version of the PSO algorithm is called dynamic mutated particle swarm optimization (DMPSO. The proposed DMPSO is then applied to solve WDN design problems. Finally, two illustrative examples are used for comparison to verify the efficiency of the proposed DMPSO as compared to other intelligent algorithms.

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

[Application of an Adaptive Inertia Weight Particle Swarm Algorithm in the Magnetic Resonance Bias Field Correction].

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Wang, Chang; Qin, Xin; Liu, Yan; Zhang, Wenchao

2016-06-01

An adaptive inertia weight particle swarm algorithm is proposed in this study to solve the local optimal problem with the method of traditional particle swarm optimization in the process of estimating magnetic resonance(MR)image bias field.An indicator measuring the degree of premature convergence was designed for the defect of traditional particle swarm optimization algorithm.The inertia weight was adjusted adaptively based on this indicator to ensure particle swarm to be optimized globally and to avoid it from falling into local optimum.The Legendre polynomial was used to fit bias field,the polynomial parameters were optimized globally,and finally the bias field was estimated and corrected.Compared to those with the improved entropy minimum algorithm,the entropy of corrected image was smaller and the estimated bias field was more accurate in this study.Then the corrected image was segmented and the segmentation accuracy obtained in this research was 10% higher than that with improved entropy minimum algorithm.This algorithm can be applied to the correction of MR image bias field.

Celestial Navigation Fix Based on Particle Swarm Optimization

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Tsou Ming-Cheng

2015-09-01

Full Text Available A technique for solving celestial fix problems is proposed in this study. This method is based on Particle Swarm Optimization from the field of swarm intelligence, utilizing its superior optimization and searching abilities to obtain the most probable astronomical vessel position. In addition to being applicable to two-body fix, multi-body fix, and high-altitude observation problems, it is also less reliant on the initial dead reckoning position. Moreover, by introducing spatial data processing and display functions in a Geographical Information System, calculation results and chart work used in Circle of Position graphical positioning can both be integrated. As a result, in addition to avoiding tedious and complicated computational and graphical procedures, this work has more flexibility and is more robust when compared to other analytical approaches.

Application of a particle swarm optimization for shape optimization in hydraulic machinery

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Moravec, Prokop; Rudolf, Pavel

A study of shape optimization has become increasingly popular in academia and industry. A typical problem is to find an optimal shape, which minimizes (or maximizes) a certain cost function and satisfies given constraints. Particle Swarm Optimization (PSO) has received a lot of attention in past years and is inspired by social behaviour of some animals such as flocking behaviour of birds. This paper focuses on a possibility of a diffuser shape optimization using particle swarm optimization (PSO), which is coupled with CFD simulation. Influence of main parameters of PSO-algorithm and later diffuser shapes obtained with this method are discussed and advantages/disadvantages summarized.

Application of a particle swarm optimization for shape optimization in hydraulic machinery

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Moravec Prokop

2017-01-01

Full Text Available A study of shape optimization has become increasingly popular in academia and industry. A typical problem is to find an optimal shape, which minimizes (or maximizes a certain cost function and satisfies given constraints. Particle Swarm Optimization (PSO has received a lot of attention in past years and is inspired by social behaviour of some animals such as flocking behaviour of birds. This paper focuses on a possibility of a diffuser shape optimization using particle swarm optimization (PSO, which is coupled with CFD simulation. Influence of main parameters of PSO-algorithm and later diffuser shapes obtained with this method are discussed and advantages/disadvantages summarized.

Particle Swarm Social Adaptive Model for Multi-Agent Based Insurgency Warfare Simulation

Energy Technology Data Exchange (ETDEWEB)

Cui, Xiaohui [ORNL; Potok, Thomas E [ORNL

2009-12-01

To better understand insurgent activities and asymmetric warfare, a social adaptive model for modeling multiple insurgent groups attacking multiple military and civilian targets is proposed and investigated. This report presents a pilot study using the particle swarm modeling, a widely used non-linear optimal tool to model the emergence of insurgency campaign. The objective of this research is to apply the particle swarm metaphor as a model of insurgent social adaptation for the dynamically changing environment and to provide insight and understanding of insurgency warfare. Our results show that unified leadership, strategic planning, and effective communication between insurgent groups are not the necessary requirements for insurgents to efficiently attain their objective.

Kinetic phenomena in charged particle transport in gases, swarm parameters and cross section data

International Nuclear Information System (INIS)

Petrovic, Z Lj; Suvakov, M; Nikitovic, Z; Dujko, S; Sasic, O; Jovanovic, J; Malovic, G; Stojanovic, V

2007-01-01

In this review we discuss the current status of the physics of charged particle swarms, mainly electrons. The whole field is analysed mainly through its relationship to plasma modelling and illustrated by some recent examples developed mainly by our group. The measurements of the swarm coefficients and the availability of the data are briefly discussed. More time is devoted to the development of complete electron-molecule cross section sets along with recent examples such as NO, CF 4 and HBr. We extend the discussion to the availability of ion and fast neutral data and how swarm experiments may serve to provide new data. As a point where new insight into the kinetics of charge particle transport is provided, the role of kinetic phenomena is discussed and recent examples are listed. We focus here on giving two examples on how non-conservative processes make dramatic effects in transport, the negative absolute mobility and the negative differential conductivity for positrons in argon. Finally we discuss the applicability of swarm data in plasma modelling and the relationship to other fields where swarm experiments and analysis make significant contributions. (topical review)

Han's model parameters for microalgae grown under intermittent illumination: Determined using particle swarm optimization.

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Pozzobon, Victor; Perre, Patrick

2018-01-21

This work provides a model and the associated set of parameters allowing for microalgae population growth computation under intermittent lightning. Han's model is coupled with a simple microalgae growth model to yield a relationship between illumination and population growth. The model parameters were obtained by fitting a dataset available in literature using Particle Swarm Optimization method. In their work, authors grew microalgae in excess of nutrients under flashing conditions. Light/dark cycles used for these experimentations are quite close to those found in photobioreactor, i.e. ranging from several seconds to one minute. In this work, in addition to producing the set of parameters, Particle Swarm Optimization robustness was assessed. To do so, two different swarm initialization techniques were used, i.e. uniform and random distribution throughout the search-space. Both yielded the same results. In addition, swarm distribution analysis reveals that the swarm converges to a unique minimum. Thus, the produced set of parameters can be trustfully used to link light intensity to population growth rate. Furthermore, the set is capable to describe photodamages effects on population growth. Hence, accounting for light overexposure effect on algal growth. Copyright © 2017 Elsevier Ltd. All rights reserved.

Improved Particle Swarm Optimization with a Collective Local Unimodal Search for Continuous Optimization Problems

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Arasomwan, Martins Akugbe; Adewumi, Aderemi Oluyinka

2014-01-01

A new local search technique is proposed and used to improve the performance of particle swarm optimization algorithms by addressing the problem of premature convergence. In the proposed local search technique, a potential particle position in the solution search space is collectively constructed by a number of randomly selected particles in the swarm. The number of times the selection is made varies with the dimension of the optimization problem and each selected particle donates the value in the location of its randomly selected dimension from its personal best. After constructing the potential particle position, some local search is done around its neighbourhood in comparison with the current swarm global best position. It is then used to replace the global best particle position if it is found to be better; otherwise no replacement is made. Using some well-studied benchmark problems with low and high dimensions, numerical simulations were used to validate the performance of the improved algorithms. Comparisons were made with four different PSO variants, two of the variants implement different local search technique while the other two do not. Results show that the improved algorithms could obtain better quality solution while demonstrating better convergence velocity and precision, stability, robustness, and global-local search ability than the competing variants. PMID:24723827

Discrete ternary particle swarm optimization for area optimization of MPRM circuits

International Nuclear Information System (INIS)

Yu Haizhen; Wang Pengjun; Wang Disheng; Zhang Huihong

2013-01-01

Having the advantage of simplicity, robustness and low computational costs, the particle swarm optimization (PSO) algorithm is a powerful evolutionary computation tool for synthesis and optimization of Reed-Muller logic based circuits. Exploring discrete PSO and probabilistic transition rules, the discrete ternary particle swarm optimization (DTPSO) is proposed for mixed polarity Reed-Muller (MPRM) circuits. According to the characteristics of mixed polarity OR/XNOR expression, a tabular technique is improved, and it is applied in the polarity conversion of MPRM functions. DTPSO is introduced to search the best polarity for an area of MPRM circuits by building parameter mapping relationships between particles and polarities. The computational results show that the proposed DTPSO outperforms the reported method using maxterm conversion starting from POS Boolean functions. The average saving in the number of terms is about 11.5%; the algorithm is quite efficient in terms of CPU time and achieves 12.2% improvement on average. (semiconductor integrated circuits)

Multi-objective mean-variance-skewness model for generation portfolio allocation in electricity markets

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Pindoriya, N.M.; Singh, S.N. [Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur 208016 (India); Singh, S.K. [Indian Institute of Management Lucknow, Lucknow 226013 (India)

2010-10-15

This paper proposes an approach for generation portfolio allocation based on mean-variance-skewness (MVS) model which is an extension of the classical mean-variance (MV) portfolio theory, to deal with assets whose return distribution is non-normal. The MVS model allocates portfolios optimally by considering the maximization of both the expected return and skewness of portfolio return while simultaneously minimizing the risk. Since, it is competing and conflicting non-smooth multi-objective optimization problem, this paper employed a multi-objective particle swarm optimization (MOPSO) based meta-heuristic technique to provide Pareto-optimal solution in a single simulation run. Using a case study of the PJM electricity market, the performance of the MVS portfolio theory based method and the classical MV method is compared. It has been found that the MVS portfolio theory based method can provide significantly better portfolios in the situation where non-normally distributed assets exist for trading. (author)

Multi-objective mean-variance-skewness model for generation portfolio allocation in electricity markets

International Nuclear Information System (INIS)

Pindoriya, N.M.; Singh, S.N.; Singh, S.K.

2010-01-01

This paper proposes an approach for generation portfolio allocation based on mean-variance-skewness (MVS) model which is an extension of the classical mean-variance (MV) portfolio theory, to deal with assets whose return distribution is non-normal. The MVS model allocates portfolios optimally by considering the maximization of both the expected return and skewness of portfolio return while simultaneously minimizing the risk. Since, it is competing and conflicting non-smooth multi-objective optimization problem, this paper employed a multi-objective particle swarm optimization (MOPSO) based meta-heuristic technique to provide Pareto-optimal solution in a single simulation run. Using a case study of the PJM electricity market, the performance of the MVS portfolio theory based method and the classical MV method is compared. It has been found that the MVS portfolio theory based method can provide significantly better portfolios in the situation where non-normally distributed assets exist for trading. (author)

Application of ant colony Algorithm and particle swarm optimization in architectural design

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Song, Ziyi; Wu, Yunfa; Song, Jianhua

2018-02-01

By studying the development of ant colony algorithm and particle swarm algorithm, this paper expounds the core idea of the algorithm, explores the combination of algorithm and architectural design, sums up the application rules of intelligent algorithm in architectural design, and combines the characteristics of the two algorithms, obtains the research route and realization way of intelligent algorithm in architecture design. To establish algorithm rules to assist architectural design. Taking intelligent algorithm as the beginning of architectural design research, the authors provide the theory foundation of ant colony Algorithm and particle swarm algorithm in architectural design, popularize the application range of intelligent algorithm in architectural design, and provide a new idea for the architects.

Simulation of microcirculatory hemodynamics: estimation of boundary condition using particle swarm optimization.

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Pan, Qing; Wang, Ruofan; Reglin, Bettina; Fang, Luping; Pries, Axel R; Ning, Gangmin

2014-01-01

Estimation of the boundary condition is a critical problem in simulating hemodynamics in microvascular networks. This paper proposed a boundary estimation strategy based on a particle swarm optimization (PSO) algorithm, which aims to minimize the number of vessels with inverted flow direction in comparison to the experimental observation. The algorithm took boundary values as the particle swarm and updated the position of the particles iteratively to approach the optimization target. The method was tested in a real rat mesenteric network. With random initial boundary values, the method achieved a minimized 9 segments with an inverted flow direction in the network with 546 vessels. Compared with reported literature, the current work has the advantage of a better fit with experimental observations and is more suitable for the boundary estimation problem in pulsatile hemodynamic models due to the experiment-based optimization target selection.

A chaotic quantum-behaved particle swarm approach applied to optimization of heat exchangers

International Nuclear Information System (INIS)

Mariani, Viviana Cocco; Klassen Duck, Anderson Rodrigo; Guerra, Fabio Alessandro; Santos Coelho, Leandro dos; Rao, Ravipudi Venkata

2012-01-01

Particle swarm optimization (PSO) method is a population-based optimization technique of swarm intelligence field in which each solution called “particle” flies around in a multidimensional problem search space. During the flight, every particle adjusts its position according to its own experience, as well as the experience of neighboring particles, using the best position encountered by itself and its neighbors. In this paper, a new quantum particle swarm optimization (QPSO) approach combined with Zaslavskii chaotic map sequences (QPSOZ) to shell and tube heat exchanger optimization is presented based on the minimization from economic view point. The results obtained in this paper for two case studies using the proposed QPSOZ approach, are compared with those obtained by using genetic algorithm, PSO and classical QPSO showing the best performance of QPSOZ. In order to verify the capability of the proposed method, two case studies are also presented showing that significant cost reductions are feasible with respect to traditionally designed exchangers. Referring to the literature test cases, reduction of capital investment up to 20% and 6% for the first and second cases, respectively, were obtained. Therefore, the annual pumping cost decreased markedly 72% and 75%, with an overall decrease of total cost up to 30% and 27%, respectively, for the cases 1 and 2, respectively, showing the improvement potential of the proposed method, QPSOZ. - Highlights: ► Shell and tube heat exchanger is minimized from economic view point. ► A new quantum particle swarm optimization (QPSO) combined with Zaslavskii chaotic map sequences (QPSOZ) is proposed. ► Reduction of capital investment up to 20% and 6% for the first and second cases was obtained. ► Annual pumping cost decreased 72% and 75%, with an overall decrease of total cost up to 30% and 27% using QPSOZ.

Genetic Learning Particle Swarm Optimization.

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Gong, Yue-Jiao; Li, Jing-Jing; Zhou, Yicong; Li, Yun; Chung, Henry Shu-Hung; Shi, Yu-Hui; Zhang, Jun

2016-10-01

Social learning in particle swarm optimization (PSO) helps collective efficiency, whereas individual reproduction in genetic algorithm (GA) facilitates global effectiveness. This observation recently leads to hybridizing PSO with GA for performance enhancement. However, existing work uses a mechanistic parallel superposition and research has shown that construction of superior exemplars in PSO is more effective. Hence, this paper first develops a new framework so as to organically hybridize PSO with another optimization technique for "learning." This leads to a generalized "learning PSO" paradigm, the *L-PSO. The paradigm is composed of two cascading layers, the first for exemplar generation and the second for particle updates as per a normal PSO algorithm. Using genetic evolution to breed promising exemplars for PSO, a specific novel *L-PSO algorithm is proposed in the paper, termed genetic learning PSO (GL-PSO). In particular, genetic operators are used to generate exemplars from which particles learn and, in turn, historical search information of particles provides guidance to the evolution of the exemplars. By performing crossover, mutation, and selection on the historical information of particles, the constructed exemplars are not only well diversified, but also high qualified. Under such guidance, the global search ability and search efficiency of PSO are both enhanced. The proposed GL-PSO is tested on 42 benchmark functions widely adopted in the literature. Experimental results verify the effectiveness, efficiency, robustness, and scalability of the GL-PSO.

Particle swarm optimization of ascent trajectories of multistage launch vehicles

Science.gov (United States)

Pontani, Mauro

2014-02-01

Multistage launch vehicles are commonly employed to place spacecraft and satellites in their operational orbits. If the rocket characteristics are specified, the optimization of its ascending trajectory consists of determining the optimal control law that leads to maximizing the final mass at orbit injection. The numerical solution of a similar problem is not trivial and has been pursued with different methods, for decades. This paper is concerned with an original approach based on the joint use of swarming theory and the necessary conditions for optimality. The particle swarm optimization technique represents a heuristic population-based optimization method inspired by the natural motion of bird flocks. Each individual (or particle) that composes the swarm corresponds to a solution of the problem and is associated with a position and a velocity vector. The formula for velocity updating is the core of the method and is composed of three terms with stochastic weights. As a result, the population migrates toward different regions of the search space taking advantage of the mechanism of information sharing that affects the overall swarm dynamics. At the end of the process the best particle is selected and corresponds to the optimal solution to the problem of interest. In this work the three-dimensional trajectory of the multistage rocket is assumed to be composed of four arcs: (i) first stage propulsion, (ii) second stage propulsion, (iii) coast arc (after release of the second stage), and (iv) third stage propulsion. The Euler-Lagrange equations and the Pontryagin minimum principle, in conjunction with the Weierstrass-Erdmann corner conditions, are employed to express the thrust angles as functions of the adjoint variables conjugate to the dynamics equations. The use of these analytical conditions coming from the calculus of variations leads to obtaining the overall rocket dynamics as a function of seven parameters only, namely the unknown values of the initial state

Swarm intelligence based on modified PSO algorithm for the optimization of axial-flow pump impeller

International Nuclear Information System (INIS)

Miao, Fuqing; Kim, Chol Min; Ahn, Seok Young; Park, Hong Seok

2015-01-01

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_h, chord angle β_c, cascade solidity of chord σ_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

Swarm intelligence based on modified PSO algorithm for the optimization of axial-flow pump impeller

Energy Technology Data Exchange (ETDEWEB)

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.

Multispecies Coevolution Particle Swarm Optimization Based on Previous Search History

Directory of Open Access Journals (Sweden)

Danping Wang

2017-01-01

Full Text Available A hybrid coevolution particle swarm optimization algorithm with dynamic multispecies strategy based on K-means clustering and nonrevisit strategy based on Binary Space Partitioning fitness tree (called MCPSO-PSH is proposed. Previous search history memorized into the Binary Space Partitioning fitness tree can effectively restrain the individuals’ revisit phenomenon. The whole population is partitioned into several subspecies and cooperative coevolution is realized by an information communication mechanism between subspecies, which can enhance the global search ability of particles and avoid premature convergence to local optimum. To demonstrate the power of the method, comparisons between the proposed algorithm and state-of-the-art algorithms are grouped into two categories: 10 basic benchmark functions (10-dimensional and 30-dimensional, 10 CEC2005 benchmark functions (30-dimensional, and a real-world problem (multilevel image segmentation problems. Experimental results show that MCPSO-PSH displays a competitive performance compared to the other swarm-based or evolutionary algorithms in terms of solution accuracy and statistical tests.

Crop Classification by Forward Neural Network with Adaptive Chaotic Particle Swarm Optimization

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Yudong Zhang

2011-05-01

Full Text Available This paper proposes a hybrid crop classifier for polarimetric synthetic aperture radar (SAR images. The feature sets consisted of span image, the H/A/α decomposition, and the gray-level co-occurrence matrix (GLCM based texture features. Then, the features were reduced by principle component analysis (PCA. Finally, a two-hidden-layer forward neural network (NN was constructed and trained by adaptive chaotic particle swarm optimization (ACPSO. K-fold cross validation was employed to enhance generation. The experimental results on Flevoland sites demonstrate the superiority of ACPSO to back-propagation (BP, adaptive BP (ABP, momentum BP (MBP, Particle Swarm Optimization (PSO, and Resilient back-propagation (RPROP methods. Moreover, the computation time for each pixel is only 1.08 × 10−7 s.

Coevolutionary particle swarm optimization using Gaussian distribution for solving constrained optimization problems.

Science.gov (United States)

Krohling, Renato A; Coelho, Leandro dos Santos

2006-12-01

In this correspondence, an approach based on coevolutionary particle swarm optimization to solve constrained optimization problems formulated as min-max problems is presented. In standard or canonical particle swarm optimization (PSO), a uniform probability distribution is used to generate random numbers for the accelerating coefficients of the local and global terms. We propose a Gaussian probability distribution to generate the accelerating coefficients of PSO. Two populations of PSO using Gaussian distribution are used on the optimization algorithm that is tested on a suite of well-known benchmark constrained optimization problems. Results have been compared with the canonical PSO (constriction factor) and with a coevolutionary genetic algorithm. Simulation results show the suitability of the proposed algorithm in terms of effectiveness and robustness.

An Adaptive Cultural Algorithm with Improved Quantum-behaved Particle Swarm Optimization for Sonar Image Detection.

Science.gov (United States)

Wang, Xingmei; Hao, Wenqian; Li, Qiming

2017-12-18

This paper proposes an adaptive cultural algorithm with improved quantum-behaved particle swarm optimization (ACA-IQPSO) to detect the underwater sonar image. In the population space, to improve searching ability of particles, iterative times and the fitness value of particles are regarded as factors to adaptively adjust the contraction-expansion coefficient of the quantum-behaved particle swarm optimization algorithm (QPSO). The improved quantum-behaved particle swarm optimization algorithm (IQPSO) can make particles adjust their behaviours according to their quality. In the belief space, a new update strategy is adopted to update cultural individuals according to the idea of the update strategy in shuffled frog leaping algorithm (SFLA). Moreover, to enhance the utilization of information in the population space and belief space, accept function and influence function are redesigned in the new communication protocol. The experimental results show that ACA-IQPSO can obtain good clustering centres according to the grey distribution information of underwater sonar images, and accurately complete underwater objects detection. Compared with other algorithms, the proposed ACA-IQPSO has good effectiveness, excellent adaptability, a powerful searching ability and high convergence efficiency. Meanwhile, the experimental results of the benchmark functions can further demonstrate that the proposed ACA-IQPSO has better searching ability, convergence efficiency and stability.

A self-adaptive chaotic particle swarm algorithm for short term hydroelectric system scheduling in deregulated environment

International Nuclear Information System (INIS)

Jiang Chuanwen; Bompard, Etorre

2005-01-01

This paper proposes a short term hydroelectric plant dispatch model based on the rule of maximizing the benefit. For the optimal dispatch model, which is a large scale nonlinear planning problem with multi-constraints and multi-variables, this paper proposes a novel self-adaptive chaotic particle swarm optimization algorithm to solve the short term generation scheduling of a hydro-system better in a deregulated environment. Since chaotic mapping enjoys certainty, ergodicity and the stochastic property, the proposed approach introduces chaos mapping and an adaptive scaling term into the particle swarm optimization algorithm, which increases its convergence rate and resulting precision. The new method has been examined and tested on a practical hydro-system. The results are promising and show the effectiveness and robustness of the proposed approach in comparison with the traditional particle swarm optimization algorithm

Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review.

Science.gov (United States)

Bonyadi, Mohammad Reza; Michalewicz, Zbigniew

2017-01-01

This paper reviews recent studies on the Particle Swarm Optimization (PSO) algorithm. The review has been focused on high impact recent articles that have analyzed and/or modified PSO algorithms. This paper also presents some potential areas for future study.

A quantum particle swarm optimizer with chaotic mutation operator

International Nuclear Information System (INIS)

Coelho, Leandro dos Santos

2008-01-01

Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm that shares many similarities with evolutionary computation techniques. However, the PSO is driven by the simulation of a social psychological metaphor motivated by collective behaviors of bird and other social organisms instead of the survival of the fittest individual. Inspired by the classical PSO method and quantum mechanics theories, this work presents a novel Quantum-behaved PSO (QPSO) using chaotic mutation operator. The application of chaotic sequences based on chaotic Zaslavskii map instead of random sequences in QPSO is a powerful strategy to diversify the QPSO population and improve the QPSO's performance in preventing premature convergence to local minima. The simulation results demonstrate good performance of the QPSO in solving a well-studied continuous optimization problem of mechanical engineering design

Anesthesiology Nurse Scheduling using Particle Swarm Optimization

Directory of Open Access Journals (Sweden)

Leopoldo Altamirano

2012-02-01

Full Text Available In this article we present an approach designed to solve a real world problem: the Anesthesiology Nurse Scheduling Problem (ANSP at a public French hospital. The anesthesiology nurses are one of the most shared resources in the hospital and we attempt to find a fair/balanced schedule for them, taking into account a set of constraints and the nursesarsquo; stated preferences, concerning the different shifts. We propose a particle swarm optimization algorithm to solve the ANSP. Finally, we compare our technique with previous results obtained using integer programming.

A comparative study on using meta-heuristic algorithms for road maintenance planning: Insights from field study in a developing country

Directory of Open Access Journals (Sweden)

Ali Gerami Matin

2017-10-01

Full Text Available Optimized road maintenance planning seeks for solutions that can minimize the life-cycle cost of a road network and concurrently maximize pavement condition. Aiming at proposing an optimal set of road maintenance solutions, robust meta-heuristic algorithms are used in research. Two main optimization techniques are applied including single-objective and multi-objective optimization. Genetic algorithms (GA, particle swarm optimization (PSO, and combination of genetic algorithm and particle swarm optimization (GAPSO as single-objective techniques are used, while the non-domination sorting genetic algorithm II (NSGAII and multi-objective particle swarm optimization (MOPSO which are sufficient for solving computationally complex large-size optimization problems as multi-objective techniques are applied and compared. A real case study from the rural transportation network of Iran is employed to illustrate the sufficiency of the optimum algorithm. The formulation of the optimization model is carried out in such a way that a cost-effective maintenance strategy is reached by preserving the performance level of the road network at a desirable level. So, the objective functions are pavement performance maximization and maintenance cost minimization. It is concluded that multi-objective algorithms including non-domination sorting genetic algorithm II (NSGAII and multi-objective particle swarm optimization performed better than the single objective algorithms due to the capability to balance between both objectives. And between multi-objective algorithms the NSGAII provides the optimum solution for the road maintenance planning.

A Local and Global Search Combine Particle Swarm Optimization Algorithm for Job-Shop Scheduling to Minimize Makespan

Directory of Open Access Journals (Sweden)

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.

An implementation of particle swarm optimization to evaluate optimal under-voltage load shedding in competitive electricity markets

Science.gov (United States)

Hosseini-Bioki, M. M.; Rashidinejad, M.; Abdollahi, A.

2013-11-01

Load shedding is a crucial issue in power systems especially under restructured electricity environment. Market-driven load shedding in reregulated power systems associated with security as well as reliability is investigated in this paper. A technoeconomic multi-objective function is introduced to reveal an optimal load shedding scheme considering maximum social welfare. The proposed optimization problem includes maximum GENCOs and loads' profits as well as maximum loadability limit under normal and contingency conditions. Particle swarm optimization (PSO) as a heuristic optimization technique, is utilized to find an optimal load shedding scheme. In a market-driven structure, generators offer their bidding blocks while the dispatchable loads will bid their price-responsive demands. An independent system operator (ISO) derives a market clearing price (MCP) while rescheduling the amount of generating power in both pre-contingency and post-contingency conditions. The proposed methodology is developed on a 3-bus system and then is applied to a modified IEEE 30-bus test system. The obtained results show the effectiveness of the proposed methodology in implementing the optimal load shedding satisfying social welfare by maintaining voltage stability margin (VSM) through technoeconomic analyses.

Application of Advanced Particle Swarm Optimization Techniques to Wind-thermal Coordination

DEFF Research Database (Denmark)

Singh, Sri Niwas; Østergaard, Jacob; Yadagiri, J.

2009-01-01

wind-thermal coordination algorithm is necessary to determine the optimal proportion of wind and thermal generator capacity that can be integrated into the system. In this paper, four versions of Particle Swarm Optimization (PSO) techniques are proposed for solving wind-thermal coordination problem...

Multi-objective PSO based optimal placement of solar power DG in radial distribution system

Directory of Open Access Journals (Sweden)

Mahesh Kumar

2017-06-01

Full Text Available Ever increasing trend of electricity demand, fossil fuel depletion and environmental issues request the integration of renewable energy into the distribution system. The optimal planning of renewable distributed generation (DG is much essential for ensuring maximum benefits. Hence, this paper proposes the optimal placement of probabilistic based solar power DG into the distribution system. The two objective functions such as power loss reduction and voltage stability index improvement are optimized. The power balance and voltage limits are kept as constraints of the problem. The non-sorting pare to-front based multi-objective particle swarm optimization (MOPSO technique is proposed on standard IEEE 33 radial distribution test system.

Multidisciplinary Optimization of a Transport Aircraft Wing using Particle Swarm Optimization

Science.gov (United States)

Sobieszczanski-Sobieski, Jaroslaw; Venter, Gerhard

2002-01-01

The purpose of this paper is to demonstrate the application of particle swarm optimization to a realistic multidisciplinary optimization test problem. The paper's new contributions to multidisciplinary optimization is the application of a new algorithm for dealing with the unique challenges associated with multidisciplinary optimization problems, and recommendations as to the utility of the algorithm in future multidisciplinary optimization applications. The selected example is a bi-level optimization problem that demonstrates severe numerical noise and has a combination of continuous and truly discrete design variables. The use of traditional gradient-based optimization algorithms is thus not practical. The numerical results presented indicate that the particle swarm optimization algorithm is able to reliably find the optimum design for the problem presented here. The algorithm is capable of dealing with the unique challenges posed by multidisciplinary optimization as well as the numerical noise and truly discrete variables present in the current example problem.

The Optimal Wavelengths for Light Absorption Spectroscopy Measurements Based on Genetic Algorithm-Particle Swarm Optimization

Science.gov (United States)

Tang, Ge; Wei, Biao; Wu, Decao; Feng, Peng; Liu, Juan; Tang, Yuan; Xiong, Shuangfei; Zhang, Zheng

2018-03-01

To select the optimal wavelengths in the light extinction spectroscopy measurement, genetic algorithm-particle swarm optimization (GAPSO) based on genetic algorithm (GA) and particle swarm optimization (PSO) is adopted. The change of the optimal wavelength positions in different feature size parameters and distribution parameters is evaluated. Moreover, the Monte Carlo method based on random probability is used to identify the number of optimal wavelengths, and good inversion effects of the particle size distribution are obtained. The method proved to have the advantage of resisting noise. In order to verify the feasibility of the algorithm, spectra with bands ranging from 200 to 1000 nm are computed. Based on this, the measured data of standard particles are used to verify the algorithm.

A particle-based simplified swarm optimization algorithm for reliability redundancy allocation problems

International Nuclear Information System (INIS)

Huang, Chia-Ling

2015-01-01

This paper proposes a new swarm intelligence method known as the Particle-based Simplified Swarm Optimization (PSSO) algorithm while undertaking a modification of the Updating Mechanism (UM), called N-UM and R-UM, and simultaneously applying an Orthogonal Array Test (OA) to solve reliability–redundancy allocation problems (RRAPs) successfully. One difficulty of RRAP is the need to maximize system reliability in cases where the number of redundant components and the reliability of corresponding components in each subsystem are simultaneously decided with nonlinear constraints. In this paper, four RRAP benchmarks are used to display the applicability of the proposed PSSO that advances the strengths of both PSO and SSO to enable optimizing the RRAP that belongs to mixed-integer nonlinear programming. When the computational results are compared with those of previously developed algorithms in existing literature, the findings indicate that the proposed PSSO is highly competitive and performs well. - Highlights: • This paper proposes a particle-based simplified swarm optimization algorithm (PSSO) to optimize RRAP. • Furthermore, the UM and an OA are adapted to advance in optimizing RRAP. • Four systems are introduced and the results demonstrate the PSSO performs particularly well

Hybrid particle swarm optimization algorithm and its application in nuclear engineering

International Nuclear Information System (INIS)

Liu, C.Y.; Yan, C.Q.; Wang, J.J.

2014-01-01

Highlights: • We propose a hybrid particle swarm optimization algorithm (HPSO). • Modified Nelder–Mead simplex search method is applied in HPSO. • The algorithm has a high search precision and rapidly calculation speed. • HPSO can be used in the nuclear engineering optimization design problems. - Abstract: A hybrid particle swarm optimization algorithm with a feasibility-based rule for solving constrained optimization problems has been developed in this research. Firstly, the global optimal solution zone can be obtained through particle swarm optimization process, and then the refined search of the global optimal solution will be achieved through the modified Nelder–Mead simplex algorithm. Simulations based on two well-studied benchmark problems demonstrate the proposed algorithm will be an efficient alternative to solving constrained optimization problems. The vertical electrical heating pressurizer is one of the key components in reactor coolant system. The mathematical model of pressurizer has been established in steady state. The optimization design of pressurizer weight has been carried out through HPSO algorithm. The results show the pressurizer weight can be reduced by 16.92%. The thermal efficiencies of conventional PWR nuclear power plants are about 31–35% so far, which are much lower than fossil fueled plants based in a steam cycle as PWR. The thermal equilibrium mathematic model for nuclear power plant secondary loop has been established. An optimization case study has been conducted to improve the efficiency of the nuclear power plant with the proposed algorithm. The results show the thermal efficiency is improved by 0.5%

A Comparison of Selected Modifications of the Particle Swarm Optimization Algorithm

Directory of Open Access Journals (Sweden)

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.

The Inertia Weight Updating Strategies in Particle Swarm Optimisation Based on the Beta Distribution

Directory of Open Access Journals (Sweden)

Petr Maca

2015-01-01

Full Text Available The presented paper deals with the comparison of selected random updating strategies of inertia weight in particle swarm optimisation. Six versions of particle swarm optimization were analysed on 28 benchmark functions, prepared for the Special Session on Real-Parameter Single Objective Optimisation at CEC2013. The random components of tested inertia weight were generated from Beta distribution with different values of shape parameters. The best analysed PSO version is the multiswarm PSO, which combines two strategies of updating the inertia weight. The first is driven by the temporally varying shape parameters, while the second is based on random control of shape parameters of Beta distribution.

Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimizer for Improving Convergence Performance

Directory of Open Access Journals (Sweden)

Narinder Singh

2017-01-01

Full Text Available A newly hybrid nature inspired algorithm called HPSOGWO is presented with the combination of Particle Swarm Optimization (PSO and Grey Wolf Optimizer (GWO. The main idea is to improve the ability of exploitation in Particle Swarm Optimization with the ability of exploration in Grey Wolf Optimizer to produce both variants’ strength. Some unimodal, multimodal, and fixed-dimension multimodal test functions are used to check the solution quality and performance of HPSOGWO variant. The numerical and statistical solutions show that the hybrid variant outperforms significantly the PSO and GWO variants in terms of solution quality, solution stability, convergence speed, and ability to find the global optimum.

Optimal power flow by particle swarm optimization with an aging ...

African Journals Online (AJOL)

In this paper, a particle swarm optimization (PSO) with an aging leader and challengers (ALC-PSO) is applied for the solution of OPF problem of power system. This study is implemented on modified IEEE 30-bus test power system with different objectives that reflect minimization of either fuel cost or active power loss or sum ...

Identification of strategy parameters for particle swarm optimizer through Taguchi method

Institute of Scientific and Technical Information of China (English)

KHOSLA Arun; KUMAR Shakti; AGGARWAL K.K.

2006-01-01

Particle swarm optimization (PSO), like other evolutionary algorithms is a population-based stochastic algorithm inspired from the metaphor of social interaction in birds, insects, wasps, etc. It has been used for finding promising solutions in complex search space through the interaction of particles in a swarm. It is a well recognized fact that the performance of evolutionary algorithms to a great extent depends on the choice of appropriate strategy/operating parameters like population size,crossover rate, mutation rate, crossover operator, etc. Generally, these parameters are selected through hit and trial process, which is very unsystematic and requires rigorous experimentation. This paper proposes a systematic based on Taguchi method reasoning scheme for rapidly identifying the strategy parameters for the PSO algorithm. The Taguchi method is a robust design approach using fractional factorial design to study a large number of parameters with small number of experiments. Computer simulations have been performed on two benchmark functions-Rosenbrock function and Griewank function-to validate the approach.

The New Multipoint Relays Selection in OLSR using Particle Swarm Optimization

Directory of Open Access Journals (Sweden)

Razali Ngah

2012-06-01

Full Text Available The standard Optimized Link State Routing (OLSR introduces an interesting concept, the multipoint relays (MPRs, to mitigate message overhead during the flooding process. We propose a new algorithm for MPRs selection to enhance the performance of OLSR using Particle Swarm Optimization Sigmoid Increasing Inertia Weight (PSOSIIW. The sigmoid increasing inertia weight has significance improve the particle swarm optimization (PSO in terms of simplicity and quick convergence towards optimum solution. The new fitness function of PSO-SIIW, packet delay of each node and degree of willingness are introduced to support MPRs selection in OLSR. We examine the throughput, packet loss and end-to-end delay of the proposed method using network simulator 2 (ns2.  Overall results indicate that OLSR-PSOSIIW has shown good performance compared to the standard OLSR and OLSR-PSO, particularly for the throughput and end-to-end delay. Generally the proposed OLSR-PSOSIIW shows advantage of using PSO for optimizing routing paths in the MPRs selection algorithm.

Solving Multiobjective Optimization Problems Using Artificial Bee Colony Algorithm

Directory of Open Access Journals (Sweden)

Wenping Zou

2011-01-01

Full Text Available Multiobjective optimization has been a difficult problem and focus for research in fields of science and engineering. This paper presents a novel algorithm based on artificial bee colony (ABC to deal with multi-objective optimization problems. ABC is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. It uses less control parameters, and it can be efficiently used for solving multimodal and multidimensional optimization problems. Our algorithm uses the concept of Pareto dominance to determine the flight direction of a bee, and it maintains nondominated solution vectors which have been found in an external archive. The proposed algorithm is validated using the standard test problems, and simulation results show that the proposed approach is highly competitive and can be considered a viable alternative to solve multi-objective optimization problems.

Combinatorial Clustering Algorithm of Quantum-Behaved Particle Swarm Optimization and Cloud Model

Directory of Open Access Journals (Sweden)

Mi-Yuan Shan

2013-01-01

Full Text Available We propose a combinatorial clustering algorithm of cloud model and quantum-behaved particle swarm optimization (COCQPSO to solve the stochastic problem. The algorithm employs a novel probability model as well as a permutation-based local search method. We are setting the parameters of COCQPSO based on the design of experiment. In the comprehensive computational study, we scrutinize the performance of COCQPSO on a set of widely used benchmark instances. By benchmarking combinatorial clustering algorithm with state-of-the-art algorithms, we can show that its performance compares very favorably. The fuzzy combinatorial optimization algorithm of cloud model and quantum-behaved particle swarm optimization (FCOCQPSO in vague sets (IVSs is more expressive than the other fuzzy sets. Finally, numerical examples show the clustering effectiveness of COCQPSO and FCOCQPSO clustering algorithms which are extremely remarkable.

A Parallel Adaptive Particle Swarm Optimization Algorithm for Economic/Environmental Power Dispatch

Directory of Open Access Journals (Sweden)

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.

Estimation of water demand in water distribution systems using particle swarm optimization

CSIR Research Space (South Africa)

Letting, LK

2017-08-01

Full Text Available and an evolutionary algorithm is a potential solution to the demand estimation problem. This paper presents a detailed process simulation model for water demand estimation using the particle swarm optimization (PSO) algorithm. Nodal water demands and pipe flows...

Particle Swarm Optimization Based on Local Attractors of Ordinary Differential Equation System

Directory of Open Access Journals (Sweden)

Wenyu Yang

2014-01-01

Full Text Available Particle swarm optimization (PSO is inspired by sociological behavior. In this paper, we interpret PSO as a finite difference scheme for solving a system of stochastic ordinary differential equations (SODE. In this framework, the position points of the swarm converge to an equilibrium point of the SODE and the local attractors, which are easily defined by the present position points, also converge to the global attractor. Inspired by this observation, we propose a class of modified PSO iteration methods (MPSO based on local attractors of the SODE. The idea of MPSO is to choose the next update state near the present local attractor, rather than the present position point as in the original PSO, according to a given probability density function. In particular, the quantum-behaved particle swarm optimization method turns out to be a special case of MPSO by taking a special probability density function. The MPSO methods with six different probability density functions are tested on a few benchmark problems. These MPSO methods behave differently for different problems. Thus, our framework not only gives an interpretation for the ordinary PSO but also, more importantly, provides a warehouse of PSO-like methods to choose from for solving different practical problems.

Setting value optimization method in integration for relay protection based on improved quantum particle swarm optimization algorithm

Science.gov (United States)

Yang, Guo Sheng; Wang, Xiao Yang; Li, Xue Dong

2018-03-01

With the establishment of the integrated model of relay protection and the scale of the power system expanding, the global setting and optimization of relay protection is an extremely difficult task. This paper presents a kind of application in relay protection of global optimization improved particle swarm optimization algorithm and the inverse time current protection as an example, selecting reliability of the relay protection, selectivity, quick action and flexibility as the four requires to establish the optimization targets, and optimizing protection setting values of the whole system. Finally, in the case of actual power system, the optimized setting value results of the proposed method in this paper are compared with the particle swarm algorithm. The results show that the improved quantum particle swarm optimization algorithm has strong search ability, good robustness, and it is suitable for optimizing setting value in the relay protection of the whole power system.

Search Improvement Process-Chaotic Optimization-Particle Swarm Optimization-Elite Retention Strategy and Improved Combined Cooling-Heating-Power Strategy Based Two-Time Scale Multi-Objective Optimization Model for Stand-Alone Microgrid Operation

Directory of Open Access Journals (Sweden)

Fei Wang

2017-11-01

Full Text Available The optimal dispatching model for a stand-alone microgrid (MG is of great importance to its operation reliability and economy. This paper aims at addressing the difficulties in improving the operational economy and maintaining the power balance under uncertain load demand and renewable generation, which could be even worse in such abnormal conditions as storms or abnormally low or high temperatures. A new two-time scale multi-objective optimization model, including day-ahead cursory scheduling and real-time scheduling for finer adjustments, is proposed to optimize the operational cost, load shedding compensation and environmental benefit of stand-alone MG through controllable load (CL and multi-distributed generations (DGs. The main novelty of the proposed model is that the synergetic response of CL and energy storage system (ESS in real-time scheduling offset the operation uncertainty quickly. And the improved dispatch strategy for combined cooling-heating-power (CCHP enhanced the system economy while the comfort is guaranteed. An improved algorithm, Search Improvement Process-Chaotic Optimization-Particle Swarm Optimization-Elite Retention Strategy (SIP-CO-PSO-ERS algorithm with strong searching capability and fast convergence speed, was presented to deal with the problem brought by the increased errors between actual renewable generation and load and prior predictions. Four typical scenarios are designed according to the combinations of day types (work day or weekend and weather categories (sunny or rainy to verify the performance of the presented dispatch strategy. The simulation results show that the proposed two-time scale model and SIP-CO-PSO-ERS algorithm exhibit better performance in adaptability, convergence speed and search ability than conventional methods for the stand-alone MG’s operation.

Elitism set based particle swarm optimization and its application

Directory of Open Access Journals (Sweden)

Yanxia Sun

2017-01-01

Full Text Available Topology plays an important role for Particle Swarm Optimization (PSO to achieve good optimization performance. It is difficult to find one topology structure for the particles to achieve better optimization performance than the others since the optimization performance not only depends on the searching abilities of the particles, also depends on the type of the optimization problems. Three elitist set based PSO algorithm without using explicit topology structure is proposed in this paper. An elitist set, which is based on the individual best experience, is used to communicate among the particles. Moreover, to avoid the premature of the particles, different statistical methods have been used in these three proposed methods. The performance of the proposed PSOs is compared with the results of the standard PSO 2011 and several PSO with different topologies, and the simulation results and comparisons demonstrate that the proposed PSO with adaptive probabilistic preference can achieve good optimization performance.

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)

Particle swarm optimization with random keys applied to the nuclear reactor reload problem

International Nuclear Information System (INIS)

Meneses, Anderson Alvarenga de Moura; Fundacao Educacional de Macae; Machado, Marcelo Dornellas; Medeiros, Jose Antonio Carlos Canedo; Schirru, Roberto

2007-01-01

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)

The application of particle swarm optimization to identify gamma spectrum with neural network

International Nuclear Information System (INIS)

Shi Dongsheng; Di Yuming; Zhou Chunlin

2006-01-01

Aiming at the shortcomings that BP algorithm is usually trapped to a local optimum and it has a low speed of convergence in the application of neural network to identify gamma spectrum, according to the advantage of the globe optimal searching of particle swarm optimization, this paper put forward a new algorithm for neural network training by combining BP algorithm and Particle Swarm Optimization-mixed PSO-BP algorithm. In the application to identify gamma spectrum, the new algorithm overcomes the shortcoming that BP algorithm is usually trapped to a local optimum and the neural network trained by it has a high ability of generalization with identification result of one hundred percent correct. Practical example shows that the mixed PSO-BP algorithm can effectively and reliably be used to identify gamma spectrum. (authors)

Multi-objective optimization of circular magnetic abrasive polishing of SUS304 and Cu materials

Energy Technology Data Exchange (ETDEWEB)

Nguyen, NhatTan; Yin, ShaoHui; Chen, FengJun; Yin, HanFeng [Hunan University, Changsha (China); Pham, VanThoan [Hanoi University, Hanoi (Viet Nam); Tran, TrongNhan [Industrial University of Ho Chi Minh City, HCM City (Viet Nam)

2016-06-15

In this paper, a Multi-objective particle swarm optimization algorithm (MOPSOA) is applied to optimize surface roughness of workpiece after circular magnetic abrasive polishing. The most important parameters of polishing model, namely current, gap between pole and workpiece, spindle speed and polishing time, were considered in this approach. The objective functions of the MOPSOA depend on the quality of surface roughness of polishing materials with both simultaneous surfaces (Ra1, Ra2), which are determined by means of experimental approach with the aid of circular magnetic field. Finally, the effectiveness of the approach is compared between the optimal results with the experimental data. The results show that the new proposed polishing optimization method is more feasible.

Adaptive swarm cluster-based dynamic multi-objective synthetic minority oversampling technique algorithm for tackling binary imbalanced datasets in biomedical data classification.

Science.gov (United States)

Li, Jinyan; Fong, Simon; Sung, Yunsick; Cho, Kyungeun; Wong, Raymond; Wong, Kelvin K L

2016-01-01

An imbalanced dataset is defined as a training dataset that has imbalanced proportions of data in both interesting and uninteresting classes. Often in biomedical applications, samples from the stimulating class are rare in a population, such as medical anomalies, positive clinical tests, and particular diseases. Although the target samples in the primitive dataset are small in number, the induction of a classification model over such training data leads to poor prediction performance due to insufficient training from the minority class. In this paper, we use a novel class-balancing method named adaptive swarm cluster-based dynamic multi-objective synthetic minority oversampling technique (ASCB_DmSMOTE) to solve this imbalanced dataset problem, which is common in biomedical applications. The proposed method combines under-sampling and over-sampling into a swarm optimisation algorithm. It adaptively selects suitable parameters for the rebalancing algorithm to find the best solution. Compared with the other versions of the SMOTE algorithm, significant improvements, which include higher accuracy and credibility, are observed with ASCB_DmSMOTE. Our proposed method tactfully combines two rebalancing techniques together. It reasonably re-allocates the majority class in the details and dynamically optimises the two parameters of SMOTE to synthesise a reasonable scale of minority class for each clustered sub-imbalanced dataset. The proposed methods ultimately overcome other conventional methods and attains higher credibility with even greater accuracy of the classification model.

Design of an Adaptive PID Neural Controller for Continuous Stirred Tank Reactor based on Particle Swarm Optimization

OpenAIRE

Khulood A. Dagher; Ahmed S. Al-Araji

2013-01-01

A particle swarm optimization algorithm and neural network like self-tuning PID controller for CSTR system is presented. The scheme of the discrete-time PID control structure is based on neural network and tuned the parameters of the PID controller by using a particle swarm optimization PSO technique as a simple and fast training algorithm. The proposed method has advantage that it is not necessary to use a combined structure of identification and decision because it used PSO. Simulation resu...

A Multiobjective Optimization Model in Automotive Supply Chain Networks

Directory of Open Access Journals (Sweden)

Abdolhossein Sadrnia

2013-01-01

Full Text Available In the new decade, green investment decisions are attracting more interest in design supply chains due to the hidden economic benefits and environmental legislative barriers. In this paper, a supply chain network design problem with both economic and environmental concerns is presented. Therefore, a multiobjective optimization model that captures the trade-off between the total logistics cost and CO2 emissions is proposed. With regard to the complexity of logistic networks, a new multiobjective swarm intelligence algorithm known as a multiobjective Gravitational search algorithm (MOGSA has been implemented for solving the proposed mathematical model. To evaluate the effectiveness of the model, a comprehensive set of numerical experiments is explained. The results obtained show that the proposed model can be applied as an effective tool in strategic planning for optimizing cost and CO2 emissions in an environmentally friendly automotive supply chain.

A new ARMAX model based on evolutionary algorithm and particle swarm optimization for short-term load forecasting

International Nuclear Information System (INIS)

Wang, Bo; Tai, Neng-ling; Zhai, Hai-qing; Ye, Jian; Zhu, Jia-dong; Qi, Liang-bo

2008-01-01

In this paper, a new ARMAX model based on evolutionary algorithm and particle swarm optimization for short-term load forecasting is proposed. Auto-regressive (AR) and moving average (MA) with exogenous variables (ARMAX) has been widely applied in the load forecasting area. Because of the nonlinear characteristics of the power system loads, the forecasting function has many local optimal points. The traditional method based on gradient searching may be trapped in local optimal points and lead to high error. While, the hybrid method based on evolutionary algorithm and particle swarm optimization can solve this problem more efficiently than the traditional ways. It takes advantage of evolutionary strategy to speed up the convergence of particle swarm optimization (PSO), and applies the crossover operation of genetic algorithm to enhance the global search ability. The new ARMAX model for short-term load forecasting has been tested based on the load data of Eastern China location market, and the results indicate that the proposed approach has achieved good accuracy. (author)

Pressure Prediction of Coal Slurry Transportation Pipeline Based on Particle Swarm Optimization Kernel Function Extreme Learning Machine

Directory of Open Access Journals (Sweden)

Xue-cun Yang

2015-01-01

Full Text Available For coal slurry pipeline blockage prediction problem, through the analysis of actual scene, it is determined that the pressure prediction from each measuring point is the premise of pipeline blockage prediction. Kernel function of support vector machine is introduced into extreme learning machine, the parameters are optimized by particle swarm algorithm, and blockage prediction method based on particle swarm optimization kernel function extreme learning machine (PSOKELM is put forward. The actual test data from HuangLing coal gangue power plant are used for simulation experiments and compared with support vector machine prediction model optimized by particle swarm algorithm (PSOSVM and kernel function extreme learning machine prediction model (KELM. The results prove that mean square error (MSE for the prediction model based on PSOKELM is 0.0038 and the correlation coefficient is 0.9955, which is superior to prediction model based on PSOSVM in speed and accuracy and superior to KELM prediction model in accuracy.

Multi-Objective Dynamic Economic Dispatch of Microgrid Systems Including Vehicle-to-Grid

Directory of Open Access Journals (Sweden)

Haitao Liu

2015-05-01

Full Text Available Based on the characteristics of electric vehicles (EVs, this paper establishes the load models of EVs under the autonomous charging mode and the coordinated charging and discharging mode. Integrating the EVs into a microgrid system which includes wind turbines (WTs, photovoltaic arrays (PVs, diesel engines (DEs, fuel cells (FCs and a storage battery (BS, this paper establishes multi-objective economic dispatch models of a microgrid, including the lowest operating cost, the least carbon dioxide emissions, and the lowest pollutant treatment cost. After converting the multi-objective functions to a single objective function by using the judgment matrix method, we analyze the dynamic economic dispatch of the microgrid system including vehicle-to-grid (V2G with an improved particle swarm optimization algorithm under different operation control strategies. With the example system, the proposed models and strategies are verified and analyzed. Simulation results show that the microgrid system with EVs under the coordinated charging and discharging mode has better operation economics than the autonomous charging mode. Meanwhile, the greater the load fluctuation is, the higher the operating cost of the microgrid system is.

The Study on Food Sensory Evaluation based on Particle Swarm Optimization Algorithm

OpenAIRE

Hairong Wang; Huijuan Xu

2015-01-01

In this study, it explores the procedures and methods of the system for establishing food sensory evaluation based on particle swarm optimization algorithm, by means of explaining the interpretation of sensory evaluation and sensory analysis, combined with the applying situation of sensory evaluation in food industry.

Design of Wire Antennas by Using an Evolved Particle Swarm Optimization Algorithm

NARCIS (Netherlands)

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

Identification of nuclear power plant transients using the Particle Swarm Optimization algorithm

International Nuclear Information System (INIS)

Canedo Medeiros, Jose Antonio Carlos; Schirru, Roberto

2008-01-01

In order to help nuclear power plant operator reduce his cognitive load and increase his available time to maintain the plant operating in a safe condition, transient identification systems have been devised to help operators identify possible plant transients and take fast and right corrective actions in due time. In the design of classification systems for identification of nuclear power plants transients, several artificial intelligence techniques, involving expert systems, neuro-fuzzy and genetic algorithms have been used. In this work we explore the ability of the Particle Swarm Optimization algorithm (PSO) as a tool for optimizing a distance-based discrimination transient classification method, giving also an innovative solution for searching the best set of prototypes for identification of transients. The Particle Swarm Optimization algorithm was successfully applied to the optimization of a nuclear power plant transient identification problem. Comparing the PSO to similar methods found in literature it has shown better results

Identification of nuclear power plant transients using the Particle Swarm Optimization algorithm

Energy Technology Data Exchange (ETDEWEB)

Canedo Medeiros, Jose Antonio Carlos [Universidade Federal do Rio de Janeiro, PEN/COPPE, UFRJ, Ilha do Fundao s/n, CEP 21945-970 Rio de Janeiro (Brazil)], E-mail: canedo@lmp.ufrj.br; Schirru, Roberto [Universidade Federal do Rio de Janeiro, PEN/COPPE, UFRJ, Ilha do Fundao s/n, CEP 21945-970 Rio de Janeiro (Brazil)], E-mail: schirru@lmp.ufrj.br

2008-04-15

In order to help nuclear power plant operator reduce his cognitive load and increase his available time to maintain the plant operating in a safe condition, transient identification systems have been devised to help operators identify possible plant transients and take fast and right corrective actions in due time. In the design of classification systems for identification of nuclear power plants transients, several artificial intelligence techniques, involving expert systems, neuro-fuzzy and genetic algorithms have been used. In this work we explore the ability of the Particle Swarm Optimization algorithm (PSO) as a tool for optimizing a distance-based discrimination transient classification method, giving also an innovative solution for searching the best set of prototypes for identification of transients. The Particle Swarm Optimization algorithm was successfully applied to the optimization of a nuclear power plant transient identification problem. Comparing the PSO to similar methods found in literature it has shown better results.

Swarming behavior of gradient-responsive Brownian particles in a porous medium

Science.gov (United States)

Grančič, Peter; Štěpánek, František

2012-07-01

Active targeting by Brownian particles in a fluid-filled porous environment is investigated by computer simulation. The random motion of the particles is enhanced by diffusiophoresis with respect to concentration gradients of chemical signals released by the particles in the proximity of a target. The mathematical model, based on a combination of the Brownian dynamics method and a diffusion problem is formulated in terms of key parameters that include the particle diffusiophoretic mobility and the signaling threshold (the distance from the target at which the particles release their chemical signals). The results demonstrate that even a relatively simple chemical signaling scheme can lead to a complex collective behavior of the particles and can be a very efficient way of guiding a swarm of Brownian particles towards a target, similarly to the way colonies of living cells communicate via secondary messengers.

A fuzzy controller design for nuclear research reactors using the particle swarm optimization algorithm

International Nuclear Information System (INIS)

Coban, Ramazan

2011-01-01

Research highlights: → A closed-loop fuzzy logic controller based on the particle swarm optimization algorithm was proposed for controlling the power level of nuclear research reactors. → The proposed control system was tested for various initial and desired power levels, and it could control the reactor successfully for most situations. → The proposed controller is robust against the disturbances. - Abstract: In this paper, a closed-loop fuzzy logic controller based on the particle swarm optimization algorithm is proposed for controlling the power level of nuclear research reactors. The principle of the fuzzy logic controller is based on the rules constructed from numerical experiments made by means of a computer code for the core dynamics calculation and from human operator's experience and knowledge. In addition to these intuitive and experimental design efforts, consequent parts of the fuzzy rules are optimally (or near optimally) determined using the particle swarm optimization algorithm. The contribution of the proposed algorithm to a reactor control system is investigated in details. The performance of the controller is also tested with numerical simulations in numerous operating conditions from various initial power levels to desired power levels, as well as under disturbance. It is shown that the proposed control system performs satisfactorily under almost all operating conditions, even in the case of very small initial power levels.

Optimization of the reflux ratio for a stage distillation column based on an improved particle swarm algorithm

DEFF Research Database (Denmark)

Ren, Jingzheng; Tan, Shiyu; Dong, Lichun

2010-01-01

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...... 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...

Constrained multi-objective optimization of storage ring lattices

Science.gov (United States)

Husain, Riyasat; Ghodke, A. D.

2018-03-01

The storage ring lattice optimization is a class of constrained multi-objective optimization problem, where in addition to low beam emittance, a large dynamic aperture for good injection efficiency and improved beam lifetime are also desirable. The convergence and computation times are of great concern for the optimization algorithms, as various objectives are to be optimized and a number of accelerator parameters to be varied over a large span with several constraints. In this paper, a study of storage ring lattice optimization using differential evolution is presented. The optimization results are compared with two most widely used optimization techniques in accelerators-genetic algorithm and particle swarm optimization. It is found that the differential evolution produces a better Pareto optimal front in reasonable computation time between two conflicting objectives-beam emittance and dispersion function in the straight section. The differential evolution was used, extensively, for the optimization of linear and nonlinear lattices of Indus-2 for exploring various operational modes within the magnet power supply capabilities.

Hybrid Optimization Algorithm of Particle Swarm Optimization and Cuckoo Search for Preventive Maintenance Period Optimization

Directory of Open Access Journals (Sweden)

Jianwen Guo

2016-01-01

Full Text Available All equipment must be maintained during its lifetime to ensure normal operation. Maintenance is one of the critical roles in the success of manufacturing enterprises. This paper proposed a preventive maintenance period optimization model (PMPOM to find an optimal preventive maintenance period. By making use of the advantages of particle swarm optimization (PSO and cuckoo search (CS algorithm, a hybrid optimization algorithm of PSO and CS is proposed to solve the PMPOM problem. The test functions show that the proposed algorithm exhibits more outstanding performance than particle swarm optimization and cuckoo search. Experiment results show that the proposed algorithm has advantages of strong optimization ability and fast convergence speed to solve the PMPOM problem.

Optimization of mechanical structures using particle swarm optimization

International Nuclear Information System (INIS)

Leite, Victor C.; Schirru, Roberto

2015-01-01

Several optimization problems are dealed with the particle swarm optimization (PSO) algorithm, there is a wide kind of optimization problems, it may be applications related to logistics or the reload of nuclear reactors. This paper discusses the use of the PSO in the treatment of problems related to mechanical structure optimization. The geometry and material characteristics of mechanical components are important for the proper functioning and performance of the systems were they are applied, particularly to the nuclear field. Calculations related to mechanical aspects are all made using ANSYS, while the PSO is programed in MATLAB. (author)

Optimization of mechanical structures using particle swarm optimization

Energy Technology Data Exchange (ETDEWEB)

Leite, Victor C.; Schirru, Roberto, E-mail: victor.coppo.leite@lmp.ufrj.br [Coordenacao dos Programas de Pos-Graduacao em Engenharia (LMP/PEN/COPPE/UFRJ), Rio de Janeiro, RJ (Brazil). Lab. de Monitoracao de Processos

2015-07-01

Several optimization problems are dealed with the particle swarm optimization (PSO) algorithm, there is a wide kind of optimization problems, it may be applications related to logistics or the reload of nuclear reactors. This paper discusses the use of the PSO in the treatment of problems related to mechanical structure optimization. The geometry and material characteristics of mechanical components are important for the proper functioning and performance of the systems were they are applied, particularly to the nuclear field. Calculations related to mechanical aspects are all made using ANSYS, while the PSO is programed in MATLAB. (author)

Robust electromagnetically guided endoscopic procedure using enhanced particle swarm optimization for multimodal information fusion

International Nuclear Information System (INIS)

Luo, Xiongbiao; Wan, Ying; He, Xiangjian

2015-01-01

Purpose: Electromagnetically guided endoscopic procedure, which aims at accurately and robustly localizing the endoscope, involves multimodal sensory information during interventions. However, it still remains challenging in how to integrate these information for precise and stable endoscopic guidance. To tackle such a challenge, this paper proposes a new framework on the basis of an enhanced particle swarm optimization method to effectively fuse these information for accurate and continuous endoscope localization. Methods: The authors use the particle swarm optimization method, which is one of stochastic evolutionary computation algorithms, to effectively fuse the multimodal information including preoperative information (i.e., computed tomography images) as a frame of reference, endoscopic camera videos, and positional sensor measurements (i.e., electromagnetic sensor outputs). Since the evolutionary computation method usually limits its possible premature convergence and evolutionary factors, the authors introduce the current (endoscopic camera and electromagnetic sensor’s) observation to boost the particle swarm optimization and also adaptively update evolutionary parameters in accordance with spatial constraints and the current observation, resulting in advantageous performance in the enhanced algorithm. Results: The experimental results demonstrate that the authors’ proposed method provides a more accurate and robust endoscopic guidance framework than state-of-the-art methods. The average guidance accuracy of the authors’ framework was about 3.0 mm and 5.6° while the previous methods show at least 3.9 mm and 7.0°. The average position and orientation smoothness of their method was 1.0 mm and 1.6°, which is significantly better than the other methods at least with (2.0 mm and 2.6°). Additionally, the average visual quality of the endoscopic guidance was improved to 0.29. Conclusions: A robust electromagnetically guided endoscopy framework was

Robust electromagnetically guided endoscopic procedure using enhanced particle swarm optimization for multimodal information fusion.

Science.gov (United States)

Luo, Xiongbiao; Wan, Ying; He, Xiangjian

2015-04-01

Electromagnetically guided endoscopic procedure, which aims at accurately and robustly localizing the endoscope, involves multimodal sensory information during interventions. However, it still remains challenging in how to integrate these information for precise and stable endoscopic guidance. To tackle such a challenge, this paper proposes a new framework on the basis of an enhanced particle swarm optimization method to effectively fuse these information for accurate and continuous endoscope localization. The authors use the particle swarm optimization method, which is one of stochastic evolutionary computation algorithms, to effectively fuse the multimodal information including preoperative information (i.e., computed tomography images) as a frame of reference, endoscopic camera videos, and positional sensor measurements (i.e., electromagnetic sensor outputs). Since the evolutionary computation method usually limits its possible premature convergence and evolutionary factors, the authors introduce the current (endoscopic camera and electromagnetic sensor's) observation to boost the particle swarm optimization and also adaptively update evolutionary parameters in accordance with spatial constraints and the current observation, resulting in advantageous performance in the enhanced algorithm. The experimental results demonstrate that the authors' proposed method provides a more accurate and robust endoscopic guidance framework than state-of-the-art methods. The average guidance accuracy of the authors' framework was about 3.0 mm and 5.6° while the previous methods show at least 3.9 mm and 7.0°. The average position and orientation smoothness of their method was 1.0 mm and 1.6°, which is significantly better than the other methods at least with (2.0 mm and 2.6°). Additionally, the average visual quality of the endoscopic guidance was improved to 0.29. A robust electromagnetically guided endoscopy framework was proposed on the basis of an enhanced particle swarm

Robust electromagnetically guided endoscopic procedure using enhanced particle swarm optimization for multimodal information fusion

Energy Technology Data Exchange (ETDEWEB)

Luo, Xiongbiao, E-mail: xluo@robarts.ca, E-mail: Ying.Wan@student.uts.edu.au [Robarts Research Institute, Western University, London, Ontario N6A 5K8 (Canada); Wan, Ying, E-mail: xluo@robarts.ca, E-mail: Ying.Wan@student.uts.edu.au; He, Xiangjian [School of Computing and Communications, University of Technology, Sydney, New South Wales 2007 (Australia)

2015-04-15

Purpose: Electromagnetically guided endoscopic procedure, which aims at accurately and robustly localizing the endoscope, involves multimodal sensory information during interventions. However, it still remains challenging in how to integrate these information for precise and stable endoscopic guidance. To tackle such a challenge, this paper proposes a new framework on the basis of an enhanced particle swarm optimization method to effectively fuse these information for accurate and continuous endoscope localization. Methods: The authors use the particle swarm optimization method, which is one of stochastic evolutionary computation algorithms, to effectively fuse the multimodal information including preoperative information (i.e., computed tomography images) as a frame of reference, endoscopic camera videos, and positional sensor measurements (i.e., electromagnetic sensor outputs). Since the evolutionary computation method usually limits its possible premature convergence and evolutionary factors, the authors introduce the current (endoscopic camera and electromagnetic sensor’s) observation to boost the particle swarm optimization and also adaptively update evolutionary parameters in accordance with spatial constraints and the current observation, resulting in advantageous performance in the enhanced algorithm. Results: The experimental results demonstrate that the authors’ proposed method provides a more accurate and robust endoscopic guidance framework than state-of-the-art methods. The average guidance accuracy of the authors’ framework was about 3.0 mm and 5.6° while the previous methods show at least 3.9 mm and 7.0°. The average position and orientation smoothness of their method was 1.0 mm and 1.6°, which is significantly better than the other methods at least with (2.0 mm and 2.6°). Additionally, the average visual quality of the endoscopic guidance was improved to 0.29. Conclusions: A robust electromagnetically guided endoscopy framework was

Multivariable optimization of liquid rocket engines using particle swarm algorithms

Science.gov (United States)

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.

Particle Swarm Optimization approach to defect detection in armour ceramics.

Science.gov (United States)

Kesharaju, Manasa; Nagarajah, Romesh

2017-03-01

In this research, various extracted features were used in the development of an automated ultrasonic sensor based inspection system that enables defect classification in each ceramic component prior to despatch to the field. Classification is an important task and large number of irrelevant, redundant features commonly introduced to a dataset reduces the classifiers performance. Feature selection aims to reduce the dimensionality of the dataset while improving the performance of a classification system. In the context of a multi-criteria optimization problem (i.e. to minimize classification error rate and reduce number of features) such as one discussed in this research, the literature suggests that evolutionary algorithms offer good results. Besides, it is noted that Particle Swarm Optimization (PSO) has not been explored especially in the field of classification of high frequency ultrasonic signals. Hence, a binary coded Particle Swarm Optimization (BPSO) technique is investigated in the implementation of feature subset selection and to optimize the classification error rate. In the proposed method, the population data is used as input to an Artificial Neural Network (ANN) based classification system to obtain the error rate, as ANN serves as an evaluator of PSO fitness function. Copyright © 2016. Published by Elsevier B.V.

Particle swarm as optimization tool in complex nuclear engineering problems

International Nuclear Information System (INIS)

Medeiros, Jose Antonio Carlos Canedo

2005-06-01

Due to its low computational cost, gradient-based search techniques associated to linear programming techniques are being used as optimization tools. These techniques, however, when applied to multimodal search spaces, can lead to local optima. When finding solutions for complex multimodal domains, random search techniques are being used with great efficacy. In this work we exploit the swarm optimization algorithm search power capacity as an optimization tool for the solution of complex high dimension and multimodal search spaces of nuclear problems. Due to its easy and natural representation of high dimension domains, the particle swarm optimization was applied with success for the solution of complex nuclear problems showing its efficacy in the search of solutions in high dimension and complex multimodal spaces. In one of these applications it enabled a natural and trivial solution in a way not obtained with other methods confirming the validity of its application. (author)

Differential Evolution and Particle Swarm Optimization for Partitional Clustering

DEFF Research Database (Denmark)

Krink, Thiemo; Paterlini, Sandra

2006-01-01

for numerical optimisation, which are hardly known outside the search heuristics field, are particle swarm optimisation (PSO) and differential evolution (DE). The performance of GAs for a representative point evolution approach to clustering is compared with PSO and DE. The empirical results show that DE......Many partitional clustering algorithms based on genetic algorithms (GA) have been proposed to tackle the problem of finding the optimal partition of a data set. Very few studies considered alternative stochastic search heuristics other than GAs or simulated annealing. Two promising algorithms...

A Novel Path Planning for Robots Based on Rapidly-Exploring Random Tree and Particle Swarm Optimizer Algorithm

Directory of Open Access Journals (Sweden)

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.

On the performance of accelerated particle swarm optimization for charging plug-in hybrid electric vehicles

Directory of Open Access Journals (Sweden)

Imran Rahman

2016-03-01

Full Text Available Transportation electrification has undergone major changes since the last decade. Success of smart grid with renewable energy integration solely depends upon the large-scale penetration of plug-in hybrid electric vehicles (PHEVs for a sustainable and carbon-free transportation sector. One of the key performance indicators in hybrid electric vehicle is the State-of-Charge (SoC which needs to be optimized for the betterment of charging infrastructure using stochastic computational methods. In this paper, a newly emerged Accelerated particle swarm optimization (APSO technique was applied and compared with standard particle swarm optimization (PSO considering charging time and battery capacity. Simulation results obtained for maximizing the highly nonlinear objective function indicate that APSO achieves some improvements in terms of best fitness and computation time.

Efficiency of particle swarm optimization applied on fuzzy logic DC motor speed control

Directory of Open Access Journals (Sweden)

Allaoua Boumediene

2008-01-01

Full Text Available This paper presents the application of Fuzzy Logic for DC motor speed control using Particle Swarm Optimization (PSO. Firstly, the controller designed according to Fuzzy Logic rules is such that the systems are fundamentally robust. Secondly, the Fuzzy Logic controller (FLC used earlier was optimized with PSO so as to obtain optimal adjustment of the membership functions only. Finally, the FLC is completely optimized by Swarm Intelligence Algorithms. Digital simulation results demonstrate that in comparison with the FLC the designed FLC-PSO speed controller obtains better dynamic behavior and superior performance of the DC motor, as well as perfect speed tracking with no overshoot.

Optimization design of a gating system for sand casting aluminium A356 using a Taguchi method and multi-objective culture-based QPSO algorithm

Directory of Open Access Journals (Sweden)

Wen-Jong Chen

2016-04-01

Full Text Available This article combined Taguchi method and analysis of variance with the culture-based quantum-behaved particle swarm optimization to determine the optimal models of gating system for aluminium (Al A356 sand casting part. First, the Taguchi method and analysis of variance were, respectively, applied to establish an L27(38 orthogonal array and determine significant process parameters, including riser diameter, pouring temperature, pouring speed, riser position and gating diameter. Subsequently, a response surface methodology was used to construct a second-order regression model, including filling time, solidification time and oxide ratio. Finally, the culture-based quantum-behaved particle swarm optimization was used to determine the multi-objective Pareto optimal solutions and identify corresponding process conditions. The results showed that the proposed method, compared with initial casting model, enabled reducing the filling time, solidification time and oxide ratio by 68.14%, 50.56% and 20.20%, respectively. A confirmation experiment was verified to be able to effectively reduce the defect of casting and improve the casting quality.

Directing orbits of chaotic systems by particle swarm optimization

International Nuclear Information System (INIS)

Liu Bo; Wang Ling; Jin Yihui; Tang Fang; Huang Dexian

2006-01-01

This paper applies a novel evolutionary computation algorithm named particle swarm optimization (PSO) to direct the orbits of discrete chaotic dynamical systems towards desired target region within a short time by adding only small bounded perturbations, which could be formulated as a multi-modal numerical optimization problem with high dimension. Moreover, the synchronization of chaotic systems is also studied, which can be dealt with as an online problem of directing orbits. Numerical simulations based on Henon Map demonstrate the effectiveness and efficiency of PSO, and the effects of some parameters are also investigated

The Study of Intelligent Vehicle Navigation Path Based on Behavior Coordination of Particle Swarm.

Science.gov (United States)

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.

New hybrid genetic particle swarm optimization algorithm to design multi-zone binary filter.

Science.gov (United States)

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.

Multiple Active Contours Driven by Particle Swarm Optimization for Cardiac Medical Image Segmentation

Science.gov (United States)

Cruz-Aceves, I.; Aviña-Cervantes, J. G.; López-Hernández, J. M.; González-Reyna, S. E.

2013-01-01

This paper presents a novel image segmentation method based on multiple active contours driven by particle swarm optimization (MACPSO). The proposed method uses particle swarm optimization over a polar coordinate system to increase the energy-minimizing capability with respect to the traditional active contour model. In the first stage, to evaluate the robustness of the proposed method, a set of synthetic images containing objects with several concavities and Gaussian noise is presented. Subsequently, MACPSO is used to segment the human heart and the human left ventricle from datasets of sequential computed tomography and magnetic resonance images, respectively. Finally, to assess the performance of the medical image segmentations with respect to regions outlined by experts and by the graph cut method objectively and quantifiably, a set of distance and similarity metrics has been adopted. The experimental results demonstrate that MACPSO outperforms the traditional active contour model in terms of segmentation accuracy and stability. PMID:23762177

Optimization of heat pump system in indoor swimming pool using particle swarm algorithm

Energy Technology Data Exchange (ETDEWEB)

Lee, Wen-Shing; Kung, Chung-Kuan [Department of Energy and Refrigerating Air-Conditioning Engineering, National Taipei University of Technology, 1, Section 3, Chung-Hsiao East Road, Taipei (China)

2008-09-15

When it comes to indoor swimming pool facilities, a large amount of energy is required to heat up low-temperature outdoor air before it is being introduced indoors to maintain indoor humidity. Since water is evaporated from the pool surface, the exhausted air contains more water and specific enthalpy. In response to this indoor air, heat pump is generally used in heat recovery for indoor swimming pools. To reduce the cost in energy consumption, this paper utilizes particle swarm algorithm to optimize the design of heat pump system. The optimized parameters include continuous parameters and discrete parameters. The former consists of outdoor air mass flow and heat conductance of heat exchangers; the latter comprises compressor type and boiler type. In a case study, life cycle energy cost is considered as an objective function. In this regard, the optimized outdoor air flow and the optimized design for heating system can be deduced by using particle swarm algorithm. (author)

DAILY SCHEDULING OF SMALL HYDRO POWER PLANTS DISPATCH WITH MODIFIED PARTICLES SWARM OPTIMIZATION

Directory of Open Access Journals (Sweden)

Sinvaldo Rodrigues Moreno

2015-04-01

Full Text Available This paper presents a new approach for short-term hydro power scheduling of reservoirs using an algorithm-based Particle Swarm Optimization (PSO. PSO is a population-based algorithm designed to find good solutions to optimization problems, its characteristics have encouraged its adoption to tackle a variety of problems in different fields. In this paper the authors consider an optimization problem related to a daily scheduling of small hydro power dispatch. The goal is construct a feasible solution that maximize the cascade electricity production, following the environmental constraints and water balance. The paper proposes an improved Particle Swarm Optimization (PSO algorithm, which takes advantage of simplicity and facility of implementation. The algorithm was successfully applied to the optimization of the daily schedule strategies of small hydro power plants, considering maximum water utilization and all constraints related to simultaneous water uses. Extensive computational tests and comparisons with other heuristics methods showed the effectiveness of the proposed approach.

Discrete Particle Swarm Optimization Routing Protocol for Wireless Sensor Networks with Multiple Mobile Sinks.

Science.gov (United States)

Yang, Jin; Liu, Fagui; Cao, Jianneng; Wang, Liangming

2016-07-14

Mobile sinks can achieve load-balancing and energy-consumption balancing across the wireless sensor networks (WSNs). However, the frequent change of the paths between source nodes and the sinks caused by sink mobility introduces significant overhead in terms of energy and packet delays. To enhance network performance of WSNs with mobile sinks (MWSNs), we present an efficient routing strategy, which is formulated as an optimization problem and employs the particle swarm optimization algorithm (PSO) to build the optimal routing paths. However, the conventional PSO is insufficient to solve discrete routing optimization problems. Therefore, a novel greedy discrete particle swarm optimization with memory (GMDPSO) is put forward to address this problem. In the GMDPSO, particle's position and velocity of traditional PSO are redefined under discrete MWSNs scenario. Particle updating rule is also reconsidered based on the subnetwork topology of MWSNs. Besides, by improving the greedy forwarding routing, a greedy search strategy is designed to drive particles to find a better position quickly. Furthermore, searching history is memorized to accelerate convergence. Simulation results demonstrate that our new protocol significantly improves the robustness and adapts to rapid topological changes with multiple mobile sinks, while efficiently reducing the communication overhead and the energy consumption.

Fault detection and isolation in GPS receiver autonomous integrity monitoring based on chaos particle swarm optimization-particle filter algorithm

Science.gov (United States)

Wang, Ershen; Jia, Chaoying; Tong, Gang; Qu, Pingping; Lan, Xiaoyu; Pang, Tao

2018-03-01

The receiver autonomous integrity monitoring (RAIM) is one of the most important parts in an avionic navigation system. Two problems need to be addressed to improve this system, namely, the degeneracy phenomenon and lack of samples for the standard particle filter (PF). However, the number of samples cannot adequately express the real distribution of the probability density function (i.e., sample impoverishment). This study presents a GPS receiver autonomous integrity monitoring (RAIM) method based on a chaos particle swarm optimization particle filter (CPSO-PF) algorithm with a log likelihood ratio. The chaos sequence generates a set of chaotic variables, which are mapped to the interval of optimization variables to improve particle quality. This chaos perturbation overcomes the potential for the search to become trapped in a local optimum in the particle swarm optimization (PSO) algorithm. Test statistics are configured based on a likelihood ratio, and satellite fault detection is then conducted by checking the consistency between the state estimate of the main PF and those of the auxiliary PFs. Based on GPS data, the experimental results demonstrate that the proposed algorithm can effectively detect and isolate satellite faults under conditions of non-Gaussian measurement noise. Moreover, the performance of the proposed novel method is better than that of RAIM based on the PF or PSO-PF algorithm.

Enhanced Particle Swarm Optimization Algorithm: Efficient Training of ReaxFF Reactive Force Fields.

Science.gov (United States)

Furman, David; Carmeli, Benny; Zeiri, Yehuda; Kosloff, Ronnie

2018-05-04

Particle swarm optimization is a powerful metaheuristic population-based global optimization algorithm. However, when applied to non-separable objective functions its performance on multimodal landscapes is significantly degraded. Here we show that a significant improvement in the search quality and efficiency on multimodal functions can be achieved by enhancing the basic rotation-invariant particle swarm optimization algorithm with isotropic Gaussian mutation operators. The new algorithm demonstrates a superior performance across several nonlinear, multimodal benchmark functions compared to the rotation-invariant Particle Swam Optimization (PSO) algorithm and the well-established simulated annealing and sequential one-parameter parabolic interpolation methods. A search for the optimal set of parameters for the dispersion interaction model in ReaxFF-lg reactive force field is carried out with respect to accurate DFT-TS calculations. The resulting optimized force field accurately describes the equations of state of several high-energy molecular crystals where such interactions are of crucial importance. The improved algorithm also presents a better performance compared to a Genetic Algorithm optimization method in the optimization of a ReaxFF-lg correction model parameters. The computational framework is implemented in a standalone C++ code that allows a straightforward development of ReaxFF reactive force fields.

Short-term cascaded hydroelectric system scheduling based on chaotic particle swarm optimization using improved logistic map

Science.gov (United States)

He, Yaoyao; Yang, Shanlin; Xu, Qifa

2013-07-01

In order to solve the model of short-term cascaded hydroelectric system scheduling, a novel chaotic particle swarm optimization (CPSO) algorithm using improved logistic map is introduced, which uses the water discharge as the decision variables combined with the death penalty function. According to the principle of maximum power generation, the proposed approach makes use of the ergodicity, symmetry and stochastic property of improved logistic chaotic map for enhancing the performance of particle swarm optimization (PSO) algorithm. The new hybrid method has been examined and tested on two test functions and a practical cascaded hydroelectric system. The experimental results show that the effectiveness and robustness of the proposed CPSO algorithm in comparison with other traditional algorithms.

3D Pattern Synthesis of Time-Modulated Conformal Arrays with a Multiobjective Optimization Approach

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Wentao Li

2014-01-01

Full Text Available This paper addresses the synthesis of the three-dimensional (3D radiation patterns of the time-modulated conformal arrays. Due to the nature of periodic time modulation, harmonic radiation patterns are generated at the multiples of the modulation frequency in time-modulated arrays. Thus, the optimization goal of the time-modulated conformal array includes the optimization of the sidelobe level at the operating frequency and the sideband levels (SBLs at the harmonic frequency, and the design can be regarded as a multiobjective problem. The multiobjective particle swarm optimization (MOPSO is applied to optimize the switch-on instants and pulse durations of the time-modulated conformal array. To significantly reduce the optimization variables, the modified Bernstein polynomial is employed in the synthesis process. Furthermore, dual polarized patch antenna is designed as radiator to achieve low cross-polarization level during the beam scanning. A 12 × 13 (156-element conical conformal microstrip array is simulated to demonstrate the proposed synthesis mechanism, and good results reveal the promising ability of the proposed algorithm in solving the synthesis of the time-modulated conformal arrays problem.

A parallel competitive Particle Swarm Optimization for non-linear first arrival traveltime tomography and uncertainty quantification

Science.gov (United States)

Luu, Keurfon; Noble, Mark; Gesret, Alexandrine; Belayouni, Nidhal; Roux, Pierre-François

2018-04-01

Seismic traveltime tomography is an optimization problem that requires large computational efforts. Therefore, linearized techniques are commonly used for their low computational cost. These local optimization methods are likely to get trapped in a local minimum as they critically depend on the initial model. On the other hand, global optimization methods based on MCMC are insensitive to the initial model but turn out to be computationally expensive. Particle Swarm Optimization (PSO) is a rather new global optimization approach with few tuning parameters that has shown excellent convergence rates and is straightforwardly parallelizable, allowing a good distribution of the workload. However, while it can traverse several local minima of the evaluated misfit function, classical implementation of PSO can get trapped in local minima at later iterations as particles inertia dim. We propose a Competitive PSO (CPSO) to help particles to escape from local minima with a simple implementation that improves swarm's diversity. The model space can be sampled by running the optimizer multiple times and by keeping all the models explored by the swarms in the different runs. A traveltime tomography algorithm based on CPSO is successfully applied on a real 3D data set in the context of induced seismicity.

Design optimization of single mixed refrigerant natural gas liquefaction process using the particle swarm paradigm with nonlinear constraints

International Nuclear Information System (INIS)

Khan, Mohd Shariq; Lee, Moonyong

2013-01-01

The particle swarm paradigm is employed to optimize single mixed refrigerant natural gas liquefaction process. Liquefaction design involves multivariable problem solving and non-optimal execution of these variables can waste energy and contribute to process irreversibilities. Design optimization requires these variables to be optimized simultaneously; minimizing the compression energy requirement is selected as the optimization objective. Liquefaction is modeled using Honeywell UniSim Design ™ and the resulting rigorous model is connected with the particle swarm paradigm coded in MATLAB. Design constraints are folded into the objective function using the penalty function method. Optimization successfully improved efficiency by reducing the compression energy requirement by ca. 10% compared with the base case. -- Highlights: ► The particle swarm paradigm (PSP) is employed for design optimization of SMR NG liquefaction process. ► Rigorous SMR process model based on UniSim is connected with PSP coded in MATLAB. ► Stochastic features of PSP give more confidence in the optimality of complex nonlinear problems. ► Optimization with PSP notably improves energy efficiency of the SMR process.

Implementation of Chaotic Gaussian Particle Swarm Optimization for Optimize Learning-to-Rank Software Defect Prediction Model Construction

Science.gov (United States)

Buchari, M. A.; Mardiyanto, S.; Hendradjaya, B.

2018-03-01

Finding the existence of software defect as early as possible is the purpose of research about software defect prediction. Software defect prediction activity is required to not only state the existence of defects, but also to be able to give a list of priorities which modules require a more intensive test. Therefore, the allocation of test resources can be managed efficiently. Learning to rank is one of the approach that can provide defect module ranking data for the purposes of software testing. In this study, we propose a meta-heuristic chaotic Gaussian particle swarm optimization to improve the accuracy of learning to rank software defect prediction approach. We have used 11 public benchmark data sets as experimental data. Our overall results has demonstrated that the prediction models construct using Chaotic Gaussian Particle Swarm Optimization gets better accuracy on 5 data sets, ties in 5 data sets and gets worse in 1 data sets. Thus, we conclude that the application of Chaotic Gaussian Particle Swarm Optimization in Learning-to-Rank approach can improve the accuracy of the defect module ranking in data sets that have high-dimensional features.

A novel e-shape communication antenna design using particle swarm optimization (PSO)

Science.gov (United States)

Mohanageetha, D.; Pavithra, R.

2013-01-01

An E-shape patch antenna is designed and demonstrated their effectiveness using Particle Swarm Optimization (PSO), which is used for wireless applications. The concept of PSO is briefly introduced in the design procedure and the design parameters are explained. This work focuses on identifying the increasing popularity of swarm intelligence specifically among the electromagnetic community. It is implemented using PSO combined with numerical algorithms for electromagnetic solutions, such as the Finite Element Method (FEM) and the Method of Moments (MOM). In both the realizations, the PSO technique drives the design variables such as antenna dimensions and geometrical features. The fitness function is evaluated for the optimizer. This is achieved by using CAD FEKO 6.1, electromagnetic simulation software. The model is designed with a resonant frequency of 2.65GHz.

Multi-Robot, Multi-Target Particle Swarm Optimization Search in Noisy Wireless Environments

Energy Technology Data Exchange (ETDEWEB)

Kurt Derr; Milos Manic

2009-05-01

Multiple small robots (swarms) can work together using Particle Swarm Optimization (PSO) to perform tasks that are difficult or impossible for a single robot to accomplish. The problem considered in this paper is exploration of an unknown environment with the goal of finding a target(s) at an unknown location(s) using multiple small mobile robots. This work demonstrates the use of a distributed PSO algorithm with a novel adaptive RSS weighting factor to guide robots for locating target(s) in high risk environments. The approach was developed and analyzed on multiple robot single and multiple target search. The approach was further enhanced by the multi-robot-multi-target search in noisy environments. The experimental results demonstrated how the availability of radio frequency signal can significantly affect robot search time to reach a target.

Application of a Continuous Particle Swarm Optimization (CPSO for the Optimal Coordination of Overcurrent Relays Considering a Penalty Method

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Abdul Wadood

2018-04-01

Full Text Available In an electrical power system, the coordination of the overcurrent relays plays an important role in protecting the electrical system by providing primary as well as backup protection. To reduce power outages, the coordination between these relays should be kept at the optimum value to minimize the total operating time and ensure that the least damage occurs under fault conditions. It is also imperative to ensure that the relay setting does not create an unintentional operation and consecutive sympathy trips. In a power system protection coordination problem, the objective function to be optimized is the sum of the total operating time of all main relays. In this paper, the coordination of overcurrent relays in a ring fed distribution system is formulated as an optimization problem. Coordination is performed using proposed continuous particle swarm optimization. In order to enhance and improve the quality of this solution a local search algorithm (LSA is implanted into the original particle swarm algorithm (PSO and, in addition to the constraints, these are amalgamated into the fitness function via the penalty method. The results achieved from the continuous particle swarm optimization algorithm (CPSO are compared with other evolutionary optimization algorithms (EA and this comparison showed that the proposed scheme is competent in dealing with the relevant problems. From further analyzing the obtained results, it was found that the continuous particle swarm approach provides the most globally optimum solution.

Quantum particle swarm approaches applied to combinatorial problems

Energy Technology Data Exchange (ETDEWEB)

Nicolau, Andressa dos S.; Schirru, Roberto; Lima, Alan M.M. de, E-mail: andressa@lmp.ufrj.br [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil). Programa de Engenharia Nuclear

2017-07-01

Quantum Particle Swarm Optimization (QPSO) is a global convergence algorithm that combines the classical PSO philosophy and quantum mechanics to improve performance of PSO. Different from PSO it only has the 'measurement' of the position equation for all particles. The process of 'measurement' in quantum mechanics, obey classic laws while the particle itself follows the quantum rules. QPSO works like PSO in search ability but has fewer parameters control. In order to improve the QPSO performance, some strategies have been proposed in the literature. Weighted QPSO (WQPSO) is a version of QPSO, where weight parameter is insert in the calculation of the balance between the global and local searching of the algorithm. It has been shown to perform well in finding the optimal solutions for many optimization problems. In this article random confinement was introduced in WQPSO. The WQPSO with random confinement was tested in two combinatorial problems. First, we execute the model on Travelling Salesman Problem (TSP) to find the parameters' values resulting in good solutions in general. Finally, the model was tested on Nuclear Reactor Reload Problem, and the performance was compared with QPSO standard. (author)

Quantum particle swarm approaches applied to combinatorial problems

International Nuclear Information System (INIS)

Nicolau, Andressa dos S.; Schirru, Roberto; Lima, Alan M.M. de

2017-01-01

Quantum Particle Swarm Optimization (QPSO) is a global convergence algorithm that combines the classical PSO philosophy and quantum mechanics to improve performance of PSO. Different from PSO it only has the 'measurement' of the position equation for all particles. The process of 'measurement' in quantum mechanics, obey classic laws while the particle itself follows the quantum rules. QPSO works like PSO in search ability but has fewer parameters control. In order to improve the QPSO performance, some strategies have been proposed in the literature. Weighted QPSO (WQPSO) is a version of QPSO, where weight parameter is insert in the calculation of the balance between the global and local searching of the algorithm. It has been shown to perform well in finding the optimal solutions for many optimization problems. In this article random confinement was introduced in WQPSO. The WQPSO with random confinement was tested in two combinatorial problems. First, we execute the model on Travelling Salesman Problem (TSP) to find the parameters' values resulting in good solutions in general. Finally, the model was tested on Nuclear Reactor Reload Problem, and the performance was compared with QPSO standard. (author)

Light Focusing through Scattering Media by Particle Swarm Optimization

International Nuclear Information System (INIS)

Huang Hui-Ling; Chen Zi-Yang; Sun Cun-Zhi; Liu Ji-Lin; Pu Ji-Xiong

2015-01-01

We demonstrate light focusing through scattering media by introducing particle swarm optimization for modulating the phase wavefront. Light refocusing is simulated numerically based on the angular spectrum method and the circular Gaussian distribution model of the scattering media. Experimentally, a spatial light modulator is used to control the phase of incident light, so as to make the scattered light converge to a focus. The influence of divided segments of input light and the effect of the number of iterations on light intensity enhancement are investigated. Simulation results are found to be in good agreement with the theoretical analysis for light refocusing. (paper)

Particle swarm optimization applied to automatic lens design

Science.gov (United States)

Qin, Hua

2011-06-01

This paper describes a novel application of Particle Swarm Optimization (PSO) technique to lens design. A mathematical model is constructed, and merit functions in an optical system are employed as fitness functions, which combined radiuses of curvature, thicknesses among lens surfaces and refractive indices regarding an optical system. By using this function, the aberration correction is carried out. A design example using PSO is given. Results show that PSO as optical design tools is practical and powerful, and this method is no longer dependent on the lens initial structure and can arbitrarily create search ranges of structural parameters of a lens system, which is an important step towards automatic design with artificial intelligence.

Multiobjective Optimization of Irreversible Thermal Engine Using Mutable Smart Bee Algorithm

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M. Gorji-Bandpy

2012-01-01

Full Text Available A new method called mutable smart bee (MSB algorithm proposed for cooperative optimizing of the maximum power output (MPO and minimum entropy generation (MEG of an Atkinson cycle as a multiobjective, multi-modal mechanical problem. This method utilizes mutable smart bee instead of classical bees. The results have been checked with some of the most common optimizing algorithms like Karaboga’s original artificial bee colony, bees algorithm (BA, improved particle swarm optimization (IPSO, Lukasik firefly algorithm (LFFA, and self-adaptive penalty function genetic algorithm (SAPF-GA. According to obtained results, it can be concluded that Mutable Smart Bee (MSB is capable to maintain its historical memory for the location and quality of food sources and also a little chance of mutation is considered for this bee. These features were found as strong elements for mining data in constraint areas and the results will prove this claim.

Basic investigation of particle swarm optimization performance in a reduced scale PWR passive safety system design

International Nuclear Information System (INIS)

Cunha, Joao J. da; Lapa, Celso Marcelo F.; Alvim, Antonio Carlos M.; Lima, Carlos A. Souza; Pereira, Claudio Marcio do N.A.

2010-01-01

This work presents a methodology to investigate the viability of using particle swarm optimization technique to obtain the best combination of physical and operational parameters that lead to the best adjusted dimensionless groups, calculated by similarity laws, that are able to simulate the most relevant physical phenomena in single-phase flow under natural circulation and to offer an appropriate alternative reduced scale design for reactor primary loops with this flow characteristics. A PWR reactor core, under natural circulation, based on LOFT test facility, was used as the case study. The particle swarm optimization technique was applied to a problem with these thermo-hydraulics conditions and results demonstrated the viability and adequacy of the method to design similar systems with these characteristics.

Basic investigation of particle swarm optimization performance in a reduced scale PWR passive safety system design

Energy Technology Data Exchange (ETDEWEB)

Cunha, Joao J. da [Eletronuclear Eletrobras Termonuclear, Gerencia de Analise de Seguranca Nuclear, Rua da Candelaria, 65, 7o andar. Centro, Rio de Janeiro 20091-906 (Brazil); Lapa, Celso Marcelo F., E-mail: lapa@ien.gov.b [Instituto de Engenharia Nuclear, Divisao de Reatores/PPGIEN, P.O. Box 68550, Rua Helio de Almeida 75 Cidade Universitaria, Ilha do Fundao, Rio de Janeiro 21941-972 (Brazil); Instituto Nacional de Ciencia e Tecnologia de Reatores Nucleares Inovadores (Brazil); Alvim, Antonio Carlos M. [Universidade Federal do Rio de Janeiro, COPPE/Nuclear, P.O. Box 68509, Cidade Universitaria, Ilha do Fundao s/n, Rio de Janeiro 21945-970 (Brazil); Instituto Nacional de Ciencia e Tecnologia de Reatores Nucleares Inovadores (Brazil); Lima, Carlos A. Souza [Instituto de Engenharia Nuclear, Divisao de Reatores/PPGIEN, P.O. Box 68550, Rua Helio de Almeida 75 Cidade Universitaria, Ilha do Fundao, Rio de Janeiro 21941-972 (Brazil); Instituto Politecnico, Universidade do Estado do Rio de Janeiro, Pos-Graduacao em Modelagem Computacional, Rua Alberto Rangel, s/n, Vila Nova, Nova Friburgo 28630-050 (Brazil); Pereira, Claudio Marcio do N.A. [Instituto de Engenharia Nuclear, Divisao de Reatores/PPGIEN, P.O. Box 68550, Rua Helio de Almeida 75 Cidade Universitaria, Ilha do Fundao, Rio de Janeiro 21941-972 (Brazil); Instituto Nacional de Ciencia e Tecnologia de Reatores Nucleares Inovadores (Brazil)

2010-03-15

This work presents a methodology to investigate the viability of using particle swarm optimization technique to obtain the best combination of physical and operational parameters that lead to the best adjusted dimensionless groups, calculated by similarity laws, that are able to simulate the most relevant physical phenomena in single-phase flow under natural circulation and to offer an appropriate alternative reduced scale design for reactor primary loops with this flow characteristics. A PWR reactor core, under natural circulation, based on LOFT test facility, was used as the case study. The particle swarm optimization technique was applied to a problem with these thermo-hydraulics conditions and results demonstrated the viability and adequacy of the method to design similar systems with these characteristics.

Hysteresis compensation of the Prandtl-Ishlinskii model for piezoelectric actuators using modified particle swarm optimization with chaotic map.

Science.gov (United States)

Long, Zhili; Wang, Rui; Fang, Jiwen; Dai, Xufei; Li, Zuohua

2017-07-01

Piezoelectric actuators invariably exhibit hysteresis nonlinearities that tend to become significant under the open-loop condition and could cause oscillations and errors in nanometer-positioning tasks. Chaotic map modified particle swarm optimization (MPSO) is proposed and implemented to identify the Prandtl-Ishlinskii model for piezoelectric actuators. Hysteresis compensation is attained through application of an inverse Prandtl-Ishlinskii model, in which the parameters are formulated based on the original model with chaotic map MPSO. To strengthen the diversity and improve the searching ergodicity of the swarm, an initial method of adaptive inertia weight based on a chaotic map is proposed. To compare and prove that the swarm's convergence occurs before stochastic initialization and to attain an optimal particle swarm optimization algorithm, the parameters of a proportional-integral-derivative controller are searched using self-tuning, and the simulated results are used to verify the search effectiveness of chaotic map MPSO. The results show that chaotic map MPSO is superior to its competitors for identifying the Prandtl-Ishlinskii model and that the inverse Prandtl-Ishlinskii model can provide hysteresis compensation under different conditions in a simple and effective manner.

Multidimensional particle swarm optimization for machine learning and pattern recognition

CERN Document Server

Kiranyaz, Serkan; Gabbouj, Moncef

2013-01-01

For many engineering problems we require optimization processes with dynamic adaptation as we aim to establish the dimension of the search space where the optimum solution resides and develop robust techniques to avoid the local optima usually associated with multimodal problems. This book explores multidimensional particle swarm optimization, a technique developed by the authors that addresses these requirements in a well-defined algorithmic approach.  After an introduction to the key optimization techniques, the authors introduce their unified framework and demonstrate its advantages in chal

An Improved Quantum-Behaved Particle Swarm Optimization Method for Economic Dispatch Problems with Multiple Fuel Options and Valve-Points Effects

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Hong-Yun Zhang

2012-09-01

Full Text Available Quantum-behaved particle swarm optimization (QPSO is an efficient and powerful population-based optimization technique, which is inspired by the conventional particle swarm optimization (PSO and quantum mechanics theories. In this paper, an improved QPSO named SQPSO is proposed, which combines QPSO with a selective probability operator to solve the economic dispatch (ED problems with valve-point effects and multiple fuel options. To show the performance of the proposed SQPSO, it is tested on five standard benchmark functions and two ED benchmark problems, including a 40-unit ED problem with valve-point effects and a 10-unit ED problem with multiple fuel options. The results are compared with differential evolution (DE, particle swarm optimization (PSO and basic QPSO, as well as a number of other methods reported in the literature in terms of solution quality, convergence speed and robustness. The simulation results confirm that the proposed SQPSO is effective and reliable for both function optimization and ED problems.

Structural optimization of Au–Pd bimetallic nanoparticles with improved particle swarm optimization method

International Nuclear Information System (INIS)

Shao Gui-Fang; Zhu Meng; Shangguan Ya-Li; Li Wen-Ran; Zhang Can; Wang Wei-Wei; Li Ling

2017-01-01

Due to the dependence of the chemical and physical properties of the bimetallic nanoparticles (NPs) on their structures, a fundamental understanding of their structural characteristics is crucial for their syntheses and wide applications. In this article, a systematical atomic-level investigation of Au–Pd bimetallic NPs is conducted by using the improved particle swarm optimization (IPSO) with quantum correction Sutton–Chen potentials (Q-SC) at different Au/Pd ratios and different sizes. In the IPSO, the simulated annealing is introduced into the classical particle swarm optimization (PSO) to improve the effectiveness and reliability. In addition, the influences of initial structure, particle size and composition on structural stability and structural features are also studied. The simulation results reveal that the initial structures have little effects on the stable structures, but influence the converging rate greatly, and the convergence rate of the mixing initial structure is clearly faster than those of the core-shell and phase structures. We find that the Au–Pd NPs prefer the structures with Au-rich in the outer layers while Pd-rich in the inner ones. Especially, when the Au/Pd ratio is 6:4, the structure of the nanoparticle (NP) presents a standardized Pd core Au shell structure. (paper)

Particle Swarm Optimization with Various Inertia Weight Variants for Optimal Power Flow Solution

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Prabha Umapathy

2010-01-01

Full Text Available This paper proposes an efficient method to solve the optimal power flow problem in power systems using Particle Swarm Optimization (PSO. The objective of the proposed method is to find the steady-state operating point which minimizes the fuel cost, while maintaining an acceptable system performance in terms of limits on generator power, line flow, and voltage. Three different inertia weights, a constant inertia weight (CIW, a time-varying inertia weight (TVIW, and global-local best inertia weight (GLbestIW, are considered with the particle swarm optimization algorithm to analyze the impact of inertia weight on the performance of PSO algorithm. The PSO algorithm is simulated for each of the method individually. It is observed that the PSO algorithm with the proposed inertia weight yields better results, both in terms of optimal solution and faster convergence. The proposed method has been tested on the standard IEEE 30 bus test system to prove its efficacy. The algorithm is computationally faster, in terms of the number of load flows executed, and provides better results than other heuristic techniques.

Particle Swarm-Based Translation Control for Immersed Tunnel Element in the Hong Kong-Zhuhai-Macao Bridge Project

Science.gov (United States)

Li, Jun-jun; Yang, Xiao-jun; Xiao, Ying-jie; Xu, Bo-wei; Wu, Hua-feng

2018-03-01

Immersed tunnel is an important part of the Hong Kong-Zhuhai-Macao Bridge (HZMB) project. In immersed tunnel floating, translation which includes straight and transverse movements is the main working mode. To decide the magnitude and direction of the towing force for each tug, a particle swarm-based translation control method is presented for non-power immersed tunnel element. A sort of linear weighted logarithmic function is exploited to avoid weak subgoals. In simulation, the particle swarm-based control method is evaluated and compared with traditional empirical method in the case of the HZMB project. Simulation results show that the presented method delivers performance improvement in terms of the enhanced surplus towing force.

A Novel Cluster Head Selection Algorithm Based on Fuzzy Clustering and Particle Swarm Optimization.

Science.gov (United States)

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.

Optimization of C4.5 algorithm-based particle swarm optimization for breast cancer diagnosis

Science.gov (United States)

Muslim, M. A.; Rukmana, S. H.; Sugiharti, E.; Prasetiyo, B.; Alimah, S.

2018-03-01

Data mining has become a basic methodology for computational applications in the field of medical domains. Data mining can be applied in the health field such as for diagnosis of breast cancer, heart disease, diabetes and others. Breast cancer is most common in women, with more than one million cases and nearly 600,000 deaths occurring worldwide each year. The most effective way to reduce breast cancer deaths was by early diagnosis. This study aims to determine the level of breast cancer diagnosis. This research data uses Wisconsin Breast Cancer dataset (WBC) from UCI machine learning. The method used in this research is the algorithm C4.5 and Particle Swarm Optimization (PSO) as a feature option and to optimize the algorithm. C4.5. Ten-fold cross-validation is used as a validation method and a confusion matrix. The result of this research is C4.5 algorithm. The particle swarm optimization C4.5 algorithm has increased by 0.88%.

A tabu search evalutionary algorithm for multiobjective optimization: Application to a bi-criterion aircraft structural reliability problem

Science.gov (United States)

Long, Kim Chenming

Real-world engineering optimization problems often require the consideration of multiple conflicting and noncommensurate objectives, subject to nonconvex constraint regions in a high-dimensional decision space. Further challenges occur for combinatorial multiobjective problems in which the decision variables are not continuous. Traditional multiobjective optimization methods of operations research, such as weighting and epsilon constraint methods, are ill-suited to solving these complex, multiobjective problems. This has given rise to the application of a wide range of metaheuristic optimization algorithms, such as evolutionary, particle swarm, simulated annealing, and ant colony methods, to multiobjective optimization. Several multiobjective evolutionary algorithms have been developed, including the strength Pareto evolutionary algorithm (SPEA) and the non-dominated sorting genetic algorithm (NSGA), for determining the Pareto-optimal set of non-dominated solutions. Although numerous researchers have developed a wide range of multiobjective optimization algorithms, there is a continuing need to construct computationally efficient algorithms with an improved ability to converge to globally non-dominated solutions along the Pareto-optimal front for complex, large-scale, multiobjective engineering optimization problems. This is particularly important when the multiple objective functions and constraints of the real-world system cannot be expressed in explicit mathematical representations. This research presents a novel metaheuristic evolutionary algorithm for complex multiobjective optimization problems, which combines the metaheuristic tabu search algorithm with the evolutionary algorithm (TSEA), as embodied in genetic algorithms. TSEA is successfully applied to bicriteria (i.e., structural reliability and retrofit cost) optimization of the aircraft tail structure fatigue life, which increases its reliability by prolonging fatigue life. A comparison for this

Techno-economic optimization of a shell and tube heat exchanger by genetic and particle swarm algorithms

International Nuclear Information System (INIS)

Sadeghzadeh, H.; Ehyaei, M.A.; Rosen, M.A.

2015-01-01

Highlights: • Calculating pressure drop and heat transfer coefficient by Delaware method. • The accuracy of the Delaware method is more than the Kern method. • The results of the PSO are better than the results of the GA. • The optimization results suggest that yields the best and most economic optimization. - Abstract: The use of genetic and particle swarm algorithms in the design of techno-economically optimum shell-and-tube heat exchangers is demonstrated. A cost function (including costs of the heat exchanger based on surface area and power consumption to overcome pressure drops) is the objective function, which is to be minimized. Selected decision variables include tube diameter, central baffles spacing and shell diameter. The Delaware method is used to calculate the heat transfer coefficient and the shell-side pressure drop. The accuracy and efficiency of the suggested algorithm and the Delaware method are investigated. A comparison of the results obtained by the two algorithms shows that results obtained with the particle swarm optimization method are superior to those obtained with the genetic algorithm method. By comparing these results with those from various references employing the Kern method and other algorithms, it is shown that the Delaware method accompanied by genetic and particle swarm algorithms achieves more optimum results, based on assessments for two case studies

hydroPSO: A Versatile Particle Swarm Optimisation R Package for Calibration of Environmental Models

Science.gov (United States)

Zambrano-Bigiarini, M.; Rojas, R.

2012-04-01

Particle Swarm Optimisation (PSO) is a recent and powerful population-based stochastic optimisation technique inspired by social behaviour of bird flocking, which shares similarities with other evolutionary techniques such as Genetic Algorithms (GA). In PSO, however, each individual of the population, known as particle in PSO terminology, adjusts its flying trajectory on the multi-dimensional search-space according to its own experience (best-known personal position) and the one of its neighbours in the swarm (best-known local position). PSO has recently received a surge of attention given its flexibility, ease of programming, low memory and CPU requirements, and efficiency. Despite these advantages, PSO may still get trapped into sub-optimal solutions, suffer from swarm explosion or premature convergence. Thus, the development of enhancements to the "canonical" PSO is an active area of research. To date, several modifications to the canonical PSO have been proposed in the literature, resulting into a large and dispersed collection of codes and algorithms which might well be used for similar if not identical purposes. In this work we present hydroPSO, a platform-independent R package implementing several enhancements to the canonical PSO that we consider of utmost importance to bring this technique to the attention of a broader community of scientists and practitioners. hydroPSO is model-independent, allowing the user to interface any model code with the calibration engine without having to invest considerable effort in customizing PSO to a new calibration problem. Some of the controlling options to fine-tune hydroPSO are: four alternative topologies, several types of inertia weight, time-variant acceleration coefficients, time-variant maximum velocity, regrouping of particles when premature convergence is detected, different types of boundary conditions and many others. Additionally, hydroPSO implements recent PSO variants such as: Improved Particle Swarm

Comparison result of inversion of gravity data of a fault by particle swarm optimization and Levenberg-Marquardt methods.

Science.gov (United States)

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.

Photothermal depth profiling for multilayered Structures by particle swarm optimization

International Nuclear Information System (INIS)

Chen, Z J; Fang, J W; Zhang, S Y

2011-01-01

This paper presents a method to reconstruct thermal conductivity depth profile of a layered medium using noisy photothermal data. The method tries to obtain an accurate reconstruction of discontinuous profile using particle swarm optimization (PSO) algorithm and total variation (TV) regularization. The reconstructions of different thermal conductivity profiles have been tested on simulated photothermal data. The simulation results show that the method can find accurately the locations of discontinuities, and the reconstructed profiles are in agreement with the original ones. Moreover, the results also show the method has good robustness and anti-noise capability.

Thermal design of an electric motor using Particle Swarm Optimization

International Nuclear Information System (INIS)

Jandaud, P-O; Harmand, S; Fakes, M

2012-01-01

In this paper, flow inside an electric machine called starter-alternator is studied parametrically with CFD in order to be used by a thermal lumped model coupled to an optimization algorithm using Particle Swarm Optimization (PSO). In a first case, the geometrical parameters are symmetric allowing us to model only one side of the machine. The optimized thermal results are not conclusive. In a second case, all the parameters are independent. In this case, the flow is strongly influenced by the dissymmetry. Optimization results are this time a clear improvement compared to the original machine.

A Performance Study on Synchronous and Asynchronous Update Rules for A Plug-In Direct Particle Swarm Repetitive Controller

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Ufnalski Bartlomiej

2014-12-01

Full Text Available In this paper two different update schemes for the recently developed plug-in direct particle swarm repetitive controller (PDPSRC are investigated and compared. The proposed approach employs the particle swarm optimizer (PSO to solve in on-line mode a dynamic optimization problem (DOP related to the control task in the constant-amplitude constant-frequency voltage-source inverter (CACF VSI with an LC output filter. The effectiveness of synchronous and asynchronous update rules, both commonly used in static optimization problems (SOPs, is assessed and compared in the case of PDPSRC. The performance of the controller, when synthesized using each of the update schemes, is studied numerically.

Multi-objective efficiency enhancement using workload spreading in an operational data center

International Nuclear Information System (INIS)

Habibi Khalaj, Ali; Scherer, Thomas; Siriwardana, Jayantha; Halgamuge, Saman K.

2015-01-01

Highlights: • Development of the heat-flow reduced order model (HFROM) for the IBM ZRL data center. • Verification of the developed HFROM with the experimentally verified CFD model. • Multi-objective efficiency enhancement of the HFROM using particle swarm optimization. • Improving the COP of the data center’s cooling system by about 17%. • Increasing the total allocated workload of the servers by about 10%. - Abstract: The cooling systems of rapidly growing Data Centers (DCs) consume a considerable amount of energy, which is one of the main concerns in designing and operating DCs. The main source of thermal inefficiency in a typical air-cooled DC is hot air recirculation from outlets of servers into their inlets, causing hot spots and leading to performance reduction of the cooling system. In this study, a thermally aware workload spreading method is proposed for reducing the hot spots while the total allocated server workload is increased. The core of this methodology lies in developing an appropriate thermal DC model for the optimization process. Given the fact that utilizing a high-fidelity thermal model of a DC is highly time consuming in the optimization process, a three dimensional reduced order model of a real DC is developed in this study. This model, whose boundary conditions are determined based on measurement data of an operational DC, is developed based on the potential flow theory updated with the Rankine vortex to account for buoyancy and air recirculation effects inside the DC. Before evaluating the proposed method, this model is verified with a computational fluid dynamic (CFD) model simulated with the same boundary conditions. The efficient load spreading method is achieved by applying a multi-objective particle swarm optimization (MOPSO) algorithm whose objectives are to minimize the hot spot occurrences and to maximize the total workload allocated to servers. In this case study, by applying the proposed method, the Coefficient of

PID control for chaotic synchronization using particle swarm optimization

International Nuclear Information System (INIS)

Chang, W.-D.

2009-01-01

In this paper, we attempt to use the proportional-integral-derivative (PID) controller to achieve the chaos synchronization for delayed discrete chaotic systems. Three PID control gains can be optimally determined by means of using a novel optimization algorithm, called the particle swarm optimization (PSO). The algorithm is motivated from the organism behavior of fish schooling and bird flocking, and involves the social psychology principles in socio-cognition human agents and evolutionary computations. It has a good numerical convergence for solving optimization problem. To show the validity of the PSO-based PID control for chaos synchronization, several cases with different initial populations are considered and some simulation results are shown.

PID control for chaotic synchronization using particle swarm optimization

Energy Technology Data Exchange (ETDEWEB)

Chang, W.-D. [Department of Computer and Communication, Shu-Te University, Kaohsiung 824, Taiwan (China)], E-mail: wdchang@mail.stu.edu.tw

2009-01-30

In this paper, we attempt to use the proportional-integral-derivative (PID) controller to achieve the chaos synchronization for delayed discrete chaotic systems. Three PID control gains can be optimally determined by means of using a novel optimization algorithm, called the particle swarm optimization (PSO). The algorithm is motivated from the organism behavior of fish schooling and bird flocking, and involves the social psychology principles in socio-cognition human agents and evolutionary computations. It has a good numerical convergence for solving optimization problem. To show the validity of the PSO-based PID control for chaos synchronization, several cases with different initial populations are considered and some simulation results are shown.

Iteration particle swarm optimization for contract capacities selection of time-of-use rates industrial customers

International Nuclear Information System (INIS)

Lee, Tsung-Ying; Chen, Chun-Lung

2007-01-01

This paper presents a new algorithm for solving the optimal contract capacities of a time-of-use (TOU) rates industrial customer. This algorithm is named iteration particle swarm optimization (IPSO). A new index, called iteration best is incorporated into particle swarm optimization (PSO) to improve solution quality and computation efficiency. Expanding line construction cost and contract recovery cost are considered, as well as demand contract capacity cost and penalty bill, in the selection of the optimal contract capacities. The resulting optimal contract capacity effectively reaches the minimum electricity charge of TOU rates users. A significant reduction in electricity costs is observed. The effects of expanding line construction cost and contract recovery cost on the selection of optimal contract capacities can also be estimated. The feasibility of the new algorithm is demonstrated by a numerical example, and the IPSO solution quality and computation efficiency are compared to those of other algorithms

Optimal risky bidding strategy for a generating company by self-organising hierarchical particle swarm optimisation

International Nuclear Information System (INIS)

Boonchuay, Chanwit; Ongsakul, Weerakorn

2011-01-01

In this paper, an optimal risky bidding strategy for a generating company (GenCo) by self-organising hierarchical particle swarm optimisation with time-varying acceleration coefficients (SPSO-TVAC) is proposed. A significant risk index based on mean-standard deviation ratio (MSR) is maximised to provide the optimal bid prices and quantities. The Monte Carlo (MC) method is employed to simulate rivals' behaviour in competitive environment. Non-convex operating cost functions of thermal generating units and minimum up/down time constraints are taken into account. The proposed bidding strategy is implemented in a multi-hourly trading in a uniform price spot market and compared to other particle swarm optimisation (PSO). Test results indicate that the proposed SPSO-TVAC approach can provide a higher MSR than the other PSO methods. It is potentially applicable to risk management of profit variation of GenCo in spot market.

An Image Enhancement Method Using the Quantum-Behaved Particle Swarm Optimization with an Adaptive Strategy

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Xiaoping Su

2013-01-01

Full Text Available Image enhancement techniques are very important to image processing, which are used to improve image quality or extract the fine details in degraded images. In this paper, two novel objective functions based on the normalized incomplete Beta transform function are proposed to evaluate the effectiveness of grayscale image enhancement and color image enhancement, respectively. Using these objective functions, the parameters of transform functions are estimated by the quantum-behaved particle swarm optimization (QPSO. We also propose an improved QPSO with an adaptive parameter control strategy. The QPSO and the AQPSO algorithms, along with genetic algorithm (GA and particle swarm optimization (PSO, are tested on several benchmark grayscale and color images. The results show that the QPSO and AQPSO perform better than GA and PSO for the enhancement of these images, and the AQPSO has some advantages over QPSO due to its adaptive parameter control strategy.

Tuning of damping controller for UPFC using quantum particle swarm optimizer

Energy Technology Data Exchange (ETDEWEB)

Shayeghi, H., E-mail: hshayeghi@gmail.co [Technical Engineering Department, University of Mohaghegh Ardabili, Ardabil (Iran, Islamic Republic of); Shayanfar, H.A. [Center of Excellence for Power System Automation and Operation, Electrical Engineering Department, Iran University of Science and Technology, Tehran (Iran, Islamic Republic of); Jalilzadeh, S.; Safari, A. [Technical Engineering Department, Zanjan University, Zanjan (Iran, Islamic Republic of)

2010-11-15

On the basis of the linearized Phillips-Herffron model of a single machine power system, we design optimally the unified power flow controller (UPFC) based damping controller in order to enhance power system low frequency oscillations. The problem of robustly UPFC based damping controller is formulated as an optimization problem according to the time domain-based objective function which is solved using quantum-behaved particle swarm optimization (QPSO) technique that has fewer parameters and stronger search capability than the particle swarm optimization (PSO), as well as is easy to implement. To ensure the robustness of the proposed damping controller, the design process takes into account a wide range of operating conditions and system configurations. The effectiveness of the proposed controller is demonstrated through non-linear time-domain simulation and some performance indices studies under various disturbance conditions of over a wide range of loading conditions. The results analysis reveals that the designed QPSO based UPFC controller has an excellent capability in damping power system low frequency oscillations in comparison with the designed classical PSO (CPSO) based UPFC controller and enhance greatly the dynamic stability of the power systems. Moreover, the system performance analysis under different operating conditions show that the {delta}{sub E} based damping controller is superior to the m{sub B} based damping controller.

Simplified particle swarm optimization algorithm - doi: 10.4025/actascitechnol.v34i1.9679

Directory of Open Access Journals (Sweden)

Ricardo Paupitz Barbosa dos Santos

2011-11-01

Full Text Available Real ants and bees are considered social insects, which present some remarkable characteristics that can be used, as inspiration, to solve complex optimization problems. This field of study is known as swarm intelligence. Therefore, this paper presents a new algorithm that can be understood as a simplified version of the well known Particle Swarm Optimization (PSO. The proposed algorithm allows saving some computational effort and obtains a considerable performance in the optimization of nonlinear functions. We employed four nonlinear benchmark functions, Sphere, Schwefel, Schaffer and Ackley functions, to test and validate the new proposal. Some simulated results were used in order to clarify the efficiency of the proposed algorithm.

Particle swarm optimization based support vector machine for damage level prediction of non-reshaped berm breakwater

Digital Repository Service at National Institute of Oceanography (India)

Harish, N.; Mandal, S.; Rao, S.; Patil, S.G.

breakwater. Soft computing tools like Artificial Neural Network, Fuzzy Logic, Support Vector Machine (SVM), etc, are successfully used to solve complex problems. In the present study, SVM and hybrid of Particle Swarm Optimization (PSO) with SVM (PSO...

Portfolio management using value at risk: A comparison between genetic algorithms and particle swarm optimization

NARCIS (Netherlands)

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

Collective motion with anticipation: flocking, spinning, and swarming.

Science.gov (United States)

Morin, Alexandre; Caussin, Jean-Baptiste; Eloy, Christophe; Bartolo, Denis

2015-01-01

We investigate the collective dynamics of self-propelled particles able to probe and anticipate the orientation of their neighbors. We show that a simple anticipation strategy hinders the emergence of homogeneous flocking patterns. Yet anticipation promotes two other forms of self-organization: collective spinning and swarming. In the spinning phase, all particles follow synchronous circular orbits, while in the swarming phase, the population condensates into a single compact swarm that cruises coherently without requiring any cohesive interactions. We quantitatively characterize and rationalize these phases of polar active matter and discuss potential applications to the design of swarming robots.

The upper surface of an Escherichia coli swarm is stationary.

Science.gov (United States)

Zhang, Rongjing; Turner, Linda; Berg, Howard C

2010-01-05

When grown in a rich medium on agar, many bacteria elongate, produce more flagella, and swim in a thin film of fluid over the agar surface in swirling packs. Cells that spread in this way are said to swarm. The agar is a solid gel, with pores smaller than the bacteria, so the swarm/agar interface is fixed. Here we show, in experiments with Escherichia coli, that the swarm/air interface also is fixed. We deposited MgO smoke particles on the top surface of an E. coli swarm near its advancing edge, where cells move in a single layer, and then followed the motion of the particles by dark-field microscopy and the motion of the underlying cells by phase-contrast microscopy. Remarkably, the smoke particles remained fixed (diffusing only a few micrometers) while the swarming cells streamed past underneath. The diffusion coefficients of the smoke particles were smaller over the virgin agar ahead of the swarm than over the swarm itself. Changes between these two modes of behavior were evident within 10-20 microm of the swarm edge, indicating an increase in depth of the fluid in advance of the swarm. The only plausible way that the swarm/air interface can be fixed is that it is covered by a surfactant monolayer pinned at its edges. When a swarm is exposed to air, such a monolayer can markedly reduce water loss. When cells invade tissue, the ability to move rapidly between closely opposed fixed surfaces is a useful trait.

Extreme Learning Machine and Particle Swarm Optimization in optimizing CNC turning operation

Science.gov (United States)

Janahiraman, Tiagrajah V.; Ahmad, Nooraziah; Hani Nordin, Farah

2018-04-01

The CNC machine is controlled by manipulating cutting parameters that could directly influence the process performance. Many optimization methods has been applied to obtain the optimal cutting parameters for the desired performance function. Nonetheless, the industry still uses the traditional technique to obtain those values. Lack of knowledge on optimization techniques is the main reason for this issue to be prolonged. Therefore, the simple yet easy to implement, Optimal Cutting Parameters Selection System is introduced to help the manufacturer to easily understand and determine the best optimal parameters for their turning operation. This new system consists of two stages which are modelling and optimization. In modelling of input-output and in-process parameters, the hybrid of Extreme Learning Machine and Particle Swarm Optimization is applied. This modelling technique tend to converge faster than other artificial intelligent technique and give accurate result. For the optimization stage, again the Particle Swarm Optimization is used to get the optimal cutting parameters based on the performance function preferred by the manufacturer. Overall, the system can reduce the gap between academic world and the industry by introducing a simple yet easy to implement optimization technique. This novel optimization technique can give accurate result besides being the fastest technique.

Prediction of Skin Sensitization with a Particle Swarm Optimized Support Vector Machine

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Chenzhong Cao

2009-07-01

Full Text Available Skin sensitization is the most commonly reported occupational illness, causing much suffering to a wide range of people. Identification and labeling of environmental allergens is urgently required to protect people from skin sensitization. The guinea pig maximization test (GPMT and murine local lymph node assay (LLNA are the two most important in vivo models for identification of skin sensitizers. In order to reduce the number of animal tests, quantitative structure-activity relationships (QSARs are strongly encouraged in the assessment of skin sensitization of chemicals. This paper has investigated the skin sensitization potential of 162 compounds with LLNA results and 92 compounds with GPMT results using a support vector machine. A particle swarm optimization algorithm was implemented for feature selection from a large number of molecular descriptors calculated by Dragon. For the LLNA data set, the classification accuracies are 95.37% and 88.89% for the training and the test sets, respectively. For the GPMT data set, the classification accuracies are 91.80% and 90.32% for the training and the test sets, respectively. The classification performances were greatly improved compared to those reported in the literature, indicating that the support vector machine optimized by particle swarm in this paper is competent for the identification of skin sensitizers.

Prediction of Skin Sensitization with a Particle Swarm Optimized Support Vector Machine

Science.gov (United States)

Yuan, Hua; Huang, Jianping; Cao, Chenzhong

2009-01-01

Skin sensitization is the most commonly reported occupational illness, causing much suffering to a wide range of people. Identification and labeling of environmental allergens is urgently required to protect people from skin sensitization. The guinea pig maximization test (GPMT) and murine local lymph node assay (LLNA) are the two most important in vivo models for identification of skin sensitizers. In order to reduce the number of animal tests, quantitative structure-activity relationships (QSARs) are strongly encouraged in the assessment of skin sensitization of chemicals. This paper has investigated the skin sensitization potential of 162 compounds with LLNA results and 92 compounds with GPMT results using a support vector machine. A particle swarm optimization algorithm was implemented for feature selection from a large number of molecular descriptors calculated by Dragon. For the LLNA data set, the classification accuracies are 95.37% and 88.89% for the training and the test sets, respectively. For the GPMT data set, the classification accuracies are 91.80% and 90.32% for the training and the test sets, respectively. The classification performances were greatly improved compared to those reported in the literature, indicating that the support vector machine optimized by particle swarm in this paper is competent for the identification of skin sensitizers. PMID:19742136

An Improved Method for Reconfiguring and Optimizing Electrical Active Distribution Network Using Evolutionary Particle Swarm Optimization

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Nur Faziera Napis

2018-05-01

Full Text Available The presence of optimized distributed generation (DG with suitable distribution network reconfiguration (DNR in the electrical distribution network has an advantage for voltage support, power losses reduction, deferment of new transmission line and distribution structure and system stability improvement. However, installation of a DG unit at non-optimal size with non-optimal DNR may lead to higher power losses, power quality problem, voltage instability and incremental of operational cost. Thus, an appropriate DG and DNR planning are essential and are considered as an objective of this research. An effective heuristic optimization technique named as improved evolutionary particle swarm optimization (IEPSO is proposed in this research. The objective function is formulated to minimize the total power losses (TPL and to improve the voltage stability index (VSI. The voltage stability index is determined for three load demand levels namely light load, nominal load, and heavy load with proper optimal DNR and DG sizing. The performance of the proposed technique is compared with other optimization techniques, namely particle swarm optimization (PSO and iteration particle swarm optimization (IPSO. Four case studies on IEEE 33-bus and IEEE 69-bus distribution systems have been conducted to validate the effectiveness of the proposed IEPSO. The optimization results show that, the best achievement is done by IEPSO technique with power losses reduction up to 79.26%, and 58.41% improvement in the voltage stability index. Moreover, IEPSO has the fastest computational time for all load conditions as compared to other algorithms.

An Improved Artificial Bee Colony Algorithm and Its Application to Multi-Objective Optimal Power Flow

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Xuanhu He

2015-03-01

Full Text Available Optimal power flow (OPF objective functions involve minimization of the total fuel costs of generating units, minimization of atmospheric pollutant emissions, minimization of active power losses and minimization of voltage deviations. In this paper, a fuzzy multi-objective OPF model is established by the fuzzy membership functions and the fuzzy satisfaction-maximizing method. The improved artificial bee colony (IABC algorithm is applied to solve the model. In the IABC algorithm, the mutation and crossover operations of a differential evolution algorithm are utilized to generate new solutions to improve exploitation capacity; tent chaos mapping is utilized to generate initial swarms, reference mutation solutions and the reference dimensions of crossover operations to improve swarm diversity. The proposed method is applied to multi-objective OPF problems in IEEE 30-bus, IEEE 57-bus and IEEE 300-bus test systems. The results are compared with those obtained by other algorithms, which demonstrates the effectiveness and superiority of the IABC algorithm, and how the optimal scheme obtained by the proposed model can make systems more economical and stable.

A Framework for Constrained Optimization Problems Based on a Modified Particle Swarm Optimization

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Biwei Tang

2016-01-01

Full Text Available This paper develops a particle swarm optimization (PSO based framework for constrained optimization problems (COPs. Aiming at enhancing the performance of PSO, a modified PSO algorithm, named SASPSO 2011, is proposed by adding a newly developed self-adaptive strategy to the standard particle swarm optimization 2011 (SPSO 2011 algorithm. Since the convergence of PSO is of great importance and significantly influences the performance of PSO, this paper first theoretically investigates the convergence of SASPSO 2011. Then, a parameter selection principle guaranteeing the convergence of SASPSO 2011 is provided. Subsequently, a SASPSO 2011-based framework is established to solve COPs. Attempting to increase the diversity of solutions and decrease optimization difficulties, the adaptive relaxation method, which is combined with the feasibility-based rule, is applied to handle constraints of COPs and evaluate candidate solutions in the developed framework. Finally, the proposed method is verified through 4 benchmark test functions and 2 real-world engineering problems against six PSO variants and some well-known methods proposed in the literature. Simulation results confirm that the proposed method is highly competitive in terms of the solution quality and can be considered as a vital alternative to solve COPs.

Quantum behaved Particle Swarm Optimization with Differential Mutation operator applied to WWER-1000 in-core fuel management optimization

International Nuclear Information System (INIS)

Jamalipour, Mostafa; Sayareh, Reza; Gharib, Morteza; Khoshahval, Farrokh; Karimi, Mahmood Reza

2013-01-01

Highlights: ► A new method called QPSO-DM is applied to BNPP in-core fuel management optimization. ► It is found that QPSO-DM performs better than PSO and QPSO. ► This method provides a permissible arrangement for optimum loading pattern. - Abstract: This paper presents a new method using Quantum Particle Swarm Optimization with Differential Mutation operator (QPSO-DM) for optimizing WWER-1000 core fuel management. Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) have shown good performance on in-core fuel management optimization (ICFMO). The objective of this paper is to show that QPSO-DM performs very well and is comparable to PSO and Quantum Particle Swarm Optimization (QPSO). Most of the strategies for ICFMO are based on maximizing multiplication factor (k eff ) to increase cycle length and minimizing power peaking factor (P q ) in order to improve fuel integrity. PSO, QPSO and QPSO-DM have been implemented to fulfill these requirements for the first operating cycle of WWER-1000 Bushehr Nuclear Power Plant (BNPP). The results show that QPSO-DM performs better than the others. A program has been written in MATLAB to map PSO, QPSO and QPSO-DM for loading pattern optimization. WIMS and CITATION have been used to simulate reactor core for neutronic calculations

Application of multi-objective controller to optimal tuning of PID gains for a hydraulic turbine regulating system using adaptive grid particle swam optimization.

Science.gov (United States)

Chen, Zhihuan; Yuan, Yanbin; Yuan, Xiaohui; Huang, Yuehua; Li, Xianshan; Li, Wenwu

2015-05-01

A hydraulic turbine regulating system (HTRS) is one of the most important components of hydropower plant, which plays a key role in maintaining safety, stability and economical operation of hydro-electrical installations. At present, the conventional PID controller is widely applied in the HTRS system for its practicability and robustness, and the primary problem with respect to this control law is how to optimally tune the parameters, i.e. the determination of PID controller gains for satisfactory performance. In this paper, a kind of multi-objective evolutionary algorithms, named adaptive grid particle swarm optimization (AGPSO) is applied to solve the PID gains tuning problem of the HTRS system. This newly AGPSO optimized method, which differs from a traditional one-single objective optimization method, is designed to take care of settling time and overshoot level simultaneously, in which a set of non-inferior alternatives solutions (i.e. Pareto solution) is generated. Furthermore, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto set. An illustrative example associated with the best compromise solution for parameter tuning of the nonlinear HTRS system is introduced to verify the feasibility and the effectiveness of the proposed AGPSO-based optimization approach, as compared with two another prominent multi-objective algorithms, i.e. Non-dominated Sorting Genetic Algorithm II (NSGAII) and Strength Pareto Evolutionary Algorithm II (SPEAII), for the quality and diversity of obtained Pareto solutions set. Consequently, simulation results show that this AGPSO optimized approach outperforms than compared methods with higher efficiency and better quality no matter whether the HTRS system works under unload or load conditions. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Biogeography-based particle swarm optimization with fuzzy elitism and its applications to constrained engineering problems

Science.gov (United States)

Guo, Weian; Li, Wuzhao; Zhang, Qun; Wang, Lei; Wu, Qidi; Ren, Hongliang

2014-11-01

In evolutionary algorithms, elites are crucial to maintain good features in solutions. However, too many elites can make the evolutionary process stagnate and cannot enhance the performance. This article employs particle swarm optimization (PSO) and biogeography-based optimization (BBO) to propose a hybrid algorithm termed biogeography-based particle swarm optimization (BPSO) which could make a large number of elites effective in searching optima. In this algorithm, the whole population is split into several subgroups; BBO is employed to search within each subgroup and PSO for the global search. Since not all the population is used in PSO, this structure overcomes the premature convergence in the original PSO. Time complexity analysis shows that the novel algorithm does not increase the time consumption. Fourteen numerical benchmarks and four engineering problems with constraints are used to test the BPSO. To better deal with constraints, a fuzzy strategy for the number of elites is investigated. The simulation results validate the feasibility and effectiveness of the proposed algorithm.

Combined Data with Particle Swarm Optimization for Structural Damage Detection

Directory of Open Access Journals (Sweden)

Fei Kang

2013-01-01

Full Text Available This paper proposes a damage detection method based on combined data of static and modal tests using particle swarm optimization (PSO. To improve the performance of PSO, some immune properties such as selection, receptor editing, and vaccination are introduced into the basic PSO and an improved PSO algorithm is formed. Simulations on three benchmark functions show that the new algorithm performs better than PSO. The efficiency of the proposed damage detection method is tested on a clamped beam, and the results demonstrate that it is more efficient than PSO, differential evolution, and an adaptive real-parameter simulated annealing genetic algorithm.

Solving the economic dispatch problem with a modified quantum-behaved particle swarm optimization method

Energy Technology Data Exchange (ETDEWEB)

Jun Sun; Wei Fang; Daojun Wang; Wenbo Xu [School of Information Technology, Jiangnan Univ., Wuxi, Jiangsu 214122 (China)

2009-12-15

In this paper, a modified quantum-behaved particle swarm optimization (QPSO) method is proposed to solve the economic dispatch (ED) problem in power systems, whose objective is to simultaneously minimize the generation cost rate while satisfying various equality and inequality constraints. The proposed method, denoted as QPSO-DM, combines the QPSO algorithm with differential mutation operation to enhance the global search ability of the algorithm. Many nonlinear characteristics of the generator, such as ramp rate limits, prohibited operating zones, and nonsmooth cost functions are considered when the proposed method is used in practical generator operation. The feasibility of the QPSO-DM method is demonstrated by three different power systems. It is compared with the QPSO, the differential evolution (DE), the particle swarm optimization (PSO), and the genetic algorithm (GA) in terms of the solution quality, robustness and convergence property. The simulation results show that the proposed QPSO-DM method is able to obtain higher quality solutions stably and efficiently in the ED problem than any other tested optimization algorithm. (author)

Solving the economic dispatch problem with a modified quantum-behaved particle swarm optimization method

International Nuclear Information System (INIS)

Sun Jun; Fang Wei; Wang Daojun; Xu Wenbo

2009-01-01

In this paper, a modified quantum-behaved particle swarm optimization (QPSO) method is proposed to solve the economic dispatch (ED) problem in power systems, whose objective is to simultaneously minimize the generation cost rate while satisfying various equality and inequality constraints. The proposed method, denoted as QPSO-DM, combines the QPSO algorithm with differential mutation operation to enhance the global search ability of the algorithm. Many nonlinear characteristics of the generator, such as ramp rate limits, prohibited operating zones, and nonsmooth cost functions are considered when the proposed method is used in practical generator operation. The feasibility of the QPSO-DM method is demonstrated by three different power systems. It is compared with the QPSO, the differential evolution (DE), the particle swarm optimization (PSO), and the genetic algorithm (GA) in terms of the solution quality, robustness and convergence property. The simulation results show that the proposed QPSO-DM method is able to obtain higher quality solutions stably and efficiently in the ED problem than any other tested optimization algorithm.

Economic dispatch using particle swarm optimization. A review

International Nuclear Information System (INIS)

Mahor, Amita; Rangnekar, Saroj; Prasad, Vishnu

2009-01-01

Electrical power industry restructuring has created highly vibrant and competitive market that altered many aspects of the power industry. In this changed scenario, scarcity of energy resources, increasing power generation cost, environment concern, ever growing demand for electrical energy necessitate optimal economic dispatch. Practical economic dispatch (ED) problems have nonlinear, non-convex type objective function with intense equality and inequality constraints. The conventional optimization methods are not able to solve such problems as due to local optimum solution convergence. Meta-heuristic optimization techniques especially particle swarm optimization (PSO) has gained an incredible recognition as the solution algorithm for such type of ED problems in last decade. The application of PSO in ED problem, which is considered as one of the most complex optimization problem has been summarized in present paper. (author)

Applying a multiobjective metaheuristic inspired by honey bees to phylogenetic inference.

Science.gov (United States)

Santander-Jiménez, Sergio; Vega-Rodríguez, Miguel A

2013-10-01

The development of increasingly popular multiobjective metaheuristics has allowed bioinformaticians to deal with optimization problems in computational biology where multiple objective functions must be taken into account. One of the most relevant research topics that can benefit from these techniques is phylogenetic inference. Throughout the years, different researchers have proposed their own view about the reconstruction of ancestral evolutionary relationships among species. As a result, biologists often report different phylogenetic trees from a same dataset when considering distinct optimality principles. In this work, we detail a multiobjective swarm intelligence approach based on the novel Artificial Bee Colony algorithm for inferring phylogenies. The aim of this paper is to propose a complementary view of phylogenetics according to the maximum parsimony and maximum likelihood criteria, in order to generate a set of phylogenetic trees that represent a compromise between these principles. Experimental results on a variety of nucleotide data sets and statistical studies highlight the relevance of the proposal with regard to other multiobjective algorithms and state-of-the-art biological methods. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Solving the Container Stowage Problem (CSP) using Particle Swarm Optimization (PSO)

Science.gov (United States)

Matsaini; Santosa, Budi

2018-04-01

Container Stowage Problem (CSP) is a problem of containers arrangement into ships by considering rules such as: total weight, weight of one stack, destination, equilibrium, and placement of containers on vessel. Container stowage problem is combinatorial problem and hard to solve with enumeration technique. It is an NP-Hard Problem. Therefore, to find a solution, metaheuristics is preferred. The objective of solving the problem is to minimize the amount of shifting such that the unloading time is minimized. Particle Swarm Optimization (PSO) is proposed to solve the problem. The implementation of PSO is combined with some steps which are stack position change rules, stack changes based on destination, and stack changes based on the weight type of the stacks (light, medium, and heavy). The proposed method was applied on five different cases. The results were compared to Bee Swarm Optimization (BSO) and heuristics method. PSO provided mean of 0.87% gap and time gap of 60 second. While BSO provided mean of 2,98% gap and 459,6 second to the heuristcs.

Biochemical systems identification by a random drift particle swarm optimization approach

Science.gov (United States)

2014-01-01

Background Finding an efficient method to solve the parameter estimation problem (inverse problem) for nonlinear biochemical dynamical systems could help promote the functional understanding at the system level for signalling pathways. The problem is stated as a data-driven nonlinear regression problem, which is converted into a nonlinear programming problem with many nonlinear differential and algebraic constraints. Due to the typical ill conditioning and multimodality nature of the problem, it is in general difficult for gradient-based local optimization methods to obtain satisfactory solutions. To surmount this limitation, many stochastic optimization methods have been employed to find the global solution of the problem. Results This paper presents an effective search strategy for a particle swarm optimization (PSO) algorithm that enhances the ability of the algorithm for estimating the parameters of complex dynamic biochemical pathways. The proposed algorithm is a new variant of random drift particle swarm optimization (RDPSO), which is used to solve the above mentioned inverse problem and compared with other well known stochastic optimization methods. Two case studies on estimating the parameters of two nonlinear biochemical dynamic models have been taken as benchmarks, under both the noise-free and noisy simulation data scenarios. Conclusions The experimental results show that the novel variant of RDPSO algorithm is able to successfully solve the problem and obtain solutions of better quality than other global optimization methods used for finding the solution to the inverse problems in this study. PMID:25078435

OPTIMASI AIRFOIL MENGGUNAKAN PARTICLE SWARM DENGAN PARAMETERISASI CST (CLASS SHAPE TRANSFORMATION

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Eva Hertnacahyani Herraprastanti

2017-11-01

Full Text Available Airfoil merupakan profil penampang yang diaplikasikan pada sarana transportasi maupun pembangkit energi sebagai penampang sudu turbin. Airfoil yang dirancang diharapkan menghasilkan gaya angkat (lift maksimal namun gaya hambat (drag seminimal mungkin. Tujuan penelitian 1 Validasi aerodinamika metode panel dengan Interaksi Viskos-Tak Viskos; 2 Analisis aerodinamika airfoil untuk menentukan koefisien lift dan drag; 3 Menerapkan metode optimasi Particle Swarm Optimization untuk mendapatkan geometri airfoil dengan rasio koefisien lift dan drag maksimum (CL/CD maks. Tahap pertama menentukan profil airfoil Class Shape Transformation (CST. Airfoil akan dianalisis menggunakan metoda panel selanjutnya diterapkan model aliran singularitas source dan doublet. Solusi yang diperoleh dari metode panel merupakan kondisi aliran yang dianggap tak viskos. Apabila sudut serang cukup tinggi solusi yang diperoleh dengan pendekatan tersebut sudah tidak akurat lagi. Untuk memperbaiki hasil maka diterapkan metode interaksi viskos-tak viskos kuasi simultan. Proses ini diulang sampai konvergensi dan diperoleh koefisien lift, dan drag. Dengan menggunakan optimasi Particle Swarm Optimization (PSO akan didapat profil airfoil dengan  koefisien lift dan drag maksimum. Namun apabila prosedur optimasi belum optimal, akan dilakukan update geometri, sampai didapat konvergensi. Kesimpulan penelitian :1 Metode panel dengan interaksi viskos tak viskos memberikan hasil yang cukup baik dan akurat, dengan rata-rata kesalahan dibawah 9.5%;  2 Semakin besar bilangan Reynold maka nilai CL/CD maksimum akan semakin tinggi; 3 Ketebalan (thickness dan camber maksimum cenderung meningkat dengan peningkatan bilangan Reynold; 4 Untuk airfoil CST optimasi dengan PSO memberikan hasil yang lebih baik.

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.

Cosmological parameter estimation using Particle Swarm Optimization

Science.gov (United States)

Prasad, J.; Souradeep, T.

2014-03-01

Constraining parameters of a theoretical model from observational data is an important exercise in cosmology. There are many theoretically motivated models, which demand greater number of cosmological parameters than the standard model of cosmology uses, and make the problem of parameter estimation challenging. It is a common practice to employ Bayesian formalism for parameter estimation for which, in general, likelihood surface is probed. For the standard cosmological model with six parameters, likelihood surface is quite smooth and does not have local maxima, and sampling based methods like Markov Chain Monte Carlo (MCMC) method are quite successful. However, when there are a large number of parameters or the likelihood surface is not smooth, other methods may be more effective. In this paper, we have demonstrated application of another method inspired from artificial intelligence, called Particle Swarm Optimization (PSO) for estimating cosmological parameters from Cosmic Microwave Background (CMB) data taken from the WMAP satellite.

Cosmological parameter estimation using Particle Swarm Optimization

International Nuclear Information System (INIS)

Prasad, J; Souradeep, T

2014-01-01

Constraining parameters of a theoretical model from observational data is an important exercise in cosmology. There are many theoretically motivated models, which demand greater number of cosmological parameters than the standard model of cosmology uses, and make the problem of parameter estimation challenging. It is a common practice to employ Bayesian formalism for parameter estimation for which, in general, likelihood surface is probed. For the standard cosmological model with six parameters, likelihood surface is quite smooth and does not have local maxima, and sampling based methods like Markov Chain Monte Carlo (MCMC) method are quite successful. However, when there are a large number of parameters or the likelihood surface is not smooth, other methods may be more effective. In this paper, we have demonstrated application of another method inspired from artificial intelligence, called Particle Swarm Optimization (PSO) for estimating cosmological parameters from Cosmic Microwave Background (CMB) data taken from the WMAP satellite

Particle Swarm Optimization for Outdoor Lighting Design

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Ana Castillo-Martinez

2017-01-01

Full Text Available Outdoor lighting is an essential service for modern life. However, the high influence of this type of facility on energy consumption makes it necessary to take extra care in the design phase. Therefore, this manuscript describes an algorithm to help light designers to get, in an easy way, the best configuration parameters and to improve energy efficiency, while ensuring a minimum level of overall uniformity. To make this possible, we used a particle swarm optimization (PSO algorithm. These algorithms are well established, and are simple and effective to solve optimization problems. To take into account the most influential parameters on lighting and energy efficiency, 500 simulations were performed using DIALux software (4.10.0.2, DIAL, Ludenscheid, Germany. Next, the relation between these parameters was studied using to data mining software. Subsequently, we conducted two experiments for setting parameters that enabled the best configuration algorithm in order to improve efficiency in the proposed process optimization.

Swarm analysis by using transport equations

International Nuclear Information System (INIS)

Dote, Toshihiko.

1985-01-01

As the basis of weak ionization plasma phenomena, the motion, i.e. swarm, of charged particles in the gas is analyzed by use of the transport equations, from which basic nature of the swarm is discussed. The present report is an overview of the studies made in the past several years. Described are principally the most basic aspects concerning behaviors of the electrons and positive ions, that is, the basic equations and their significance, characteristics of the behaviors of the electron and positive ion swarms as revealed by solving the equations, and various characteristics of the swarm parameters. Contents are: Maxwell-Boltzmann's transport equations, behavior of the electron swarm, energy loss of the electrons, and behavior of the positive ion swarm. (Mori, K.)

Research on vehicle routing optimization for the terminal distribution of B2C E-commerce firms

Science.gov (United States)

Zhang, Shiyun; Lu, Yapei; Li, Shasha

2018-05-01

In this paper, we established a half open multi-objective optimization model for the vehicle routing problem of B2C (business-to-customer) E-Commerce firms. To minimize the current transport distance as well as the disparity between the excepted shipments and the transport capacity in the next distribution, we applied the concept of dominated solution and Pareto solutions to the standard particle swarm optimization and proposed a MOPSO (multi-objective particle swarm optimization) algorithm to support the model. Besides, we also obtained the optimization solution of MOPSO algorithm based on data randomly generated through the system, which verified the validity of the model.

Intelligent sizing of a series hybrid electric power-train system based on Chaos-enhanced accelerated particle swarm optimization

International Nuclear Information System (INIS)

Zhou, Quan; Zhang, Wei; Cash, Scott; Olatunbosun, Oluremi; Xu, Hongming; Lu, Guoxiang

2017-01-01

Highlights: • A novel algorithm for hybrid electric powertrain intelligent sizing is introduced and applied. • The proposed CAPSO algorithm is capable of finding the real optimal result with much higher reputation. • Logistic mapping is the most effective strategy to build CAPSO. • The CAPSO gave more reliable results and increased the efficiency by 1.71%. - Abstract: This paper firstly proposed a novel HEV sizing method using the Chaos-enhanced Accelerated Particle Swarm Optimization (CAPSO) algorithm and secondly provided a demonstration on sizing a series hybrid electric powertrain with investigations of chaotic mapping strategies to achieve the global optimization. In this paper, the intelligent sizing of a series hybrid electric powertrain is formulated as an integer multi-objective optimization issue by modelling the powertrain system. The intelligent sizing mechanism based on APSO is then introduced, and 4 types of the most effective chaotic mapping strategy are investigated to upgrade the standard APSO into CAPSO algorithms for intelligent sizing. The evaluation of the intelligent sizing systems based on standard APSO and CAPSOs are then performed. The Monte Carlo analysis and reputation evaluation indicate that the CAPSO outperforms the standard APSO for finding the real optimal sizing result with much higher reputation, and CAPSO with logistic mapping strategy is the most effective algorithm for HEV powertrain components intelligent sizing. In addition, this paper also performs the sensitivity analysis and Pareto analysis to help engineers customize the intelligent sizing system.

Advantages of Task-Specific Multi-Objective Optimisation in Evolutionary Robotics.

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Trianni, Vito; López-Ibáñez, Manuel

2015-01-01

The application of multi-objective optimisation to evolutionary robotics is receiving increasing attention. A survey of the literature reveals the different possibilities it offers to improve the automatic design of efficient and adaptive robotic systems, and points to the successful demonstrations available for both task-specific and task-agnostic approaches (i.e., with or without reference to the specific design problem to be tackled). However, the advantages of multi-objective approaches over single-objective ones have not been clearly spelled out and experimentally demonstrated. This paper fills this gap for task-specific approaches: starting from well-known results in multi-objective optimisation, we discuss how to tackle commonly recognised problems in evolutionary robotics. In particular, we show that multi-objective optimisation (i) allows evolving a more varied set of behaviours by exploring multiple trade-offs of the objectives to optimise, (ii) supports the evolution of the desired behaviour through the introduction of objectives as proxies, (iii) avoids the premature convergence to local optima possibly introduced by multi-component fitness functions, and (iv) solves the bootstrap problem exploiting ancillary objectives to guide evolution in the early phases. We present an experimental demonstration of these benefits in three different case studies: maze navigation in a single robot domain, flocking in a swarm robotics context, and a strictly collaborative task in collective robotics.

Advantages of Task-Specific Multi-Objective Optimisation in Evolutionary Robotics.

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Vito Trianni

Full Text Available The application of multi-objective optimisation to evolutionary robotics is receiving increasing attention. A survey of the literature reveals the different possibilities it offers to improve the automatic design of efficient and adaptive robotic systems, and points to the successful demonstrations available for both task-specific and task-agnostic approaches (i.e., with or without reference to the specific design problem to be tackled. However, the advantages of multi-objective approaches over single-objective ones have not been clearly spelled out and experimentally demonstrated. This paper fills this gap for task-specific approaches: starting from well-known results in multi-objective optimisation, we discuss how to tackle commonly recognised problems in evolutionary robotics. In particular, we show that multi-objective optimisation (i allows evolving a more varied set of behaviours by exploring multiple trade-offs of the objectives to optimise, (ii supports the evolution of the desired behaviour through the introduction of objectives as proxies, (iii avoids the premature convergence to local optima possibly introduced by multi-component fitness functions, and (iv solves the bootstrap problem exploiting ancillary objectives to guide evolution in the early phases. We present an experimental demonstration of these benefits in three different case studies: maze navigation in a single robot domain, flocking in a swarm robotics context, and a strictly collaborative task in collective robotics.

A practical multi-objective PSO algorithm for optimal operation management of distribution network with regard to fuel cell power plants

Energy Technology Data Exchange (ETDEWEB)

Niknam, Taher; Meymand, Hamed Zeinoddini; Mojarrad, Hasan Doagou [Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, P.O. 71555-313 (Iran, Islamic Republic of)

2011-05-15

In this paper a novel Multi-objective fuzzy self adaptive hybrid particle swarm optimization (MFSAHPSO) evolutionary algorithm to solve the Multi-objective optimal operation management (MOOM) is presented. The purposes of the MOOM problem are to decrease the total electrical energy losses, the total electrical energy cost and the total pollutant emission produced by fuel cells and substation bus. Conventional algorithms used to solve the multi-objective optimization problems convert the multiple objectives into a single objective, using a vector of the user-predefined weights. In this conversion several deficiencies can be detected. For instance, the optimal solution of the algorithms depends greatly on the values of the weights and also some of the information may be lost in the conversion process and so this strategy is not expected to provide a robust solution. This paper presents a new MFSAHPSO algorithm for the MOOM problem. The proposed algorithm maintains a finite-sized repository of non-dominated solutions which gets iteratively updated in the presence of new solutions. Since the objective functions are not the same, a fuzzy clustering technique is used to control the size of the repository, within the limits. The proposed algorithm is tested on a distribution test feeder and the results demonstrate the capabilities of the proposed approach, to generate true and well-distributed Pareto-optimal non-dominated solutions of the MOOM problem. (author)

OPTIMIZED PARTICLE SWARM OPTIMIZATION BASED DEADLINE CONSTRAINED TASK SCHEDULING IN HYBRID CLOUD

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Dhananjay Kumar

2016-01-01

Full Text Available Cloud Computing is a dominant way of sharing of computing resources that can be configured and provisioned easily. Task scheduling in Hybrid cloud is a challenge as it suffers from producing the best QoS (Quality of Service when there is a high demand. In this paper a new resource allocation algorithm, to find the best External Cloud provider when the intermediate provider’s resources aren’t enough to satisfy the customer’s demand is proposed. The proposed algorithm called Optimized Particle Swarm Optimization (OPSO combines the two metaheuristic algorithms namely Particle Swarm Optimization and Ant Colony Optimization (ACO. These metaheuristic algorithms are used for the purpose of optimization in the search space of the required solution, to find the best resource from the pool of resources and to obtain maximum profit even when the number of tasks submitted for execution is very high. This optimization is performed to allocate job requests to internal and external cloud providers to obtain maximum profit. It helps to improve the system performance by improving the CPU utilization, and handle multiple requests at the same time. The simulation result shows that an OPSO yields 0.1% - 5% profit to the intermediate cloud provider compared with standard PSO and ACO algorithms and it also increases the CPU utilization by 0.1%.

Hybrid Bacterial Foraging and Particle Swarm Optimization for detecting Bundle Branch Block.

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Kora, Padmavathi; Kalva, Sri Ramakrishna

2015-01-01

Abnormal cardiac beat identification is a key process in the detection of heart diseases. Our present study describes a procedure for the detection of left and right bundle branch block (LBBB and RBBB) Electrocardiogram (ECG) patterns. The electrical impulses that control the cardiac beat face difficulty in moving inside the heart. This problem is termed as bundle branch block (BBB). BBB makes it harder for the heart to pump blood effectively through the heart circulatory system. ECG feature extraction is a key process in detecting heart ailments. Our present study comes up with a hybrid method combining two heuristic optimization methods: Bacterial Forging Optimization (BFO) and Particle Swarm Optimization (PSO) for the feature selection of ECG signals. One of the major controlling forces of BFO algorithm is the chemotactic movement of a bacterium that models a test solution. The chemotaxis process of the BFO depends on random search directions which may lead to a delay in achieving the global optimum solution. The hybrid technique: Bacterial Forging-Particle Swarm Optimization (BFPSO) incorporates the concepts from BFO and PSO and it creates individuals in a new generation. This BFPSO method performs local search through the chemotactic movement of BFO and the global search over the entire search domain is accomplished by a PSO operator. The BFPSO feature values are given as the input for the Levenberg-Marquardt Neural Network classifier.

Constructing DNA Barcode Sets Based on Particle Swarm Optimization.

Science.gov (United States)

Wang, Bin; Zheng, Xuedong; Zhou, Shihua; Zhou, Changjun; Wei, Xiaopeng; Zhang, Qiang; Wei, Ziqi

2018-01-01

Following the completion of the human genome project, a large amount of high-throughput bio-data was generated. To analyze these data, massively parallel sequencing, namely next-generation sequencing, was rapidly developed. DNA barcodes are used to identify the ownership between sequences and samples when they are attached at the beginning or end of sequencing reads. Constructing DNA barcode sets provides the candidate DNA barcodes for this application. To increase the accuracy of DNA barcode sets, a particle swarm optimization (PSO) algorithm has been modified and used to construct the DNA barcode sets in this paper. Compared with the extant results, some lower bounds of DNA barcode sets are improved. The results show that the proposed algorithm is effective in constructing DNA barcode sets.

Strategic bidding in electricity markets using particle swarm optimization

International Nuclear Information System (INIS)

Yucekaya, Ahmet D.; Valenzuela, Jorge; Dozier, Gerry

2009-01-01

Profit maximization for power companies is highly related to the bidding strategies used. In order to sell electricity at high prices and maximize profit, power companies need suitable bidding models that consider power operating constraints and price uncertainty within the market. In this paper, we present two particle swarm optimization (PSO) algorithms to determine bid prices and quantities under the rules of a competitive power market. The first method uses a conventional PSO technique to find solutions. The second method uses a decomposition technique in conjunction with the PSO approach. This new decomposition-based PSO dramatically outperforms the conventional form of PSO. We show that for nonlinear cost functions PSO solutions provide higher expected profits than marginal cost-based bidding. (author)

Optimization of Reinforced Concrete Reservoir with Circumferential Stiffeners Strips by Particle Swarm Algorithm

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GholamReza Havaei

2015-09-01

Full Text Available Reinforced concrete reservoirs (RCR have been used extensively in municipal and industrial facilities for several decades. The design of these structures requires that attention be given not only to strength requirements, but to serviceability requirements as well. These types of structures will be square, round, and oval reinforced concrete structures which may be above, below, or partially below ground. The main challenge is to design concrete liquid containing structures which will resist the extremes of seasonal temperature changes, a variety of loading conditions, and remain liquid tight for useful life of 50 to 60 years. In this study, optimization is performed by particle swarm algorithm basd on structural design. Firstly by structural analysis all range of shell thickness and areas of rebar find. In the second step by parameter identification system interchange algorithm, source code which developed in particle swarm algorithm by MATLAB software linked to analysis software. Therefore best and optimized thicknesses and total area of bars for each element find. Lastly with circumferential stiffeners structure optimize and show 19% decrease in weight of rebar, 20% decrease in volume of concrete, and 13% minimum cost reduction in construction procedure compared with conventional 10,000 m3 RCR structures.

A constriction factor based particle swarm optimisation algorithm to solve the economic dispatch problem including losses

Energy Technology Data Exchange (ETDEWEB)

Young, Steven; Montakhab, Mohammad; Nouri, Hassan

2011-07-15

Economic dispatch (ED) is one of the most important problems to be solved in power generation as fractional percentage fuel reductions represent significant cost savings. ED wishes to optimise the power generated by each generating unit in a system in order to find the minimum operating cost at a required load demand, whilst ensuring both equality and inequality constraints are met. For the process of optimisation, a model must be created for each generating unit. The particle swarm optimisation technique is an evolutionary computation technique with one of the most powerful methods for solving global optimisation problems. The aim of this paper is to add in a constriction factor to the particle swarm optimisation algorithm (CFBPSO). Results show that the algorithm is very good at solving the ED problem and that CFBPSO must be able to work in a practical environment and so a valve point effect with transmission losses should be included in future work.

Time-delayed autosynchronous swarm control.

Science.gov (United States)

Biggs, James D; Bennet, Derek J; Dadzie, S Kokou

2012-01-01

In this paper a general Morse potential model of self-propelling particles is considered in the presence of a time-delayed term and a spring potential. It is shown that the emergent swarm behavior is dependent on the delay term and weights of the time-delayed function, which can be set to induce a stationary swarm, a rotating swarm with uniform translation, and a rotating swarm with a stationary center of mass. An analysis of the mean field equations shows that without a spring potential the motion of the center of mass is determined explicitly by a multivalued function. For a nonzero spring potential the swarm converges to a vortex formation about a stationary center of mass, except at discrete bifurcation points where the center of mass will periodically trace an ellipse. The analytical results defining the behavior of the center of mass are shown to correspond with the numerical swarm simulations.

Thermal Depth Profiling Reconstruction by Multilayer Thermal Quadrupole Modeling and Particle Swarm Optimization

International Nuclear Information System (INIS)

Zhao-Jiang, Chen; Shu-Yi, Zhang

2010-01-01

A new hybrid inversion method for depth profiling reconstruction of thermal conductivities of inhomogeneous solids is proposed based on multilayer quadrupole formalism of thermal waves, particle swarm optimization and sequential quadratic programming. The reconstruction simulations for several thermal conductivity profiles are performed to evaluate the applicability of the method. The numerical simulations demonstrate that the precision and insensitivity to noise of the inversion method are very satisfactory. (condensed matter: structure, mechanical and thermal properties)

Feature and Intensity Based Medical Image Registration Using Particle Swarm Optimization.

Science.gov (United States)

Abdel-Basset, Mohamed; Fakhry, Ahmed E; El-Henawy, Ibrahim; Qiu, Tie; Sangaiah, Arun Kumar

2017-11-03

Image registration is an important aspect in medical image analysis, and kinds use in a variety of medical applications. Examples include diagnosis, pre/post surgery guidance, comparing/merging/integrating images from multi-modal like Magnetic Resonance Imaging (MRI), and Computed Tomography (CT). Whether registering images across modalities for a single patient or registering across patients for a single modality, registration is an effective way to combine information from different images into a normalized frame for reference. Registered datasets can be used for providing information relating to the structure, function, and pathology of the organ or individual being imaged. In this paper a hybrid approach for medical images registration has been developed. It employs a modified Mutual Information (MI) as a similarity metric and Particle Swarm Optimization (PSO) method. Computation of mutual information is modified using a weighted linear combination of image intensity and image gradient vector flow (GVF) intensity. In this manner, statistical as well as spatial image information is included into the image registration process. Maximization of the modified mutual information is effected using the versatile Particle Swarm Optimization which is developed easily with adjusted less parameter. The developed approach has been tested and verified successfully on a number of medical image data sets that include images with missing parts, noise contamination, and/or of different modalities (CT, MRI). The registration results indicate the proposed model as accurate and effective, and show the posture contribution in inclusion of both statistical and spatial image data to the developed approach.

Multiobjective Location Model Design Based on Government Subsidy in the Recycling of CDW

Directory of Open Access Journals (Sweden)

Peihua Fu

2017-01-01

Full Text Available With the generation of a large amount of construction and demolition waste (CDW, many scholars have recently paid more attention to the recycling of CDW. In this paper, we design a classification recycling method based on the degree of CDW availability in the recycling of CDW. Considering the important role of the government in reverse logistics, a model of reverse logistics network based on the trade-off between cost and recycling rate is put forward, which is subject to government subsidy. The model includes the location of classification processing center and ensures the best route of transportation. Then, the improved particle swarm optimization algorithm is applied to solve the model to get Pareto frontier by transforming it into a multiobjective integer-programming problem. As a case study, the results of the statistical modeling used in this study indicate the feasibility of the model. Finally, according to the sensitivity analysis of government’s subsidy, we evaluate the effectiveness of this program and its applicability.

Environment-Aware Production Schedulingfor Paint Shops in Automobile Manufacturing: A Multi-Objective Optimization Approach.

Science.gov (United States)

Zhang, Rui

2017-12-25

The traditional way of scheduling production processes often focuses on profit-driven goals (such as cycle time or material cost) while tending to overlook the negative impacts of manufacturing activities on the environment in the form of carbon emissions and other undesirable by-products. To bridge the gap, this paper investigates an environment-aware production scheduling problem that arises from a typical paint shop in the automobile manufacturing industry. In the studied problem, an objective function is defined to minimize the emission of chemical pollutants caused by the cleaning of painting devices which must be performed each time before a color change occurs. Meanwhile, minimization of due date violations in the downstream assembly shop is also considered because the two shops are interrelated and connected by a limited-capacity buffer. First, we have developed a mixed-integer programming formulation to describe this bi-objective optimization problem. Then, to solve problems of practical size, we have proposed a novel multi-objective particle swarm optimization (MOPSO) algorithm characterized by problem-specific improvement strategies. A branch-and-bound algorithm is designed for accurately assessing the most promising solutions. Finally, extensive computational experiments have shown that the proposed MOPSO is able to match the solution quality of an exact solver on small instances and outperform two state-of-the-art multi-objective optimizers in literature on large instances with up to 200 cars.

Order-2 Stability Analysis of Particle Swarm Optimization.

Science.gov (United States)

Liu, Qunfeng

2015-01-01

Several stability analyses and stable regions of particle swarm optimization (PSO) have been proposed before. The assumption of stagnation and different definitions of stability are adopted in these analyses. In this paper, the order-2 stability of PSO is analyzed based on a weak stagnation assumption. A new definition of stability is proposed and an order-2 stable region is obtained. Several existing stable analyses for canonical PSO are compared, especially their definitions of stability and the corresponding stable regions. It is shown that the classical stagnation assumption is too strict and not necessary. Moreover, among all these definitions of stability, it is shown that our definition requires the weakest conditions, and additional conditions bring no benefit. Finally, numerical experiments are reported to show that the obtained stable region is meaningful. A new parameter combination of PSO is also shown to be good, even better than some known best parameter combinations.

Resource planning and scheduling of payload for satellite with particle swarm optimization

Science.gov (United States)

Li, Jian; Wang, Cheng

2007-11-01

The resource planning and scheduling technology of payload is a key technology to realize an automated control for earth observing satellite with limited resources on satellite, which is implemented to arrange the works states of various payloads to carry out missions by optimizing the scheme of the resources. The scheduling task is a difficult constraint optimization problem with various and mutative requests and constraints. Based on the analysis of the satellite's functions and the payload's resource constraints, a proactive planning and scheduling strategy based on the availability of consumable and replenishable resources in time-order is introduced along with dividing the planning and scheduling period to several pieces. A particle swarm optimization algorithm is proposed to address the problem with an adaptive mutation operator selection, where the swarm is divided into groups with different probabilities to employ various mutation operators viz., differential evolution, Gaussian and random mutation operators. The probabilities are adjusted adaptively by comparing the effectiveness of the groups to select a proper operator. The simulation results have shown the feasibility and effectiveness of the method.

Artificial Fish Swarm Algorithm-Based Particle Filter for Li-Ion Battery Life Prediction

Directory of Open Access Journals (Sweden)

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.

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.

Constrained Fuzzy Predictive Control Using Particle Swarm Optimization

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Oussama Ait Sahed

2015-01-01

Full Text Available A fuzzy predictive controller using particle swarm optimization (PSO approach is proposed. The aim is to develop an efficient algorithm that is able to handle the relatively complex optimization problem with minimal computational time. This can be achieved using reduced population size and small number of iterations. In this algorithm, instead of using the uniform distribution as in the conventional PSO algorithm, the initial particles positions are distributed according to the normal distribution law, within the area around the best position. The radius limiting this area is adaptively changed according to the tracking error values. Moreover, the choice of the initial best position is based on prior knowledge about the search space landscape and the fact that in most practical applications the dynamic optimization problem changes are gradual. The efficiency of the proposed control algorithm is evaluated by considering the control of the model of a 4 × 4 Multi-Input Multi-Output industrial boiler. This model is characterized by being nonlinear with high interactions between its inputs and outputs, having a nonminimum phase behaviour, and containing instabilities and time delays. The obtained results are compared to those of the control algorithms based on the conventional PSO and the linear approach.

Particle swarm optimization with scale-free interactions.

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Chen Liu

Full Text Available The particle swarm optimization (PSO algorithm, in which individuals collaborate with their interacted neighbors like bird flocking to search for the optima, has been successfully applied in a wide range of fields pertaining to searching and convergence. Here we employ the scale-free network to represent the inter-individual interactions in the population, named SF-PSO. In contrast to the traditional PSO with fully-connected topology or regular topology, the scale-free topology used in SF-PSO incorporates the diversity of individuals in searching and information dissemination ability, leading to a quite different optimization process. Systematic results with respect to several standard test functions demonstrate that SF-PSO gives rise to a better balance between the convergence speed and the optimum quality, accounting for its much better performance than that of the traditional PSO algorithms. We further explore the dynamical searching process microscopically, finding that the cooperation of hub nodes and non-hub nodes play a crucial role in optimizing the convergence process. Our work may have implications in computational intelligence and complex networks.

A decoupled power flow algorithm using particle swarm optimization technique

International Nuclear Information System (INIS)

Acharjee, P.; Goswami, S.K.

2009-01-01

A robust, nondivergent power flow method has been developed using the particle swarm optimization (PSO) technique. The decoupling properties between the power system quantities have been exploited in developing the power flow algorithm. The speed of the power flow algorithm has been improved using a simple perturbation technique. The basic power flow algorithm and the improvement scheme have been designed to retain the simplicity of the evolutionary approach. The power flow is rugged, can determine the critical loading conditions and also can handle the flexible alternating current transmission system (FACTS) devices efficiently. Test results on standard test systems show that the proposed method can find the solution when the standard power flows fail.

Multiobjective hyper heuristic scheme for system design and optimization

Science.gov (United States)

Rafique, Amer Farhan

2012-11-01

As system design is becoming more and more multifaceted, integrated, and complex, the traditional single objective optimization trends of optimal design are becoming less and less efficient and effective. Single objective optimization methods present a unique optimal solution whereas multiobjective methods present pareto front. The foremost intent is to predict a reasonable distributed pareto-optimal solution set independent of the problem instance through multiobjective scheme. Other objective of application of intended approach is to improve the worthiness of outputs of the complex engineering system design process at the conceptual design phase. The process is automated in order to provide the system designer with the leverage of the possibility of studying and analyzing a large multiple of possible solutions in a short time. This article presents Multiobjective Hyper Heuristic Optimization Scheme based on low level meta-heuristics developed for the application in engineering system design. Herein, we present a stochastic function to manage meta-heuristics (low-level) to augment surety of global optimum solution. Generic Algorithm, Simulated Annealing and Swarm Intelligence are used as low-level meta-heuristics in this study. Performance of the proposed scheme is investigated through a comprehensive empirical analysis yielding acceptable results. One of the primary motives for performing multiobjective optimization is that the current engineering systems require simultaneous optimization of conflicting and multiple. Random decision making makes the implementation of this scheme attractive and easy. Injecting feasible solutions significantly alters the search direction and also adds diversity of population resulting in accomplishment of pre-defined goals set in the proposed scheme.

Agent based Particle Swarm Optimization for Load Frequency Control of Distribution Grid

DEFF Research Database (Denmark)

Cha, Seung-Tae; Saleem, Arshad; Wu, Qiuwei

2012-01-01

This paper presents a Particle Swarm Optimization (PSO) based on multi-agent controller. Real-time digital simulator (RTDS) is used for modelling the power system, while a PSO based multi-agent LFC algorithm is developed in JAVA for communicating with resource agents and determines the scenario...... to stabilize the frequency and voltage after the system enters into the islanding operation mode. The proposed algorithm is based on the formulation of an optimization problem using agent based PSO. The modified IEEE 9-bus system is employed to illustrate the performance of the proposed controller via RTDS...

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.

Comparing genetic algorithm and particle swarm optimization for solving capacitated vehicle routing problem

Science.gov (United States)

Iswari, T.; Asih, A. M. S.

2018-04-01

In the logistics system, transportation plays an important role to connect every element in the supply chain, but it can produces the greatest cost. Therefore, it is important to make the transportation costs as minimum as possible. Reducing the transportation cost can be done in several ways. One of the ways to minimizing the transportation cost is by optimizing the routing of its vehicles. It refers to Vehicle Routing Problem (VRP). The most common type of VRP is Capacitated Vehicle Routing Problem (CVRP). In CVRP, the vehicles have their own capacity and the total demands from the customer should not exceed the capacity of the vehicle. CVRP belongs to the class of NP-hard problems. These NP-hard problems make it more complex to solve such that exact algorithms become highly time-consuming with the increases in problem sizes. Thus, for large-scale problem instances, as typically found in industrial applications, finding an optimal solution is not practicable. Therefore, this paper uses two kinds of metaheuristics approach to solving CVRP. Those are Genetic Algorithm and Particle Swarm Optimization. This paper compares the results of both algorithms and see the performance of each algorithm. The results show that both algorithms perform well in solving CVRP but still needs to be improved. From algorithm testing and numerical example, Genetic Algorithm yields a better solution than Particle Swarm Optimization in total distance travelled.

The Fate of Colloidal Swarms in Fractures

Science.gov (United States)

Pyrak-Nolte, L. J.; Olander, M. K.

2009-12-01

In the next 10-20 years, nano- and micro-sensor engineering will advance to the stage where sensor swarms could be deployed in the subsurface to probe rock formations and the fluids contained in them. Sensor swarms are groups of nano- or micro- sensors that are maintained as a coherent group to enable either sensor-to-sensor communication and/or coherent transmission of information as a group. The ability to maintain a swarm of sensors depends on the complexity of the flow paths in the rock, on the size and shape of the sensors and on the chemical interaction among the sensors, fluids, and rock surfaces. In this study, we investigate the effect of fracture aperture and fluid currents on the formation, evolution and break-up of colloidal swarms under gravity. Transparent cubic samples (100 mm x 100 mm x 100 mm) containing synthetic fractures with uniform and non-uniform aperture distributions were used to quantify the effect of aperture on swarm formation, swarm velocity, and swarm geometry using optical imaging. A fracture with a uniform aperture distribution was fabricated from two polished rectangular prisms of acrylic. A fracture with a non-uniform aperture distribution was created with a polished rectangular acrylic prism and an acrylic replica of an induced fracture surface from a carbonate rock. A series of experiments were performed to determine how swarm movement and geometry are affected as the walls of the fracture are brought closer together from 50 mm to 1 mm. During the experiments, the fracture was fully saturated with water. We created the swarms using two different particle sizes in dilute suspension (~ 1.0% by mass) . The particles were 3 micron diameter fluorescent polymer beads and 25 micron diameter soda-lime glass beads. The swarm behavior was imaged using an optical fluorescent imaging system composed of a CCD camera illuminated by a 100 mW diode-pumped doubled YAG laser. A swam was created when approximately 0.01 g drop of the suspension was

An adaptive image enhancement technique by combining cuckoo search and particle swarm optimization algorithm.

Science.gov (United States)

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.

An Adaptive Image Enhancement Technique by Combining Cuckoo Search and Particle Swarm Optimization Algorithm

Directory of Open Access Journals (Sweden)

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.

Hybrid particle swarm optimization with Cauchy distribution for solving reentrant flexible flow shop with blocking constraint

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Chatnugrob Sangsawang

2016-06-01

Full Text Available This paper addresses a problem of the two-stage flexible flow shop with reentrant and blocking constraints in Hard Disk Drive Manufacturing. This problem can be formulated as a deterministic FFS|stage=2,rcrc, block|Cmax problem. In this study, adaptive Hybrid Particle Swarm Optimization with Cauchy distribution (HPSO was developed to solve the problem. The objective of this research is to find the sequences in order to minimize the makespan. To show their performances, computational experiments were performed on a number of test problems and the results are reported. Experimental results show that the proposed algorithms give better solutions than the classical Particle Swarm Optimization (PSO for all test problems. Additionally, the relative improvement (RI of the makespan solutions obtained by the proposed algorithms with respect to those of the current practice is performed in order to measure the quality of the makespan solutions generated by the proposed algorithms. The RI results show that the HPSO algorithm can improve the makespan solution by averages of 14.78%.

Optimal Piezoelectric Actuators and Sensors Configuration for Vibration Suppression of Aircraft Framework Using Particle Swarm Algorithm

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Quanzhen Huang

2017-01-01

Full Text Available Numbers and locations of sensors and actuators play an important role in cost and control performance for active vibration control system of piezoelectric smart structure. This may lead to a diverse control system if sensors and actuators were not configured properly. An optimal location method of piezoelectric actuators and sensors is proposed in this paper based on particle swarm algorithm (PSA. Due to the complexity of the frame structure, it can be taken as a combination of many piezoelectric intelligent beams and L-type structures. Firstly, an optimal criterion of sensors and actuators is proposed with an optimal objective function. Secondly, each order natural frequency and modal strain are calculated and substituted into the optimal objective function. Preliminary optimal allocation is done using the particle swarm algorithm, based on the similar optimization method and the combination of the vibration stress and strain distribution at the lower modal frequency. Finally, the optimal location is given. An experimental platform was established and the experimental results indirectly verified the feasibility and effectiveness of the proposed method.

Performance evaluation of different types of particle representation procedures of Particle Swarm Optimization in Job-shop Scheduling Problems

Science.gov (United States)

Izah Anuar, Nurul; Saptari, Adi

2016-02-01

This paper addresses the types of particle representation (encoding) procedures in a population-based stochastic optimization technique in solving scheduling problems known in the job-shop manufacturing environment. It intends to evaluate and compare the performance of different particle representation procedures in Particle Swarm Optimization (PSO) in the case of solving Job-shop Scheduling Problems (JSP). Particle representation procedures refer to the mapping between the particle position in PSO and the scheduling solution in JSP. It is an important step to be carried out so that each particle in PSO can represent a schedule in JSP. Three procedures such as Operation and Particle Position Sequence (OPPS), random keys representation and random-key encoding scheme are used in this study. These procedures have been tested on FT06 and FT10 benchmark problems available in the OR-Library, where the objective function is to minimize the makespan by the use of MATLAB software. Based on the experimental results, it is discovered that OPPS gives the best performance in solving both benchmark problems. The contribution of this paper is the fact that it demonstrates to the practitioners involved in complex scheduling problems that different particle representation procedures can have significant effects on the performance of PSO in solving JSP.

Numerical thermal mathematical model correlation to thermal balance test using adaptive particle swarm optimization (APSO)

International Nuclear Information System (INIS)

Beck, T.; Bieler, A.; Thomas, N.

2012-01-01

We present structural and thermal model (STM) tests of the BepiColombo laser altimeter (BELA) receiver baffle with emphasis on the correlation of the data with a thermal mathematical model. The test unit is a part of the thermal and optical protection of the BELA instrument being tested under infrared and solar irradiation at University of Bern. An iterative optimization method known as particle swarm optimization has been adapted to adjust the model parameters, mainly the linear conductivity, in such a way that model and test results match. The thermal model reproduces the thermal tests to an accuracy of 4.2 °C ± 3.2 °C in a temperature range of 200 °C after using only 600 iteration steps of the correlation algorithm. The use of this method brings major benefits to the accuracy of the results as well as to the computational time required for the correlation. - Highlights: ► We present model correlations of the BELA receiver baffle to thermal balance tests. ► Adaptive particle swarm optimization has been adapted for the correlation. ► The method improves the accuracy of the correlation and the computational time.

A Particle Swarm Optimization of Natural Ventilation Parameters in a Greenhouse with Continuous Roof Vents

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Abdelhafid HASNI

2009-03-01

Full Text Available Although natural ventilation plays an important role in the affecting greenhouse climate, as defined by temperature, humidity and CO2 concentration, particularly in Mediterranean countries, little information and data are presently available on full-scale greenhouse ventilation mechanisms. In this paper, we present a new method for selecting the parameters based on a particle swarm optimization (PSO algorithm which optimize the choice of parameters by minimizing a cost function. The simulator was based on a published model with some minor modifications as we were interested in the parameter of ventilation. The function is defined by a reduced model that could be used to simulate and predict the greenhouse environment, as well as the tuning methods to compute their parameters. This study focuses on the dynamic behavior of the inside air temperature and humidity during ventilation. Our approach is validated by comparison with some experimental results. Various experimental techniques were used to make full-scale measurements of the air exchange rate in a 400 m2 plastic greenhouse. The model which we propose based on natural ventilation parameters optimized by a particle swarm optimization was compared with the measurements results.

Cost Minimization for Joint Energy Management and Production Scheduling Using Particle Swarm Optimization

Science.gov (United States)

Shah, Rahul H.

production planning framework are discussed. A modified Particle Swarm Optimization solution technique is adopted to solve the proposed scheduling problem. The algorithm is described in detail and compared to Genetic Algorithm. Case studies are presented to illustrate the benefits of using the proposed model and the effectiveness of the Particle Swarm Optimization approach. Numerical Experiments are implemented and analyzed to test the effectiveness of the proposed model. The proposed scheduling strategy can achieve savings of around 19 to 27 % in cost per part when compared to the baseline scheduling scenarios. By optimizing key production system parameters from the cost per part model, the baseline scenarios can obtain around 20 to 35 % in savings for the cost per part. These savings further increase by 42 to 55 % when system parameter optimization is integrated with the proposed scheduling problem. Using this method, the most influential parameters on the cost per part are the rated power from production, the production rate, and the initial machine reliabilities. The modified Particle Swarm Optimization algorithm adopted allows greater diversity and exploration compared to Genetic Algorithm for the proposed joint model which results in it being more computationally efficient in determining the optimal scheduling. While Genetic Algorithm could achieve a solution quality of 2,279.63 at an expense of 2,300 seconds in computational effort. In comparison, the proposed Particle Swarm Optimization algorithm achieved a solution quality of 2,167.26 in less than half the computation effort which is required by Genetic Algorithm.

Short-Term Wind Power Forecasting Using the Enhanced Particle Swarm Optimization Based Hybrid Method

OpenAIRE

Wen-Yeau Chang

2013-01-01

High penetration of wind power in the electricity system provides many challenges to power system operators, mainly due to the unpredictability and variability of wind power generation. Although wind energy may not be dispatched, an accurate forecasting method of wind speed and power generation can help power system operators reduce the risk of an unreliable electricity supply. This paper proposes an enhanced particle swarm optimization (EPSO) based hybrid forecasting method for short-term wi...

Swarm analysis by using transport equations, 1

International Nuclear Information System (INIS)

Dote, Toshihiko; Shimada, Masatoshi

1980-01-01

By evolving Maxwell-Boltzmann transport equations, various quantities on swarm of charged particles have been analyzed. Although this treatment is properly general, and common transport equations for charged particles ought to be given, in particular, equations only for electrons were presented here. The relation between the random energy and the drift energy was first derived and the general expression of the electron velocity was deduced too. For a simple example, one dimensional steady-state electron swarm in a uniform medium was treated. Electron swarm characteristics numerically calculated in He, Ne or Ar exhibited some interesting properties, which were physically clearly elucidated. These results were also compared with several data already published. Agreements between them were qualitatively rather well in detailed structures. (author)

Acceleration of the Particle Swarm Optimization for Peierls–Nabarro modeling of dislocations in conventional and high-entropy alloys

International Nuclear Information System (INIS)

Pei, Zongrui; Eisenbach, Markus

2017-01-01

Dislocations are among the most important defects in determining the mechanical properties of both conventional alloys and high-entropy alloys. The Peierls-Nabarro model supplies an efficient pathway to their geometries and mobility. The difficulty in solving the integro-differential Peierls-Nabarro equation is how to effectively avoid the local minima in the energy landscape of a dislocation core. Among the other methods to optimize the dislocation core structures, we choose the algorithm of Particle Swarm Optimization, an algorithm that simulates the social behaviors of organisms. By employing more particles (bigger swarm) and more iterative steps (allowing them to explore for longer time), the local minima can be effectively avoided. But this would require more computational cost. The advantage of this algorithm is that it is readily parallelized in modern high computing architecture. We demonstrate the performance of our parallelized algorithm scales linearly with the number of employed cores.

PSOVina: The hybrid particle swarm optimization algorithm for protein-ligand docking.

Science.gov (United States)

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 .

Short-term hydro-thermal scheduling using particle swarm optimization method

International Nuclear Information System (INIS)

Yu, Binghui; Yuan, Xiaohui; Wang, Jinwen

2007-01-01

The approaches based on different particle swarm optimization (PSO) techniques are applied to solve the short-term hydro-thermal scheduling problem. In the proposed methods, many constraints of the hydro-thermal system, such as power balance, water balance, reservoir volume limits and the operation limits of hydro and thermal plants, are considered. The feasibility of the proposed algorithm is demonstrated through an example system, and the results are compared with the results of a genetic algorithm and evolutionary programming approaches. The experimental results show that all the PSO algorithms have the ability to achieve nearly global solutions, but a local version of PSO with inertia weight appears to be the best amongst all the PSOs in terms of high quality solution

Generation Expansion Planning as Particle Swarm Optimization with Gridified SATyrus

International Nuclear Information System (INIS)

Diacovo, R.; Franca, F. M. G.; Lima, P. M. V.

2007-01-01

This work introduces our first attempt on using the Grid to solve a real-life problem with the SATyrus platform. In electrical engineering, a challenging task is to find the less expensive ways to expand the energy production capacity, supporting an increasing demand. This is the definition of the generation expansion planning problem (GEP). We decided to investigate the Particle Swarm Optimization (PSO) paradigm for this task, due to its efficiency and arbitrary memory requirements, the last one being a desirable characteristic for any solver running on a Grid environment. The PSO was used in conjunction with the SATyrus platform, which stands for an energy function synthesizer. We hope the results presented here will help to evolve SATyrus into a reliable generic problem solver. (Author)

Fault Diagnosis of Plunger Pump in Truck Crane Based on Relevance Vector Machine with Particle Swarm Optimization Algorithm

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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.

Exergetic optimization of a thermoacoustic engine using the particle swarm optimization method

International Nuclear Information System (INIS)

Chaitou, Hussein; Nika, Philippe

2012-01-01

Highlights: ► Optimization of a thermoacoustic engine using the particle swarm optimization method. ► Exergetic efficiency, acoustic power and their product are the optimized functions. ► PSO method is used successfully for the first time in the TA research. ► The powerful PSO tool is advised to be more involved in the TA research and design. ► EE times AP optimized function is highly recommended to design any new TA devices. - Abstract: Thermoacoustic engines convert heat energy into acoustic energy. Then, the acoustic energy can be used to pump heat or to generate electricity. It is well-known that the acoustic energy and therefore the exergetic efficiency depend on parameters such as the stack’s hydraulic radius, the stack’s position in the resonator and the traveling–standing-wave ratio. In this paper, these three parameters are investigated in order to study and analyze the best value of the produced acoustic energy, the exergetic efficiency and the product of the acoustic energy by the exergetic efficiency of a thermoacoustic engine with a parallel-plate stack. The dimensionless expressions of the thermoacoustic equations are derived and calculated. Then, the Particle Swarm Optimization method (PSO) is introduced and used for the first time in the thermoacoustic research. The use of the PSO method and the optimization of the acoustic energy multiplied by the exergetic efficiency are novel contributions to this domain of research. This paper discusses some significant conclusions which are useful for the design of new thermoacoustic engines.

Multi-objective optimization with estimation of distribution algorithm in a noisy environment.

Science.gov (United States)

Shim, Vui Ann; Tan, Kay Chen; Chia, Jun Yong; Al Mamun, Abdullah

2013-01-01

Many real-world optimization problems are subjected to uncertainties that may be characterized by the presence of noise in the objective functions. The estimation of distribution algorithm (EDA), which models the global distribution of the population for searching tasks, is one of the evolutionary computation techniques that deals with noisy information. This paper studies the potential of EDAs; particularly an EDA based on restricted Boltzmann machines that handles multi-objective optimization problems in a noisy environment. Noise is introduced to the objective functions in the form of a Gaussian distribution. In order to reduce the detrimental effect of noise, a likelihood correction feature is proposed to tune the marginal probability distribution of each decision variable. The EDA is subsequently hybridized with a particle swarm optimization algorithm in a discrete domain to improve its search ability. The effectiveness of the proposed algorithm is examined via eight benchmark instances with different characteristics and shapes of the Pareto optimal front. The scalability, hybridization, and computational time are rigorously studied. Comparative studies show that the proposed approach outperforms other state of the art algorithms.

Optimizing bi-objective, multi-echelon supply chain model using particle swarm intelligence algorithm

Science.gov (United States)

Sathish Kumar, V. R.; Anbuudayasankar, S. P.; Rameshkumar, K.

2018-02-01

In the current globalized scenario, business organizations are more dependent on cost effective supply chain to enhance profitability and better handle competition. Demand uncertainty is an important factor in success or failure of a supply chain. An efficient supply chain limits the stock held at all echelons to the extent of avoiding a stock-out situation. In this paper, a three echelon supply chain model consisting of supplier, manufacturing plant and market is developed and the same is optimized using particle swarm intelligence algorithm.

A Particle Swarm Optimization Variant with an Inner Variable Learning Strategy

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Guohua Wu

2014-01-01

Full Text Available Although Particle Swarm Optimization (PSO has demonstrated competitive performance in solving global optimization problems, it exhibits some limitations when dealing with optimization problems with high dimensionality and complex landscape. In this paper, we integrate some problem-oriented knowledge into the design of a certain PSO variant. The resulting novel PSO algorithm with an inner variable learning strategy (PSO-IVL is particularly efficient for optimizing functions with symmetric variables. Symmetric variables of the optimized function have to satisfy a certain quantitative relation. Based on this knowledge, the inner variable learning (IVL strategy helps the particle to inspect the relation among its inner variables, determine the exemplar variable for all other variables, and then make each variable learn from the exemplar variable in terms of their quantitative relations. In addition, we design a new trap detection and jumping out strategy to help particles escape from local optima. The trap detection operation is employed at the level of individual particles whereas the trap jumping out strategy is adaptive in its nature. Experimental simulations completed for some representative optimization functions demonstrate the excellent performance of PSO-IVL. The effectiveness of the PSO-IVL stresses a usefulness of augmenting evolutionary algorithms by problem-oriented domain knowledge.

Particle Swarm Optimization for Structural Design Problems

Directory of Open Access Journals (Sweden)

Hamit SARUHAN

2010-02-01

Full Text Available The aim of this paper is to employ the Particle Swarm Optimization (PSO technique to a mechanical engineering design problem which is minimizing the volume of a cantilevered beam subject to bending strength constraints. Mechanical engineering design problems are complex activities which are computing capability are more and more required. The most of these problems are solved by conventional mathematical programming techniques that require gradient information. These techniques have several drawbacks from which the main one is becoming trapped in local optima. As an alternative to gradient-based techniques, the PSO does not require the evaluation of gradients of the objective function. The PSO algorithm employs the generation of guided random positions when they search for the global optimum point. The PSO which is a nature inspired heuristics search technique imitates the social behavior of bird flocking. The results obtained by the PSO are compared with Mathematical Programming (MP. It is demonstrated that the PSO performed and obtained better convergence reliability on the global optimum point than the MP. Using the MP, the volume of 2961000 mm3 was obtained while the beam volume of 2945345 mm3 was obtained by the PSO.

Tour Route Multiobjective Optimization Design Based on the Tourist Satisfaction

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Yan Han

2014-01-01

Full Text Available The question prompted is how to design the tour route to make the tourists get the maximum satisfactions considering the tourists’ demand. The influence factors of the tour route choices of tourists were analyzed and tourists’ behavior characteristics and psychological preferences were regarded as the important influence factors based on the tourist behavioral theories. A questionnaire of tourists’ tour route information and satisfaction degree was carried out. Some information about the scene spot and tourists demand and tour behaviors characteristic such as visit frequency, number of attractions visited was obtained and analyzed. Based on the convey datum, tour routes multiobjective optimization functions were prompted for the tour route design regarding the maximum satisfaction and the minimum tour distance as the optimal objective. The available routes are listed and categorized. Based on the particle swarm optimization model, the priorities of the tour route are calculated and finally the suggestion depth tour route and quick route tour routes are given considering the different tour demands of tourists. The results can offer constructive suggestions on how to design tour routes on the part of tourism enterprises and how to choose a proper tour route on the part of tourists.

Control parameter optimization for AP1000 reactor using Particle Swarm Optimization

International Nuclear Information System (INIS)

Wang, Pengfei; Wan, Jiashuang; Luo, Run; Zhao, Fuyu; Wei, Xinyu

2016-01-01

Highlights: • The PSO algorithm is applied for control parameter optimization of AP1000 reactor. • Key parameters of the MSHIM control system are optimized. • Optimization results are evaluated though simulations and quantitative analysis. - Abstract: The advanced mechanical shim (MSHIM) core control strategy is implemented in the AP1000 reactor for core reactivity and axial power distribution control simultaneously. The MSHIM core control system can provide superior reactor control capabilities via automatic rod control only. This enables the AP1000 to perform power change operations automatically without the soluble boron concentration adjustments. In this paper, the Particle Swarm Optimization (PSO) algorithm has been applied for the parameter optimization of the MSHIM control system to acquire better reactor control performance for AP1000. System requirements such as power control performance, control bank movement and AO control constraints are reflected in the objective function. Dynamic simulations are performed based on an AP1000 reactor simulation platform in each iteration of the optimization process to calculate the fitness values of particles in the swarm. The simulation platform is developed in Matlab/Simulink environment with implementation of a nodal core model and the MSHIM control strategy. Based on the simulation platform, the typical 10% step load decrease transient from 100% to 90% full power is simulated and the objective function used for control parameter tuning is directly incorporated in the simulation results. With successful implementation of the PSO algorithm in the control parameter optimization of AP1000 reactor, four key parameters of the MSHIM control system are optimized. It has been demonstrated by the calculation results that the optimized MSHIM control system parameters can improve the reactor power control capability and reduce the control rod movement without compromising AO control. Therefore, the PSO based optimization

PID Controller Design of Nonlinear System using a New Modified Particle Swarm Optimization with Time-Varying Constriction Coefficient

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Alrijadjis .

2014-12-01

Full Text Available The proportional integral derivative (PID controllers have been widely used in most process control systems for a long time. However, it is a very important problem how to choose PID parameters, because these parameters give a great influence on the control performance. Especially, it is difficult to tune these parameters for nonlinear systems. In this paper, a new modified particle swarm optimization (PSO is presented to search for optimal PID parameters for such system. The proposed algorithm is to modify constriction coefficient which is nonlinearly decreased time-varying for improving the final accuracy and the convergence speed of PSO. To validate the control performance of the proposed method, a typical nonlinear system control, a continuous stirred tank reactor (CSTR process, is illustrated. The results testify that a new modified PSO algorithm can perform well in the nonlinear PID control system design in term of lesser overshoot, rise-time, settling-time, IAE and ISE. Keywords: PID controller, Particle Swarm Optimization (PSO,constriction factor, nonlinear system.

Particle swarm optimization based feature enhancement and feature selection for improved emotion recognition in speech and glottal signals.

Science.gov (United States)

Muthusamy, Hariharan; Polat, Kemal; Yaacob, Sazali

2015-01-01

In the recent years, many research works have been published using speech related features for speech emotion recognition, however, recent studies show that there is a strong correlation between emotional states and glottal features. In this work, Mel-frequency cepstralcoefficients (MFCCs), linear predictive cepstral coefficients (LPCCs), perceptual linear predictive (PLP) features, gammatone filter outputs, timbral texture features, stationary wavelet transform based timbral texture features and relative wavelet packet energy and entropy features were extracted from the emotional speech (ES) signals and its glottal waveforms(GW). Particle swarm optimization based clustering (PSOC) and wrapper based particle swarm optimization (WPSO) were proposed to enhance the discerning ability of the features and to select the discriminating features respectively. Three different emotional speech databases were utilized to gauge the proposed method. Extreme learning machine (ELM) was employed to classify the different types of emotions. Different experiments were conducted and the results show that the proposed method significantly improves the speech emotion recognition performance compared to previous works published in the literature.

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

Pebble bed reactor fuel cycle optimization using particle swarm algorithm

International Nuclear Information System (INIS)

Tavron, Barak; Shwageraus, Eugene

2016-01-01

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

Parameters estimation online for Lorenz system by a novel quantum-behaved particle swarm optimization

International Nuclear Information System (INIS)

Gao Fei; Tong Hengqing; Li Zhuoqiu

2008-01-01

This paper proposes a novel quantum-behaved particle swarm optimization (NQPSO) for the estimation of chaos' unknown parameters by transforming them into nonlinear functions' optimization. By means of the techniques in the following three aspects: contracting the searching space self-adaptively; boundaries restriction strategy; substituting the particles' convex combination for their centre of mass, this paper achieves a quite effective search mechanism with fine equilibrium between exploitation and exploration. Details of applying the proposed method and other methods into Lorenz systems are given, and experiments done show that NQPSO has better adaptability, dependability and robustness. It is a successful approach in unknown parameter estimation online especially in the cases with white noises

Multiple R&D projects scheduling optimization with improved particle swarm algorithm.

Science.gov (United States)

Liu, Mengqi; Shan, Miyuan; Wu, Juan

2014-01-01

For most enterprises, in order to win the initiative in the fierce competition of market, a key step is to improve their R&D ability to meet the various demands of customers more timely and less costly. This paper discusses the features of multiple R&D environments in large make-to-order enterprises under constrained human resource and budget, and puts forward a multi-project scheduling model during a certain period. Furthermore, we make some improvements to existed particle swarm algorithm and apply the one developed here to the resource-constrained multi-project scheduling model for a simulation experiment. Simultaneously, the feasibility of model and the validity of algorithm are proved in the experiment.

Concept of combinatorial de novo design of drug-like molecules by particle swarm optimization.

Science.gov (United States)

Hartenfeller, Markus; Proschak, Ewgenij; Schüller, Andreas; Schneider, Gisbert

2008-07-01

We present a fast stochastic optimization algorithm for fragment-based molecular de novo design (COLIBREE, Combinatorial Library Breeding). The search strategy is based on a discrete version of particle swarm optimization. Molecules are represented by a scaffold, which remains constant during optimization, and variable linkers and side chains. Different linkers represent virtual chemical reactions. Side-chain building blocks were obtained from pseudo-retrosynthetic dissection of large compound databases. Here, ligand-based design was performed using chemically advanced template search (CATS) topological pharmacophore similarity to reference ligands as fitness function. A weighting scheme was included for particle swarm optimization-based molecular design, which permits the use of many reference ligands and allows for positive and negative design to be performed simultaneously. In a case study, the approach was applied to the de novo design of potential peroxisome proliferator-activated receptor subtype-selective agonists. The results demonstrate the ability of the technique to cope with large combinatorial chemistry spaces and its applicability to focused library design. The technique was able to perform exploitation of a known scheme and at the same time explorative search for novel ligands within the framework of a given molecular core structure. It thereby represents a practical solution for compound screening in the early hit and lead finding phase of a drug discovery project.

1-D DC Resistivity Modeling and Interpretation in Anisotropic Media Using Particle Swarm Optimization

Science.gov (United States)

Pekşen, Ertan; Yas, Türker; Kıyak, Alper

2014-09-01

We examine the one-dimensional direct current method in anisotropic earth formation. We derive an analytic expression of a simple, two-layered anisotropic earth model. Further, we also consider a horizontally layered anisotropic earth response with respect to the digital filter method, which yields a quasi-analytic solution over anisotropic media. These analytic and quasi-analytic solutions are useful tests for numerical codes. A two-dimensional finite difference earth model in anisotropic media is presented in order to generate a synthetic data set for a simple one-dimensional earth. Further, we propose a particle swarm optimization method for estimating the model parameters of a layered anisotropic earth model such as horizontal and vertical resistivities, and thickness. The particle swarm optimization is a naturally inspired meta-heuristic algorithm. The proposed method finds model parameters quite successfully based on synthetic and field data. However, adding 5 % Gaussian noise to the synthetic data increases the ambiguity of the value of the model parameters. For this reason, the results should be controlled by a number of statistical tests. In this study, we use probability density function within 95 % confidence interval, parameter variation of each iteration and frequency distribution of the model parameters to reduce the ambiguity. The result is promising and the proposed method can be used for evaluating one-dimensional direct current data in anisotropic media.

A Chaotic Particle Swarm Optimization-Based Heuristic for Market-Oriented Task-Level Scheduling in Cloud Workflow Systems.

Science.gov (United States)

Li, Xuejun; Xu, Jia; Yang, Yun

2015-01-01

Cloud workflow system is a kind of platform service based on cloud computing. It facilitates the automation of workflow applications. Between cloud workflow system and its counterparts, market-oriented business model is one of the most prominent factors. The optimization of task-level scheduling in cloud workflow system is a hot topic. As the scheduling is a NP problem, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) have been proposed to optimize the cost. However, they have the characteristic of premature convergence in optimization process and therefore cannot effectively reduce the cost. To solve these problems, Chaotic Particle Swarm Optimization (CPSO) algorithm with chaotic sequence and adaptive inertia weight factor is applied to present the task-level scheduling. Chaotic sequence with high randomness improves the diversity of solutions, and its regularity assures a good global convergence. Adaptive inertia weight factor depends on the estimate value of cost. It makes the scheduling avoid premature convergence by properly balancing between global and local exploration. The experimental simulation shows that the cost obtained by our scheduling is always lower than the other two representative counterparts.

Effects of Random Values for Particle Swarm Optimization Algorithm

Directory of Open Access Journals (Sweden)

Hou-Ping Dai

2018-02-01

Full Text Available Particle swarm optimization (PSO algorithm is generally improved by adaptively adjusting the inertia weight or combining with other evolution algorithms. However, in most modified PSO algorithms, the random values are always generated by uniform distribution in the range of [0, 1]. In this study, the random values, which are generated by uniform distribution in the ranges of [0, 1] and [−1, 1], and Gauss distribution with mean 0 and variance 1 ( U [ 0 , 1 ] , U [ − 1 , 1 ] and G ( 0 , 1 , are respectively used in the standard PSO and linear decreasing inertia weight (LDIW PSO algorithms. For comparison, the deterministic PSO algorithm, in which the random values are set as 0.5, is also investigated in this study. Some benchmark functions and the pressure vessel design problem are selected to test these algorithms with different types of random values in three space dimensions (10, 30, and 100. The experimental results show that the standard PSO and LDIW-PSO algorithms with random values generated by U [ − 1 , 1 ] or G ( 0 , 1 are more likely to avoid falling into local optima and quickly obtain the global optima. This is because the large-scale random values can expand the range of particle velocity to make the particle more likely to escape from local optima and obtain the global optima. Although the random values generated by U [ − 1 , 1 ] or G ( 0 , 1 are beneficial to improve the global searching ability, the local searching ability for a low dimensional practical optimization problem may be decreased due to the finite particles.

Applying Particle Swarm Optimization for Solving Team Orienteering Problem with Time Windows

Directory of Open Access Journals (Sweden)

The Jin Ai

2014-01-01

Full Text Available The Team Orienteering Problem With Time Windows (TOPTW is a transportation problem case that have a set of vertices with a score, service time, and the time windows, start and final at a depot location. A number of paths are constructed to maximize the total collected score by the vertices which is visited. Each vertice can be visited only once and the visit can only start during the  time window of vertices. This paper proposes a Particle Swarm Optimization algorithm for solving the TOPTW, by defining a specific particle for representing the solution of TOPTW within the PSO algorithm and two alternatives, called PSO_TOPTW1 and PSO_TOPTW2, for translating the particle position to form the routes of the path. The performance of the proposed PSO algorithm is evaluated through some benchmark data problem available in the literature. The computational results show that the proposed PSO is able to produce sufficiently good TOPTW solutions that are comparable with corresponding solutions from other existing methods for solving the TOPTW.

A Decomposition Model for HPLC-DAD Data Set and Its Solution by Particle Swarm Optimization

Directory of Open Access Journals (Sweden)

Lizhi Cui

2014-01-01

Full Text Available This paper proposes a separation method, based on the model of Generalized Reference Curve Measurement and the algorithm of Particle Swarm Optimization (GRCM-PSO, for the High Performance Liquid Chromatography with Diode Array Detection (HPLC-DAD data set. Firstly, initial parameters are generated to construct reference curves for the chromatogram peaks of the compounds based on its physical principle. Then, a General Reference Curve Measurement (GRCM model is designed to transform these parameters to scalar values, which indicate the fitness for all parameters. Thirdly, rough solutions are found by searching individual target for every parameter, and reinitialization only around these rough solutions is executed. Then, the Particle Swarm Optimization (PSO algorithm is adopted to obtain the optimal parameters by minimizing the fitness of these new parameters given by the GRCM model. Finally, spectra for the compounds are estimated based on the optimal parameters and the HPLC-DAD data set. Through simulations and experiments, following conclusions are drawn: (1 the GRCM-PSO method can separate the chromatogram peaks and spectra from the HPLC-DAD data set without knowing the number of the compounds in advance even when severe overlap and white noise exist; (2 the GRCM-PSO method is able to handle the real HPLC-DAD data set.

Energy-Aware Multipath Routing Scheme Based on Particle Swarm Optimization in Mobile Ad Hoc Networks.

Science.gov (United States)

Robinson, Y Harold; Rajaram, M

2015-01-01

Mobile ad hoc network (MANET) is a collection of autonomous mobile nodes forming an ad hoc network without fixed infrastructure. Dynamic topology property of MANET may degrade the performance of the network. However, multipath selection is a great challenging task to improve the network lifetime. We proposed an energy-aware multipath routing scheme based on particle swarm optimization (EMPSO) that uses continuous time recurrent neural network (CTRNN) to solve optimization problems. CTRNN finds the optimal loop-free paths to solve link disjoint paths in a MANET. The CTRNN is used as an optimum path selection technique that produces a set of optimal paths between source and destination. In CTRNN, particle swarm optimization (PSO) method is primly used for training the RNN. The proposed scheme uses the reliability measures such as transmission cost, energy factor, and the optimal traffic ratio between source and destination to increase routing performance. In this scheme, optimal loop-free paths can be found using PSO to seek better link quality nodes in route discovery phase. PSO optimizes a problem by iteratively trying to get a better solution with regard to a measure of quality. The proposed scheme discovers multiple loop-free paths by using PSO technique.

Feature selection and classifier parameters estimation for EEG signals peak detection using particle swarm optimization.

Science.gov (United States)

Adam, Asrul; Shapiai, Mohd Ibrahim; Tumari, Mohd Zaidi Mohd; Mohamad, Mohd Saberi; Mubin, Marizan

2014-01-01

Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO) are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RA-PSO). The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model.

An improved quantum-behaved particle swarm optimization method for short-term combined economic emission hydrothermal scheduling

Energy Technology Data Exchange (ETDEWEB)

Lu, Songfeng; Sun, Chengfu; Lu, Zhengding [School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074 (China)

2010-03-15

This paper presents a modified quantum-behaved particle swarm optimization (QPSO) for short-term combined economic emission scheduling (CEES) of hydrothermal power systems with several equality and inequality constraints. The hydrothermal scheduling is formulated as a bi-objective problem: (i) minimizing fuel cost and (ii) minimizing pollutant emission. The bi-objective problem is converted into a single objective one by price penalty factor. The proposed method, denoted as QPSO-DM, combines the QPSO algorithm with differential mutation operation to enhance the global search ability. In this study, heuristic strategies are proposed to handle the equality constraints especially water dynamic balance constraints and active power balance constraints. A feasibility-based selection technique is also employed to meet the reservoir storage volumes constraints. To show the efficiency of the proposed method, different case studies are carried out and QPSO-DM is compared with the differential evolution (DE), the particle swarm optimization (PSO) with same heuristic strategies in terms of the solution quality, robustness and convergence property. The simulation results show that the proposed method is capable of yielding higher-quality solutions stably and efficiently in the short-term hydrothermal scheduling than any other tested optimization algorithms. (author)

An improved quantum-behaved particle swarm optimization method for short-term combined economic emission hydrothermal scheduling

Energy Technology Data Exchange (ETDEWEB)

Lu Songfeng [School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074 (China); Sun Chengfu, E-mail: ajason_369@sina.co [School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074 (China); Lu Zhengding [School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074 (China)

2010-03-15

This paper presents a modified quantum-behaved particle swarm optimization (QPSO) for short-term combined economic emission scheduling (CEES) of hydrothermal power systems with several equality and inequality constraints. The hydrothermal scheduling is formulated as a bi-objective problem: (i) minimizing fuel cost and (ii) minimizing pollutant emission. The bi-objective problem is converted into a single objective one by price penalty factor. The proposed method, denoted as QPSO-DM, combines the QPSO algorithm with differential mutation operation to enhance the global search ability. In this study, heuristic strategies are proposed to handle the equality constraints especially water dynamic balance constraints and active power balance constraints. A feasibility-based selection technique is also employed to meet the reservoir storage volumes constraints. To show the efficiency of the proposed method, different case studies are carried out and QPSO-DM is compared with the differential evolution (DE), the particle swarm optimization (PSO) with same heuristic strategies in terms of the solution quality, robustness and convergence property. The simulation results show that the proposed method is capable of yielding higher-quality solutions stably and efficiently in the short-term hydrothermal scheduling than any other tested optimization algorithms.

An improved quantum-behaved particle swarm optimization method for short-term combined economic emission hydrothermal scheduling

International Nuclear Information System (INIS)

Lu Songfeng; Sun Chengfu; Lu Zhengding

2010-01-01

This paper presents a modified quantum-behaved particle swarm optimization (QPSO) for short-term combined economic emission scheduling (CEES) of hydrothermal power systems with several equality and inequality constraints. The hydrothermal scheduling is formulated as a bi-objective problem: (i) minimizing fuel cost and (ii) minimizing pollutant emission. The bi-objective problem is converted into a single objective one by price penalty factor. The proposed method, denoted as QPSO-DM, combines the QPSO algorithm with differential mutation operation to enhance the global search ability. In this study, heuristic strategies are proposed to handle the equality constraints especially water dynamic balance constraints and active power balance constraints. A feasibility-based selection technique is also employed to meet the reservoir storage volumes constraints. To show the efficiency of the proposed method, different case studies are carried out and QPSO-DM is compared with the differential evolution (DE), the particle swarm optimization (PSO) with same heuristic strategies in terms of the solution quality, robustness and convergence property. The simulation results show that the proposed method is capable of yielding higher-quality solutions stably and efficiently in the short-term hydrothermal scheduling than any other tested optimization algorithms.

Swarm of bees and particles algorithms in the problem of gradual failure reliability assurance

Directory of Open Access Journals (Sweden)

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

Locating multiple optima using particle swarm optimization

CSIR Research Space (South Africa)

Brits, R

2007-01-01

Full Text Available in [37]). Faure-sequences are distributed with high uniformity within a n-dimensional unit cube. Other pseudo-random uniform number generators, such as Sobol-sequences [33], may also be used. Main swarm training: In the nbest algorithm, overlapping...

Generation and reserve dispatch in a competitive market using constrained particle swarm optimization

International Nuclear Information System (INIS)

Azadani, E. Nasr; Hosseinian, S.H.; Moradzadeh, B.

2010-01-01

Competitive bidding for energy and ancillary services is increasingly recognized as an important part of electricity markets. In addition, the transmission capacity limits should be considered to optimize the total market cost. In this paper, a new approach based on constrained particle swarm optimization (CPSO) is developed to deal with the multi-product (energy and reserve) and multi-area electricity market dispatch problem. Constraint handling is based on particle ranking and uniform distribution. CPSO method offers a new solution for optimizing the total market cost in a multi-area competitive electricity market considering the system constraints. The proposed technique shows promising performance for smooth and non smooth cost function as well. Three different systems are examined to demonstrate the effectiveness and the accuracy of the proposed algorithm. (author)

Improved SpikeProp for Using Particle Swarm Optimization

Directory of Open Access Journals (Sweden)

Falah Y. H. Ahmed

2013-01-01

Full Text Available A spiking neurons network encodes information in the timing of individual spike times. A novel supervised learning rule for SpikeProp is derived to overcome the discontinuities introduced by the spiking thresholding. This algorithm is based on an error-backpropagation learning rule suited for supervised learning of spiking neurons that use exact spike time coding. The SpikeProp is able to demonstrate the spiking neurons that can perform complex nonlinear classification in fast temporal coding. This study proposes enhancements of SpikeProp learning algorithm for supervised training of spiking networks which can deal with complex patterns. The proposed methods include the SpikeProp particle swarm optimization (PSO and angle driven dependency learning rate. These methods are presented to SpikeProp network for multilayer learning enhancement and weights optimization. Input and output patterns are encoded as spike trains of precisely timed spikes, and the network learns to transform the input trains into target output trains. With these enhancements, our proposed methods outperformed other conventional neural network architectures.

A new multiple robot path planning algorithm: dynamic distributed particle swarm optimization.

Science.gov (United States)

Ayari, Asma; Bouamama, Sadok

2017-01-01

Multiple robot systems have become a major study concern in the field of robotic research. Their control becomes unreliable and even infeasible if the number of robots increases. In this paper, a new dynamic distributed particle swarm optimization (D 2 PSO) algorithm is proposed for trajectory path planning of multiple robots in order to find collision-free optimal path for each robot in the environment. The proposed approach consists in calculating two local optima detectors, LOD pBest and LOD gBest . Particles which are unable to improve their personal best and global best for predefined number of successive iterations would be replaced with restructured ones. Stagnation and local optima problems would be avoided by adding diversity to the population, without losing the fast convergence characteristic of PSO. Experiments with multiple robots are provided and proved effectiveness of such approach compared with the distributed PSO.

Neuro-Fuzzy DC Motor Speed Control Using Particle Swarm Optimization