Drilling Path Optimization Based on Particle Swarm Optimization Algorithm
Institute of Scientific and Technical Information of China (English)
ZHU Guangyu; ZHANG Weibo; DU Yuexiang
2006-01-01
This paper presents a new approach based on the particle swarm optimization (PSO) algorithm for solving the drilling path optimization problem belonging to discrete space. Because the standard PSO algorithm is not guaranteed to be global convergence or local convergence, based on the mathematical algorithm model, the algorithm is improved by adopting the method of generate the stop evolution particle over again to get the ability of convergence to the global optimization solution. And the operators are improved by establishing the duality transposition method and the handle manner for the elements of the operator, the improved operator can satisfy the need of integer coding in drilling path optimization. The experiment with small node numbers indicates that the improved algorithm has the characteristics of easy realize, fast convergence speed, and better global convergence characteristics, hence the new PSO can play a role in solving the problem of drilling path optimization in drilling holes.
Genetic algorithm based separation cascade optimization
International Nuclear Information System (INIS)
The conventional separation cascade design procedure does not give an optimum design because of squaring-off, variation of flow rates and separation factor of the element with respect to stage location. Multi-component isotope separation further complicates the design procedure. Cascade design can be stated as a constrained multi-objective optimization. Cascade's expectation from the separating element is multi-objective i.e. overall separation factor, cut, optimum feed and separative power. Decision maker may aspire for more comprehensive multi-objective goals where optimization of cascade is coupled with the exploration of separating element optimization vector space. In real life there are many issues which make it important to understand the decision maker's perception of cost-quality-speed trade-off and consistency of preferences. Genetic algorithm (GA) is one such evolutionary technique that can be used for cascade design optimization. This paper addresses various issues involved in the GA based multi-objective optimization of the separation cascade. Reference point based optimization methodology with GA based Pareto optimality concept for separation cascade was found pragmatic and promising. This method should be explored, tested, examined and further developed for binary as well as multi-component separations. (author)
Bell-Curve Based Evolutionary Optimization Algorithm
Sobieszczanski-Sobieski, J.; Laba, K.; Kincaid, R.
1998-01-01
The paper presents an optimization algorithm that falls in the category of genetic, or evolutionary algorithms. While the bit exchange is the basis of most of the Genetic Algorithms (GA) in research and applications in America, some alternatives, also in the category of evolutionary algorithms, but use a direct, geometrical approach have gained popularity in Europe and Asia. The Bell-Curve Based Evolutionary Algorithm (BCB) is in this alternative category and is distinguished by the use of a combination of n-dimensional geometry and the normal distribution, the bell-curve, in the generation of the offspring. The tool for creating a child is a geometrical construct comprising a line connecting two parents and a weighted point on that line. The point that defines the child deviates from the weighted point in two directions: parallel and orthogonal to the connecting line, the deviation in each direction obeying a probabilistic distribution. Tests showed satisfactory performance of BCB. The principal advantage of BCB is its controllability via the normal distribution parameters and the geometrical construct variables.
An optimization algorithm for volumetrically segmented aperture-based IMRT
International Nuclear Information System (INIS)
There are few algorithms to create aperture-based IMRT such as Iterative least-square algorithm, Simultaneous projection algorithm (Cimmino's algorithm), Mixed integer programming, etc. In this present work, a Volumetrically Segmented Aperture Optimization (VSAO) algorithm is introduced and its usefulness in generating aperture-based IMRT plans is investigated in different case studies
Warehouse Optimization Model Based on Genetic Algorithm
Directory of Open Access Journals (Sweden)
Guofeng Qin
2013-01-01
Full Text Available This paper takes Bao Steel logistics automated warehouse system as an example. The premise is to maintain the focus of the shelf below half of the height of the shelf. As a result, the cost time of getting or putting goods on the shelf is reduced, and the distance of the same kind of goods is also reduced. Construct a multiobjective optimization model, using genetic algorithm to optimize problem. At last, we get a local optimal solution. Before optimization, the average cost time of getting or putting goods is 4.52996 s, and the average distance of the same kinds of goods is 2.35318 m. After optimization, the average cost time is 4.28859 s, and the average distance is 1.97366 m. After analysis, we can draw the conclusion that this model can improve the efficiency of cargo storage.
Adaptive Bacterial Foraging Optimization Algorithm Based on Social Foraging Strategy
Hai Shen; Yunlong Zhu
2014-01-01
In 2002, K. M. Passino proposed Bacterial Foraging Optimization Algorithm (BFOA) for distributed optimization and control. Biologic foraging strategies are diverse. Based on social and intelligent foraging theory, this paper proposed an adaptive bacterial foraging optimization algorithm, and introduced six foraging operators: chaos run operator, assimilation run operator, tumble operator, swimming operator, reproduction operator and elimination-dispersal operator. Among those operators, chaos...
Qingyang Zhang; Guolin Yu; Hui Song
2015-01-01
Bird Mating Optimizer (BMO) is a novel meta-heuristic optimization algorithm inspired by intelligent mating behavior of birds. However, it is still insufficient in convergence of speed and quality of solution. To overcome these drawbacks, this paper proposes a hybrid algorithm (TLBMO), which is established by combining the advantages of Teaching-learning-based optimization (TLBO) and Bird Mating Optimizer (BMO). The performance of TLBMO is evaluated on 23 benchmark functions, and compared wit...
XOR-based artificial bee colony algorithm for binary optimization
KIRAN, Mustafa Servet; Gündüz, Mesut
2012-01-01
The artificial bee colony (ABC) algorithm, which was inspired by the foraging and dance behaviors of real honey bee colonies, was first introduced for solving numerical optimization problems. When the solution space of the optimization problem is binary-structured, the basic ABC algorithm should be modified for solving this class of problems. In this study, we propose XOR-based modification for the solution-updating equation of the ABC algorithm in order to solve binary optimization pro...
AN OPTIMIZATION ALGORITHM BASED ON BACTERIA BEHAVIOR
Directory of Open Access Journals (Sweden)
Ricardo Contreras
2014-09-01
Full Text Available Paradigms based on competition have shown to be useful for solving difficult problems. In this paper we present a new approach for solving hard problems using a collaborative philosophy. A collaborative philosophy can produce paradigms as interesting as the ones found in algorithms based on a competitive philosophy. Furthermore, we show that the performance - in problems associated to explosive combinatorial - is comparable to the performance obtained using a classic evolutive approach.
Analog circuit design optimization based on evolutionary algorithms
Barari, Mansour; Karimi, Hamid Reza; Razaghian, Farhad
2014-01-01
This paper investigates an evolutionary-based designing system for automated sizing of analog integrated circuits (ICs). Two evolutionary algorithms, genetic algorithm and PSO (Parswal particle swarm optimization) algorithm, are proposed to design analog ICs with practical user-defined specifications. On the basis of the combination of HSPICE and MATLAB, the system links circuit performances, evaluated through specific electrical simulation, to the optimization system in the MATLAB environmen...
Analog Circuit Design Optimization Based on Evolutionary Algorithms
Mansour Barari; Hamid Reza Karimi; Farhad Razaghian
2014-01-01
This paper investigates an evolutionary-based designing system for automated sizing of analog integrated circuits (ICs). Two evolutionary algorithms, genetic algorithm and PSO (Parswal particle swarm optimization) algorithm, are proposed to design analog ICs with practical user-defined specifications. On the basis of the combination of HSPICE and MATLAB, the system links circuit performances, evaluated through specific electrical simulation, to the optimization system in the MATLAB environmen...
Directory of Open Access Journals (Sweden)
Qingyang Zhang
2015-02-01
Full Text Available Bird Mating Optimizer (BMO is a novel meta-heuristic optimization algorithm inspired by intelligent mating behavior of birds. However, it is still insufficient in convergence of speed and quality of solution. To overcome these drawbacks, this paper proposes a hybrid algorithm (TLBMO, which is established by combining the advantages of Teaching-learning-based optimization (TLBO and Bird Mating Optimizer (BMO. The performance of TLBMO is evaluated on 23 benchmark functions, and compared with seven state-of-the-art approaches, namely BMO, TLBO, Artificial Bee Bolony (ABC, Particle Swarm Optimization (PSO, Fast Evolution Programming (FEP, Differential Evolution (DE, Group Search Optimization (GSO. Experimental results indicate that the proposed method performs better than other existing algorithms for global numerical optimization.
Teaching learning based optimization algorithm and its engineering applications
Rao, R Venkata
2016-01-01
Describing a new optimization algorithm, the “Teaching-Learning-Based Optimization (TLBO),” in a clear and lucid style, this book maximizes reader insights into how the TLBO algorithm can be used to solve continuous and discrete optimization problems involving single or multiple objectives. As the algorithm operates on the principle of teaching and learning, where teachers influence the quality of learners’ results, the elitist version of TLBO algorithm (ETLBO) is described along with applications of the TLBO algorithm in the fields of electrical engineering, mechanical design, thermal engineering, manufacturing engineering, civil engineering, structural engineering, computer engineering, electronics engineering, physics and biotechnology. The book offers a valuable resource for scientists, engineers and practitioners involved in the development and usage of advanced optimization algorithms.
Wireless Sensor Network Path Optimization Based on Hybrid Algorithm
Zeyu Sun; Li, Zhenping
2013-01-01
One merit of genetic algorithm is fast overall searching, but this algorithm usually results in low efficiency because of large quantities of redundant codes. The advantages of ant colony algorithm are strong suitability and good robustness while its disadvantages are tendency to stagnation, slow speed of convergence. Put forward based on improved ant colony algorithm for wireless sensor network path optimization approach will first need to pass the data in the shortest path for transmission,...
Genetic Algorithm-Based Optimization Used in Rolling Schedule
Institute of Scientific and Technical Information of China (English)
YANG Jing-ming; CHE Hai-jun; DOU Fu-ping; ZHOU Tao
2008-01-01
A genetic algorithm-based optimization was used for 1 370 mm tandem cold rolling schedule, in which the press rates were coded and operated. The superiority individual is reserved in every generation. Analysis and comparison of optimized schedule with the existing schedule were offered. It is seen that the performance of the optimal rolling schedule is satisfactory and promising.
QOS-BASED MULTICAST ROUTING OPTIMIZATION ALGORITHMS FOR INTERNET
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
Most of the multimedia applications require strict Quality-of-Service (QoS) guarantee during the communication between a single source and multiple destinations. The paper mainly presents a QoS Multicast Routing algorithms based on Genetic Algorithm (QMRGA). Simulation results demonstrate that the algorithm is capable of discovering a set of QoS-based near optimized, non-dominated multicast routes within a few iterations, even for the networks environment with uncertain parameters.
Analog Circuit Design Optimization Based on Evolutionary Algorithms
Directory of Open Access Journals (Sweden)
Mansour Barari
2014-01-01
Full Text Available This paper investigates an evolutionary-based designing system for automated sizing of analog integrated circuits (ICs. Two evolutionary algorithms, genetic algorithm and PSO (Parswal particle swarm optimization algorithm, are proposed to design analog ICs with practical user-defined specifications. On the basis of the combination of HSPICE and MATLAB, the system links circuit performances, evaluated through specific electrical simulation, to the optimization system in the MATLAB environment, for the selected topology. The system has been tested by typical and hard-to-design cases, such as complex analog blocks with stringent design requirements. The results show that the design specifications are closely met. Comparisons with available methods like genetic algorithms show that the proposed algorithm offers important advantages in terms of optimization quality and robustness. Moreover, the algorithm is shown to be efficient.
PRACTICAL APPLICATION OF POPULATION BASED ANT COLONY OPTIMIZATION ALGORITHM
Valeeva, A.; Goncharova, Yu
2013-01-01
In this paper we consider the Split Delivery Vehicle Routing Problem, which has a wide practical application. The SDVRP is NP-hard problem. We propose a population based ant colony optimization algorithm for solving the SDVRP. Computational experiments for developed algorithm are reported.
Antenna synthesis based on the ant colony optimization algorithm
Slyusar, V. I.; Ermolaev, S. Y.
2009-01-01
This report are described the versions and the synthesis results of new designs of electrically small antenna based on ant colony optimization algorithms. To study the parameters of the frame and non-loopback vibrators MMANA package was used. Geometric forms that were obtained might be used as contour lines of printed, slot antenna or as forming surface of the crystal dielectric resonator antenna. A constructive meta-heuristic search algorithm for optimization of the antennas form...
Optimal fractional order PID design via Tabu Search based algorithm.
Ateş, Abdullah; Yeroglu, Celaleddin
2016-01-01
This paper presents an optimization method based on the Tabu Search Algorithm (TSA) to design a Fractional-Order Proportional-Integral-Derivative (FOPID) controller. All parameter computations of the FOPID employ random initial conditions, using the proposed optimization method. Illustrative examples demonstrate the performance of the proposed FOPID controller design method. PMID:26652128
Support vector machines optimization based theory, algorithms, and extensions
Deng, Naiyang; Zhang, Chunhua
2013-01-01
Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions presents an accessible treatment of the two main components of support vector machines (SVMs)-classification problems and regression problems. The book emphasizes the close connection between optimization theory and SVMs since optimization is one of the pillars on which SVMs are built.The authors share insight on many of their research achievements. They give a precise interpretation of statistical leaning theory for C-support vector classification. They also discuss regularized twi
OPTIMIZATION BASED ON LMPROVED REAL—CODED GENETIC ALGORITHM
Institute of Scientific and Technical Information of China (English)
ShiYu; YuShenglin
2002-01-01
An improved real-coded genetic algorithm is pro-posed for global optimization of functionsl.The new algo-rithm is based om the judgement of the searching perfor-mance of basic real-coded genetic algorithm.The opera-tions of basic real-coded genetic algorithm are briefly dis-cussed and selected.A kind of chaos sequence is described in detail and added in the new algorithm ad a disturbance factor.The strategy of field partition is also used to im-prove the strcture of the new algorithm.Numerical ex-periment shows that the mew genetic algorithm can find the global optimum of complex funtions with satistaiting precision.
Optimizing Combination of Units Commitment Based on Improved Genetic Algorithms
Institute of Scientific and Technical Information of China (English)
LAI Yifei; ZHANG Qianhua; JIA Junping
2007-01-01
GAs are general purpose optimization techniques based on principles inspired from the biological evolution using metaphors of mechanisms, such as natural selection, genetic recombination and survival of the fittest. By use of coding betterment, the dynamic changes of the mutation rate and the crossover probability, the dynamic choice of subsistence, the reservation of the optimal fitness value, a modified genetic algorithm for optimizing combination of units in thermal power plants is proposed.And through taking examples, test result are analyzed and compared with results of some different algorithms. Numerical results show available value for the unit commitment problem with examples.
A Hybrid Optimization Algorithm based on Genetic Algorithm and Ant Colony Optimization
Zainudin Zukhri; Irving Vitra Paputungan
2013-01-01
In optimization problem, Genetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO) have been known as good alternative techniques. GA is designed by adopting the natural evolution process, while ACO is inspired by the foraging behaviour of ant species. This paper presents a hybrid GA-ACO for Travelling Salesman Problem (TSP), called Genetic Ant Colony Optimization (GACO). In this method, GA will observe and preserve the fittest ant in each cycle in every generation and on...
Electronic Commerce Logistics Network Optimization Based on Swarm Intelligent Algorithm
Directory of Open Access Journals (Sweden)
Yabing Jiao
2013-09-01
Full Text Available This article establish an efficient electronic commerce logistics operation system to reduce distribution costs and build a logistics network operation model based on around the B2C electronic commerce enterprise logistics network operation system. B2C electronic commerce transactions features in the enterprise network platform. To solve the NP-hard problem this article use hybrid ant colony algorithm, particle swarm algorithm and group swarm intelligence algorithm to get a best solution. According to the intelligent algorithm, design of electronic commerce logistics network optimization system, enter the national 22 electronic commerce logistics network for validation. Through the experiment to verify the optimized logistics cost greatly decreased. This research can help B2C electronic commerce enterprise logistics network to optimize decision-making under the premise of ensuring the interests of consumers and service levels also can be an effective way for enterprises to improve the efficiency of logistics services and reduce operation costs
Optimization algorithm based characterization scheme for tunable semiconductor lasers.
Chen, Quanan; Liu, Gonghai; Lu, Qiaoyin; Guo, Weihua
2016-09-01
In this paper, an optimization algorithm based characterization scheme for tunable semiconductor lasers is proposed and demonstrated. In the process of optimization, the ratio between the power of the desired frequency and the power except of the desired frequency is used as the figure of merit, which approximately represents the side-mode suppression ratio. In practice, we use tunable optical band-pass and band-stop filters to obtain the power of the desired frequency and the power except of the desired frequency separately. With the assistance of optimization algorithms, such as the particle swarm optimization (PSO) algorithm, we can get stable operation conditions for tunable lasers at designated frequencies directly and efficiently. PMID:27607701
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
A novel method of global optimal path planning for mobile robot was proposed based on the improved Dijkstra algorithm and ant system algorithm. This method includes three steps: the first step is adopting the MAKLINK graph theory to establish the free space model of the mobile robot, the second step is adopting the improved Dijkstra algorithm to find out a sub-optimal collision-free path, and the third step is using the ant system algorithm to adjust and optimize the location of the sub-optimal path so as to generate the global optimal path for the mobile robot. The computer simulation experiment was carried out and the results show that this method is correct and effective. The comparison of the results confirms that the proposed method is better than the hybrid genetic algorithm in the global optimal path planning.
Form Errors Evaluation Based on a Hybrid Optimization Algorithm
Directory of Open Access Journals (Sweden)
Ke Zhang
2011-08-01
Full Text Available In order to realize precision measurement of parts, based on the analysis of existent evaluation methods for form errors, a hybrid evaluation method is provided in this paper. The hybrid global optimization algorithm based on ant colony optimization and simplex search method is proposed. The optimum model and the calculation process are introduced, where the planar straightness error is discussed as an example. The hybrid optimization algorithm can improve the efficiency and accuracy of searching in the whole field by gradually shrinking the area of optimization variable. Finally, a control experiment is carried out, and the calculation results by using different method such as the least square, simplex search, Powell optimum methods and GA, show that the hybrid evaluation method is feasible and satisfactory in the evaluation of form errors.
Wireless Sensor Network Path Optimization Based on Hybrid Algorithm
Directory of Open Access Journals (Sweden)
Zeyu Sun
2013-09-01
Full Text Available One merit of genetic algorithm is fast overall searching, but this algorithm usually results in low efficiency because of large quantities of redundant codes. The advantages of ant colony algorithm are strong suitability and good robustness while its disadvantages are tendency to stagnation, slow speed of convergence. Put forward based on improved ant colony algorithm for wireless sensor network path optimization approach will first need to pass the data in the shortest path for transmission, assuming that transmission path jam, it will clog information sent to the initial position, so the follow-up need to pass data can choose other reasonable path so as to avoid the defects of the traditional method. Genetic ant colony is proposed to avoid the faults of both algorithms above. The proposed algorithm determines distribution of pheromones on path through fast searching and changing the operation of selection operator, crossover operator and mutation operator of genetic ant colony, and then solves the problems efficiently through parallelism, positive feedback and iteration of ant colony algorithm. Therefore, the faults of both algorithms are conquered and the aim of combinational optimization is achieved. At last, the validity and feasibility is demonstrated by means of simulation experiment of traveling salesman problem.
Optimal design of the heat pipe using TLBO (teaching–learning-based optimization) algorithm
International Nuclear Information System (INIS)
Heat pipe is a highly efficient and reliable heat transfer component. It is a closed container designed to transfer a large amount of heat in system. Since the heat pipe operates on a closed two-phase cycle, the heat transfer capacity is greater than for solid conductors. Also, the thermal response time is less than with solid conductors. The three major elemental parts of the rotating heat pipe are: a cylindrical evaporator, a truncated cone condenser, and a fixed amount of working fluid. In this paper, a recently proposed new stochastic advanced optimization algorithm called TLBO (Teaching–Learning-Based Optimization) algorithm is used for single objective as well as multi-objective design optimization of heat pipe. It is easy to implement, does not make use of derivatives and it can be applied to unconstrained or constrained problems. Two examples of heat pipe are presented in this paper. The results of application of TLBO algorithm for the design optimization of heat pipe are compared with the NPGA (Niched Pareto Genetic Algorithm), GEM (Grenade Explosion Method) and GEO (Generalized External optimization). It is found that the TLBO algorithm has produced better results as compared to those obtained by using NPGA, GEM and GEO algorithms. - Highlights: • The TLBO (Teaching–Learning-Based Optimization) algorithm is used for the design and optimization of a heat pipe. • Two examples of heat pipe design and optimization are presented. • The TLBO algorithm is proved better than the other optimization algorithms in terms of results and the convergence
A HYBRID OPTIMIZATION ALGORITHM BASED ON GENETIC ALGORITHM AND ANT COLONY OPTIMIZATION
Directory of Open Access Journals (Sweden)
Zainudin Zukhri
2013-09-01
Full Text Available In optimization problem, Genetic Algorithm (GA and Ant Colony Optimization Algorithm (ACO have been known as good alternative techniques. GA is designed by adopting the natural evolution process, while ACO is inspired by the foraging behaviour of ant species. This paper presents a hybrid GA-ACO for Travelling Salesman Problem (TSP, called Genetic Ant Colony Optimization (GACO. In this method, GA will observe and preserve the fittest ant in each cycle in every generation and only unvisited cities will be assessed by ACO. From experimental result, GACO performance is significantly improved and its time complexity is fairly equal compared to the GA and ACO.
Routing Optimization Based on Taboo Search Algorithm for Logistic Distribution
Directory of Open Access Journals (Sweden)
Hongxue Yang
2014-04-01
Full Text Available Along with the widespread application of the electronic commerce in the modern business, the logistic distribution has become increasingly important. More and more enterprises recognize that the logistic distribution plays an important role in the process of production and sales. A good routing for logistic distribution can cut down transport cost and improve efficiency. In order to cut down transport cost and improve efficiency, a routing optimization based on taboo search for logistic distribution is proposed in this paper. Taboo search is a metaheuristic search method to perform local search used for logistic optimization. The taboo search is employed to accelerate convergence and the aspiration criterion is combined with the heuristics algorithm to solve routing optimization. Simulation experimental results demonstrate that the optimal routing in the logistic distribution can be quickly obtained by the taboo search algorithm
Kriging-based algorithm for nuclear reactor neutronic design optimization
International Nuclear Information System (INIS)
Highlights: ► A Kriging-based algorithm was selected to guide research reactor optimization. ► We examined impacts of parameter values upon the algorithm. ► The best parameter values were incorporated into a set of best practices. ► Algorithm with best practices used to optimize thermal flux of concept. ► Final design produces thermal flux 30% higher than other 5 MW reactors. - Abstract: Kriging, a geospatial interpolation technique, has been used in the present work to drive a search-and-optimization algorithm which produces the optimum geometric parameters for a 5 MW research reactor design. The technique has been demonstrated to produce an optimal neutronic solution after a relatively small number of core calculations. It has additionally been successful in producing a design which significantly improves thermal neutron fluxes by 30% over existing reactors of the same power rating. Best practices for use of this algorithm in reactor design were identified and indicated the importance of selecting proper correlation functions.
Optimization Planning based on Improved Ant Colony Algorithm for Robot
Xin Zhang; Zhanwen Wu
2014-01-01
As the ant colony algorithm has the defects in robot optimization path planning such as that low convergence cause local optimum, an improved ant colony algorithm is proposed to apply to the planning of path finding for robot. This algorithm uses the search way of exhumation ant to realize the complementation of advantages and accelerate the convergence of algorithm. The experimental result shows that the algorithm of this paper make the optimization planning of robot more reasonable
GENETIC ALGORITHM BASED CONCEPT DESIGN TO OPTIMIZE NETWORK LOAD BALANCE
Directory of Open Access Journals (Sweden)
Ashish Jain
2012-07-01
Full Text Available Multiconstraints optimal network load balancing is an NP-hard problem and it is an important part of traffic engineering. In this research we balance the network load using classical method (brute force approach and dynamic programming is used but result shows the limitation of this method but at a certain level we recognized that the optimization of balanced network load with increased number of nodes and demands is intractable using the classical method because the solution set increases exponentially. In such case the optimization techniques like evolutionary techniques can employ for optimizing network load balance. In this paper we analyzed proposed classical algorithm and evolutionary based genetic approach is devise as well as proposed in this paper for optimizing the balance network load.
Sampling-based Algorithms for Optimal Motion Planning
Karaman, Sertac
2011-01-01
During the last decade, sampling-based path planning algorithms, such as Probabilistic RoadMaps (PRM) and Rapidly-exploring Random Trees (RRT), have been shown to work well in practice and possess theoretical guarantees such as probabilistic completeness. However, little effort has been devoted to the formal analysis of the quality of the solution returned by such algorithms, e.g., as a function of the number of samples. The purpose of this paper is to fill this gap, by rigorously analyzing the asymptotic behavior of the cost of the solution returned by stochastic sampling-based algorithms as the number of samples increases. A number of negative results are provided, characterizing existing algorithms, e.g., showing that, under mild technical conditions, the cost of the solution returned by broadly used sampling-based algorithms converges almost surely to a non-optimal value. The main contribution of the paper is the introduction of new algorithms, namely, PRM* and RRT*, which are provably asymptotically opti...
Rao, R. V.; Savsani, V. J.; Balic, J.
2012-12-01
An efficient optimization algorithm called teaching-learning-based optimization (TLBO) is proposed in this article to solve continuous unconstrained and constrained optimization problems. The proposed method is based on the effect of the influence of a teacher on the output of learners in a class. The basic philosophy of the method is explained in detail. The algorithm is tested on 25 different unconstrained benchmark functions and 35 constrained benchmark functions with different characteristics. For the constrained benchmark functions, TLBO is tested with different constraint handling techniques such as superiority of feasible solutions, self-adaptive penalty, ɛ-constraint, stochastic ranking and ensemble of constraints. The performance of the TLBO algorithm is compared with that of other optimization algorithms and the results show the better performance of the proposed algorithm.
Quantum Behaved Particle Swarm Optimization Algorithm Based on Artificial Fish Swarm
Dong Yumin; Zhao Li
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...
Genetic based optimization for multicast routing algorithm for MANET
Indian Academy of Sciences (India)
C Rajan; N Shanthi
2015-12-01
Mobile Ad hoc Network (MANET) is established for a limited period, for special extemporaneous services related to mobile applications. This ad hoc network is set up for a limited period, in environments that change with the application. While in Internet the TCP/IP protocol suite supports a wide range of application, in MANETs protocols are tuned to specific customer/application. Multicasting is emerging as a popular communication format where the same packet is sent to multiple nodes in a network. Routing in multicasting involves maintaining routes and finding new node locations in a group and is NP-complete due to the dynamic nature of the network. In this paper, a Hybrid Genetic Based Optimization for Multicast Routing algorithm is proposed. The proposed algorithm uses the best features of Genetic Algorithm (GA) and particle swarm optimization (PSO) to improve the solution. Simulations were conducted by varying number of mobile nodes and results compared with Multicast AODV (MAODV) protocol, PSO based and GA based solution. The proposed optimization improves jitter, end to end delay and Packet Delivery Ratio (PDR) with faster convergence.
Ting Jiang; Wei Zang; Chenglin Zhao; Jiong Shi
2010-01-01
We optimize the cluster structure to solve problems such as the uneven energy consumption of the radar sensor nodes and random cluster head selection in the traditional clustering routing algorithm. According to the defined cost function for clusters, we present the clustering algorithm which is based on radio-free space path loss. In addition, we propose the energy and distance pheromones based on the residual energy and aggregation of the radar sensor nodes. According to bionic heuristic a...
CACONET: Ant Colony Optimization (ACO) Based Clustering Algorithm for VANET
Bajwa, Khalid Bashir; Khan, Salabat; Chaudary, Nadeem Majeed; Akram, Adeel
2016-01-01
A vehicular ad hoc network (VANET) is a wirelessly connected network of vehicular nodes. A number of techniques, such as message ferrying, data aggregation, and vehicular node clustering aim to improve communication efficiency in VANETs. Cluster heads (CHs), selected in the process of clustering, manage inter-cluster and intra-cluster communication. The lifetime of clusters and number of CHs determines the efficiency of network. In this paper a Clustering algorithm based on Ant Colony Optimization (ACO) for VANETs (CACONET) is proposed. CACONET forms optimized clusters for robust communication. CACONET is compared empirically with state-of-the-art baseline techniques like Multi-Objective Particle Swarm Optimization (MOPSO) and Comprehensive Learning Particle Swarm Optimization (CLPSO). Experiments varying the grid size of the network, the transmission range of nodes, and number of nodes in the network were performed to evaluate the comparative effectiveness of these algorithms. For optimized clustering, the parameters considered are the transmission range, direction and speed of the nodes. The results indicate that CACONET significantly outperforms MOPSO and CLPSO. PMID:27149517
CACONET: Ant Colony Optimization (ACO) Based Clustering Algorithm for VANET.
Aadil, Farhan; Bajwa, Khalid Bashir; Khan, Salabat; Chaudary, Nadeem Majeed; Akram, Adeel
2016-01-01
A vehicular ad hoc network (VANET) is a wirelessly connected network of vehicular nodes. A number of techniques, such as message ferrying, data aggregation, and vehicular node clustering aim to improve communication efficiency in VANETs. Cluster heads (CHs), selected in the process of clustering, manage inter-cluster and intra-cluster communication. The lifetime of clusters and number of CHs determines the efficiency of network. In this paper a Clustering algorithm based on Ant Colony Optimization (ACO) for VANETs (CACONET) is proposed. CACONET forms optimized clusters for robust communication. CACONET is compared empirically with state-of-the-art baseline techniques like Multi-Objective Particle Swarm Optimization (MOPSO) and Comprehensive Learning Particle Swarm Optimization (CLPSO). Experiments varying the grid size of the network, the transmission range of nodes, and number of nodes in the network were performed to evaluate the comparative effectiveness of these algorithms. For optimized clustering, the parameters considered are the transmission range, direction and speed of the nodes. The results indicate that CACONET significantly outperforms MOPSO and CLPSO. PMID:27149517
Ziwen Sun; Li Tao; Xinyu Wang; Zhiping Zhou
2015-01-01
Based on multiobjective particle swarm optimization, a localization algorithm named multiobjective particle swarm optimization localization algorithm (MOPSOLA) is proposed to solve the multiobjective optimization localization issues in wireless sensor networks. The multiobjective functions consist of the space distance constraint and the geometric topology constraint. The optimal solution is found by multiobjective particle swarm optimization algorithm. Dynamic method is adopted to maintain t...
Microwave-based medical diagnosis using particle swarm optimization algorithm
Modiri, Arezoo
This dissertation proposes and investigates a novel architecture intended for microwave-based medical diagnosis (MBMD). Furthermore, this investigation proposes novel modifications of particle swarm optimization algorithm for achieving enhanced convergence performance. MBMD has been investigated through a variety of innovative techniques in the literature since the 1990's and has shown significant promise in early detection of some specific health threats. In comparison to the X-ray- and gamma-ray-based diagnostic tools, MBMD does not expose patients to ionizing radiation; and due to the maturity of microwave technology, it lends itself to miniaturization of the supporting systems. This modality has been shown to be effective in detecting breast malignancy, and hence, this study focuses on the same modality. A novel radiator device and detection technique is proposed and investigated in this dissertation. As expected, hardware design and implementation are of paramount importance in such a study, and a good deal of research, analysis, and evaluation has been done in this regard which will be reported in ensuing chapters of this dissertation. It is noteworthy that an important element of any detection system is the algorithm used for extracting signatures. Herein, the strong intrinsic potential of the swarm-intelligence-based algorithms in solving complicated electromagnetic problems is brought to bear. This task is accomplished through addressing both mathematical and electromagnetic problems. These problems are called benchmark problems throughout this dissertation, since they have known answers. After evaluating the performance of the algorithm for the chosen benchmark problems, the algorithm is applied to MBMD tumor detection problem. The chosen benchmark problems have already been tackled by solution techniques other than particle swarm optimization (PSO) algorithm, the results of which can be found in the literature. However, due to the relatively high level
Chaos Time Series Prediction Based on Membrane Optimization Algorithms
Directory of Open Access Journals (Sweden)
Meng Li
2015-01-01
Full Text Available This paper puts forward a prediction model based on membrane computing optimization algorithm for chaos time series; the model optimizes simultaneously the parameters of phase space reconstruction (τ,m and least squares support vector machine (LS-SVM (γ,σ by using membrane computing optimization algorithm. It is an important basis for spectrum management to predict accurately the change trend of parameters in the electromagnetic environment, which can help decision makers to adopt an optimal action. Then, the model presented in this paper is used to forecast band occupancy rate of frequency modulation (FM broadcasting band and interphone band. To show the applicability and superiority of the proposed model, this paper will compare the forecast model presented in it with conventional similar models. The experimental results show that whether single-step prediction or multistep prediction, the proposed model performs best based on three error measures, namely, normalized mean square error (NMSE, root mean square error (RMSE, and mean absolute percentage error (MAPE.
An optimized hybrid encode based compression algorithm for hyperspectral image
Wang, Cheng; Miao, Zhuang; Feng, Weiyi; He, Weiji; Chen, Qian; Gu, Guohua
2013-12-01
Compression is a kernel procedure in hyperspectral image processing due to its massive data which will bring great difficulty in date storage and transmission. In this paper, a novel hyperspectral compression algorithm based on hybrid encoding which combines with the methods of the band optimized grouping and the wavelet transform is proposed. Given the characteristic of correlation coefficients between adjacent spectral bands, an optimized band grouping and reference frame selection method is first utilized to group bands adaptively. Then according to the band number of each group, the redundancy in the spatial and spectral domain is removed through the spatial domain entropy coding and the minimum residual based linear prediction method. Thus, embedded code streams are obtained by encoding the residual images using the improved embedded zerotree wavelet based SPIHT encode method. In the experments, hyperspectral images collected by the Airborne Visible/ Infrared Imaging Spectrometer (AVIRIS) were used to validate the performance of the proposed algorithm. The results show that the proposed approach achieves a good performance in reconstructed image quality and computation complexity.The average peak signal to noise ratio (PSNR) is increased by 0.21~0.81dB compared with other off-the-shelf algorithms under the same compression ratio.
Directory of Open Access Journals (Sweden)
Jiang Ting
2010-01-01
Full Text Available We optimize the cluster structure to solve problems such as the uneven energy consumption of the radar sensor nodes and random cluster head selection in the traditional clustering routing algorithm. According to the defined cost function for clusters, we present the clustering algorithm which is based on radio-free space path loss. In addition, we propose the energy and distance pheromones based on the residual energy and aggregation of the radar sensor nodes. According to bionic heuristic algorithm, a new ant colony-based clustering algorithm for radar sensor networks is also proposed. Simulation results show that this algorithm can get a better balance of the energy consumption and then remarkably prolong the lifetime of the radar sensor network.
A novel Retinex algorithm based on alternating direction optimization
Fu, Xueyang; Lin, Qin; Guo, Wei; Huang, Yue; Zeng, Delu; Ding, Xinghao
2013-10-01
The goal of the Retinex theory is to removed the effects of illumination from the observed images. To address this typical ill-posed inverse problem, many existing Retinex algorithms obtain an enhanced image by using different assumptions either on the illumination or on the reflectance. One significant limitation of these Retinex algorithms is that if the assumption is false, the result is unsatisfactory. In this paper, we firstly build a Retinex model which includes two variables: the illumination and the reflectance. We propose an efficient and effective algorithm based on alternating direction optimization to solve this problem where FFT (Fast Fourier Transform) is used to speed up the computation. Comparing with most existing Retinex algorithms, the proposed method solve the illumination image and reflectance image without converting images to the logarithmic domain. One of the advantages in this paper is that, unlike other traditional Retinex algorithms, our method can simultaneously estimate the illumination image and the reflectance image, the later of which is the ideal image without the illumination effect. Since our method can directly separate the illumination and the reflectance, and the two variables constrain each other mutually in the computing process, the result is robust to some degree. Another advantage is that our method has less computational cost and can be applied to real-time processing.
SNMP Based Network Optimization Technique Using Genetic Algorithms
M. Mohamed Surputheen; G Ravi; Srinivasan, R.
2012-01-01
Genetic Algorithms (GAs) has innumerable applications through the optimization techniques and network optimization is one of them. SNMP (Simple Network Management Protocol) is used as the basic network protocol for monitoring the network activities health of the systems. This paper deals with adding Intelligence to the various aspects of SNMP by adding optimization techniques derived out of genetic algorithms, which enhances the performance of SNMP processes like routing.
SNMP Based Network Optimization Technique Using Genetic Algorithms
Directory of Open Access Journals (Sweden)
M. Mohamed Surputheen
2012-03-01
Full Text Available Genetic Algorithms (GAs has innumerable applications through the optimization techniques and network optimization is one of them. SNMP (Simple Network Management Protocol is used as the basic network protocol for monitoring the network activities health of the systems. This paper deals with adding Intelligence to the various aspects of SNMP by adding optimization techniques derived out of genetic algorithms, which enhances the performance of SNMP processes like routing.
A new efficient optimal path planner for mobile robot based on Invasive Weed Optimization algorithm
Mohanty, Prases K.; Parhi, Dayal R.
2014-12-01
Planning of the shortest/optimal route is essential for efficient operation of autonomous mobile robot or vehicle. In this paper Invasive Weed Optimization (IWO), a new meta-heuristic algorithm, has been implemented for solving the path planning problem of mobile robot in partially or totally unknown environments. This meta-heuristic optimization is based on the colonizing property of weeds. First we have framed an objective function that satisfied the conditions of obstacle avoidance and target seeking behavior of robot in partially or completely unknown environments. Depending upon the value of objective function of each weed in colony, the robot avoids obstacles and proceeds towards destination. The optimal trajectory is generated with this navigational algorithm when robot reaches its destination. The effectiveness, feasibility, and robustness of the proposed algorithm has been demonstrated through series of simulation and experimental results. Finally, it has been found that the developed path planning algorithm can be effectively applied to any kinds of complex situation.
Optimization of Pressurizer Based on Genetic-Simplex Algorithm
Energy Technology Data Exchange (ETDEWEB)
Wang, Cheng; Yan, Chang Qi; Wang, Jian Jun [Harbin Engineering University, Harbin (China)
2014-08-15
Pressurizer is one of key components in nuclear power system. It's important to control the dimension in the design of pressurizer through optimization techniques. In this work, a mathematic model of a vertical electric heating pressurizer was established. A new Genetic-Simplex Algorithm (GSA) that combines genetic algorithm and simplex algorithm was developed to enhance the searching ability, and the comparison among modified and original algorithms is conducted by calculating the benchmark function. Furthermore, the optimization design of pressurizer, taking minimization of volume and net weight as objectives, was carried out considering thermal-hydraulic and geometric constraints through GSA. The results indicate that the mathematical model is agreeable for the pressurizer and the new algorithm is more effective than the traditional genetic algorithm. The optimization design shows obvious validity and can provide guidance for real engineering design.
Optimization of Pressurizer Based on Genetic-Simplex Algorithm
International Nuclear Information System (INIS)
Pressurizer is one of key components in nuclear power system. It's important to control the dimension in the design of pressurizer through optimization techniques. In this work, a mathematic model of a vertical electric heating pressurizer was established. A new Genetic-Simplex Algorithm (GSA) that combines genetic algorithm and simplex algorithm was developed to enhance the searching ability, and the comparison among modified and original algorithms is conducted by calculating the benchmark function. Furthermore, the optimization design of pressurizer, taking minimization of volume and net weight as objectives, was carried out considering thermal-hydraulic and geometric constraints through GSA. The results indicate that the mathematical model is agreeable for the pressurizer and the new algorithm is more effective than the traditional genetic algorithm. The optimization design shows obvious validity and can provide guidance for real engineering design
Optimization of Catalysts Using Specific, Description-Based Genetic Algorithms
Czech Academy of Sciences Publication Activity Database
Holeňa, Martin; Čukić, T.; Rodemerck, U.; Linke, D.
2008-01-01
Roč. 48, č. 2 (2008), s. 274-282. ISSN 1549-9596 R&D Projects: GA ČR GA201/08/1744 Institutional research plan: CEZ:AV0Z10300504 Keywords : optimization of catalytic materials * genetic algorithm s * mixed optimization * constrained optimization Subject RIV: IN - Informatics, Computer Science Impact factor: 3.643, year: 2008
Optimal Design of Materials for DJMP Based on Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
FENG Zhong-ren; WANG Xiong-jiang
2004-01-01
The genetic algorithm was used in optimal design of deep jet method pile. The cost of deep jetmethod pile in one unit area of foundation was taken as the objective function. All the restrains were listed followingthe corresponding specification. Suggestions were proposed and the modified. The real-coded Genetic Algorithm wasgiven to deal with the problems of excessive computational cost and premature convergence. Software system of opti-mal design of deep jet method pile was developed.
ESSENTIAL MODIFICATIONS ON BIOGEOGRAPHY-BASED OPTIMIZATION ALGORITHM
Directory of Open Access Journals (Sweden)
Ali R. Alroomi
2013-11-01
Full Text Available Biogeography-based optimization (BBO is a new population-based evolutionary algorithm and is based on an old theory of island biogeography that explains the geographical distribution of biological organisms. BBO was introduced in 2008 and then a lot of modifications were employed to enhance its performance. This paper proposes two modifications; firstly, modifying the probabilistic selection process of the migration and mutation stages to give a fairly randomized selection for all the features of the islands. Secondly, the clear duplication process after the mutation stage is sized to avoid any corruption on the suitability index variables. The obtained results through wide variety range of test functions with different dimensions and complexities proved that the BBO performance can be enhanced effectively without using any complicated form of the immigration and emigration rates. This essential modification has to be considered as an initial step for any other modification.
Xiaobing Yu; Jie Cao; Haiyan Shan; Li Zhu; Jun Guo
2014-01-01
Particle swarm optimization (PSO) and differential evolution (DE) are both efficient and powerful population-based stochastic search techniques for solving optimization problems, which have been widely applied in many scientific and engineering fields. Unfortunately, both of them can easily fly into local optima and lack the ability of jumping out of local optima. A novel adaptive hybrid algorithm based on PSO and DE (HPSO-DE) is formulated by developing a balanced parameter between PSO and D...
Bionic Intelligent Optimization Algorithm Based on MMAS and Fish-Swarm Algorithm
Jingjing Yang; Benzhen Guo; Jixiang Gou; Xiao Zhang
2013-01-01
With large number of ants, the ant colony algorithm would always take a long time or is rather difficult to find the optimal path from complex chapter path, further more, there exists a contradiction between stagnation, accelerated convergence and precocity. In this paper, we propose a new bionic optimization algorithm. The main idea of the algorithm is to introduce the horizons concept in the MMAS fish swarm algorithm, so it would take shorter time to find the optimal path with numerous ants...
Optimization of Submarine Hydrodynamic Coefficients Based on Immune Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
HU Kun; XU Yi-fan
2010-01-01
Aiming at the demand for optimization of hydrodynamic coefficients in submarine's motion equations, an adaptive weight immune genetic algorithm was proposed to optimize hydrodynamic coefficients in motion equations. Some hydrody-namic coefficients of high sensitivity to control and maneuver were chosen as the optimization objects in the algorithm. By using adaptive weight method to determine the weight and target function, the multi-objective optimization could be transla-ted into single-objective optimization. For a certain kind of submarine, three typical maneuvers were chosen to be the objects of study: overshoot maneuver in horizontal plane, overshoot maneuver in vertical plane and turning circle maneuver in horizontal plane. From the results of computer simulations using primal hydrodynamic coefficient and optimized hydrody-namic coefficient, the efficiency of proposed method is proved.
Study on Ice Regime Forecast Based on SVR Optimized by Particle Swarm Optimization Algorithm
Institute of Scientific and Technical Information of China (English)
WANG; Fu-qiang; RONG; Fei
2012-01-01
[Objective] The research aimed to study forecast models for frozen and melted dates of the river water in Ningxia-Inner Mongolia section of the Yellow River based on SVR optimized by particle swarm optimization algorithm. [Method] Correlation analysis and cause analysis were used to select suitable forecast factor combination of the ice regime. Particle swarm optimization algorithm was used to determine the optimal parameter to construct forecast model. The model was used to forecast frozen and melted dates of the river water in Ningxia-Inner Mongolia section of the Yellow River. [Result] The model had high prediction accuracy and short running time. Average forecast error was 3.51 d, and average running time was 10.464 s. Its forecast effect was better than that of the support vector regression optimized by genetic algorithm (GA) and back propagation type neural network (BPNN). It could accurately forecast frozen and melted dates of the river water. [Conclusion] SVR based on particle swarm optimization algorithm could be used for ice regime forecast.
An Improved Particle Swarm Optimization Algorithm Based on Centroid and Exponential Inertia Weight
2014-01-01
Particle swarm optimization algorithm (PSO) is a global stochastic tool, which has ability to search the global optima. However, PSO algorithm is easily trapped into local optima with low accuracy in convergence. In this paper, in order to overcome the shortcoming of PSO algorithm, an improved particle swarm optimization algorithm (IPSO), based on two forms of exponential inertia weight and two types of centroids, is proposed. By means of comparing the optimization ability of IPSO algorithm w...
Design and optimization of DBSCAN Algorithm based on CUDA
Wang, Bingchen; Zhang, Chenglong; Song, Lei; Zhao, Lianhe; Dou, Yu; Yu, Zihao
2015-01-01
DBSCAN is a very classic algorithm for data clus- tering, which is widely used in many fields. However, with the data scale growing much more bigger than before, the traditional serial algorithm can not meet the performance requirement. Recently, parallel computing based on CUDA has developed very fast and has great advantage on big data. This paper summarizes the algorithms proposed before and improves the performance of the old DBSCAN algorithm by using CUDA and parallel computing. The algo...
Optimization of heat transfer utilizing graph based evolutionary algorithms
International Nuclear Information System (INIS)
This paper examines the use of graph based evolutionary algorithms (GBEAs) for optimization of heat transfer in a complex system. The specific case examined in this paper is the optimization of heat transfer in a biomass cookstove utilizing three-dimensional computational fluid dynamics to generate the fitness function. In this stove hot combustion gases are used to heat a cooking surface. The goal is to provide an even spatial temperature distribution on the cooking surface by redirecting the flow of combustion gases with baffles. The variables in the optimization are the position and size of the baffles, which are described by integer values. GBEAs are a novel type of EA in which a topology or geography is imposed on an evolving population of solutions. The choice of graph controls the rate at which solutions can spread within the population, impacting the diversity of solutions and convergence rate of the EAs. In this study, the choice of graph in the GBEAs changes the number of mating events required for convergence by a factor of approximately 2.25 and the diversity of the population by a factor of 2. These results confirm that by tuning the graph and parameters in GBEAs, computational time can be significantly reduced
Particle swarm optimization algorithm based low cost magnetometer calibration
Ali, A. S.; Siddharth, S., Syed, Z., El-Sheimy, N.
2011-12-01
Inertial Navigation Systems (INS) consist of accelerometers, gyroscopes and a microprocessor provide inertial digital data from which position and orientation is obtained by integrating the specific forces and rotation rates. In addition to the accelerometers and gyroscopes, magnetometers can be used to derive the absolute user heading based on Earth's magnetic field. Unfortunately, the measurements of the magnetic field obtained with low cost sensors are corrupted by several errors including manufacturing defects and external electro-magnetic fields. Consequently, proper calibration of the magnetometer is required to achieve high accuracy heading measurements. In this paper, a Particle Swarm Optimization (PSO) based calibration algorithm is presented to estimate the values of the bias and scale factor of low cost magnetometer. The main advantage of this technique is the use of the artificial intelligence which does not need any error modeling or awareness of the nonlinearity. The estimated bias and scale factor errors from the proposed algorithm improve the heading accuracy and the results are also statistically significant. Also, it can help in the development of the Pedestrian Navigation Devices (PNDs) when combined with the INS and GPS/Wi-Fi especially in the indoor environments
An optimization-based iterative algorithm for recovering fluorophore location
Yi, Huangjian; Peng, Jinye; Jin, Chen; He, Xiaowei
2015-10-01
Fluorescence molecular tomography (FMT) is a non-invasive technique that allows three-dimensional visualization of fluorophore in vivo in small animals. In practical applications of FMT, however, there are challenges in the image reconstruction since it is a highly ill-posed problem due to the diffusive behaviour of light transportation in tissue and the limited measurement data. In this paper, we presented an iterative algorithm based on an optimization problem for three dimensional reconstruction of fluorescent target. This method alternates weighted algebraic reconstruction technique (WART) with steepest descent method (SDM) for image reconstruction. Numerical simulations experiments and physical phantom experiment are performed to validate our method. Furthermore, compared to conjugate gradient method, the proposed method provides a better three-dimensional (3D) localization of fluorescent target.
Xu Bao
2013-01-01
One of the most important targets of routing algorithm for Wireless Sensor Network (WSN) is to prolong the network lifetime. Aimed at the features of WSN, a new routing optimization approach based on cloud adaptive particle swarm optimization algorithm is put forward in this paper. All paths appear at the same time in one round are fused in one particle, and the coding rule of particle is set down. The particle itself is defined as its position, the number of replaceable relay nodes in paths ...
Distribution System Optimization Planning Based on Plant Growth Simulation Algorithm
Institute of Scientific and Technical Information of China (English)
WANG Chun; CHENG Hao-zhong; HU Ze-chun; WANG Yi
2008-01-01
An approach for the integrated optimization of the construction/expansion capacity of high-voltage/medium-voltage (HV/MV) substations and the configuration of MV radial distribution network was presented using plant growth simulation algorithm (PGSA). In the optimization process, fixed costs correspondent to the investment in lines and substations and the variable costs associated to the operation of the system were considered under the constraints of branch capacity, substation capacity and bus voltage. The optimization variables considerably reduce the dimension of variables and speed up the process of optimizing. The effectiveness of the proposed approach was tested by a distribution system planning.
Improved Ant Colony Optimization Algorithm based Expert System on Nephrology
Directory of Open Access Journals (Sweden)
Sri.N.V.Ramana Murty
2010-07-01
Full Text Available Expert system Nephrology is a computer program that exhibits, within a specific domain, a degree of expertise in problem solving that is comparable to that of a human expert. The knowledge base consistsof information about a particular problem area. This information is collected from domain experts (doctors. This system mainly contains two modules one is Information System and the other is Expert Advisory system. The Information System contains the static information about different diseases and drugs in the field of Nephrology. This information system helps the patients /users to know about the problems related to kidneys. The Nephrology Advisory system helps the Patients /users to get the required and suitable advice depending on their queries. This medical expert system is developedusing Java Server Pages (JSP as front-end and MYSQL database as Backend in such a way that all the activities are carried out in a user-friendly manner. Improved Ant Colony Optimization Algorithm (ACO along with RETE algorithm is also used for better results.
A constrained optimization algorithm based on the simplex search method
Mehta, Vivek Kumar; Dasgupta, Bhaskar
2012-05-01
In this article, a robust method is presented for handling constraints with the Nelder and Mead simplex search method, which is a direct search algorithm for multidimensional unconstrained optimization. The proposed method is free from the limitations of previous attempts that demand the initial simplex to be feasible or a projection of infeasible points to the nonlinear constraint boundaries. The method is tested on several benchmark problems and the results are compared with various evolutionary algorithms available in the literature. The proposed method is found to be competitive with respect to the existing algorithms in terms of effectiveness and efficiency.
Graph-based optimization algorithm and software on kidney exchanges.
Chen, Yanhua; Li, Yijiang; Kalbfleisch, John D; Zhou, Yan; Leichtman, Alan; Song, Peter X-K
2012-07-01
Kidney transplantation is typically the most effective treatment for patients with end-stage renal disease. However, the supply of kidneys is far short of the fast-growing demand. Kidney paired donation (KPD) programs provide an innovative approach for increasing the number of available kidneys. In a KPD program, willing but incompatible donor-candidate pairs may exchange donor organs to achieve mutual benefit. Recently, research on exchanges initiated by altruistic donors (ADs) has attracted great attention because the resultant organ exchange mechanisms offer advantages that increase the effectiveness of KPD programs. Currently, most KPD programs focus on rule-based strategies of prioritizing kidney donation. In this paper, we consider and compare two graph-based organ allocation algorithms to optimize an outcome-based strategy defined by the overall expected utility of kidney exchanges in a KPD program with both incompatible pairs and ADs. We develop an interactive software-based decision support system to model, monitor, and visualize a conceptual KPD program, which aims to assist clinicians in the evaluation of different kidney allocation strategies. Using this system, we demonstrate empirically that an outcome-based strategy for kidney exchanges leads to improvement in both the quantity and quality of kidney transplantation through comprehensive simulation experiments. PMID:22542649
Logit Model based Performance Analysis of an Optimization Algorithm
Hernández, J. A.; Ospina, J. D.; Villada, D.
2011-09-01
In this paper, the performance of the Multi Dynamics Algorithm for Global Optimization (MAGO) is studied through simulation using five standard test functions. To guarantee that the algorithm converges to a global optimum, a set of experiments searching for the best combination between the only two MAGO parameters -number of iterations and number of potential solutions, are considered. These parameters are sequentially varied, while increasing the dimension of several test functions, and performance curves were obtained. The MAGO was originally designed to perform well with small populations; therefore, the self-adaptation task with small populations is more challenging while the problem dimension is higher. The results showed that the convergence probability to an optimal solution increases according to growing patterns of the number of iterations and the number of potential solutions. However, the success rates slow down when the dimension of the problem escalates. Logit Model is used to determine the mutual effects between the parameters of the algorithm.
Singh, R.; Verma, H. K.
2013-12-01
This paper presents a teaching-learning-based optimization (TLBO) algorithm to solve parameter identification problems in the designing of digital infinite impulse response (IIR) filter. TLBO based filter modelling is applied to calculate the parameters of unknown plant in simulations. Unlike other heuristic search algorithms, TLBO algorithm is an algorithm-specific parameter-less algorithm. In this paper big bang-big crunch (BB-BC) optimization and PSO algorithms are also applied to filter design for comparison. Unknown filter parameters are considered as a vector to be optimized by these algorithms. MATLAB programming is used for implementation of proposed algorithms. Experimental results show that the TLBO is more accurate to estimate the filter parameters than the BB-BC optimization algorithm and has faster convergence rate when compared to PSO algorithm. TLBO is used where accuracy is more essential than the convergence speed.
Liqiang Liu; Yuntao Dai; Jinyu Gao
2014-01-01
Ant colony optimization algorithm for continuous domains is a major research direction for ant colony optimization algorithm. In this paper, we propose a distribution model of ant colony foraging, through analysis of the relationship between the position distribution and food source in the process of ant colony foraging. We design a continuous domain optimization algorithm based on the model and give the form of solution for the algorithm, the distribution model of pheromone, the update rules...
Optimal high speed CMOS inverter design using craziness based Particle Swarm Optimization Algorithm
De, Bishnu P.; Kar, Rajib; Mandal, Durbadal; Ghoshal, Sakti P.
2015-07-01
The inverter is the most fundamental logic gate that performs a Boolean operation on a single input variable. In this paper, an optimal design of CMOS inverter using an improved version of particle swarm optimization technique called Craziness based Particle Swarm Optimization (CRPSO) is proposed. CRPSO is very simple in concept, easy to implement and computationally efficient algorithm with two main advantages: it has fast, nearglobal convergence, and it uses nearly robust control parameters. The performance of PSO depends on its control parameters and may be influenced by premature convergence and stagnation problems. To overcome these problems the PSO algorithm has been modiffed to CRPSO in this paper and is used for CMOS inverter design. In birds' flocking or ffsh schooling, a bird or a ffsh often changes direction suddenly. In the proposed technique, the sudden change of velocity is modelled by a direction reversal factor associated with the previous velocity and a "craziness" velocity factor associated with another direction reversal factor. The second condition is introduced depending on a predeffned craziness probability to maintain the diversity of particles. The performance of CRPSO is compared with real code.gnetic algorithm (RGA), and conventional PSO reported in the recent literature. CRPSO based design results are also compared with the PSPICE based results. The simulation results show that the CRPSO is superior to the other algorithms for the examples considered and can be efficiently used for the CMOS inverter design.
Chaotic Genetic Algorithm based on Lorenz Chaotic System for Optimization Problems
Reza Ebrahimzadeh; Mahdi Jampour
2013-01-01
Very recently evolutionary optimization algorithms use the Genetic Algorithm to improve the result of Optimization problems. Several processes of the Genetic Algorithm are based on 'Random', that is fundamental to evolutionary algorithms, but important defections in the Genetic Algorithm are local convergence and high tolerances in the results, they have happened for randomness reason. In this paper we have prepared pseudo random numbers by Lorenz chaotic system for operators of Genetic Algor...
Bionic Intelligent Optimization Algorithm Based on MMAS and Fish-Swarm Algorithm
Directory of Open Access Journals (Sweden)
Jingjing Yang
2013-09-01
Full Text Available With large number of ants, the ant colony algorithm would always take a long time or is rather difficult to find the optimal path from complex chapter path, further more, there exists a contradiction between stagnation, accelerated convergence and precocity. In this paper, we propose a new bionic optimization algorithm. The main idea of the algorithm is to introduce the horizons concept in the MMAS fish swarm algorithm, so it would take shorter time to find the optimal path with numerous ants, and the introduction of the concept of fish swarm algorithm congestion level would enable the ant colony find the path of global optimization with a strong crowding limit which avoids the emergence of local extreme and improves the accuracy and efficiency of the algorithm.
Optimization of Actuators in Smart Truss Based on Genetic Algorithms
Directory of Open Access Journals (Sweden)
Ruizhen Gao
2012-11-01
Full Text Available Actuators formed from piezoelectric ceramics were embedded in truss rods to make up active rods. The paper used mechanical knowledge, static stiffness method and the finite element method to analyze the active rod and the smart truss structure and then model them. In order to solve the difficult problem of number optimization, the paper put forward the actuator existence variable and optimized number and locations of actuators at the same time, made the structure have the best output effect, so it can reduce the displacement at the designated location of the truss structure and the structure vibration. It also can improve the truss structure accuracy. Then find the optimal solution by genetic algorithms（GA） and MATLAB programming. The results of the example show that the model this paper builds is correct and genetic algorithms are effective in solving the optimization question.
Tugrul Talaslioglu
2009-01-01
A new genetic algorithm (GA) methodology, Bipopulation-Based Genetic Algorithm with Enhanced Interval Search (BGAwEIS), is introduced and used to optimize the design of truss structures with various complexities. The results of BGAwEIS are compared with those obtained by the sequential genetic algorithm (SGA) utilizing a single population, a multipopulation-based genetic algorithm (MPGA) proposed for this study and other existing approaches presented in literature. This study has two goals: o...
Directory of Open Access Journals (Sweden)
Jing Chen
2015-06-01
Full Text Available This study takes the concept of food logistics distribution as the breakthrough point, by means of the aim of optimization of food logistics distribution routes and analysis of the optimization model of food logistics route, as well as the interpretation of the genetic algorithm, it discusses the optimization of food logistics distribution route based on genetic and cluster scheme algorithm.
Directory of Open Access Journals (Sweden)
Xu Bao
2013-11-01
Full Text Available One of the most important targets of routing algorithm for Wireless Sensor Network (WSN is to prolong the network lifetime. Aimed at the features of WSN, a new routing optimization approach based on cloud adaptive particle swarm optimization algorithm is put forward in this paper. All paths appear at the same time in one round are fused in one particle, and the coding rule of particle is set down. The particle itself is defined as its position, the number of replaceable relay nodes in paths is defined as the velocity of particle. Cloud algorithm is used to optimize the inertia weight of particle. Optimize rules are laid out, and both residual energy of nodes and variance of all paths’ length are considered in objective function. Simulations find out the best value of balance factor in objective function, also prove that this approach can control the energy consumption of network, enhance the viability of nodes, and prolong the lifetime of network.
Design of SVC Controller Based on Improved Biogeography-Based Optimization Algorithm
Directory of Open Access Journals (Sweden)
Feifei Dong
2014-01-01
Full Text Available Considering that common subsynchronous resonance controllers cannot adapt to the characteristics of the time-varying and nonlinear behavior of a power system, the cosine migration model, the improved migration operator, and the mutative scale of chaos and Cauchy mutation strategy are introduced into an improved biogeography-based optimization (IBBO algorithm in order to design an optimal subsynchronous damping controller based on the mechanism of suppressing SSR by static var compensator (SVC. The effectiveness of the improved controller is verified by eigenvalue analysis and electromagnetic simulations. The simulation results of Jinjie plant indicate that the subsynchronous damping controller optimized by the IBBO algorithm can remarkably improve the damping of torsional modes and thus effectively depress SSR, and ensure the safety and stability of units and power grid operation. Moreover, the IBBO algorithm has the merits of a faster searching speed and higher searching accuracy in seeking the optimal control parameters over traditional algorithms, such as BBO algorithm, PSO algorithm, and GA algorithm.
Rolling optimization algorithm based on collision window for single machine scheduling problem
Institute of Scientific and Technical Information of China (English)
Wang Changjun; Xi Yugeng
2005-01-01
Focusing on the single machine scheduling problem which minimizes the total completion time in the presence of dynamic job arrivals, a rolling optimization scheduling algorithm is proposed based on the analysis of the character and structure of scheduling. An optimal scheduling strategy in collision window is presented. Performance evaluation of this algorithm is given. Simulation indicates that the proposed algorithm is better than other common heuristic algorithms on both the total performance and stability.
Directory of Open Access Journals (Sweden)
Shi Chen-guang
2014-08-01
Full Text Available A novel optimal power allocation algorithm for radar network systems is proposed for Low Probability of Intercept (LPI technology in modern electronic warfare. The algorithm is based on the LPI optimization. First, the Schleher intercept factor for a radar network is derived, and then the Schleher intercept factor is minimized by optimizing the transmission power allocation among netted radars in the network to guarantee target-tracking performance. Furthermore, the Nonlinear Programming Genetic Algorithm (NPGA is used to solve the resulting nonconvex, nonlinear, and constrained optimization problem. Numerical simulation results show the effectiveness of the proposed algorithm.
Algorithm Optimization of Resources Scheduling Based on Cloud Computing
Directory of Open Access Journals (Sweden)
Zhongli Liu
2014-07-01
Full Text Available In some service areas, such as cloud computing of virtual technology, the specific virtual technology’s level configuration processing power is limited and it has defects. [1] Under normal circumstances, the traditional cloud computing resource sharing configuration can not be effectively applied, for it has a time difference and several machines can not be connected to the network by a broadband link and work simultaneously, making the virtual technology particularly difficult in the efficiency and effectiveness of the implementation. This paper presents a cloud computing of ant colony and genetic combination of resources virtual scheduling optimization, which is applied to multiple machines physical layer simultaneously, at the same time it can optimize the allocation of resources to maximization. Experimental results show that the model algorithm is more convergent and more practical than traditional methods.
Liu, Liqiang; Dai, Yuntao; Gao, Jinyu
2014-01-01
Ant colony optimization algorithm for continuous domains is a major research direction for ant colony optimization algorithm. In this paper, we propose a distribution model of ant colony foraging, through analysis of the relationship between the position distribution and food source in the process of ant colony foraging. We design a continuous domain optimization algorithm based on the model and give the form of solution for the algorithm, the distribution model of pheromone, the update rules of ant colony position, and the processing method of constraint condition. Algorithm performance against a set of test trials was unconstrained optimization test functions and a set of optimization test functions, and test results of other algorithms are compared and analyzed to verify the correctness and effectiveness of the proposed algorithm. PMID:24955402
Design of SVC Controller Based on Improved Biogeography-Based Optimization Algorithm
Feifei Dong; Dichen Liu; Jun Wu; Bingcheng Cen; Haolei Wang; Chunli Song; Lina Ke
2014-01-01
Considering that common subsynchronous resonance controllers cannot adapt to the characteristics of the time-varying and nonlinear behavior of a power system, the cosine migration model, the improved migration operator, and the mutative scale of chaos and Cauchy mutation strategy are introduced into an improved biogeography-based optimization (IBBO) algorithm in order to design an optimal subsynchronous damping controller based on the mechanism of suppressing SSR by static var compensator (SV...
Evolutionary optimization algorithms for sunlight-based positioning sensor networks
Jose Pardeiro; Javier V. Gómez; Alberto Brunete; Frode Eika Sandnes
2015-01-01
The sunlight intensity-based global positioning system (SGPS) is able to geolocate outdoor objects by means of the sunlight intensity detection. This paper presents the integration of SGPS into a sensor network in order to improve the overall accuracy using evolutionary algorithms. Another contribution of the paper is to theoretically solve both global and relative positioning of the sensors composing the network within the same framework without satellite-based GPS technology. Results show t...
Global path planning approach based on ant colony optimization algorithm
Institute of Scientific and Technical Information of China (English)
WEN Zhi-qiang; CAI Zi-xing
2006-01-01
Ant colony optimization (ACO) algorithm was modified to optimize the global path. In order to simulate the real ant colonies, according to the foraging behavior of ant colonies and the characteristic of food, conceptions of neighboring area and smell area were presented. The former can ensure the diversity of paths and the latter ensures that each ant can reach the goal. Then the whole path was divided into three parts and ACO was used to search the second part path. When the three parts pathes were adjusted,the final path was found. The valid path and invalid path were defined to ensure the path valid. Finally, the strategies of the pheromone search were applied to search the optimum path. However, when only the pheromone was used to search the optimum path, ACO converges easily. In order to avoid this premature convergence, combining pheromone search and random search, a hybrid ant colony algorithm(HACO) was used to find the optimum path. The comparison between ACO and HACO shows that HACO can be used to find the shortest path.
Li Hui; Zhang Jingxiao; Ren Lieyan; Shi Zhen
2013-01-01
In this paper, the basic theory and procedure for working out solutions of ant colony genetic algorithm were first introduced; the optimization, constraints and objectives of construction project scheduling were described; then a basic model for optimization of construction project scheduling was established; and an improved ant colony genetic algorithm for solving the basic model was put forward. Performance of ant colony genetic algorithm was analyzed and evaluated from the aspect of schedu...
Tran, Huu-Khoa; Chiou, Juing -Shian; Peng, Shou-Tao
2016-01-01
In this paper, the feasibility of a Genetic Algorithm Optimization (GAO) education software based Fuzzy Logic Controller (GAO-FLC) for simulating the flight motion control of Unmanned Aerial Vehicles (UAVs) is designed. The generated flight trajectories integrate the optimized Scaling Factors (SF) fuzzy controller gains by using GAO algorithm. The…
The optimal time-frequency atom search based on a modified ant colony algorithm
Institute of Scientific and Technical Information of China (English)
GUO Jun-feng; LI Yan-jun; YU Rui-xing; ZHANG Ke
2008-01-01
In this paper,a new optimal time-frequency atom search method based on a modified ant colony algorithm is proposed to improve the precision of the traditional methods.First,the discretization formula of finite length time-frequency atom is inferred at length.Second; a modified ant colony algorithm in continuous space is proposed.Finally,the optimal timefrequency atom search algorithm based on the modified ant colony algorithm is described in detail and the simulation experiment is carried on.The result indicates that the developed algorithm is valid and stable,and the precision of the method is higher than that of the traditional method.
Rate-optimal scheduling of recursive DSP algorithms based on the scheduling-range chart
Heemstra de Groot, Sonia M.; Herrmann, Otto E.
1990-01-01
A method for rate-optimal scheduling of recursive DSP algorithms is presented. The approach is based on the determination of the scheduling window of each operation and the construction of a scheduling-range chart. The information in the chart is used during scheduling to optimize some quality criteria (number of hardware resources, latency, register life time) at the same time that a rate-optimal solution is guaranteed. An algorithm based on this approach is introduced. It can schedule cycli...
Pixel-based ant colony algorithm for source mask optimization
Kuo, Hung-Fei; Wu, Wei-Chen; Li, Frederick
2015-03-01
Source mask optimization (SMO) was considered to be one of the key resolution enhancement techniques for node technology below 20 nm prior to the availability of extreme-ultraviolet tools. SMO has been shown to enlarge the process margins for the critical layer in SRAM and memory cells. In this study, a new illumination shape optimization approach was developed on the basis of the ant colony optimization (ACO) principle. The use of this heuristic pixel-based ACO method in the SMO process provides an advantage over the extant SMO method because of the gradient of the cost function associated with the rapid and stable searching capability of the proposed method. This study was conducted to provide lithographic engineers with references for the quick determination of the optimal illumination shape for complex mask patterns. The test pattern used in this study was a contact layer for SRAM design, with a critical dimension and a minimum pitch of 55 and 110 nm, respectively. The optimized freeform source shape obtained using the ACO method was numerically verified by performing an aerial image investigation, and the result showed that the optimized freeform source shape generated an aerial image profile different from the nominal image profile and with an overall error rate of 9.64%. Furthermore, the overall average critical shape difference was determined to be 1.41, which was lower than that for the other off-axis illumination exposure. The process window results showed an improvement in exposure latitude (EL) and depth of focus (DOF) for the ACO-based freeform source shape compared with those of the Quasar source shape. The maximum EL of the ACO-based freeform source shape reached 7.4% and the DOF was 56 nm at an EL of 5%.
Institute of Scientific and Technical Information of China (English)
Shao Wei; Qian Zuping; Yuan Feng
2007-01-01
A robust phase-only Direct Data Domain Least Squares (D3LS) algorithm based on generalized Rayleigh quotient optimization using hybrid Genetic Algorithm (GA) is presented in this letter. The optimization efficiency and computational speed are improved via the hybrid GA composed of standard GA and Nelder-Mead simplex algorithms. First, the objective function, with a form of generalized Rayleigh quotient, is derived via the standard D3LS algorithm. It is then taken as a fitness function and the unknown phases of all adaptive weights are taken as decision variables.Then, the nonlinear optimization is performed via the hybrid GA to obtain the optimized solution of phase-only adaptive weights. As a phase-only adaptive algorithm, the proposed algorithm is simpler than conventional algorithms when it comes to hardware implementation. Moreover, it processes only a single snapshot data as opposed to forming sample covariance matrix and operating matrix inversion. Simulation results show that the proposed algorithm has a good signal recovery and interferences nulling performance, which are superior to that of the phase-only D3LS algorithm based on standard GA.
Que, Dashun; Li, Gang; Yue, Peng
2007-12-01
An adaptive optimization watermarking algorithm based on Genetic Algorithm (GA) and discrete wavelet transform (DWT) is proposed in this paper. The core of this algorithm is the fitness function optimization model for digital watermarking based on GA. The embedding intensity for digital watermarking can be modified adaptively, and the algorithm can effectively ensure the imperceptibility of watermarking while the robustness is ensured. The optimization model research may provide a new idea for anti-coalition attacks of digital watermarking algorithm. The paper has fulfilled many experiments, including the embedding and extracting experiments of watermarking, the influence experiments by the weighting factor, the experiments of embedding same watermarking to the different cover image, the experiments of embedding different watermarking to the same cover image, the comparative analysis experiments between this optimization algorithm and human visual system (HVS) algorithm and etc. The simulation results and the further analysis show the effectiveness and advantage of the new algorithm, which also has versatility and expandability. And meanwhile it has better ability of anti-coalition attacks. Moreover, the robustness and security of watermarking algorithm are improved by scrambling transformation and chaotic encryption while preprocessing the watermarking.
Directory of Open Access Journals (Sweden)
R. Venkata Rao
2014-01-01
Full Text Available The present work proposes a multi-objective improved teaching-learning based optimization (MO-ITLBO algorithm for unconstrained and constrained multi-objective function optimization. The MO-ITLBO algorithm is the improved version of basic teaching-learning based optimization (TLBO algorithm adapted for multi-objective problems. The basic TLBO algorithm is improved to enhance its exploration and exploitation capacities by introducing the concept of number of teachers, adaptive teaching factor, tutorial training and self-motivated learning. The MO-ITLBO algorithm uses a grid-based approach to adaptively assess the non-dominated solutions (i.e. Pareto front maintained in an external archive. The performance of the MO-ITLBO algorithm is assessed by implementing it on unconstrained and constrained test problems proposed for the Congress on Evolutionary Computation 2009 (CEC 2009 competition. The performance assessment is done by using the inverted generational distance (IGD measure. The IGD measures obtained by using the MO-ITLBO algorithm are compared with the IGD measures of the other state-of-the-art algorithms available in the literature. Finally, Lexicographic ordering is used to assess the overall performance of competitive algorithms. Results have shown that the proposed MO-ITLBO algorithm has obtained the 1st rank in the optimization of unconstrained test functions and the 3rd rank in the optimization of constrained test functions.
Karla Vittori; Alexandre C B Delbem; Pereira, Sérgio L
2008-01-01
We propose a new distance algorithm for phylogenetic estimation based on Ant Colony Optimization (ACO), named Ant-Based Phylogenetic Reconstruction (ABPR). ABPR joins two taxa iteratively based on evolutionary distance among sequences, while also accounting for the quality of the phylogenetic tree built according to the total length of the tree. Similar to optimization algorithms for phylogenetic estimation, the algorithm allows exploration of a larger set of nearly optimal solutions. We appl...
The Role of Vertex Consistency in Sampling-based Algorithms for Optimal Motion Planning
Arslan, Oktay
2012-01-01
Motion planning problems have been studied by both the robotics and the controls research communities for a long time, and many algorithms have been developed for their solution. Among them, incremental sampling-based motion planning algorithms, such as the Rapidly-exploring Random Trees (RRTs), and the Probabilistic Road Maps (PRMs) have become very popular recently, owing to their implementation simplicity and their advantages in handling high-dimensional problems. Although these algorithms work very well in practice, the quality of the computed solution is often not good, i.e., the solution can be far from the optimal one. A recent variation of RRT, namely the RRT* algorithm, bypasses this drawback of the traditional RRT algorithm, by ensuring asymptotic optimality as the number of samples tends to infinity. Nonetheless, the convergence rate to the optimal solution may still be slow. This paper presents a new incremental sampling-based motion planning algorithm based on Rapidly-exploring Random Graphs (RRG...
Muhammad Farhan Ausaf; Liang Gao; Xinyu Li; Ghiath Al Aqel
2015-01-01
Process planning and scheduling are two important components of a manufacturing setup. It is important to integrate them to achieve better global optimality and improved system performance. To find optimal solutions for integrated process planning and scheduling (IPPS) problem, numerous algorithm-based approaches exist. Most of these approaches try to use existing meta-heuristic algorithms for solving the IPPS problem. Although these approaches have been shown to be effective in optimizing th...
The optimal extraction of feature algorithm based on KAZE
Yao, Zheyi; Gu, Guohua; Qian, Weixian; Wang, Pengcheng
2015-10-01
As a novel method of 2D features extraction algorithm over the nonlinear scale space, KAZE provide a special method. However, the computation of nonlinear scale space and the construction of KAZE feature vectors are more expensive than the SIFT and SURF significantly. In this paper, the given image is used to build the nonlinear space up to a maximum evolution time through the efficient Additive Operator Splitting (AOS) techniques and the variable conductance diffusion. Changing the parameter can improve the construction of nonlinear scale space and simplify the image conductivities for each dimension space, with the predigest computation. Then, the detection for points of interest can exhibit a maxima of the scale-normalized determinant with the Hessian response in the nonlinear scale space. At the same time, the detection of feature vectors is optimized by the Wavelet Transform method, which can avoid the second Gaussian smoothing in the KAZE Features and cut down the complexity of the algorithm distinctly in the building and describing vectors steps. In this way, the dominant orientation is obtained, similar to SURF, by summing the responses within a sliding circle segment covering an angle of π/3 in the circular area of radius 6σ with a sampling step of size σ one by one. Finally, the extraction in the multidimensional patch at the given scale, centered over the points of interest and rotated to align its dominant orientation to a canonical direction, is able to simplify the description of feature by reducing the description dimensions, just as the PCA-SIFT method. Even though the features are somewhat more expensive to compute than SIFT due to the construction of nonlinear scale space, but compared to SURF, the result revels a step forward in performance in detection, description and application against the previous ways by the following contrast experiments.
Simulated Annealing-Based Krill Herd Algorithm for Global Optimization
Directory of Open Access Journals (Sweden)
Gai-Ge Wang
2013-01-01
Full Text Available Recently, Gandomi and Alavi proposed a novel swarm intelligent method, called krill herd (KH, for global optimization. To enhance the performance of the KH method, in this paper, a new improved meta-heuristic simulated annealing-based krill herd (SKH method is proposed for optimization tasks. A new krill selecting (KS operator is used to refine krill behavior when updating krill’s position so as to enhance its reliability and robustness dealing with optimization problems. The introduced KS operator involves greedy strategy and accepting few not-so-good solutions with a low probability originally used in simulated annealing (SA. In addition, a kind of elitism scheme is used to save the best individuals in the population in the process of the krill updating. The merits of these improvements are verified by fourteen standard benchmarking functions and experimental results show that, in most cases, the performance of this improved meta-heuristic SKH method is superior to, or at least highly competitive with, the standard KH and other optimization methods.
A hybrid multi-agent based particle swarm optimization algorithm for economic power dispatch
Energy Technology Data Exchange (ETDEWEB)
Kumar, Rajesh; Sharma, Devendra; Sadu, Abhinav [Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur 302 017 (India)
2011-01-15
This paper presents a new multi-agent based hybrid particle swarm optimization technique (HMAPSO) applied to the economic power dispatch. The earlier PSO suffers from tuning of variables, randomness and uniqueness of solution. The algorithm integrates the deterministic search, the Multi-agent system (MAS), the particle swarm optimization (PSO) algorithm and the bee decision-making process. Thus making use of deterministic search, multi-agent and bee PSO, the HMAPSO realizes the purpose of optimization. The economic power dispatch problem is a non-linear constrained optimization problem. Classical optimization techniques like direct search and gradient methods fails to give the global optimum solution. Other Evolutionary algorithms provide only a good enough solution. To show the capability, the proposed algorithm is applied to two cases 13 and 40 generators, respectively. The results show that this algorithm is more accurate and robust in finding the global optimum than its counterparts. (author)
Optimized Laplacian image sharpening algorithm based on graphic processing unit
Ma, Tinghuai; Li, Lu; Ji, Sai; Wang, Xin; Tian, Yuan; Al-Dhelaan, Abdullah; Al-Rodhaan, Mznah
2014-12-01
In classical Laplacian image sharpening, all pixels are processed one by one, which leads to large amount of computation. Traditional Laplacian sharpening processed on CPU is considerably time-consuming especially for those large pictures. In this paper, we propose a parallel implementation of Laplacian sharpening based on Compute Unified Device Architecture (CUDA), which is a computing platform of Graphic Processing Units (GPU), and analyze the impact of picture size on performance and the relationship between the processing time of between data transfer time and parallel computing time. Further, according to different features of different memory, an improved scheme of our method is developed, which exploits shared memory in GPU instead of global memory and further increases the efficiency. Experimental results prove that two novel algorithms outperform traditional consequentially method based on OpenCV in the aspect of computing speed.
Directory of Open Access Journals (Sweden)
Mingjian Sun
2015-01-01
Full Text Available Photoacoustic imaging is an innovative imaging technique to image biomedical tissues. The time reversal reconstruction algorithm in which a numerical model of the acoustic forward problem is run backwards in time is widely used. In the paper, a time reversal reconstruction algorithm based on particle swarm optimization (PSO optimized support vector machine (SVM interpolation method is proposed for photoacoustics imaging. Numerical results show that the reconstructed images of the proposed algorithm are more accurate than those of the nearest neighbor interpolation, linear interpolation, and cubic convolution interpolation based time reversal algorithm, which can provide higher imaging quality by using significantly fewer measurement positions or scanning times.
Identification of Dynamic Parameters Based on Pseudo-Parallel Ant Colony Optimization Algorithm
Institute of Scientific and Technical Information of China (English)
ZHAO Feng-yao; MA Zhen-yue; ZHANG Yun-liang
2007-01-01
For the parameter identification of dynamic problems, a pseudo-parallel ant colony optimization (PPACO) algorithm based on graph-based ant system (AS) was introduced. On the platform of ANSYS dynamic analysis, the PPACO algorithm was applied to the identification of dynamic parameters successfully. Using simulated data of forces and displacements, elastic modulus E and damping ratio ξ was identified for a designed 3D finite element model, and the detailed identification step was given. Mathematical example and simulation example show that the proposed method has higher precision, faster convergence speed and stronger antinoise ability compared with the standard genetic algorithm and the ant colony optimization (ACO) algorithms.
Optimal design of hydraulic manifold blocks based on niching genetic simulated annealing algorithm
Institute of Scientific and Technical Information of China (English)
Jia Chunqiang; Yu Ling; Tian Shujun; Gao Yanming
2007-01-01
To solve the combinatorial optimization problem of outer layout and inner connection integrated schemes in the design of hydraulic manifold blocks(HMB),a hybrid genetic simulated annealing algorithm based on niche technology is presented.This hybrid algorithm,which combines genetic algorithm,simulated annealing algorithm and niche technology,has a strong capability in global and local search,and all extrema can be found in a short time without strict requests for preferences.For the complex restricted solid spatial layout problems in HMB,the optimizing mathematical model is presented.The key technologies in the integrated layout and connection design of HMB,including the realization of coding,annealing operation and genetic operation,are discussed.The framework of HMB optimal design system based on hybrid optimization strategy is proposed.An example is given to testify the effectiveness and feasibility of the algorithm.
Optimization of unit commitment based on genetic algorithms
Institute of Scientific and Technical Information of China (English)
蔡兴国; 初壮
2002-01-01
How to solve unit commitment and load dispatch of power system by genetic algorithms is discussed in this paper. A combination encoding scheme of binary encoding and floating number encoding and corresponding genetic operators are developed. Meanwhile a contract mapping genetic algorithm is used to enhance traditional GA' s convergence. The result of a practical example shows that this algorithm is effective.
An optimized outlier detection algorithm for jury-based grading of engineering design projects
DEFF Research Database (Denmark)
Thompson, Mary Kathryn; Espensen, Christina; Clemmensen, Line Katrine Harder
2016-01-01
This work characterizes and optimizes an outlier detection algorithm to identify potentially invalid scores produced by jury members while grading engineering design projects. The paper describes the original algorithm and the associated adjudication process in detail. The impact of the various...... conditions in the algorithm on the false positive and false negative rates is explored. Aresponse surface design is performed to optimize the algorithm using a data set from Fall 2010. Finally, the results are tested against a data set from Fall 2011. It is shown that all elements of the original algorithm...... (the base rule and the three additional conditions) play a role in the algorithm's performance and should be included in the algorithm. Because there is significant interaction between the base rule and the additional conditions, many acceptable combinations that balance the FPR and FNR can be found...
Function Optimization and Parameter Performance Analysis Based on Gravitation Search Algorithm
Directory of Open Access Journals (Sweden)
Jie-Sheng Wang
2015-12-01
Full Text Available The gravitational search algorithm (GSA is a kind of swarm intelligence optimization algorithm based on the law of gravitation. The parameter initialization of all swarm intelligence optimization algorithms has an important influence on the global optimization ability. Seen from the basic principle of GSA, the convergence rate of GSA is determined by the gravitational constant and the acceleration of the particles. The optimization performances on six typical test functions are verified by the simulation experiments. The simulation results show that the convergence speed of the GSA algorithm is relatively sensitive to the setting of the algorithm parameters, and the GSA parameter can be used flexibly to improve the algorithm’s convergence velocity and improve the accuracy of the solutions.
Directory of Open Access Journals (Sweden)
Dac-Nhuong Le
2013-01-01
Full Text Available The wireless access networks design problem is formulated as a constrained optimization problem, where the goal is to find a network topology such that an objective function is optimized, subject to a set of constraints. The objective function may be the total cost, or some performance measure like utilization, call blocking or throughput. The constraints may be bounds on link capacities, cost elements, or some network performance measure. However, the optimization problem is too complex. In this paper, we propose a novel Particle Swarm Optimization (PSO algorithm to finding the total cost of connecting the BSs to the MSCs, and connecting the MSCs to the LE called by the optimal centralized wireless access network. Numerical results show that performance of our proposed algorithm is much better than previous studies.
Directory of Open Access Journals (Sweden)
R. Venkata Rao
2015-12-01
Full Text Available This paper presents the performance of teaching–learning-based optimization (TLBO algorithm to obtain the optimum set of design and operating parameters for a smooth flat plate solar air heater (SFPSAH. The TLBO algorithm is a recently proposed population-based algorithm, which simulates the teaching–learning process of the classroom. Maximization of thermal efficiency is considered as an objective function for the thermal performance of SFPSAH. The number of glass plates, irradiance, and the Reynolds number are considered as the design parameters and wind velocity, tilt angle, ambient temperature, and emissivity of the plate are considered as the operating parameters to obtain the thermal performance of the SFPSAH using the TLBO algorithm. The computational results have shown that the TLBO algorithm is better or competitive to other optimization algorithms recently reported in the literature for the considered problem.
Institute of Scientific and Technical Information of China (English)
Feng Yi; Li Li; Tian Shujun
2003-01-01
Optimization design of hydraulic manifold blocks (HMB) is studied as a complex solid spatial layout problem. Based on comprehensive research into structure features and design rules of HMB, an optimal mathematical model for this problem is presented. Using human-computer cooperative genetic algorithm (GA) and its hybrid optimization strategies, integrated layout and connection design schemes of HMB can be automatically optimized. An example is given to testify it.
Zhang, Zili; Gao, Chao; Liu, Yuxin; Qian, Tao
2014-09-01
Ant colony optimization (ACO) algorithms often fall into the local optimal solution and have lower search efficiency for solving the travelling salesman problem (TSP). According to these shortcomings, this paper proposes a universal optimization strategy for updating the pheromone matrix in the ACO algorithms. The new optimization strategy takes advantages of the unique feature of critical paths reserved in the process of evolving adaptive networks of the Physarum-inspired mathematical model (PMM). The optimized algorithms, denoted as PMACO algorithms, can enhance the amount of pheromone in the critical paths and promote the exploitation of the optimal solution. Experimental results in synthetic and real networks show that the PMACO algorithms are more efficient and robust than the traditional ACO algorithms, which are adaptable to solve the TSP with single or multiple objectives. Meanwhile, we further analyse the influence of parameters on the performance of the PMACO algorithms. Based on these analyses, the best values of these parameters are worked out for the TSP. PMID:24613939
International Nuclear Information System (INIS)
Ant colony optimization (ACO) algorithms often fall into the local optimal solution and have lower search efficiency for solving the travelling salesman problem (TSP). According to these shortcomings, this paper proposes a universal optimization strategy for updating the pheromone matrix in the ACO algorithms. The new optimization strategy takes advantages of the unique feature of critical paths reserved in the process of evolving adaptive networks of the Physarum-inspired mathematical model (PMM). The optimized algorithms, denoted as PMACO algorithms, can enhance the amount of pheromone in the critical paths and promote the exploitation of the optimal solution. Experimental results in synthetic and real networks show that the PMACO algorithms are more efficient and robust than the traditional ACO algorithms, which are adaptable to solve the TSP with single or multiple objectives. Meanwhile, we further analyse the influence of parameters on the performance of the PMACO algorithms. Based on these analyses, the best values of these parameters are worked out for the TSP. (paper)
An Information Entropy-Based Animal Migration Optimization Algorithm for Data Clustering
Directory of Open Access Journals (Sweden)
Lei Hou
2016-05-01
Full Text Available Data clustering is useful in a wide range of application areas. The Animal Migration Optimization (AMO algorithm is one of the recently introduced swarm-based algorithms, which has demonstrated good performances for solving numeric optimization problems. In this paper, we presented a modified AMO algorithm with an entropy-based heuristic strategy for data clustering. The main contribution is that we calculate the information entropy of each attribute for a given data set and propose an adaptive strategy that can automatically balance convergence speed and global search efforts according to its entropy in both migration and updating steps. A series of well-known benchmark clustering problems are employed to evaluate the performance of our approach. We compare experimental results with k-means, Artificial Bee Colony (ABC, AMO, and the state-of-the-art algorithms for clustering and show that the proposed AMO algorithm generally performs better than the compared algorithms on the considered clustering problems.
Fuzzy C Means (FCM Clustering Based Hybrid Swarm Intelligence Algorithm for Test Case Optimization
Directory of Open Access Journals (Sweden)
Abraham Kiran Joseph
2014-07-01
Full Text Available The main objective of an operative testing strategy is the delivery of a reliable and quality oriented software product to the end user. Testing an application entirely from end to end is a time consuming and laborious process. Exhaustive testing utilizes a good chunk of the resources in a project for meticulous scrutiny to identify even a minor bug. A need to optimize the existing suite is highly recommended, with minimum resources and a shorter time span. To achieve this optimization in testing, a technique based on combining Artificial Bee Colony algorithm (ABC integrated with Fuzzy C-Means (FCM and Particle Swarm Optimization (PSO is described here. The initiation is done with the ABC algorithm that consists of three phases-the employed bee, the onlooker bee and the scout bee phase. The artificial bees that are initialized in the ABC algorithm identify the nodes with the highest coverage. This results in the ABC algorithm generating an optimal number of test-cases, which are sufficient to cover the entire paths within the application. The node with the highest usage by a given test case is determined by the PSO algorithm. Based on the above ‘hybrid’ optimization approach of ABC and PSO algorithms, a set of test cases that are optimal are obtained by repeated pruning of the original set of test cases. The performance of the proposed method is evaluated and is compared with other optimization techniques to emphasize the fact of improved quality and reduced complexity.
A constraint programming-based genetic algorithm (CPGA) for capacity output optimization
Goh, Kate Ean Nee; Chin, Jeng Feng; Loh, Wei Ping; Tan, Melissa Chea-Ling
2014-01-01
Purpose: The manuscript presents an investigation into a constraint programming-based genetic algorithm for capacity output optimization in a back-end semiconductor manufacturing company. Design/methodology/approach: In the first stage, constraint programming defining the relationships between variables was formulated into the objective function. A genetic algorithm model was created in the second stage to optimize capacity output. Three demand scenarios were applied to test...
An Adaptive Particle Swarm Optimization Algorithm Based on Directed Weighted Complex Network
Ming Li; Wenqiang Du; Fuzhong Nian
2014-01-01
The disadvantages of particle swarm optimization (PSO) algorithm are that it is easy to fall into local optimum in high-dimensional space and has a low convergence rate in the iterative process. To deal with these problems, an adaptive particle swarm optimization algorithm based on directed weighted complex network (DWCNPSO) is proposed. Particles can be scattered uniformly over the search space by using the topology of small-world network to initialize the particles position. At the same tim...
Institute of Scientific and Technical Information of China (English)
ZHAO Peng; MU Xin; YAO Jin-hua; WANG Yong; YANG Xiu-tai
2007-01-01
We established an integrated and optimized model of vehicle scheduling problem and vehicle filling problem for solving an extremely complex delivery mode-multi-type vehicles, non-full loads, pickup and delivery in logistics and delivery system. The integrated and optimized model is based on our previous research result-effective space method. An integrated algorithm suitable for the integrated and optimized model was proposed and corresponding computer programs were designed to solve practical problems. The results indicates the programs can work out optimized delivery routes and concrete loading projects. The model and algorithm have many virtues and are valuable in practice.
A Novel Hybrid Crossover based Artificial Bee Colony Algorithm for Optimization Problem
Kumar, Sandeep; Sharma, Vivek Kumar; Kumari, Rajani
2014-01-01
Artificial bee colony (ABC) algorithm has proved its importance in solving a number of problems including engineering optimization problems. ABC algorithm is one of the most popular and youngest member of the family of population based nature inspired meta-heuristic swarm intelligence method. ABC has been proved its superiority over some other Nature Inspired Algorithms (NIA) when applied for both benchmark functions and real world problems. The performance of search process of ABC depends on...
Dynamic Deployment of Wireless Sensor Networks by Biogeography Based Optimization Algorithm
Directory of Open Access Journals (Sweden)
Luo Liu
2012-06-01
Full Text Available As the usage and development of wireless sensor networks increases, problems related to these networks are becoming apparent. Dynamic deployment is one of the main topics that directly affects the performance of the wireless sensor networks. In this paper, biogeography-based optimization is applied to the dynamic deployment of static and mobile sensor networks to achieve better performance by trying to increase the coverage area of the network. A binary detection model is considered to obtain realistic results while computing the effectively covered area. Performance of the algorithm is compared with that of the artificial bee colony algorithm, Homo-H-VFCPSO and stud genetic algorithm that are also population-based optimization algorithms. Results show biogeography-based optimization can be preferable in the dynamic deployment of wireless sensor networks.
Wang, Xuewu; Shi, Yingpan; Ding, Dongyan; Gu, Xingsheng
2016-02-01
Spot-welding robots have a wide range of applications in manufacturing industries. There are usually many weld joints in a welding task, and a reasonable welding path to traverse these weld joints has a significant impact on welding efficiency. Traditional manual path planning techniques can handle a few weld joints effectively, but when the number of weld joints is large, it is difficult to obtain the optimal path. The traditional manual path planning method is also time consuming and inefficient, and cannot guarantee optimality. Double global optimum genetic algorithm-particle swarm optimization (GA-PSO) based on the GA and PSO algorithms is proposed to solve the welding robot path planning problem, where the shortest collision-free paths are used as the criteria to optimize the welding path. Besides algorithm effectiveness analysis and verification, the simulation results indicate that the algorithm has strong searching ability and practicality, and is suitable for welding robot path planning.
Institute of Scientific and Technical Information of China (English)
Hui Li; Xiong Wan; Taoli Liu; Zhongshou Liu; Yanhua Zhu
2007-01-01
Although emission spectral tomography (EST) combines emission spectral measurement with optical computed tomography (OCT), it is difficult to gain transient emission data from a large number of views,therefore, high precision OCT algorithms with few views ought to be studied for EST application. To improve the reconstruction precision in the case of few views, a new computed tomography reconstruction algorithm based on multipurpose optimal criterion and simulated annealing theory (multi-criterion simulated annealing reconstruction technique, MCSART) is proposed. This algorithm can suffice criterion of least squares, criterion of most uniformity, and criterion of most smoothness synchronously. We can get global optimal solution by MCSART algorithm with simulated annealing theory. The simulating experiment result shows that this algorithm is superior to the traditional algorithms under various noises.
Zeyu Chen; Rui Xiong; Kunyu Wang; Bin Jiao
2015-01-01
Plug-in hybrid electric vehicles (PHEVs) have been recognized as one of the most promising vehicle categories nowadays due to their low fuel consumption and reduced emissions. Energy management is critical for improving the performance of PHEVs. This paper proposes an energy management approach based on a particle swarm optimization (PSO) algorithm. The optimization objective is to minimize total energy cost (summation of oil and electricity) from vehicle utilization. A main drawback of optim...
Institute of Scientific and Technical Information of China (English)
Xiong LUO; Xiaoping FAN; Heng ZHANG; Tefang CHEN
2004-01-01
Optimal trajectory planning for robot manipulators plays an important role in implementing the high productivity for robots.The performance indexes used in optimal trajectory planning are classified into two main categories:optimum traveling time and optimum mechanical energy of the actuators.The current trajectory planning algorithms are designed based on one of the above two performance indexes.So far,there have been few planning algorithms designed to satisfy two performance indexes simultaneously.On the other hand,some deficiencies arise in the existing integrated optimization algorithms of trajectory planning.In order to overcome those deficiencies,the integrated optimization algorithms of trajectory planning are presented based on the complete analysis for trajectory planning of robot manipulators.In the algorithm,two object functions are designed based on the specific weight coefficient method and "ideal point" strategy.Moreover,based on the features of optimization problem,the intensified evolutionary programming is proposed to solve the corresponding optimization model.Especially,for the Stanford Robot,the high-quality solutions are found at a lower cost.
Target distribution in cooperative combat based on Bayesian optimization algorithm
Institute of Scientific and Technical Information of China (English)
Shi Zhifu; Zhang An; Wang Anli
2006-01-01
Target distribution in cooperative combat is a difficult and emphases. We build up the optimization model according to the rule of fire distribution. We have researched on the optimization model with BOA. The BOA can estimate the joint probability distribution of the variables with Bayesian network, and the new candidate solutions also can be generated by the joint distribution. The simulation example verified that the method could be used to solve the complex question, the operation was quickly and the solution was best.
Zhou, Xu; Liu, Yanheng; Li, Bin; Sun, Geng
2015-10-01
Identifying community structures in static network misses the opportunity to capture the evolutionary patterns. So community detection in dynamic network has attracted many researchers. In this paper, a multiobjective biogeography based optimization algorithm with decomposition (MBBOD) is proposed to solve community detection problem in dynamic networks. In the proposed algorithm, the decomposition mechanism is adopted to optimize two evaluation objectives named modularity and normalized mutual information simultaneously, which measure the quality of the community partitions and temporal cost respectively. A novel sorting strategy for multiobjective biogeography based optimization is presented for comparing quality of habitats to get species counts. In addition, problem-specific migration and mutation model are introduced to improve the effectiveness of the new algorithm. Experimental results both on synthetic and real networks demonstrate that our algorithm is effective and promising, and it can detect communities more accurately in dynamic networks compared with DYNMOGA and FaceNet.
RELIABILITY-BASED DESIGN OF COMPOSITES UNDER THE MIXED UNCERTAINTIES AND THE OPTIMIZATION ALGORITHM
Institute of Scientific and Technical Information of China (English)
Rui Ge; Jianqiao Chen; Jnnhong Wei
2008-01-01
This paper proposed a reliability design model for composite materials under the mixture of random and interval variables. Together with the inverse reliability analysis technique, the sequential single-loop optimization method is applied to the reliability-based design of com-posites. In the sequential single-loop optimization, the optimization and the reliability analysis are decoupled to improve the computational efficiency. As shown in examples, the minimum weight problems under the constraint of structural reliability are solved for laminated composites. The Particle Swarm Optimization (PSO) algorithm is utilized to search for the optimal solutions. The design results indicate that, under the mixture of random and interval variables, the method that combines the sequential single-loop optimization and the PSO algorithm can deal effectively with the reliability-based design of composites.
Guo, Y C; Wang, H; Wu, H P; Zhang, M Q
2015-01-01
Aimed to address the defects of the large mean square error (MSE), and the slow convergence speed in equalizing the multi-modulus signals of the constant modulus algorithm (CMA), a multi-modulus algorithm (MMA) based on global artificial fish swarm (GAFS) intelligent optimization of DNA encoding sequences (GAFS-DNA-MMA) was proposed. To improve the convergence rate and reduce the MSE, this proposed algorithm adopted an encoding method based on DNA nucleotide chains to provide a possible solution to the problem. Furthermore, the GAFS algorithm, with its fast convergence and global search ability, was used to find the best sequence. The real and imaginary parts of the initial optimal weight vector of MMA were obtained through DNA coding of the best sequence. The simulation results show that the proposed algorithm has a faster convergence speed and smaller MSE in comparison with the CMA, the MMA, and the AFS-DNA-MMA. PMID:26782395
New Tabu Search based global optimization methods outline of algorithms and study of efficiency.
Stepanenko, Svetlana; Engels, Bernd
2008-04-15
The study presents two new nonlinear global optimization routines; the Gradient Only Tabu Search (GOTS) and the Tabu Search with Powell's Algorithm (TSPA). They are based on the Tabu-Search strategy, which tries to determine the global minimum of a function by the steepest descent-mildest ascent strategy. The new algorithms are explained and their efficiency is compared with other approaches by determining the global minima of various well-known test functions with varying dimensionality. These tests show that for most tests the GOTS possesses a much faster convergence than global optimizer taken from the literature. The efficiency of the TSPA compares to the efficiency of genetic algorithms. PMID:17910004
Transport path optimization algorithm based on fuzzy integrated weights
Hou, Yuan-Da; Xu, Xiao-Hao
2014-11-01
Natural disasters cause significant damage to roads, making route selection a complicated logistical problem. To overcome this complexity, we present a method of using a trapezoidal fuzzy number to select the optimal transport path. Using the given trapezoidal fuzzy edge coefficients, we calculate a fuzzy integrated matrix, and incorporate the fuzzy multi-weights into fuzzy integrated weights. The optimal path is determined by taking two sets of vertices and transforming undiscovered vertices into discoverable ones. Our experimental results show that the model is highly accurate, and requires only a few measurement data to confirm the optimal path. The model provides an effective, feasible, and convenient method to obtain weights for different road sections, and can be applied to road planning in intelligent transportation systems.
Guidelines for Interactive Reliability-Based Structural Optimization using Quasi-Newton Algorithms
DEFF Research Database (Denmark)
Pedersen, C.; Thoft-Christensen, Palle
Guidelines for interactive reliability-based structural optimization problems are outlined in terms of modifications of standard quasi-Newton algorithms. The proposed modifications minimize the condition number of the approximate Hessian matrix in each iteration, restrict the relative and absolute...... increase of the condition number and preserve positive definiteness without discarding previously obtained information. All proposed modifications are also valid for non-interactive optimization problems. Heuristic rules from various optimization problems concerning when and how to impose interactions such...
An adaptive grid algorithm for 3-D GIS landform optimization based on improved ant algorithm
Wu, Chenhan; Meng, Lingkui; Deng, Shijun
2005-07-01
The key technique of 3-D GIS is to realize quick and high-quality 3-D visualization, in which 3-D roaming system based on landform plays an important role. However how to increase efficiency of 3-D roaming engine and process a large amount of landform data is a key problem in 3-D landform roaming system and improper process of the problem would result in tremendous consumption of system resources. Therefore it has become the key of 3-D roaming system design that how to realize high-speed process of distributed data for landform DEM (Digital Elevation Model) and high-speed distributed modulation of various 3-D landform data resources. In the paper we improved the basic ant algorithm and designed the modulation strategy of 3-D GIS landform resources based on the improved ant algorithm. By initially hypothetic road weights σi , the change of the information factors in the original algorithm would transform from ˜τj to ∆τj+σi and the weights was decided by 3-D computative capacity of various nodes in network environment. So during the course of initial phase of task assignment, increasing the resource information factors of high task-accomplishing rate and decreasing ones of low accomplishing rate would make load accomplishing rate approach the same value as quickly as possible, then in the later process of task assignment, the load balanced ability of the system was further improved. Experimental results show by improving ant algorithm, our system not only decreases many disadvantage of the traditional ant algorithm, but also like ants looking for food effectively distributes the complicated landform algorithm to many computers to process cooperatively and gains a satisfying search result.
Directory of Open Access Journals (Sweden)
Yong-sheng Wang
2014-04-01
Full Text Available Image segmentation is one of the key techniques in the field of image understanding and computer vision. To determine the optimal threshold in image segmentation, an effective image threshold segmentation method based on fuzzy logic is presented. A new kind of fuzzy entropy is defined, that is not only related to the membership, but also related to probability distribution. According to the maximum entropy criterion, the improved particle swarm optimization algorithm based on chaos bee colony is used to determine the optimal parameters of membership function to automatically determine the optimal threshold segmentation. The experiment results show that proposed algorithm based on fuzzy entropy and chaos bee colony particle swarm optimization has good performance.
Stellarator Optimization Using a Distributed Swarm Intelligence-Based Algorithm
Antonio Gómez-Iglesias; Francisco Castejón; Miguel A. Vega-Rodríguez
2012-01-01
The design of enhanced fusion devices constitutes a key element for the development of fusion as a commercial source of energy. Stellarator optimization presents high computational requirements because of the complexity of the numerical methods needed as well as the size of the solution space regarding all the possible configurations satisfying the characteristics of a feasible reactor. The size of the solution space does not allow to explore every single feasible configuration. Hence, a meta...
Elevator Group-Control Policy Based on Neural Network Optimized by Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
SHEN Hong; WAN Jianru; ZHANG Zhichao; LIU Yingpei; LI Guangye
2009-01-01
Aiming at the diversity and nonlinearity of the elevator system control target, an effective group method based on a hybrid algorithm of genetic algorithm and neural network is presented in this paper. The genetic algo-rithm is used to search the weight of the neural network. At the same time, the multi-objective-based evaluation function is adopted, in which there are three main indicators including the passenger waiting time, car passengers number and the number of stops. Different weights are given to meet the actual needs. The optimal values of the evaluation function are obtained, and the optimal dispatch control of the elevator group control system based on neural network is realized. By analyzing the running of the elevator group control system, all the processes and steps are presented. The validity of the hybrid algorithm is verified by the dynamic imitation performance.
COStar: a D-star Lite-based dynamic search algorithm for codon optimization.
Liu, Xiaowu; Deng, Riqiang; Wang, Jinwen; Wang, Xunzhang
2014-03-01
Codon optimized genes have two major advantages: they simplify de novo gene synthesis and increase the expression level in target hosts. Often they achieve this by altering codon usage in a given gene. Codon optimization is complex because it usually needs to achieve multiple opposing goals. In practice, finding an optimal sequence from the massive number of possible combinations of synonymous codons that can code for the same amino acid sequence is a challenging task. In this article, we introduce COStar, a D-star Lite-based dynamic search algorithm for codon optimization. The algorithm first maps the codon optimization problem into a weighted directed acyclic graph using a sliding window approach. Then, the D-star Lite algorithm is used to compute the shortest path from the start site to the target site in the resulting graph. Optimizing a gene is thus converted to a search in real-time for a shortest path in a generated graph. Using in silico experiments, the performance of the algorithm was shown by optimizing the different genes including the human genome. The results suggest that COStar is a promising codon optimization tool for de novo gene synthesis and heterologous gene expression. PMID:24316385
Boiler combustion optimization based on ANN and PSO-Powell algorithm
Institute of Scientific and Technical Information of China (English)
DAI Wei-bao; ZOU Ping-hua; FENG Ming-hua; DONG Zhan-shuang
2009-01-01
To improve the thermal efficiency and reduce nitrogen oxides (NOx) emissions in a power plant for energy conservation and environment protection, based on the reconstructed section temperature field and other related parameters, dynamic radial basis function (RBF) artificial neural network (ANN) models for forecasting unburned carbon in fly ash and NOx emissions in flue gas ware developed in this paper, together with a multi-objective optimization system utilizing particle swarm optimization and Powell (PSO-Powell) algorithm.To validate the proposed approach, a series of field tests were conducted in a 350 MW power plant. The results indicate that PSO-Powell algorithm can improve the capability to search optimization solution of PSO algorithm,and the effectiveness of system. Its prospective application in the optimization of a pulverized coal (PC) fired boiler is presented as well.
Yong-sheng Wang
2014-01-01
Image segmentation is one of the key techniques in the field of image understanding and computer vision. To determine the optimal threshold in image segmentation, an effective image threshold segmentation method based on fuzzy logic is presented. A new kind of fuzzy entropy is defined, that is not only related to the membership, but also related to probability distribution. According to the maximum entropy criterion, the improved particle swarm optimization algorithm based on chaos bee colony...
Jiang, Wenjuan; Shi, Yunbo; Zhao, Wenjie; Wang, Xiangxin
2016-01-01
The main part of the magnetic fluxgate sensor is the magnetic core, the hysteresis characteristic of which affects the performance of the sensor. When the fluxgate sensors are modelled for design purposes, an accurate model of hysteresis characteristic of the cores is necessary to achieve good agreement between modelled and experimental data. The Jiles-Atherton model is simple and can reflect the hysteresis properties of the magnetic material precisely, which makes it widely used in hysteresis modelling and simulation of ferromagnetic materials. However, in practice, it is difficult to determine the parameters accurately owing to the sensitivity of the parameters. In this paper, the Biogeography-Based Optimization (BBO) algorithm is applied to identify the Jiles-Atherton model parameters. To enhance the performances of the BBO algorithm such as global search capability, search accuracy and convergence rate, an improved Biogeography-Based Optimization (IBBO) algorithm is put forward by using Arnold map and mutation strategy of Differential Evolution (DE) algorithm. Simulation results show that IBBO algorithm is superior to Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, Differential Evolution algorithm and BBO algorithm in identification accuracy and convergence rate. The IBBO algorithm is applied to identify Jiles-Atherton model parameters of selected permalloy. The simulation hysteresis loop is in high agreement with experimental data. Using permalloy as core of fluxgate probe, the simulation output is consistent with experimental output. The IBBO algorithm can identify the parameters of Jiles-Atherton model accurately, which provides a basis for the precise analysis and design of instruments and equipment with magnetic core. PMID:27347974
Ruan, Cong; Sun, Xiao-Min; Song, Yi-Xu
In this paper, we propose a method to optimize etching yield parameters. By means of defining a fitness function between the actual etching profile and the simulation profile, the etching yield parameters solving problem is transformed into an optimization problem. The problem is nonlinear and high dimensional, and each simulation is computationally expensive. To solve this problem, we need to search a better solution in a multidimensional space. Ordinal optimization and tabu search hybrid algorithm is introduced to solve this complex problem. This method ensures getting good enough solution in an acceptable time. The experimental results illustrate that simulation profile obtained by this method is very similar with the actual etching profile in surface topography. It also proves that our proposed method has feasibility and validity.
Directory of Open Access Journals (Sweden)
Muhammad Farhan Ausaf
2015-12-01
Full Text Available Process planning and scheduling are two important components of a manufacturing setup. It is important to integrate them to achieve better global optimality and improved system performance. To find optimal solutions for integrated process planning and scheduling (IPPS problem, numerous algorithm-based approaches exist. Most of these approaches try to use existing meta-heuristic algorithms for solving the IPPS problem. Although these approaches have been shown to be effective in optimizing the IPPS problem, there is still room for improvement in terms of quality of solution and algorithm efficiency, especially for more complicated problems. Dispatching rules have been successfully utilized for solving complicated scheduling problems, but haven’t been considered extensively for the IPPS problem. This approach incorporates dispatching rules with the concept of prioritizing jobs, in an algorithm called priority-based heuristic algorithm (PBHA. PBHA tries to establish job and machine priority for selecting operations. Priority assignment and a set of dispatching rules are simultaneously used to generate both the process plans and schedules for all jobs and machines. The algorithm was tested for a series of benchmark problems. The proposed algorithm was able to achieve superior results for most complex problems presented in recent literature while utilizing lesser computational resources.
Lin, Kuan-Cheng; Hsieh, Yi-Hsiu
2015-10-01
The classification and analysis of data is an important issue in today's research. Selecting a suitable set of features makes it possible to classify an enormous quantity of data quickly and efficiently. Feature selection is generally viewed as a problem of feature subset selection, such as combination optimization problems. Evolutionary algorithms using random search methods have proven highly effective in obtaining solutions to problems of optimization in a diversity of applications. In this study, we developed a hybrid evolutionary algorithm based on endocrine-based particle swarm optimization (EPSO) and artificial bee colony (ABC) algorithms in conjunction with a support vector machine (SVM) for the selection of optimal feature subsets for the classification of datasets. The results of experiments using specific UCI medical datasets demonstrate that the accuracy of the proposed hybrid evolutionary algorithm is superior to that of basic PSO, EPSO and ABC algorithms, with regard to classification accuracy using subsets with a reduced number of features. PMID:26289628
Nuclear reactors project optimization based on neural network and genetic algorithm
International Nuclear Information System (INIS)
This work presents a prototype of a system for nuclear reactor core design optimization based on genetic algorithms and artificial neural networks. A neural network is modeled and trained in order to predict the flux and the neutron multiplication factor values based in the enrichment, network pitch and cladding thickness, with average error less than 2%. The values predicted by the neural network are used by a genetic algorithm in this heuristic search, guided by an objective function that rewards the high flux values and penalizes multiplication factors far from the required value. Associating the quick prediction - that may substitute the reactor physics calculation code - with the global optimization capacity of the genetic algorithm, it was obtained a quick and effective system for nuclear reactor core design optimization. (author). 11 refs., 8 figs., 3 tabs
Mavaddaty, Samira; Ebrahimzadeh, Ata
2011-01-01
Blind source separation technique separates mixed signals blindly without any information on the mixing system. In this paper, we have used two evolutionary algorithms, genetic algorithm and particle swarm optimization for blind source separation. In these techniques a novel fitness function that is based on the mutual information and high order statistics is proposed. In order to evaluate and compare the performance of these methods, we have focused on separation of noisy and noiseless sourc...
The Distribution Population-based Genetic Algorithm for Parameter Optimization PID Controller
Institute of Scientific and Technical Information of China (English)
CHENQing-Geng; WANGNing; HUANGShao-Feng
2005-01-01
Enlightened by distribution of creatures in natural ecology environment, the distribution population-based genetic algorithm (DPGA) is presented in this paper. The searching capability of the algorithm is improved by competition between distribution populations to reduce the search zone.This method is applied to design of optimal parameters of PID controllers with examples, and the simulation results show that satisfactory performances are obtained.
Uncertainty-based Optimization Algorithms in Designing Fractionated Spacecraft
Ning, Xin; Yuan, Jianping; Yue, Xiaokui
2016-03-01
A fractionated spacecraft is an innovative application of a distributive space system. To fully understand the impact of various uncertainties on its development, launch and in-orbit operation, we use the stochastic missioncycle cost to comprehensively evaluate the survivability, flexibility, reliability and economy of the ways of dividing the various modules of the different configurations of fractionated spacecraft. We systematically describe its concept and then analyze its evaluation and optimal design method that exists during recent years and propose the stochastic missioncycle cost for comprehensive evaluation. We also establish the models of the costs such as module development, launch and deployment and the impacts of their uncertainties respectively. Finally, we carry out the Monte Carlo simulation of the complete missioncycle costs of various configurations of the fractionated spacecraft under various uncertainties and give and compare the probability density distribution and statistical characteristics of its stochastic missioncycle cost, using the two strategies of timing module replacement and non-timing module replacement. The simulation results verify the effectiveness of the comprehensive evaluation method and show that our evaluation method can comprehensively evaluate the adaptability of the fractionated spacecraft under different technical and mission conditions.
Multi-objective optimization of membrane structures based on Pareto Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
SAN Bing-bing; SUN Xiao-ying; WU Yue
2010-01-01
A multi-objective optimization method based on Pareto Genetic Algorithm is presented for shape design of membrane structures from a structural view point.Several non-dimensional variables are defined as optimization variables,which are decision factors of shapes of membrane structures.Three objectives are proposed including maximization of stiffness,maximum uniformity of stress and minimum reaction under external loads.Pareto Muhi-objective Genetic Algorithm is introduced to solve the Pareto solutions.Consequently,the dependence of the optimality upon the optimization variables is derived to provide guidelines on how to determine design parameters.Moreover,several examples illustrate the proposed methods and applications.The study shows that the multi-objective optimization method in this paper is feasible and efficient for membrane structures; the research on Pareto solutions can provide explicit and useful guidelines for shape design of membrane structures.
Minimal K-Covering Set Algorithm based on Particle Swarm Optimizer
Directory of Open Access Journals (Sweden)
Yong Hu
2013-12-01
Full Text Available For random high density distribution in wireless sensor networks in this article have serious redundancy problems. In order to maximize the cost savings network resources for wireless sensor networks, extend the life network, this paper proposed a algorithm for the minimal k-covering set based on particle swarm optimizer. Firstly, the network monitoring area is divided into a number of grid points. Utilization rate and the node minimum are used as optional objective, and a combinatorial optimization mathematical model is established. Then using Particle Swarm Optimizer to solve optimization model, thus the optimal network coverage and the utilization od sensor nodes are obtained. Simulation results that algorithm has reduced node redundancy and the energy consumption, and improved the network coverage effectively
Optimal Design of Fuzzy Based Power System Stabilizer Self Tuned by Robust Search Algorithm
Linda, M Mary
2009-01-01
In the interconnected power system network, instability problems are caused mainly by the low frequency oscillations of 0.2 to 2.5 Hz .The supplementary control signal in addition with AVR and high gain excitation systems are provided by means of Power System Stabilizer (PSS). Conventional power system stabilizers provide effective damping only on a particular operating point. But fuzzy based PSS provides good damping for a wide range of operating points. The bottlenecks faced in designing a fuzzy logic controller can be minimized by using appropriate optimization techniques like Genetic Algorithm, Particle Swam Optimization, Ant Colony Optimization etc.In this paper the membership functions of FLC are optimized by the new breed optimization technique called Genetic Algorithm. This design methodology is implemented on a Single Machine Infinite Bus (SMIB) system. Simulation results on SMIB show the effectiveness and robustness of the proposed PSS over a wide range of operating conditions and system configurati...
Wu, Qiong; Wang, Jihua; Wang, Cheng; Xu, Tongyu
2016-09-01
Genetic algorithm (GA) has a significant effect in the band optimization selection of Partial Least Squares (PLS) correction model. Application of genetic algorithm in selection of characteristic bands can achieve the optimal solution more rapidly, effectively improve measurement accuracy and reduce variables used for modeling. In this study, genetic algorithm as a module conducted band selection for the application of hyperspectral imaging in nondestructive testing of corn seedling leaves, and GA-PLS model was established. In addition, PLS quantitative model of full spectrum and experienced-spectrum region were established in order to suggest the feasibility of genetic algorithm optimizing wave bands, and model robustness was evaluated. There were 12 characteristic bands selected by genetic algorithm. With reflectance values of corn seedling component information at spectral characteristic wavelengths corresponding to 12 characteristic bands as variables, a model about SPAD values of corn leaves acquired was established by PLS, and modeling results showed r = 0.7825. The model results were better than those of PLS model established in full spectrum and experience-based selected bands. The results suggested that genetic algorithm can be used for data optimization and screening before establishing the corn seedling component information model by PLS method and effectively increase measurement accuracy and greatly reduce variables used for modeling.
Genetic algorithm based optimization and post optimality analysis of multi-pass face milling
Saha, Sourabh
2009-01-01
This paper presents an optimization technique for the multi-pass face milling process. Genetic algorithm (GA) is used to obtain the optimum cutting parameters by minimizing the unit production cost for a given amount of material removal. Cutting speed, feed and depth of cut for the finish and rough passes are the cutting parameters. An equal depth of cut for roughing passes has been considered. A lookup table containing the feasible combinations of depth of cut in finish and rough passes is g...
Directory of Open Access Journals (Sweden)
Fu-Kwun Wang
2012-01-01
Full Text Available It is important for executives to predict the future trends. Otherwise, their companies cannot make profitable decisions and investments. The Bass diffusion model can describe the empirical adoption curve for new products and technological innovations. The Grey model provides short-term forecasts using four data points. This study develops a combined model based on the rolling Grey model (RGM and the Bass diffusion model to forecast motherboard shipments. In addition, we investigate evolutionary optimization algorithms to determine the optimal parameters. Our results indicate that the combined model using a hybrid algorithm outperforms other methods for the fitting and forecasting processes in terms of mean absolute percentage error.
Tian, Shu; Zhao, Min
2013-03-01
To solve the difficult problem that exists in the location of single-phase ground fault for coal mine underground distribution network, a fault location method using RBF network optimized by improved PSO algorithm based on the mapping relationship between wavelet packet transform modulus maxima of specific frequency bands transient state zero sequence current in the fault line and fault point position is presented. The simulation analysis results in the cases of different transition resistances and fault distances show that the RBF network optimized by improved PSO algorithm can obtain accurate and reliable fault location results, and the fault location perfor- mance is better than traditional RBF network.
Minimal K-Covering Set Algorithm based on Particle Swarm Optimizer
Yong Hu
2013-01-01
For random high density distribution in wireless sensor networks in this article have serious redundancy problems. In order to maximize the cost savings network resources for wireless sensor networks, extend the life network, this paper proposed a algorithm for the minimal k-covering set based on particle swarm optimizer. Firstly, the network monitoring area is divided into a number of grid points. Utilization rate and the node minimum are used as optional objective, and a combinatorial optim...
Energy Technology Data Exchange (ETDEWEB)
Osman, M.S. [High Institute of Technology, 10th Ramadan City (Egypt); Abo-Sinna, M.A.; Mousa, A.A. [Faculty of Engineering, Shebin El-Kom, Menoufia University (Egypt)
2009-11-15
In this paper, a novel multiobjective genetic algorithm approach for economic emission load dispatch (EELD) optimization problem is presented. The EELD problem is formulated as a non-linear constrained multiobjective optimization problem with both equality and inequality constraints. A new optimization algorithm which is based on concept of co-evolution and repair algorithm for handling non-linear constraints is presented. The algorithm maintains a finite-sized archive of non-dominated solutions which gets iteratively updated in the presence of new solutions based on the concept of {epsilon}-dominance. The use of {epsilon}-dominance also makes the algorithms practical by allowing a decision maker to control the resolution of the Pareto-set approximation by choosing an appropriate {epsilon} value. The proposed approach is carried out on the standard IEEE 30-bus 6-genrator test system. The results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto-optimal non-dominated solutions of the multiobjective EELD problem in one single run. Simulation results with the proposed approach have been compared to those reported in the literature. The comparison demonstrates the superiority of the proposed approach and confirms its potential to solve the multiobjective EELD problem. (author)
Parallel simulation algorithm for maintenance optimization based on directed Acyclic Graph
International Nuclear Information System (INIS)
An efficient simulation algorithm for the quantification of reliability performance indicators of a complex system is demonstrated in the paper that is based on Monte Carlo method. A directed Acyclic Graph is used as a useful system representation. A parallel simulation technique is used in the algorithm which is based on the construction of the special course of life sequence of transformed transition times subjected to the corresponding part of the Acyclic Graph. The parts of the Acyclic Graph represent individual subsystems of a given system and may be effectively evaluated from the reliability point of view. The wide range of models for both deterministic and stochastic processes applied on the terminal nodes of the Acyclic Graph is allowed in the algorithm. The use of the algorithm for comparative theoretical calculations as well as for industrial applications is shown by a visual demonstration. A cost-optimization problem is shortly introduced which may be fully solved by the algorithm using additional genetic algorithms as an applicable optimization technique. The problem takes into account also additional objective that is defined as a prescribed constraint of a selected reliability performance indicator. The solution of the cost-optimization problem is demonstrated on two practical examples
Khehra, Baljit Singh; Pharwaha, Amar Partap Singh
2016-06-01
Ductal carcinoma in situ (DCIS) is one type of breast cancer. Clusters of microcalcifications (MCCs) are symptoms of DCIS that are recognized by mammography. Selection of robust features vector is the process of selecting an optimal subset of features from a large number of available features in a given problem domain after the feature extraction and before any classification scheme. Feature selection reduces the feature space that improves the performance of classifier and decreases the computational burden imposed by using many features on classifier. Selection of an optimal subset of features from a large number of available features in a given problem domain is a difficult search problem. For n features, the total numbers of possible subsets of features are 2n. Thus, selection of an optimal subset of features problem belongs to the category of NP-hard problems. In this paper, an attempt is made to find the optimal subset of MCCs features from all possible subsets of features using genetic algorithm (GA), particle swarm optimization (PSO) and biogeography-based optimization (BBO). For simulation, a total of 380 benign and malignant MCCs samples have been selected from mammogram images of DDSM database. A total of 50 features extracted from benign and malignant MCCs samples are used in this study. In these algorithms, fitness function is correct classification rate of classifier. Support vector machine is used as a classifier. From experimental results, it is also observed that the performance of PSO-based and BBO-based algorithms to select an optimal subset of features for classifying MCCs as benign or malignant is better as compared to GA-based algorithm.
Distribution Grid Reactive Power Optimization Based on Improved Cloud Particle Swarm Algorithm
Directory of Open Access Journals (Sweden)
Hongsheng Su
2013-01-01
Full Text Available To resolve the problems that cloud particle swarm optimization(CPSO was easily trapped in local minimum and possessed slow convergence speed and early-maturing during distribution grid reactive power optimization, CPSO algorithm was improved based on cloud digital features in this paper. The method firstly combined Local search with global search together based on solution space transform, where the crossover and mutation operation of the particles were implemented based on normal cloud operator. And then the dramatic achievements were acquired in time-consuming and storage-cost using the improved algorithm. Finally, applied in bus IEEE30 system, the simulation results show that the better global solution is attained using the improved CPSO algorithm, and its convergence speed and accuracy possesses a dramatic improvement.
DEFF Research Database (Denmark)
Meng, Lexuan; Dragicevic, Tomislav; Guerrero, Josep M.;
2014-01-01
. Accordingly, this paper proposes a dynamic consensus algorithm based distributed optimization method aiming at improving the system efficiency while offering higher expandability and flexibility when compared to centralized control. Hardware-in-the-loop (HIL) results are shown to demonstrate the effectiveness...
A Genetic Algorithms-based Approach for Optimized Self-protection in a Pervasive Service Middleware
DEFF Research Database (Denmark)
Zhang, Weishan; Ingstrup, Mads; Hansen, Klaus Marius;
2009-01-01
constraints of heterogeneous devices and networks. In this paper, we present a Genetic Algorithms-based approach for obtaining optimized security configurations at run time, supported by a set of security OWL ontologies and an event-driven framework. This approach has been realized as a prototype for self...
The Study on Food Sensory Evaluation based on Particle Swarm Optimization Algorithm
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.
The Study on Food Sensory Evaluation based on Particle Swarm Optimization Algorithm
Directory of Open Access Journals (Sweden)
Hairong Wang
2015-07-01
Full Text Available 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.
Wong, Ling Ai; Shareef, Hussain; Mohamed, Azah; Ibrahim, Ahmad Asrul
2014-01-01
This paper presents the application of enhanced opposition-based firefly algorithm in obtaining the optimal battery energy storage systems (BESS) sizing in photovoltaic generation integrated radial distribution network in order to mitigate the voltage rise problem. Initially, the performance of the original firefly algorithm is enhanced by utilizing the opposition-based learning and introducing inertia weight. After evaluating the performance of the enhanced opposition-based firefly algorithm (EOFA) with fifteen benchmark functions, it is then adopted to determine the optimal size for BESS. Two optimization processes are conducted where the first optimization aims to obtain the optimal battery output power on hourly basis and the second optimization aims to obtain the optimal BESS capacity by considering the state of charge constraint of BESS. The effectiveness of the proposed method is validated by applying the algorithm to the 69-bus distribution system and by comparing the performance of EOFA with conventional firefly algorithm and gravitational search algorithm. Results show that EOFA has the best performance comparatively in terms of mitigating the voltage rise problem. PMID:25054184
Institute of Scientific and Technical Information of China (English)
Chen Xiaofang; Gui Weihua; Wang Yalin
2005-01-01
Considering premature convergence in the searching process of genetic algorithm, a chaotic migration-based pseudo parallel genetic algorithm (CMPPGA) is proposed, which applies the idea of isolated evolution and information exchanging in distributed Parallel Genetic Algorithm by serial program structure to solve optimization problem of low real-time demand. In this algorithm,asynchronic migration of individuals during parallel evolution is guided by a chaotic migration sequence. Infcrmation exchanging among sub-populations is ensured to be efficient and sufficient due to that the sequence is ergodic and stochastic. Simulation study of CMPPGA shows its strong global search ability, superiority to standard genetic algorithm and high immunity against premature convergence. According to the practice of raw material supply, an inventory prcgramming model is set up and solved by CMPPGA with satisfactory results returned.
Damping of Power Systems Oscillations by using Genetic Algorithm-Based Optimal Controller
Directory of Open Access Journals (Sweden)
Akram F. Bat
2010-06-01
Full Text Available In this paper, the power system stabilizer (PSS and Thyristor controlled phase shifter(TCPS interaction is investigated . The objective of this work is to study and design a controller capable of doing the task of damping in less economical control effort, and to globally link all controllers of national network in an optimal manner , toward smarter grids . This can be well done if a specific coordination between PSS and FACTS devices , is accomplished . Firstly, A genetic algorithm-based controller is used. Genetic Algorithm (GA is utilized to search for optimum controller parameter settings that optimize a given eigenvalue based objective function. Secondly, an optimal pole shifting, based on modern control theory for multi-input multi-output systems, is used. It requires solving first order or second order linear matrix Lyapunov equation for shifting dominant poles to much better location that guaranteed less overshoot and less settling time of system transient response following a disturbance.
Multi-equipment condition based maintenance optimization by multi- objective genetic algorithm
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Š. Valčuha
2011-04-01
Full Text Available Purpose: This paper deals with the optimization of the condition based maintenance (CBM applied on manufacturing multi-equipment system under cost and benefit criteria.Design/methodology/approach: The system is modeled using Discrete Event Simulation (DES and optimized by means of the application of a Multi-Objective Evolutionary Algorithm (MOEA.Findings: Solution for the joint optimization of the condition based maintenance model applied on several equipment has been obtained.Research limitations/implications: The developed approach has been successfully applied to the optimization of condition based maintenance activities of a hubcap production system composed by three plastic injection machines and a painting station, for management decision support.Originality/value: This paper provides a solution for the joint optimization of CBM strategies applied on several equipments
iMASKO: A Genetic Algorithm Based Optimization Framework for Wireless Sensor Networks
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Nanhao Zhu
2013-10-01
Full Text Available In this paper we present the design and implementation of a generic GA-based optimization framework iMASKO (iNL@MATLAB Genetic Algorithm-based Sensor NetworK Optimizer to optimize the performance metrics of wireless sensor networks. Due to the global search property of genetic algorithms, the framework is able to automatically and quickly fine tune hundreds of possible solutions for the given task to find the best suitable tradeoff. We test and evaluate the framework by using it to explore a SystemC-based simulation process to tune the configuration of the unslotted CSMA/CA algorithm of IEEE 802.15.4, aiming to discover the most available tradeoff solutions for the required performance metrics. In particular, in the test cases different sensor node platforms are under investigation. A weighted sum based cost function is used to measure the optimization effectiveness and capability of the framework. In the meantime, another experiment is performed to test the framework’s optimization characteristic in multi-scenario and multi-objectives conditions.
Resource Optimization in Mobile Communication Networks With User Profile-Based Algorithms
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R. Bajcsy,
2012-04-01
Full Text Available In mobile communications networks, Location Management enables the roaming of the user in the coverage area. The employment of the call and mobility patterns of the user can help minimize the signaling costs involved in Location Management, and optimize the available radio resources. In this paper, we carry out an exhaustive analysis of the location update costs involved in a user profile-based Location Management algorithm, and compare its performance with the classical strategy of static location areas. As original contributions, we introduce two new algorithms to obtain the β parameters, useful for the calculation of the Location Management signaling costs. Making use of these new algorithms, we show the convenience of the application of user profile-based strategies for Location Management in order to optimize the available radio resources, and we obtain practical guidelines for the optimum design of mobile communications networks.
Optimizing of large-number-patterns string matching algorithms based on definite-state automata
Institute of Scientific and Technical Information of China (English)
CHEN Xun-xun; FANG Bin-xing
2007-01-01
Because the small CACHE size of computers, the scanning speed of DFA based multi-pattern stringmatching algorithms slows down rapidly especially when the number of patterns is very large. For solving such problems, we cut down the scanning time of those algorithms (i.e. DFA based) by rearranging the states table and shrinking the DFA alphabet size. Both the methods can decrease the probability of large-scale random memory accessing and increase the probability of continuously memory accessing. Then the hitting rate of the CACHE is increased and the searching time of on the DFA is reduced. Shrinking the alphabet size of the DFA also reduces the storage complication. The AC + + algorithm, by optimizing the Aho-Corasick ( i. e. AC) algorithm using such methods, proves the theoretical analysis. And the experimentation results show that the scanning time of AC + + and the storage occupied is better than that of AC in most cases and the result is much attractive when the number of patterns is very large. Because DFA is a widely used base algorithm in may string matching algorithms, such as DAWG, SBOM etc. , the optimizing method discussed is significant in practice.
Optimization of Nano-Process Deposition Parameters Based on Gravitational Search Algorithm
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Norlina Mohd Sabri
2016-06-01
Full Text Available This research is focusing on the radio frequency (RF magnetron sputtering process, a physical vapor deposition technique which is widely used in thin film production. This process requires the optimized combination of deposition parameters in order to obtain the desirable thin film. The conventional method in the optimization of the deposition parameters had been reported to be costly and time consuming due to its trial and error nature. Thus, gravitational search algorithm (GSA technique had been proposed to solve this nano-process parameters optimization problem. In this research, the optimized parameter combination was expected to produce the desirable electrical and optical properties of the thin film. The performance of GSA in this research was compared with that of Particle Swarm Optimization (PSO, Genetic Algorithm (GA, Artificial Immune System (AIS and Ant Colony Optimization (ACO. Based on the overall results, the GSA optimized parameter combination had generated the best electrical and an acceptable optical properties of thin film compared to the others. This computational experiment is expected to overcome the problem of having to conduct repetitive laboratory experiments in obtaining the most optimized parameter combination. Based on this initial experiment, the adaptation of GSA into this problem could offer a more efficient and productive way of depositing quality thin film in the fabrication process.
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E.Kayalvizhi
2015-08-01
Full Text Available Mitigation of global warming gases from burning gasoline for transportation in vehicles is one of the biggest and most complex issues the world has ever faced. In an intention to eradicate the environmental crisis caused due to global warming, electric vehicles were been introduced that are powered by electric motor which works on the energy stored in a battery pack. Inspired by the research on power management in electric vehicles, this paper focuses on the development of an energy management system for electric vehicles (EMSEV to optimally balance the energy from battery pack. The proposed methodology uses firefly optimization algorithm to optimize the power consumption of the devices like electric motor, power steering, air conditioner, power window, automatic door locks, radio, speaker, horn, wiper, GPS, internal and external lights etc., from the battery in electric vehicles. Depending upon the distance to cover and the battery availability, the devices are made to switch down automatically through dynamic EDF scheduling. CAN protocol is used for effective communication between the devices and the controller. Simulation results are obtained using MATLAB.
Nature-inspired optimization algorithms
Yang, Xin-She
2014-01-01
Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning
Institute of Scientific and Technical Information of China (English)
Wenjing Du; Peili Wang; Lipeng Song; Lin Cheng
2015-01-01
A conduction heat transfer process is enhanced by filling prescribed quantity and optimized-shaped high thermal conductivity materials to the substrate. Numerical simulations and analyses are performed on a volume to point conduction problem based on the principle of minimum entropy generation. In the optimization, the arrange-ment of high thermal conductivity materials is variable, the quantity of high thermal-conductivity material is constrained, and the objective is to obtain the maximum heat conduction rate as the entropy is the minimum. A novel algorithm of thermal conductivity discretization is proposed based on large quantity of calculations. Compared with other algorithms in literature, the average temperature in the substrate by the new algorithm is lower, while the highest temperature in the substrate is in a reasonable range. Thus the new algorithm is fea-sible. The optimization of volume to point heat conduction is carried out in a rectangular model with radiation boundary condition and constant surface temperature boundary condition. The results demonstrate that the al-gorithm of thermal conductivity discretization is applicable for volume to point heat conduction problems.
Adaptive Search Protocol Based on Optimized Ant Colony Algorithm in Peer-to-Peer Network
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Chun-Ying Liu
2013-04-01
Full Text Available In order to solve the low searching efficiency in the peer-to-peer (P2P network, introduce the ant colony algorithm with the particle swarm optimization in searching procedure. Present a new adaptive search protocol (SACASP based on the ant colony algorithm with the particle swarm optimization in the Peer-to-Peer Network. The approach simulates the process of the ants’ searching food, and can direct the query routing efficiently according to the adaptive strategy and the positive feedback principle of the pheromone. Decrease the blindness of the messages transmitting in early searching stage by adding the particle swarm optimization to the ant colony algorithm. Give the adaptive P2P search model based on the fusion algorithm, and design the data structure and steps of the model. The simulation experiment shows, PSACASP can effectively shorten the time and reduce the search query packets comparing with the other search algorithms, and it can achieve better search performance and decrease the network loads.
Method of transient identification based on a possibilistic approach, optimized by genetic algorithm
International Nuclear Information System (INIS)
This work develops a method for transient identification based on a possible approach, optimized by Genetic Algorithm to optimize the number of the centroids of the classes that represent the transients. The basic idea of the proposed method is to optimize the partition of the search space, generating subsets in the classes within a partition, defined as subclasses, whose centroids are able to distinguish the classes with the maximum correct classifications. The interpretation of the subclasses as fuzzy sets and the possible approach provided a heuristic to establish influence zones of the centroids, allowing to achieve the 'don't know' answer for unknown transients, that is, outside the training set. (author)
Directory of Open Access Journals (Sweden)
Karla Vittori
2008-12-01
Full Text Available We propose a new distance algorithm for phylogenetic estimation based on Ant Colony Optimization (ACO, named Ant-Based Phylogenetic Reconstruction (ABPR. ABPR joins two taxa iteratively based on evolutionary distance among sequences, while also accounting for the quality of the phylogenetic tree built according to the total length of the tree. Similar to optimization algorithms for phylogenetic estimation, the algorithm allows exploration of a larger set of nearly optimal solutions. We applied the algorithm to four empirical data sets of mitochondrial DNA ranging from 12 to 186 sequences, and from 898 to 16,608 base pairs, and covering taxonomic levels from populations to orders. We show that ABPR performs better than the commonly used Neighbor-Joining algorithm, except when sequences are too closely related (e.g., population-level sequences. The phylogenetic relationships recovered at and above species level by ABPR agree with conventional views. However, like other algorithms of phylogenetic estimation, the proposed algorithm failed to recover expected relationships when distances are too similar or when rates of evolution are very variable, leading to the problem of long-branch attraction. ABPR, as well as other ACO-based algorithms, is emerging as a fast and accurate alternative method of phylogenetic estimation for large data sets.
Directory of Open Access Journals (Sweden)
Mahdi M. M. El-Arini
2013-01-01
Full Text Available In recent years, the solar energy has become one of the most important alternative sources of electric energy, so it is important to operate photovoltaic (PV panel at the optimal point to obtain the possible maximum efficiency. This paper presents a new optimization approach to maximize the electrical power of a PV panel. The technique which is based on objective function represents the output power of the PV panel and constraints, equality and inequality. First the dummy variables that have effect on the output power are classified into two categories: dependent and independent. The proposed approach is a multistage one as the genetic algorithm, GA, is used to obtain the best initial population at optimal solution and this initial population is fed to Lagrange multiplier algorithm (LM, then a comparison between the two algorithms, GA and LM, is performed. The proposed technique is applied to solar radiation measured at Helwan city at latitude 29.87°, Egypt. The results showed that the proposed technique is applicable.
Arteaga-Sierra, F R; Torres-Gómez, I; Torres-Cisneros, M; Moltó, G; Ferrando, A
2014-01-01
We present a numerical strategy to design fiber based dual pulse light sources exhibiting two predefined spectral peaks in the anomalous group velocity dispersion regime. The frequency conversion is based on the soliton fission and soliton self-frequency shift occurring during supercontinuum generation. The optimization process is carried out by a genetic algorithm that provides the optimum input pulse parameters: wavelength, temporal width and peak power. This algorithm is implemented in a Grid platform in order to take advantage of distributed computing. These results are useful for optical coherence tomography applications where bell-shaped pulses located in the second near-infrared window are needed.
An Enhanced PSO-Based Clustering Energy Optimization Algorithm for Wireless Sensor Network
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C. Vimalarani
2016-01-01
Full Text Available Wireless Sensor Network (WSN is a network which formed with a maximum number of sensor nodes which are positioned in an application environment to monitor the physical entities in a target area, for example, temperature monitoring environment, water level, monitoring pressure, and health care, and various military applications. Mostly sensor nodes are equipped with self-supported battery power through which they can perform adequate operations and communication among neighboring nodes. Maximizing the lifetime of the Wireless Sensor networks, energy conservation measures are essential for improving the performance of WSNs. This paper proposes an Enhanced PSO-Based Clustering Energy Optimization (EPSO-CEO algorithm for Wireless Sensor Network in which clustering and clustering head selection are done by using Particle Swarm Optimization (PSO algorithm with respect to minimizing the power consumption in WSN. The performance metrics are evaluated and results are compared with competitive clustering algorithm to validate the reduction in energy consumption.
A fuzzy c-means bi-sonar-based Metaheuristic Optimization Algorithm
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Koffka Khan
2012-12-01
Full Text Available Fuzzy clustering is an important problem which is the subject of active research in several real world applications. Fuzzy c-means (FCM algorithm is one of the most popular fuzzy clustering techniques because it is efficient, straightforward, and easy to implement. Fuzzy clustering methods allow the objects to belong to several clusters simultaneously, with different degrees of membership. Objects on the boundaries between several classes are not forced to fully belong to one of the classes, but rather are assigned membership degrees between 0 and 1 indicating their partial membership. However FCM is sensitive to initialization and is easily trapped in local optima. Bi-sonar optimization (BSO is a stochastic global Metaheuristic optimization tool and is a relatively new algorithm. In this paper a hybrid fuzzy clustering method FCB based on FCM and BSO is proposed which makes use of the merits of both algorithms. Experimental results show that this proposed method is efficient and reveals encouraging results.
An Enhanced PSO-Based Clustering Energy Optimization Algorithm for Wireless Sensor Network.
Vimalarani, C; Subramanian, R; Sivanandam, S N
2016-01-01
Wireless Sensor Network (WSN) is a network which formed with a maximum number of sensor nodes which are positioned in an application environment to monitor the physical entities in a target area, for example, temperature monitoring environment, water level, monitoring pressure, and health care, and various military applications. Mostly sensor nodes are equipped with self-supported battery power through which they can perform adequate operations and communication among neighboring nodes. Maximizing the lifetime of the Wireless Sensor networks, energy conservation measures are essential for improving the performance of WSNs. This paper proposes an Enhanced PSO-Based Clustering Energy Optimization (EPSO-CEO) algorithm for Wireless Sensor Network in which clustering and clustering head selection are done by using Particle Swarm Optimization (PSO) algorithm with respect to minimizing the power consumption in WSN. The performance metrics are evaluated and results are compared with competitive clustering algorithm to validate the reduction in energy consumption. PMID:26881273
Directory of Open Access Journals (Sweden)
Elias David Nino Ruiz
2013-05-01
Full Text Available This paper states a novel hybrid-metaheuristic based on the Theory of Deterministic Swapping, Theory of Evolution and Simulated Annealing Metaheuristic for the multi-objective optimization of combinatorial problems. The proposed algorithm is named EMSA. It is an improvement of MODS algorithm. Unlike MODS, EMSA works using a search direction given through the assignation of weights to each function of the combinatorial problem to optimize. Also, in order to avoid local optimums, EMSA uses crossover strategy of Genetic Algorithm. Lastly, EMSA is tested using well know instances of the Bi-Objective Traveling Salesman Problem (TSP from TSPLIB. Its results were compared with MODS Metaheuristic (its precessor. The comparison was made using metrics from the specialized literature such as Spacing, Generational Distance, Inverse Generational Distance and Non-Dominated Generation Vectors. In every case, the EMSA results on the metrics were always better and in some of those cases, the superiority was 100%.
International Nuclear Information System (INIS)
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
Lim, Wee Loon; Wibowo, Antoni; Desa, Mohammad Ishak; Haron, Habibollah
2016-01-01
The quadratic assignment problem (QAP) is an NP-hard combinatorial optimization problem with a wide variety of applications. Biogeography-based optimization (BBO), a relatively new optimization technique based on the biogeography concept, uses the idea of migration strategy of species to derive algorithm for solving optimization problems. It has been shown that BBO provides performance on a par with other optimization methods. A classical BBO algorithm employs the mutation operator as its diversification strategy. However, this process will often ruin the quality of solutions in QAP. In this paper, we propose a hybrid technique to overcome the weakness of classical BBO algorithm to solve QAP, by replacing the mutation operator with a tabu search procedure. Our experiments using the benchmark instances from QAPLIB show that the proposed hybrid method is able to find good solutions for them within reasonable computational times. Out of 61 benchmark instances tested, the proposed method is able to obtain the best known solutions for 57 of them. PMID:26819585
Lim, Wee Loon; Wibowo, Antoni; Desa, Mohammad Ishak; Haron, Habibollah
2016-01-01
The quadratic assignment problem (QAP) is an NP-hard combinatorial optimization problem with a wide variety of applications. Biogeography-based optimization (BBO), a relatively new optimization technique based on the biogeography concept, uses the idea of migration strategy of species to derive algorithm for solving optimization problems. It has been shown that BBO provides performance on a par with other optimization methods. A classical BBO algorithm employs the mutation operator as its diversification strategy. However, this process will often ruin the quality of solutions in QAP. In this paper, we propose a hybrid technique to overcome the weakness of classical BBO algorithm to solve QAP, by replacing the mutation operator with a tabu search procedure. Our experiments using the benchmark instances from QAPLIB show that the proposed hybrid method is able to find good solutions for them within reasonable computational times. Out of 61 benchmark instances tested, the proposed method is able to obtain the best known solutions for 57 of them. PMID:26819585
Yang, Dixiong; Liu, Zhenjun; Zhou, Jilei
2014-04-01
Chaos optimization algorithms (COAs) usually utilize the chaotic map like Logistic map to generate the pseudo-random numbers mapped as the design variables for global optimization. Many existing researches indicated that COA can more easily escape from the local minima than classical stochastic optimization algorithms. This paper reveals the inherent mechanism of high efficiency and superior performance of COA, from a new perspective of both the probability distribution property and search speed of chaotic sequences generated by different chaotic maps. The statistical property and search speed of chaotic sequences are represented by the probability density function (PDF) and the Lyapunov exponent, respectively. Meanwhile, the computational performances of hybrid chaos-BFGS algorithms based on eight one-dimensional chaotic maps with different PDF and Lyapunov exponents are compared, in which BFGS is a quasi-Newton method for local optimization. Moreover, several multimodal benchmark examples illustrate that, the probability distribution property and search speed of chaotic sequences from different chaotic maps significantly affect the global searching capability and optimization efficiency of COA. To achieve the high efficiency of COA, it is recommended to adopt the appropriate chaotic map generating the desired chaotic sequences with uniform or nearly uniform probability distribution and large Lyapunov exponent.
International Nuclear Information System (INIS)
This study presents a model-based optimization strategy for an actual chiller driven dehumidifier of liquid desiccant dehumidification system operating with lithium chloride solution. By analyzing the characteristics of the components, energy predictive models for the components in the dehumidifier are developed. To minimize the energy usage while maintaining the outlet air conditions at the pre-specified set-points, an optimization problem is formulated with an objective function, the constraints of mechanical limitations and components interactions. Model-based optimization strategy using genetic algorithm is proposed to obtain the optimal set-points for desiccant solution temperature and flow rate, to minimize the energy usage in the dehumidifier. Experimental studies on an actual system are carried out to compare energy consumption between the proposed optimization and the conventional strategies. The results demonstrate that energy consumption using the proposed optimization strategy can be reduced by 12.2% in the dehumidifier operation. - Highlights: • Present a model-based optimization strategy for energy saving in LDDS. • Energy predictive models for components in dehumidifier are developed. • The Optimization strategy are applied and tested in an actual LDDS. • Optimization strategy can achieve energy savings by 12% during operation
Wei Zhang; Guozhen Tan; Nan Ding; Guangyuan Wang
2012-01-01
This paper presents the model and algorithms for traffic flow data monitoring and optimal traffic light control based on wireless sensor networks. Given the scenario that sensor nodes are sparsely deployed along the segments between signalized intersections, an analytical model is built using continuum traffic equation and develops the method to estimate traffic parameter with the scattered sensor data. Based on the traffic data and principle of traffic congestion formation, we introduce the ...
Directory of Open Access Journals (Sweden)
Yu-Feng Sun
2016-04-01
Full Text Available The fireworks algorithm (FA is a new parallel diffuse optimization algorithm to simulate the fireworks explosion phenomenon, which realizes the balance between global exploration and local searching by means of adjusting the explosion mode of fireworks bombs. By introducing the grouping strategy of the shuffled frog leaping algorithm (SFLA, an improved FA-SFLA hybrid algorithm is put forward, which can effectively make the FA jump out of the local optimum and accelerate the global search ability. The simulation results show that the hybrid algorithm greatly improves the accuracy and convergence velocity for solving the function optimization problems.
Optimal fuzzy PID controller with adjustable factors based on flexible polyhedron search algorithm
Institute of Scientific and Technical Information of China (English)
谭冠政; 肖宏峰; 王越超
2002-01-01
A new kind of optimal fuzzy PID controller is proposed, which contains two parts. One is an on-line fuzzy inference system, and the other is a conventional PID controller. In the fuzzy inference system, three adjustable factors xp, xi, and xd are introduced. Their functions are to further modify and optimize the result of the fuzzy inference so as to make the controller have the optimal control effect on a given object. The optimal values of these adjustable factors are determined based on the ITAE criterion and the Nelder and Mead′s flexible polyhedron search algorithm. This optimal fuzzy PID controller has been used to control the executive motor of the intelligent artificial leg designed by the authors. The result of computer simulation indicates that this controller is very effective and can be widely used to control different kinds of objects and processes.
Kaboli, M.; Akhlaghi, M.
2016-06-01
A new efficient binary optimization method based on Teaching-Learning-Based Optimization (TLBO) algorithm is proposed to design an array of plasmonic nanodisks in order to achieve maximum scattering coefficient spectrum. In binary TLBO (BTLBO), a group of learner consists of a matrix with binary entries; control the presence (`1') or the absence (`0') of nanodisks in the array. Simulation results show that scattering coefficient strongly depends on the localized position of nanoparticles and non-periodic structures have more appropriate response in term of scattering coefficient. This approach can be useful in optical applications such as plasmonic nanoantennas.
Institute of Scientific and Technical Information of China (English)
LIZhihong
2002-01-01
A new superstructure from of heat exchanger networks(HEN) is proposed based on expert system (ES). The new superstructure from is combined with the practical engineering.The different investment cost formula for different heat exchanger is also presented based on ES.The mathematical model for the simultaneous optimization of network configuration is established and solved by a genetic algorithm.This method can deal with larger scale HEN synthesis and the optimal HEN configuration is obtained automatically.Finally,a case study is presented to demonstrate the effectiveness of the method.
Integrated Multiobjective Optimal Design for Active Control System Based on Genetic Algorithm
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Ma Yong-Quan
2014-01-01
Full Text Available The integrated multiobjective optimal design method for structural active control system is put forward based on improved Pareto multiobjective genetic algorithm, through which the position of actuator is synchronously optimized with active controller. External excitation is simulated by stationary filtered white noise. The root-mean-square (RMS of structural response and active control force can be achieved by solving Lyapunov equation in the state space. The design of active controller adopts linear quadratic regulator (LQR control algorithm. Minimum ratio of the maximum RMS of controlled structural displacement divided by the maximum RMS of uncontrolled structural displacement and minimum ratio of the maximum RMS of controlled structural shear divided by the maximum RMS of uncontrolled structural shear, together with minimization of the sum of RMS of active control force, are used as the three objective functions of multiobjective optimization. The optimization process takes the impact of structure and excitation parameter on the optimized results. An eight-storey six-span plane steel frame was used as an emulational example to demonstrate the validity of this optimization method. Results show that the proposed integrated multiobjective optimal design method is simple, efficient, and practical with good universality.
Wang, Jie-sheng; Li, Shu-xia; Song, Jiang-di
2015-01-01
In order to improve convergence velocity and optimization accuracy of the cuckoo search (CS) algorithm for solving the function optimization problems, a new improved cuckoo search algorithm based on the repeat-cycle asymptotic self-learning and self-evolving disturbance (RC-SSCS) is proposed. A disturbance operation is added into the algorithm by constructing a disturbance factor to make a more careful and thorough search near the bird's nests location. In order to select a reasonable repeat-cycled disturbance number, a further study on the choice of disturbance times is made. Finally, six typical test functions are adopted to carry out simulation experiments, meanwhile, compare algorithms of this paper with two typical swarm intelligence algorithms particle swarm optimization (PSO) algorithm and artificial bee colony (ABC) algorithm. The results show that the improved cuckoo search algorithm has better convergence velocity and optimization accuracy. PMID:26366164
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Jie-sheng Wang
2015-01-01
Full Text Available In order to improve convergence velocity and optimization accuracy of the cuckoo search (CS algorithm for solving the function optimization problems, a new improved cuckoo search algorithm based on the repeat-cycle asymptotic self-learning and self-evolving disturbance (RC-SSCS is proposed. A disturbance operation is added into the algorithm by constructing a disturbance factor to make a more careful and thorough search near the bird’s nests location. In order to select a reasonable repeat-cycled disturbance number, a further study on the choice of disturbance times is made. Finally, six typical test functions are adopted to carry out simulation experiments, meanwhile, compare algorithms of this paper with two typical swarm intelligence algorithms particle swarm optimization (PSO algorithm and artificial bee colony (ABC algorithm. The results show that the improved cuckoo search algorithm has better convergence velocity and optimization accuracy.
A Method for Crude Oil Selection and Blending Optimization Based on Improved Cuckoo Search Algorithm
Institute of Scientific and Technical Information of China (English)
Yang Huihua; Ma Wei; Zhang Xiaofeng; Li Hu; Tian Songbai
2014-01-01
Reifneries often need to ifnd similar crude oil to replace the scarce crude oil for stabilizing the feedstock prop-erty. We introduced the method for calculation of crude blended properties ifrstly, and then created a crude oil selection and blending optimization model based on the data of crude oil property. The model is a mixed-integer nonlinear programming (MINLP) with constraints, and the target is to maximize the similarity between the blended crude oil and the objective crude oil. Furthermore, the model takes into account the selection of crude oils and their blending ratios simultaneously, and trans-forms the problem of looking for similar crude oil into the crude oil selection and blending optimization problem. We ap-plied the Improved Cuckoo Search (ICS) algorithm to solving the model. Through the simulations, ICS was compared with the genetic algorithm, the particle swarm optimization algorithm and the CPLEX solver. The results show that ICS has very good optimization efifciency. The blending solution can provide a reference for reifneries to ifnd the similar crude oil. And the method proposed can also give some references to selection and blending optimization of other materials.
An ant colony optimization based algorithm for identifying gene regulatory elements.
Liu, Wei; Chen, Hanwu; Chen, Ling
2013-08-01
It is one of the most important tasks in bioinformatics to identify the regulatory elements in gene sequences. Most of the existing algorithms for identifying regulatory elements are inclined to converge into a local optimum, and have high time complexity. Ant Colony Optimization (ACO) is a meta-heuristic method based on swarm intelligence and is derived from a model inspired by the collective foraging behavior of real ants. Taking advantage of the ACO in traits such as self-organization and robustness, this paper designs and implements an ACO based algorithm named ACRI (ant-colony-regulatory-identification) for identifying all possible binding sites of transcription factor from the upstream of co-expressed genes. To accelerate the ants' searching process, a strategy of local optimization is presented to adjust the ants' start positions on the searched sequences. By exploiting the powerful optimization ability of ACO, the algorithm ACRI can not only improve precision of the results, but also achieve a very high speed. Experimental results on real world datasets show that ACRI can outperform other traditional algorithms in the respects of speed and quality of solutions. PMID:23746735
A Novel Rough Set Reduct Algorithm for Medical Domain Based on Bee Colony Optimization
Suguna, N
2010-01-01
Feature selection refers to the problem of selecting relevant features which produce the most predictive outcome. In particular, feature selection task is involved in datasets containing huge number of features. Rough set theory has been one of the most successful methods used for feature selection. However, this method is still not able to find optimal subsets. This paper proposes a new feature selection method based on Rough set theory hybrid with Bee Colony Optimization (BCO) in an attempt to combat this. This proposed work is applied in the medical domain to find the minimal reducts and experimentally compared with the Quick Reduct, Entropy Based Reduct, and other hybrid Rough Set methods such as Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO).
Institute of Scientific and Technical Information of China (English)
YANG Jian-qiu; WANG Yan-rong
2011-01-01
Several structural design parameters for the description of the geometric features of a hollow fan blade were determined. A structural design optimization model of a hollow fan blade which based on the strength constraint and minimum mass was established based on the finite element method through these parameters. Then, the sequential quadratic programming algorithm was employed to search the optimal solutions. Several groups of value for initial design variables were chosen, for the purpose of not only finding much more local optimal results but also analyzing which discipline that the variables according to could be benefit for the convergence and robustness. Response surface method and Monte Carlo simulations were used to analyze whether the objective function and constraint function are sensitive to the variation of variables or not. Then the robust results could be found among a group of different local optimal solutions.
International Nuclear Information System (INIS)
The study aims to introduce a hybrid optimization algorithm for anatomy-based intensity modulated radiotherapy (AB-IMRT). Our proposal is that by integrating an exact optimization algorithm with a heuristic optimization algorithm, the advantages of both the algorithms can be combined, which will lead to an efficient global optimizer solving the problem at a very fast rate. Our hybrid approach combines Gaussian elimination algorithm (exact optimizer) with fast simulated annealing algorithm (a heuristic global optimizer) for the optimization of beam weights in AB-IMRT. The algorithm has been implemented using MATLAB software. The optimization efficiency of the hybrid algorithm is clarified by (i) analysis of the numerical characteristics of the algorithm and (ii) analysis of the clinical capabilities of the algorithm. The numerical and clinical characteristics of the hybrid algorithm are compared with Gaussian elimination method (GEM) and fast simulated annealing (FSA). The numerical characteristics include convergence, consistency, number of iterations and overall optimization speed, which were analyzed for the respective cases of 8 patients. The clinical capabilities of the hybrid algorithm are demonstrated in cases of (a) prostate and (b) brain. The analyses reveal that (i) the convergence speed of the hybrid algorithm is approximately three times higher than that of FSA algorithm (ii) the convergence (percentage reduction in the cost function) in hybrid algorithm is about 20% improved as compared to that in GEM algorithm (iii) the hybrid algorithm is capable of producing relatively better treatment plans in terms of Conformity Index (CI) (∼ 2% - 5% improvement) and Homogeneity Index (HI) (∼ 4% - 10% improvement) as compared to GEM and FSA algorithms (iv) the sparing of organs at risk in hybrid algorithm-based plans is better than that in GEM-based plans and comparable to that in FSA-based plans; and (v) the beam weights resulting from the hybrid algorithm are
Xu Yang; Chang-bin Hu; Kai-xiang Peng; Chao-nan Tong
2013-01-01
Based on the hot rolling process, a load distribution optimization model is established, which includes rolling force model, thickness distribution model, and temperature model. The rolling force ratio distribution and good strip shape are integrated as two indicators of objective function in the optimization model. Then, the evolutionary algorithm for complex-process optimization (EACOP) is introduced in the following optimization algorithm. Due to its flexible framework structure on search ...
A non-linear camera calibration with modified teaching-learning-based optimization algorithm
Zhang, Buyang; Yang, Hua; Yang, Shuo
2015-12-01
In this paper, we put forward a novel approach based on hierarchical teaching-and-learning-based optimization (HTLBO) algorithm for nonlinear camera calibration. This algorithm simulates the teaching-learning ability of teachers and learners of a classroom. Different from traditional calibration approach, the proposed technique can find the nearoptimal solution without the need of accurate initial parameters estimation (with only very loose parameter bounds). With the introduction of cascade of teaching, the convergence speed is rapid and the global search ability is improved. Results from our study demonstrate the excellent performance of the proposed technique in terms of convergence, accuracy, and robustness. The HTLBO can also be used to solve many other complex non-linear calibration optimization problems for its good portability.
Histogram-Based Estimation of Distribution Algorithm: A Competent Method for Continuous Optimization
Institute of Scientific and Technical Information of China (English)
Nan Ding; Shu-De Zhou; Zeng-Qi Sun
2008-01-01
Designing efficient estimation of distribution algorithms for optimizing complex continuous problems is still a challenging task. This paper utilizes histogram probabilistic model to describe the distribution of population and to generate promising solutions. The advantage of histogram model, its intrinsic multimodality, makes it proper to describe the solution distribution of complex and multimodal continuous problems. To make histogram model more efficiently explore and exploit the search space, several strategies are brought into the algorithms: the surrounding effect reduces the population size in estimating the model with a certain number of the bins and the shrinking strategy guarantees the accuracy of optimal solutions. Furthermore, this paper shows that histogram-based EDA (Estimation of distributiona lgorithm) can give comparable or even much better performance than those predominant EDAs based on Gaussianmodels.
Managing Emergencies Optimally Using a Random Neural Network-Based Algorithm
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Qing Han
2013-10-01
Full Text Available Emergency rescues require that first responders provide support to evacuate injured and other civilians who are obstructed by the hazards. In this case, the emergency personnel can take actions strategically in order to rescue people maximally, efficiently and quickly. The paper studies the effectiveness of a random neural network (RNN-based task assignment algorithm involving optimally matching emergency personnel and injured civilians, so that the emergency personnel can aid trapped people to move towards evacuation exits in real-time. The evaluations are run on a decision support evacuation system using the Distributed Building Evacuation Simulator (DBES multi-agent platform in various emergency scenarios. The simulation results indicate that the RNN-based task assignment algorithm provides a near-optimal solution to resource allocation problems, which avoids resource wastage and improves the efficiency of the emergency rescue process.
Broadband and Broad-Angle Low-Scattering Metasurface Based on Hybrid Optimization Algorithm
Wang, Ke; Zhao, Jie; Cheng, Qiang; Dong, Di Sha; Cui, Tie Jun
2014-01-01
A broadband and broad-angle low-scattering metasurface is designed, fabricated, and characterized. Based on the optimization algorithm and far-field scattering pattern analysis, we propose a rapid and efficient method to design metasurfaces, which avoids the large amount of time-consuming electromagnetic simulations. Full-wave simulation and measurement results show that the proposed metasurface is insensitive to the polarization of incident waves, and presents good scattering-reduction properties for oblique incident waves. PMID:25089367
Broadband and Broad-Angle Low-Scattering Metasurface Based on Hybrid Optimization Algorithm
Ke Wang; Jie Zhao; Qiang Cheng; Di Sha Dong; Tie Jun Cui
2014-01-01
A broadband and broad-angle low-scattering metasurface is designed, fabricated, and characterized. Based on the optimization algorithm and far-field scattering pattern analysis, we propose a rapid and efficient method to design metasurfaces, which avoids the large amount of time-consuming electromagnetic simulations. Full-wave simulation and measurement results show that the proposed metasurface is insensitive to the polarization of incident waves, and presents good scattering-reduction prope...
Performance Evaluation Of Biped Robot Optimal Gait Based On Genetic Algorithm
Capi, G.; Nasu, Y.; Barolli, L.; Mitobe, K.; Yamano, M.; Takeda, K
2001-01-01
A Genetic Algorithm (GA) gait synthesis method for walking biped robots is considered in this paper. The walking occupy most of the time during the task performance, therefore its gait is analyzed based on the minimum consumed energy (CE) and minimum torque change (TC). The biped robot optimal gait is considered starting from static standing state and continuing with normal walking. The proposed method can be applied for wide ranges of step lengths and step times and for other tasks that migh...
Institute of Scientific and Technical Information of China (English)
LIU Zi-ping; LI Li-xin
2013-01-01
Based on the niche genetic algorithm, the intelligent and optimizing model for the rolling force distribution in hot strip mills was put forward. The research showed that the model had many advantages such as fast searching speed, high calculating pre-cision and suiting for on-line calculation. A good strip shape could be achieved by using the model and it is appropriate and practica-ble for rolling producing.
Yunlong Yu; Le Ru; Sheng Mao; Kangning Sun; Qiangqiang Yu; Kun Fang
2016-01-01
Airborne highly dynamic ad hoc UAV network has features of high node mobility, fast changing network topology, and complex application environment. The performance of traditional routing algorithms is so poor over aspects such as end to end delay, data packet delivery ratio, and routing overhead that they cannot provide efficient communication for multi-UAVs carrying out missions synergistically. A bionic optimization based stability and congestion aware routing algorithm—BSCAR algorithm—is p...
Herath, Manudha T; Natarajan, Sundararajan; Prusty, B Gangadhara; John, Nigel St
2013-01-01
An optimization scheme using the Cell-based Smoothed Finite Element Method (CS-FEM) combined with a Genetic Algorithm (GA) framework is proposed in this paper to design shape adaptive laminated composite marine propellers. The proposed scheme utilise the bend-twist coupling characteristics of the composites to achieve the required performance. An iterative procedure to evaluate the unloaded shape of the propeller blade is proposed, confirming the manufacturing requirements at the initial stag...
A unified model for the speed of sound in cranial bone based on genetic algorithm optimization
International Nuclear Information System (INIS)
The density and structure of bone is highly heterogeneous, causing wide variations in the reported speed of sound for ultrasound propagation. Current research on the propagation of high intensity focused ultrasound through an intact human skull for non-invasive therapeutic action on brain tissue requires a detailed model for the acoustic velocity in cranial bone. Such models have been difficult to derive empirically due to the aforementioned heterogeneity of bone itself. We propose a single unified model for the speed of sound in cranial bone based upon the apparent density of bone by CT scan. This model is based upon the coupling of empirical measurement, theoretical acoustic simulation and genetic algorithm optimization. The phase distortion caused by the presence of skull in an acoustic path is empirically measured. The ability of a theoretical acoustic simulation coupled with a particular speed-of-sound model to predict this phase distortion is compared against the empirical data, thus providing the fitness function needed to perform genetic algorithm optimization. By performing genetic algorithm optimization over an initial population of candidate speed-of-sound models, an ultimate single unified model for the speed of sound in both the cortical and trabecular regions of cranial bone is produced. The final model produced by genetic algorithm optimization has a nonlinear dependency of speed of sound upon local bone density. This model is shown by statistical significance to be a suitable model of the speed of sound in bone. Furthermore, using a skull that was not part of the optimization process, this model is also tested against a published homogeneous speed-of-sound model and shown to return an improved prediction of transcranial ultrasound propagation
International Nuclear Information System (INIS)
This paper presents a solution based on genetic algorithm and probabilistic safety analysis that can be applied in the optimization of the preventive maintenance politic of nuclear power plant safety systems. The goal of this approach is to improve the average availability of the system through the optimization of the preventive maintenance scheduling politic. The auxiliary feed water system of a two loops pressurized water reactor is used as a sample case, in order to demonstrate the effectiveness of the proposed method. The results, when compared to those obtained by some standard maintenance politics, reveal quantitative gains and operational safety levels. (author)
Directory of Open Access Journals (Sweden)
Rhythm Suren Wadhwa
2011-11-01
Full Text Available The paper presents a comparison and application of metaheuristic population-based optimization algorithms to a flexible manufacturing automation scenario in a metacasting foundry. It presents a novel application and comparison of Bee Colony Algorithm (BCA with variations of Particle Swarm Optimization (PSO and Ant Colony Optimization (ACO for object recognition problem in a robot material handling system. To enable robust pick and place activity of metalcasted parts by a six axis industrial robot manipulator, it is important that the correct orientation of the parts is input to the manipulator, via the digital image captured by the vision system. This information is then used for orienting the robot gripper to grip the part from a moving conveyor belt. The objective is to find the reference templates on the manufactured parts from the target landscape picture which may contain noise. The Normalized cross-correlation (NCC function is used as an objection function in the optimization procedure. The ultimate goal is to test improved algorithms that could prove useful in practical manufacturing automation scenarios.
Genetic algorithm based optimization of advanced solar cell designs modeled in Silvaco AtlasTM
Utsler, James
2006-01-01
A genetic algorithm was used to optimize the power output of multi-junction solar cells. Solar cell operation was modeled using the Silvaco ATLASTM software. The output of the ATLASTM simulation runs served as the input to the genetic algorithm. The genetic algorithm was run as a diffusing computation on a network of eighteen dual processor nodes. Results showed that the genetic algorithm produced better power output optimizations when compared with the results obtained using the hill cli...
Algorithm of axial fuel optimization based in progressive steps of turned search
International Nuclear Information System (INIS)
The development of an algorithm for the axial optimization of fuel of boiling water reactors (BWR) is presented. The algorithm is based in a serial optimizations process in the one that the best solution in each stage is the starting point of the following stage. The objective function of each stage adapts to orient the search toward better values of one or two parameters leaving the rest like restrictions. Conform to it advances in those optimization stages, it is increased the fineness of the evaluation of the investigated designs. The algorithm is based on three stages, in the first one are used Genetic algorithms and in the two following Tabu Search. The objective function of the first stage it looks for to minimize the average enrichment of the one it assembles and to fulfill with the generation of specified energy for the operation cycle besides not violating none of the limits of the design base. In the following stages the objective function looks for to minimize the power factor peak (PPF) and to maximize the margin of shutdown (SDM), having as restrictions the one average enrichment obtained for the best design in the first stage and those other restrictions. The third stage, very similar to the previous one, it begins with the design of the previous stage but it carries out a search of the margin of shutdown to different exhibition steps with calculations in three dimensions (3D). An application to the case of the design of the fresh assemble for the fourth fuel reload of the Unit 1 reactor of the Laguna Verde power plant (U1-CLV) is presented. The obtained results show an advance in the handling of optimization methods and in the construction of the objective functions that should be used for the different design stages of the fuel assemblies. (Author)
Optimization problem of radiation protection for site work based on genetic algorithm
International Nuclear Information System (INIS)
In this paper, Genetic Algorithm is applied for optimizing the temporary shielding plan of pipes when a valve needs to be repaired in the field of radiation protection. Firstly, the mathematical model of the radiation dose for shielding of two pipes is established, and then the model is converted to a constrained combinational optimization problem The Genetic Algorithm is used to solve this optimization problem. The experimental results show that the Genetic Algorithm can give an accurate complete radiation protection scheme. (authors)
A hybrid genetic algorithm based on mutative scale chaos optimization strategy
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
In order to avoid such problems as low convergent speed and local optimal solution in simple genetic algorithms, a new hybrid genetic algorithm is proposed. In this algorithm, a mutative scale chaos optimization strategy is operated on the population after a genetic operation. And according to the searching process, the searching space of the optimal variables is gradually diminished and the regulating coefficient of the secondary searching process is gradually changed which will lead to the quick evolution of the population. The algorithm has such advantages as fast search, precise results and convenient using etc. The simulation results show that the performance of the method is better than that of simple genetic algorithms.
Wang, Ping; Wu, Guangqiang
2013-03-01
Typical multidisciplinary design optimization(MDO) has gradually been proposed to balance performances of lightweight, noise, vibration and harshness(NVH) and safety for instrument panel(IP) structure in the automotive development. Nevertheless, plastic constitutive relation of Polypropylene(PP) under different strain rates, has not been taken into consideration in current reliability-based and collaborative IP MDO design. In this paper, based on tensile test under different strain rates, the constitutive relation of Polypropylene material is studied. Impact simulation tests for head and knee bolster are carried out to meet the regulation of FMVSS 201 and FMVSS 208, respectively. NVH analysis is performed to obtain mainly the natural frequencies and corresponding mode shapes, while the crashworthiness analysis is employed to examine the crash behavior of IP structure. With the consideration of lightweight, NVH, head and knee bolster impact performance, design of experiment(DOE), response surface model(RSM), and collaborative optimization(CO) are applied to realize the determined and reliability-based optimizations, respectively. Furthermore, based on multi-objective genetic algorithm(MOGA), the optimal Pareto sets are completed to solve the multi-objective optimization(MOO) problem. The proposed research ensures the smoothness of Pareto set, enhances the ability of engineers to make a comprehensive decision about multi-objectives and choose the optimal design, and improves the quality and efficiency of MDO.
Knee Joint Optimization Design of Intelligent Bionic Leg Based on Genetic Algorithm
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Hualong Xie
2014-09-01
Full Text Available Intelligent bionic leg (IBL is an advanced prosthesis which can maximum functionally simulate and approach the motion trajectory of human leg. Knee joint is the most important bone of human leg and its bionic design has great significance to prosthesis performance. The structural components of IBL are introduced and virtual prototype is given. The advantages of 4-bar knee joint are analyzed and are adopted in IBL design. The kinematics model of 4-bar knee joint is established. The objective function, constraint condition, parameters selection and setting of genetic algorithm are discussed in detail. Based on genetic algorithm, the optimization design of IBL knee joint is done. The optimization results indicate that the 4-bar mechanism can achieve better anthropomorphic characteristics of human knee joint.
Implementation of Web-Based Chilli Expert Advisory System Using ABC Optimization Algorithm
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D.V.P.R.Sridhar,
2010-09-01
Full Text Available The presently developed paper deals with the concepts of web based online expert systems and machine learning Algorithms in the field of Artificial Intelligence. An expert system follows the methodology of task-based specification and it is independent in case of problem solving. Where as, the machine learning technique is used to find the good optimal solution. This paper mainly focuses on the investigations on the diseases and treatment to the diseases which were effected to the chilli plants by using the mechanism of Rule based system and Artificial Bee Colony1 (ABC algorithm. The rules in the database is processed by the rule based system and if the required rules are not present in the database, then the system goes to the Machine learning algorithm technique used expert system. Thus, by applying machine learning techniques, resulting to best global optimized solution for recognizing the diseases in chilli plants. This expert system is a web based online application for online users with java as front end and MySQL as backend.
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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.
Institute of Scientific and Technical Information of China (English)
周杰; 卓芳; 黄磊; 罗艳
2015-01-01
To obtain the optimal process parameters of stamping forming, finite element analysis and optimization technique were integrated via transforming multi-objective issue into a single-objective issue. A Pareto-based genetic algorithm was applied to optimizing the head stamping forming process. In the proposed optimal model, fracture, wrinkle and thickness varying are a function of several factors, such as fillet radius, draw-bead position, blank size and blank-holding force. Hence, it is necessary to investigate the relationship between the objective functions and the variables in order to make objective functions varying minimized simultaneously. Firstly, the central composite experimental (CCD) with four factors and five levels was applied, and the experimental data based on the central composite experimental were acquired. Then, the response surface model (RSM) was set up and the results of the analysis of variance (ANOVA) show that it is reliable to predict the fracture, wrinkle and thickness varying functions by the response surface model. Finally, a Pareto-based genetic algorithm was used to find out a set of Pareto front, which makes fracture, wrinkle and thickness varying minimized integrally. A head stamping case indicates that the present method has higher precision and practicability compared with the“trial and error”procedure.
International Nuclear Information System (INIS)
People have paid more attention to enhancing voltage stability margin since voltage collapses happened in some power systems recently. This paper proposes an optimal reactive power flow (ORPF) incorporating static voltage stability based on a multi-objective adaptive immune algorithm (MOAIA). The main idea of the proposed algorithm is to add two parts to an existing immune algorithm. The first part defines both partial affinity and global affinity to evaluate the antibody affinity to the multi-objective functions. The second part uses adaptive crossover, mutation and clone rates for antibodies to maintain the antibodies diversity. Hence, the proposed algorithm can achieve a dynamic balance between individual diversity and population convergence. The paper describes ORPF's multi-objective functional mathematical model and the constraint conditions. The problems associated with the antibody are also discussed in detail. The proposed method has been tested in the IEEE-30 system and compared with IGA (immune genetic algorithm). The results show that the proposed algorithm has improved performance over the IGA
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Lejiang Guo
2011-05-01
Full Text Available Wireless Sensor Networks (WSN represent a new dimension in the field of network research. The cluster algorithm can significantly reduce the energy consumption of wireless sensor networks and prolong the network lifetime. This paper uses neuron to describe the WSN node and constructs neural network model for WSN. The neural network model includes three aspects: WSN node neuron model, WSN node control model and WSN node connection model. Through learning the framework of cluster algorithm for wireless sensor networks, this paper presents a weighted average of cluster-head selection algorithm based on an improved Genetic Optimization which makes the node weights directly related to the decision-making predictions. The Algorithm consists of two stages: single-parent evolution and population evolution. The initial population is formed in the stage of single-parent evolution by using gene pool, then the algorithm continues to the next further evolution process, finally the best solution will be generated and saved in the population. The simulation results illustrate that the new algorithm has the high convergence speed and good global searching capacity. It is to effectively balance the network energy consumption, improve the network life-cycle, ensure the communication quality and provide a certain theoretical foundation for the applications of the neural networks.
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Zeyu Chen
2015-04-01
Full Text Available Plug-in hybrid electric vehicles (PHEVs have been recognized as one of the most promising vehicle categories nowadays due to their low fuel consumption and reduced emissions. Energy management is critical for improving the performance of PHEVs. This paper proposes an energy management approach based on a particle swarm optimization (PSO algorithm. The optimization objective is to minimize total energy cost (summation of oil and electricity from vehicle utilization. A main drawback of optimal strategies is that they can hardly be used in real-time control. In order to solve this problem, a rule-based strategy containing three operation modes is proposed first, and then the PSO algorithm is implemented on four threshold values in the presented rule-based strategy. The proposed strategy has been verified by the US06 driving cycle under the MATLAB/Simulink software environment. Two different driving cycles are adopted to evaluate the generalization ability of the proposed strategy. Simulation results indicate that the proposed PSO-based energy management method can achieve better energy efficiency compared with traditional blended strategies. Online control performance of the proposed approach has been demonstrated through a driver-in-the-loop real-time experiment.
International Nuclear Information System (INIS)
The static delivery technique (also called step-and-shoot technique) has been widely used in intensity-modulated radiotherapy (IMRT) because of the simple delivery and easy quality assurance. Conventional static IMRT consists of two steps: first to calculate the intensity-modulated beam profiles using an inverse planning algorithm, and then to translate these profiles into a series of uniform segments using a leaf-sequencing tool. In order to simplify the procedure and shorten the treatment time of the static mode, an efficient technique, called genetic algorithm based deliverable segments optimization (GADSO), is developed in our work, which combines these two steps into one. Taking the pre-defined beams and the total number of segments per treatment as input, the number of segments for each beam, the segment shapes and weights are determined automatically. A group of interim modulated beam profiles quickly calculated using a conjugate gradient (CG) method are used to determine the segment number for each beam and to initialize segment shapes. A modified genetic algorithm based on a two-dimensional binary coding scheme is used to optimize the segment shapes, and a CG method is used to optimize the segment weights. The physical characters of a multileaf collimator, such as the leaves interdigitation limitation and leaves maximum over-travel distance, are incorporated into the optimization. The algorithm is applied to some examples and the results demonstrate that GADSO is able to produce highly conformal dose distributions using 20-30 deliverable segments per treatment within a clinically acceptable computation time
Abdeljaber, Osama; Avci, Onur; Inman, Daniel J.
2016-05-01
One of the major challenges in civil, mechanical, and aerospace engineering is to develop vibration suppression systems with high efficiency and low cost. Recent studies have shown that high damping performance at broadband frequencies can be achieved by incorporating periodic inserts with tunable dynamic properties as internal resonators in structural systems. Structures featuring these kinds of inserts are referred to as metamaterials inspired structures or metastructures. Chiral lattice inserts exhibit unique characteristics such as frequency bandgaps which can be tuned by varying the parameters that define the lattice topology. Recent analytical and experimental investigations have shown that broadband vibration attenuation can be achieved by including chiral lattices as internal resonators in beam-like structures. However, these studies have suggested that the performance of chiral lattice inserts can be maximized by utilizing an efficient optimization technique to obtain the optimal topology of the inserted lattice. In this study, an automated optimization procedure based on a genetic algorithm is applied to obtain the optimal set of parameters that will result in chiral lattice inserts tuned properly to reduce the global vibration levels of a finite-sized beam. Genetic algorithms are considered in this study due to their capability of dealing with complex and insufficiently understood optimization problems. In the optimization process, the basic parameters that govern the geometry of periodic chiral lattices including the number of circular nodes, the thickness of the ligaments, and the characteristic angle are considered. Additionally, a new set of parameters is introduced to enable the optimization process to explore non-periodic chiral designs. Numerical simulations are carried out to demonstrate the efficiency of the optimization process.
Jie-sheng Wang; Shu-xia Li; Jiang-di Song
2015-01-01
In order to improve convergence velocity and optimization accuracy of the cuckoo search (CS) algorithm for solving the function optimization problems, a new improved cuckoo search algorithm based on the repeat-cycle asymptotic self-learning and self-evolving disturbance (RC-SSCS) is proposed. A disturbance operation is added into the algorithm by constructing a disturbance factor to make a more careful and thorough search near the bird’s nests location. In order to select a reasonable repeat-...
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J. Anitha
2010-10-01
Full Text Available This paper deals with the development of Web based online expert systems using Evolutionary Algorithms. An expert system is a computer application that performs a task that would otherwise be performed by a human expert. Here one of the evolutionary algorithms (ACO Algorithm is considered to find a good match of symptoms in the database. In the present paper, Ant Colony Optimization1 (ACO algorithm has been taken as the base and the concept of optimization is included, so that the new algorithm mainly focuses on the determination of the quality of eggs in the poultry farms. At first, the symptoms provided by the user are processed by a rule based expert system for identifying the quality of the eggs. If the rules required for processing the data by the above are not present in the database, then the system automatically calls the machine learning algorithm technique. As a whole, the system results good optimized solution for recognizing the quality and viruses if any affected to eggs in poultry farms. And corresponding treatments to the viruses may also be suggested to the users. This expert system is designed with JSP as front end and MySQL as backend.
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Jing Xu
2016-07-01
Full Text Available As the sound signal of a machine contains abundant information and is easy to measure, acoustic-based monitoring or diagnosis systems exhibit obvious superiority, especially in some extreme conditions. However, the sound directly collected from industrial field is always polluted. In order to eliminate noise components from machinery sound, a wavelet threshold denoising method optimized by an improved fruit fly optimization algorithm (WTD-IFOA is proposed in this paper. The sound is firstly decomposed by wavelet transform (WT to obtain coefficients of each level. As the wavelet threshold functions proposed by Donoho were discontinuous, many modified functions with continuous first and second order derivative were presented to realize adaptively denoising. However, the function-based denoising process is time-consuming and it is difficult to find optimal thresholds. To overcome these problems, fruit fly optimization algorithm (FOA was introduced to the process. Moreover, to avoid falling into local extremes, an improved fly distance range obeying normal distribution was proposed on the basis of original FOA. Then, sound signal of a motor was recorded in a soundproof laboratory, and Gauss white noise was added into the signal. The simulation results illustrated the effectiveness and superiority of the proposed approach by a comprehensive comparison among five typical methods. Finally, an industrial application on a shearer in coal mining working face was performed to demonstrate the practical effect.
Norlina, M. S.; Diyana, M. S. Nor; Mazidah, P.; Rusop, M.
2016-07-01
In the RF magnetron sputtering process, the desirable layer properties are largely influenced by the process parameters and conditions. If the quality of the thin film has not reached up to its intended level, the experiments have to be repeated until the desirable quality has been met. This research is proposing Gravitational Search Algorithm (GSA) as the optimization model to reduce the time and cost to be spent in the thin film fabrication. The optimization model's engine has been developed using Java. The model is developed based on GSA concept, which is inspired by the Newtonian laws of gravity and motion. In this research, the model is expected to optimize four deposition parameters which are RF power, deposition time, oxygen flow rate and substrate temperature. The results have turned out to be promising and it could be concluded that the performance of the model is satisfying in this parameter optimization problem. Future work could compare GSA with other nature based algorithms and test them with various set of data.
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Puneet Rai
2014-02-01
Full Text Available Ant Colony Optimization (ACO is nature inspired algorithm based on foraging behavior of ants. The algorithm is based on the fact how ants deposit pheromone while searching for food. ACO generates a pheromone matrix which gives the edge information present at each pixel position of image, formed by ants dispatched on image. The movement of ants depends on local variance of image's intensity value. This paper proposes an improved method based on heuristic which assigns weight to the neighborhood. Thus by assigning the weights or priority to the neighboring pixels, the ant decides in which direction it can move. The method is applied on Medical images and experimental results are provided to support the superior performance of the proposed approach and the existing method.
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Mithun Kumar PK
2014-11-01
Full Text Available Medical image segmentation is a fundamental task in the medical imaging field. Optimal segmentation is required for the accurate judgment or appropriate clinical diagnosis. In this paper, we proposed automatically gradient threshold estimator of anisotropic diffusion for Meyer’s Watershed algorithm based optimal segmentation. The Meyer’s Watershed algorithm is the most significant for a large number of regions separations but the over segmentation is the major drawback of the Meyer’s Watershed algorithm. We are able to remove over segmentation after using anisotropic diffusion as a preprocessing step of segmentation in the Meyer’s Watershed algorithm. We used a fixed window size for dynamically gradient threshold estimation. The gradient threshold is the most important parameter of the anisotropic diffusion for image smoothing. The proposed method is able to segment medical image accurately because of obtaining the enhancement image. The introducing method demonstrates better performance without loss of any clinical information while preserving edges. Our investigated method is more efficient and effective in order to segment the region of interests in the medical images indeed.
Exergetic optimization of shell and tube heat exchangers using a genetic based algorithm
Energy Technology Data Exchange (ETDEWEB)
Oezcelik, Yavuz [Ege University, Bornova, Izmir (Turkey). Engineering Faculty, Chemical Engineering Department
2007-08-15
In the computer-based optimization, many thousands of alternative shell and tube heat exchangers may be examined by varying the high number of exchanger parameters such as tube length, tube outer diameter, pitch size, layout angle, baffle space ratio, number of tube side passes. In the present study, a genetic based algorithm was developed, programmed, and applied to estimate the optimum values of discrete and continuous variables of the MINLP (mixed integer nonlinear programming) test problems. The results of the test problems show that the genetic based algorithm programmed can estimate the acceptable values of continuous variables and optimum values of integer variables. Finally the genetic based algorithm was extended to make parametric studies and to find optimum configuration of heat exchangers by minimizing the sum of the annual capital cost and exergetic cost of the shell and tube heat exchangers. The results of the example problems show that the proposed algorithm is applicable to find optimum and near optimum alternatives of the shell and tube heat exchanger configurations. (author)
A Constraint programming-based genetic algorithm for capacity output optimization
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Kate Ean Nee Goh
2014-10-01
Full Text Available Purpose: The manuscript presents an investigation into a constraint programming-based genetic algorithm for capacity output optimization in a back-end semiconductor manufacturing company.Design/methodology/approach: In the first stage, constraint programming defining the relationships between variables was formulated into the objective function. A genetic algorithm model was created in the second stage to optimize capacity output. Three demand scenarios were applied to test the robustness of the proposed algorithm.Findings: CPGA improved both the machine utilization and capacity output once the minimum requirements of a demand scenario were fulfilled. Capacity outputs of the three scenarios were improved by 157%, 7%, and 69%, respectively.Research limitations/implications: The work relates to aggregate planning of machine capacity in a single case study. The constraints and constructed scenarios were therefore industry-specific.Practical implications: Capacity planning in a semiconductor manufacturing facility need to consider multiple mutually influenced constraints in resource availability, process flow and product demand. The findings prove that CPGA is a practical and an efficient alternative to optimize the capacity output and to allow the company to review its capacity with quick feedback.Originality/value: The work integrates two contemporary computational methods for a real industry application conventionally reliant on human judgement.
An ECN-based Optimal Flow Control Algorithm for the Internet
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
According to the Wide Area Network model, we formulate Internet flow control as a constrained convex programming problem, where the objective is to maximize the total utility of all sources over their transmission rates. Based on this formulation, flow control can be converted to a normal unconstrained optimization problem through the barrier function method, so that it can be solved by means of a gradient projection algorithm with properly rate iterations. We prove that the algorithm converges to the global optimal point, which is also a stable proportional fair rate allocation point, provided that the step size is properly chosen. The main difficulty facing the realization of iteration algorithm is the distributed computation of congestion measure. Fortunately, Explicit Congestion Notification (ECN) is likely to be used to improve the performance of TCP in the near future. By using ECN, it is possible to realize the iteration algorithm in IP networks. Our algorithm is divided into two parts, algorithms in the router and in the source. The router marks the ECN bit with a probability that varies as its buffer occupancy varies, so that the congestion measure of links can be communicated to the source when the marked ECN bits are reflected back from its destination. Source rates are then updated by all sessions according to the received congestion measure. The main advantage of our scheme is its fast convergence ability and robustness; it can also provide the network with zero packet loss by properly choosing the queue threshold and provide differentiated service to users by applying different utility functions.
Hongjian Zhang; Ping He; Xudong Yang
2015-01-01
Aiming to effectively recognize train center plate bolt loss faults, this paper presents an improved fault detection method. A multi-scale local binary pattern operator containing the local texture information of different radii is designed to extract more efficient discrimination information. An improved teaching-learning-based optimization algorithm is established to optimize the classification results in the decision level. Two new phases including the worst recombination phase and the cuc...
Sangyong Kim; Hee-Bok Choi; Yoonseok Shin; Gwang-Hee Kim; Deok-Seok Seo
2013-01-01
This research aims to optimize the mixing proportion of recycled aggregate concrete (RAC) using neural networks (NNs) based on genetic algorithms (GAs) for increasing the use of recycled aggregate (RA). NN and GA were used to predict the compressive strength of the concrete at 28 days. And sensitivity analysis of the NN based on GA was used to find the mixing ratio of RAC. The mixing criteria for RAC were determined and the replacement ratio of RAs was identified. This research reveal that th...
Research on the Optimization and Simulation of the Shortest Path Based on Algorithm of Dijkstra
Institute of Scientific and Technical Information of China (English)
2010-01-01
<正>Dijkstra algorithm is a theoretical basis to solve transportation network problems of the shortest path, which has a wide range of application in path optimization. Through analyzing traditional Dijkstra algorithm,on account of the insufficiency of this algorithm in path optimization,this paper uses adjacency list and circular linked list with combination to store date,and through the improved quick sorting algorithm for weight sorting, accomplish a quick search to the adjacent node,and so an improved Dijkstra algorithm is got.Then apply it to the optimal path search,and make simulation analysis for this algorithm through the example,also verify the effectiveness of the proposed algorithm.
Directory of Open Access Journals (Sweden)
Zhi Chen
2016-01-01
Full Text Available The extensive applications of support vector machines (SVMs require efficient method of constructing a SVM classifier with high classification ability. The performance of SVM crucially depends on whether optimal feature subset and parameter of SVM can be efficiently obtained. In this paper, a coarse-grained parallel genetic algorithm (CGPGA is used to simultaneously optimize the feature subset and parameters for SVM. The distributed topology and migration policy of CGPGA can help find optimal feature subset and parameters for SVM in significantly shorter time, so as to increase the quality of solution found. In addition, a new fitness function, which combines the classification accuracy obtained from bootstrap method, the number of chosen features, and the number of support vectors, is proposed to lead the search of CGPGA to the direction of optimal generalization error. Experiment results on 12 benchmark datasets show that our proposed approach outperforms genetic algorithm (GA based method and grid search method in terms of classification accuracy, number of chosen features, number of support vectors, and running time.
Optimizing on multiple constrained QoS multicast routing algorithms based on GA
Institute of Scientific and Technical Information of China (English)
孙宝林; 李腊元
2004-01-01
With the rapid development of Internet, mobile networks and high-performance networking technology,multiple constrained QoS multicast routing optimization in networks with uncertain parameters has become a very important research issue in the areas of networks and distributed systems. It is also a challenging and hard problem to the next generation Internet and high-performance networks, and has attracted the interests of many people. This paper discusses the multiple constrained QoS multicast routing problem, which may deal with the delay, delay jitter,bandwidth and packet loss metrics, and describes a network model for researching the routing problem. The paper mainly presents multiple constrained QoS multicast routing algorithm (MCQMRA), a QoS multicast routing policy for Internet,mobile network or other high-performance networks, which is based on the genetic algorithm (GA) and can provide QoS-sensitive paths in a scalable and flexible wayin the network environment with uncertain parameters. The MCQMRA can also optimize the network resources such as bandwidth, delay, packet loss metrics and can converge to the optimal or near-optimal solution within few iterations, even for the network environment with uncertain parameters. Simulation results show that MCQMRA is an available approach to QoS multicast routing decision.
Risk-Based, genetic algorithm approach to optimize outage maintenance schedule
Energy Technology Data Exchange (ETDEWEB)
Hadavi, S. Mohammad Hadi [Department of Nuclear Engineering, School of Engineering, Shiraz University, Shiraz (Iran, Islamic Republic of)], E-mail: smhadihadavi@yahoo.com
2008-04-15
A huge number of components are typically scheduled for maintenance when a nuclear power plant is shut down for its planned outage. Among these components, a number of them are risk significant so that their operability as well as reliability is of prime concern. Lack of proper maintenance for such components during the outage would impose substantial risk on the nuclear power plant (NPP) operation. In this paper, a new approach based on genetic algorithm (GA) is presented for the optimization of the NPP maintenance schedule during plant outage/overhaul, and an optimizer is developed accordingly. The developed optimizer, coupled with the suggested risk-cost model, compromises the cost in favor of maintaining the risk imposed by each schedule below regulatory/industry set limits. The suggested cost model consists of two elements, one considering the cost incurred by maintenance activities and the other incorporating the loss of revenues if needed, but unscheduled component maintenance causes further plant shutdown. The optimizer is developed in such a way that any risk and/or cost models the user desires can be applied. The performance of the developed GA/optimizer is evaluated by comparing its predictions with Monte Carlo simulation results. It is shown that the GA/optimizer performs significantly better.
A grid based multi-objective evolutionary algorithm for the optimization of power plants
International Nuclear Information System (INIS)
There is an increasing need for optimization of energy conversion systems, in particular concerning energy consumption and efficiency to reduce their environmental impact. Usually, optimization is based on designers' backgrounds, which are able to analyze system performances and modify appropriate operating parameters. However, if these changes aim to optimize simultaneously multiple conflicting objectives, the task becomes quite complex and the use of sophisticated tools is mandatory. This paper presents a multi-objective optimization method that permits solutions that simultaneously satisfy multiple conflicting objectives to be determined. The optimization process is carried out by using an evolutionary algorithm developed around an innovative technique that consists of partitioning the solution search space (i.e., a population of solutions) into parallel corridors. Within these corridors, 'header' solutions are trapped to be then involved in a reproduction process of new populations by using genetic operators. The proposed methodology is coupled to specific power plant models that are used to optimize two different power plants: (i) a cogeneration thermal plant and (ii) an advanced steam power station. In both cases the proposed technique has shown to be very powerful, robust and reliable. Further, this methodology can be used as an effective tool to find the set of best solutions and thus providing a realistic support to the decision-making.
Directory of Open Access Journals (Sweden)
Alaa M. Allakany
Full Text Available With the fast evolution of real time and multimedia applications, some quality of service (QoS constraints need to be guaranteed in the underlying network. In this paper, we present a new method for least-cost QoS multicast routing problem based on genet ...
Directory of Open Access Journals (Sweden)
Ebrahim BARATI
2013-03-01
Full Text Available In this paper the optimization of kinematics, which has great influence in performance of flapping foil propulsion, is investigated. The purpose of optimization is to design a flapping-wing micro aircraft with appropriate kinematics and aerodynamics features, making the micro aircraft suitable for transportation over large distance with minimum energy consumption. On the point of optimal design, the pitch amplitude, wing reduced frequency and phase difference between plunging and pitching are considered as given parameters and consumed energy, generated thrust by wings and lost power are computed using the 2D quasi-steady aerodynamic model and multi-objective genetic algorithm. Based on the thrust optimization, the increase in pitch amplitude reduces the power consumption. In this case the lost power increases and the maximum thrust coefficient is computed of 2.43. Based on the power optimization, the results show that the increase in pitch amplitude leads to power consumption increase. Additionally, the minimum lost power obtained in this case is 23% at pitch amplitude of 25°, wing reduced frequency of 0.42 and phase angle difference between plunging and pitching of 77°. Furthermore, the wing reduced frequency can be estimated using regression with respect to pitch amplitude, because reduced frequency variations with pitch amplitude is approximately a linear function.
Directory of Open Access Journals (Sweden)
Kamal Hossain
2013-04-01
Full Text Available Cognitive radio (CR is to detect the presence of primary users (PUs reliably in order to reduce theinterference to licensed communications. Genetic algorithms (GAs are well suited for CR optimizationproblems to increase efficiency of bandwidth utilization by manipulating its unused portions of theapparent spectrum. In this paper, a binary genetic algorithm (BGA-based soft fusion (SF scheme forcooperative spectrum sensing in cognitive radio network is proposed to improve detection performance andbandwidth utilization. The BGA-based optimization method is implemented at the fusion centre of a linearSF scheme to optimize the weighting coefficients vector to maximize global probability of detectionperformance. Simulation results and analyses confirm that the proposed scheme meets real timerequirements of cognitive radio spectrum sensing and it outperforms conventional natural deflectioncoefficient- (NDC-, modified deflection coefficient- (MDC-, maximal ratio combining- (MRC- and equalgain combining- (EGC- based SDF schemes as well as the OR-rule based hard decision fusion (HDF. Thepropose BGA scheme also converges fast and achieves the optimum performance, which means that BGAbasedmethod is efficient and quite stable also.
Zhao, Yi; Cao, Xiangyu; Gao, Jun; Sun, Yu; Yang, Huanhuan; Liu, Xiao; Zhou, Yulong; Han, Tong; Chen, Wei
2016-04-01
We propose a new strategy to design broadband and wide angle diffusion metasurfaces. An anisotropic structure which has opposite phases under x- and y-polarized incidence is employed as the “0” and “1” elements base on the concept of coding metamaterial. To obtain a uniform backward scattering under normal incidence, Simulated Annealing algorithm is utilized in this paper to calculate the optimal layout. The proposed method provides an efficient way to design diffusion metasurface with a simple structure, which has been proved by both simulations and measurements.
Optimization of an Accelerometer and Gyroscope-Based Fall Detection Algorithm
Huynh, Quoc T.; Nguyen, Uyen D; Irazabal, Lucia B.; Nazanin Ghassemian; Tran, Binh Q.
2015-01-01
Falling is a common and significant cause of injury in elderly adults (>65 yrs old), often leading to disability and death. In the USA, one in three of the elderly suffers from fall injuries annually. This study’s purpose is to develop, optimize, and assess the efficacy of a falls detection algorithm based upon a wireless, wearable sensor system (WSS) comprised of a 3-axis accelerometer and gyroscope. For this study, the WSS is placed at the chest center to collect real-time motion data of va...
Zhao, Yi; Cao, Xiangyu; Gao, Jun; Sun, Yu; Yang, Huanhuan; Liu, Xiao; Zhou, Yulong; Han, Tong; Chen, Wei
2016-01-01
We propose a new strategy to design broadband and wide angle diffusion metasurfaces. An anisotropic structure which has opposite phases under x- and y-polarized incidence is employed as the “0” and “1” elements base on the concept of coding metamaterial. To obtain a uniform backward scattering under normal incidence, Simulated Annealing algorithm is utilized in this paper to calculate the optimal layout. The proposed method provides an efficient way to design diffusion metasurface with a simple structure, which has been proved by both simulations and measurements. PMID:27034110
Adjoint Algorithm for CAD-Based Shape Optimization Using a Cartesian Method
Nemec, Marian; Aftosmis, Michael J.
2004-01-01
Adjoint solutions of the governing flow equations are becoming increasingly important for the development of efficient analysis and optimization algorithms. A well-known use of the adjoint method is gradient-based shape optimization. Given an objective function that defines some measure of performance, such as the lift and drag functionals, its gradient is computed at a cost that is essentially independent of the number of design variables (geometric parameters that control the shape). More recently, emerging adjoint applications focus on the analysis problem, where the adjoint solution is used to drive mesh adaptation, as well as to provide estimates of functional error bounds and corrections. The attractive feature of this approach is that the mesh-adaptation procedure targets a specific functional, thereby localizing the mesh refinement and reducing computational cost. Our focus is on the development of adjoint-based optimization techniques for a Cartesian method with embedded boundaries.12 In contrast t o implementations on structured and unstructured grids, Cartesian methods decouple the surface discretization from the volume mesh. This feature makes Cartesian methods well suited for the automated analysis of complex geometry problems, and consequently a promising approach to aerodynamic optimization. Melvin et developed an adjoint formulation for the TRANAIR code, which is based on the full-potential equation with viscous corrections. More recently, Dadone and Grossman presented an adjoint formulation for the Euler equations. In both approaches, a boundary condition is introduced to approximate the effects of the evolving surface shape that results in accurate gradient computation. Central to automated shape optimization algorithms is the issue of geometry modeling and control. The need to optimize complex, "real-life" geometry provides a strong incentive for the use of parametric-CAD systems within the optimization procedure. In previous work, we presented
Optimization of lamp arrangement in a closed-conduit UV reactor based on a genetic algorithm.
Sultan, Tipu; Ahmad, Zeshan; Cho, Jinsoo
2016-01-01
The choice for the arrangement of the UV lamps in a closed-conduit ultraviolet (CCUV) reactor significantly affects the performance. However, a systematic methodology for the optimal lamp arrangement within the chamber of the CCUV reactor is not well established in the literature. In this research work, we propose a viable systematic methodology for the lamp arrangement based on a genetic algorithm (GA). In addition, we analyze the impacts of the diameter, angle, and symmetry of the lamp arrangement on the reduction equivalent dose (RED). The results are compared based on the simulated RED values and evaluated using the computational fluid dynamics simulations software ANSYS FLUENT. The fluence rate was calculated using commercial software UVCalc3D, and the GA-based lamp arrangement optimization was achieved using MATLAB. The simulation results provide detailed information about the GA-based methodology for the lamp arrangement, the pathogen transport, and the simulated RED values. A significant increase in the RED values was achieved by using the GA-based lamp arrangement methodology. This increase in RED value was highest for the asymmetric lamp arrangement within the chamber of the CCUV reactor. These results demonstrate that the proposed GA-based methodology for symmetric and asymmetric lamp arrangement provides a viable technical solution to the design and optimization of the CCUV reactor. PMID:27191576
Directory of Open Access Journals (Sweden)
Sheng Lu
2015-01-01
Full Text Available To solve the problem of parameter selection during the design of magnetically coupled resonant wireless power transmission system (MCR-WPT, this paper proposed an improved genetic simulated annealing algorithm. Firstly, the equivalent circuit of the system is analysis in this study and a nonlinear programming mathematical model is built. Secondly, in place of the penalty function method in the genetic algorithm, the selection strategy based on the distance between individuals is adopted to select individual. In this way, it reduces the excess empirical parameters. Meanwhile, it can improve the convergence rate and the searching ability by calculating crossover probability and mutation probability according to the variance of population’s fitness. At last, the simulated annealing operator is added to increase local search ability of the method. The simulation shows that the improved method can break the limit of the local optimum solution and get the global optimum solution faster. The optimized system can achieve the practical requirements.
Jie-Sheng Wang; Chen-Xu Ning
2015-01-01
In order to improve the accuracy and real-time of all kinds of information in the cash business, and solve the problem which accuracy and stability is not high of the data linkage between cash inventory forecasting and cash management information in the commercial bank, a hybrid learning algorithm is proposed based on adaptive population activity particle swarm optimization (APAPSO) algorithm combined with the least squares method (LMS) to optimize the adaptive network-based fuzzy inference s...
International Nuclear Information System (INIS)
Invasive Weed Optimization (IWO) algorithm is a simple but powerful algorithm which is capable of solving general multi-dimensional, linear and nonlinear optimization problems with appreciable efficiency. Recently IWO algorithm is being used in several engineering design owing to its superior performance in comparison with many other existing algorithms. This paper presents a Chaotic IWO (CIWO) algorithms based on chaos, and investigates its performance for optimal settings of Optimal Power Flow (OPF) control variables of OPF problem with non-smooth and non-convex generator fuel cost curves (non-smooth and non-convex OPF). The performance of CIWO algorithms are studied and evaluated on the standard IEEE 30-bus test system with different objective functions. The experimental results suggest that IWO algorithm holds immense promise to appear as an efficient and powerful algorithm for optimization in the power system. - Highlights: • OPF problem has been solved considering non-smooth and non-convex fuel cost curves. • CIWO algorithms have been used based on chaos for solving OPF problem. • A comparative study of the proposed algorithms has been presented comprehensively
Optimization of an Accelerometer and Gyroscope-Based Fall Detection Algorithm
Directory of Open Access Journals (Sweden)
Quoc T. Huynh
2015-01-01
Full Text Available Falling is a common and significant cause of injury in elderly adults (>65 yrs old, often leading to disability and death. In the USA, one in three of the elderly suffers from fall injuries annually. This study’s purpose is to develop, optimize, and assess the efficacy of a falls detection algorithm based upon a wireless, wearable sensor system (WSS comprised of a 3-axis accelerometer and gyroscope. For this study, the WSS is placed at the chest center to collect real-time motion data of various simulated daily activities (i.e., walking, running, stepping, and falling. Tests were conducted on 36 human subjects with a total of 702 different movements collected in a laboratory setting. Half of the dataset was used for development of the fall detection algorithm including investigations of critical sensor thresholds and the remaining dataset was used for assessment of algorithm sensitivity and specificity. Experimental results show that the algorithm detects falls compared to other daily movements with a sensitivity and specificity of 96.3% and 96.2%, respectively. The addition of gyroscope information enhances sensitivity dramatically from results in the literature as angular velocity changes provide further delineation of a fall event from other activities that may also experience high acceleration peaks.
Directory of Open Access Journals (Sweden)
Hong Chang
2012-10-01
Full Text Available Accurate power generation forecasting provides the basis of decision making for electric power industry development plans, energy conservation and environmental protection. Since the power generation time series are rarely purely linear or nonlinear, no single forecasting model can identify the true data trends exactly in all situations. To combine forecasts from different models can reduce the model selection risk and effectively improve accuracy. In this paper, we propose a novel technique called the Harmony Search (HS algorithm-based joint parameters optimization combination model. In this model, the single forecasting model adopts power function form with unfixed exponential parameters. The exponential parameters of the single model and the combination weights are called joint parameters which are optimized by the HS algorithm by optimizing the objective function. Real power generation time series data sets of China, Japan, Russian Federation and India were used as samples to examine the forecasting accuracy of the presented model. The forecasting performance was compared with four single models and four combination models, respectively. The MAPE of our presented model is the lowest, which shows that the proposed model outperforms other comparative ones. Especially, the proposed combination model could better fit significant turning points of power generation time series. We can conclude that the proposed model can obviously improve forecasting accuracy and it can treat nonlinear time series with fluctuations better than other single models or combination models.
Genetic algorithm based approach to optimize phenotypical traits of virtual rice.
Ding, Weilong; Xu, Lifeng; Wei, Yang; Wu, Fuli; Zhu, Defeng; Zhang, Yuping; Max, Nelson
2016-08-21
How to select and combine good traits of rice to get high-production individuals is one of the key points in developing crop ideotype cultivation technologies. Existing cultivation methods for producing ideal plants, such as field trials and crop modeling, have some limits. In this paper, we propose a method based on a genetic algorithm (GA) and a functional-structural plant model (FSPM) to optimize plant types of virtual rice by dynamically adjusting phenotypical traits. In this algorithm, phenotypical traits such as leaf angles, plant heights, the maximum number of tiller, and the angle of tiller are considered as input parameters of our virtual rice model. We evaluate the photosynthetic output as a function of these parameters, and optimized them using a GA. This method has been implemented on GroIMP using the modeling language XL (eXtended L-System) and RGG (Relational Growth Grammar). A double haploid population of rice is adopted as test material in a case study. Our experimental results show that our method can not only optimize the parameters of rice plant type and increase the amount of light absorption, but can also significantly increase crop yield. PMID:27179460
International Nuclear Information System (INIS)
A comprehensive thermodynamic modeling and optimization is reported of a polygeneration energy system for the simultaneous production of heating, cooling, electricity and hot water from a common energy source. This polygeneration system is composed of four major parts: gas turbine (GT) cycle, Rankine cycle, absorption cooling cycle and domestic hot water heater. A multi-objective optimization method based on an evolutionary algorithm is applied to determine the best design parameters for the system. The two objective functions utilized in the analysis are the total cost rate of the system, which is the cost associated with fuel, component purchasing and environmental impact, and the system exergy efficiency. The total cost rate of the system is minimized while the cycle exergy efficiency is maximized by using an evolutionary algorithm. To provide a deeper insight, the Pareto frontier is shown for multi-objective optimization. In addition, a closed form equation for the relationship between exergy efficiency and total cost rate is derived. Finally, a sensitivity analysis is performed to assess the effects of several design parameters on the system total exergy destruction rate, CO2 emission and exergy efficiency.
Optimal Management Of Renewable-Based Mgs An Intelligent Approach Through The Evolutionary Algorithm
Directory of Open Access Journals (Sweden)
Mehdi Nafar
2015-08-01
Full Text Available Abstract- This article proposes a probabilistic frame built on Scenario fabrication to considerate the uncertainties in the finest action managing of Micro Grids MGs. The MG contains different recoverable energy resources such as Wind Turbine WT Micro Turbine MT Photovoltaic PV Fuel Cell FC and one battery as the storing device. The advised frame is based on scenario generation and Roulette wheel mechanism to produce different circumstances for handling the uncertainties of altered factors. It habits typical spreading role as a probability scattering function of random factors. The uncertainties which are measured in this paper are grid bid alterations cargo request calculating error and PV and WT yield power productions. It is well-intentioned to asset that solving the MG difficult for 24 hours of a day by considering diverse uncertainties and different constraints needs one powerful optimization method that can converge fast when it doesnt fall in local optimal topic. Simultaneously single Group Search Optimization GSO system is presented to vision the total search space globally. The GSO algorithm is instigated from group active of beasts. Also the GSO procedure one change is similarly planned for this algorithm. The planned context and way is applied o one test grid-connected MG as a typical grid.
Zhu, Binqi; Gao, Yesheng; Wang, Kaizhi; Liu, Xingzhao
2016-04-01
A computational method for suppressing clutter and generating clear microwave images of targets is proposed in this paper, which combines synthetic aperture radar (SAR) principles with recursive method and waveform design theory, and it is suitable for SAR for special applications. The nonlinear recursive model is introduced into the SAR operation principle, and the cubature Kalman filter algorithm is used to estimate target and clutter responses in each azimuth position based on their previous states, which are both assumed to be Gaussian distributions. NP criteria-based optimal waveforms are designed repeatedly as the sensor flies along its azimuth path and are used as the transmitting signals. A clutter suppression filter is then designed and added to suppress the clutter response while maintaining most of the target response. Thus, with fewer disturbances from the clutter response, we can generate the SAR image with traditional azimuth matched filters. Our simulations show that the clutter suppression filter significantly reduces the clutter response, and our algorithm greatly improves the SINR of the SAR image based on different clutter suppression filter parameters. As such, this algorithm may be preferable for special target imaging when prior information on the target is available.
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.
FLEXIBLE ASSEMBLY FIXTURING LA-YOUT MODELING AND OPTIMIZATION BASED ON GENETIC ALGORITHM
Institute of Scientific and Technical Information of China (English)
Lai Xinmin; Luo Laijun; Lin Zhongqin
2004-01-01
There are many welding fixture layout design problems of flexible parts in body-in-white assembly process, which directly cause body assemble variation.The fixture layout design quality is mainly influenced by the position and quantity of fixture locators and clamps.A general analysis model of flexible assembles deformation caused by fixture is set up based on "N-2-1" locating principle, in which the locator and clamper are treated as the same fixture layout elements.An analysis model for the flexible part deformation in fixturing is set up in order to obtain the optimization object function and constraints accordingly.The final fixture element layout could be obtained through global optimal research by using improved genetic algorithm, which effectively decreases fixture elements layout influence on flexible assembles deformation.
Matsui, Futoshi; Goriki, Shin'ichi; Shimizu, Yukio; Tomizawa, Hiromitsu; Kawato, Sakae; Kobayashi, Takao
2008-05-01
Arbitrary spatial beam shaping was demonstrated with a membrane electrostatic actuator type deformable mirror (DM). An automatic closed loop system must optimize such beam shapes as flattop. Well-characterized short pulse laser beam is widely required for a photocathode RF gun or for microscopic processing, etc. We propose a new sophisticated optimizing method based on a genetic algorithm (GA) for spatial shaping. A membrane type DM is driven by electrostatic attraction power, and applied electrode voltages vs displacement of membrane surface have a square function relationship. We prepare discrete electrode voltages to linearly change displacement as a utilized gene of the initial population in GA. Using uniform crossover without mutation in this method, we can make an arbitrary spatial beam shape quasi-flattop.
Bhowmik, Tanmoy; Liu, Hanli; Ye, Zhou; Oraintara, Soontorn
2016-03-01
Diffuse optical tomography (DOT) is a relatively low cost and portable imaging modality for reconstruction of optical properties in a highly scattering medium, such as human tissue. The inverse problem in DOT is highly ill-posed, making reconstruction of high-quality image a critical challenge. Because of the nature of sparsity in DOT, sparsity regularization has been utilized to achieve high-quality DOT reconstruction. However, conventional approaches using sparse optimization are computationally expensive and have no selection criteria to optimize the regularization parameter. In this paper, a novel algorithm, Dimensionality Reduction based Optimization for DOT (DRO-DOT), is proposed. It reduces the dimensionality of the inverse DOT problem by reducing the number of unknowns in two steps and thereby makes the overall process fast. First, it constructs a low resolution voxel basis based on the sensing-matrix properties to find an image support. Second, it reconstructs the sparse image inside this support. To compensate for the reduced sensitivity with increasing depth, depth compensation is incorporated in DRO-DOT. An efficient method to optimally select the regularization parameter is proposed for obtaining a high-quality DOT image. DRO-DOT is also able to reconstruct high-resolution images even with a limited number of optodes in a spatially limited imaging set-up.
International Nuclear Information System (INIS)
Highlights: • BBO algorithm is capable of finding suitably optimized loading pattern. • It seems BBO reaches to better final parameter value in comparison with the PSO. • PSO exhibits faster convergence characteristics in comparison with BBO. • Even with same initial random patterns the BBO is found to outperform PSO. - Abstract: In this investigation, we developed a new optimization method, i.e., biogeography based optimization (BBO), for loading pattern optimization problem of pressurized water reactors. BBO is a novel stochastic force based on the science of biogeography. Biogeography is the schoolwork of geographical allotment of biological organisms. BBO make use of migration operator to share information between the problem solutions. The problem solutions are called as habitats and sharing of features is called migration. For the evaluation of the proposed method, we applied a multi-objective fitness function i.e., the maximization of reactivity at BOC and the flattening of power distribution are achieved efficiently and simultaneously. The neutronic calculation is done by CITATION and WIMS codes
Directory of Open Access Journals (Sweden)
Luman Zhao
2015-01-01
Full Text Available A thrust allocation method was proposed based on a hybrid optimization algorithm to efficiently and dynamically position a semisubmersible drilling rig. That is, the thrust allocation was optimized to produce the generalized forces and moment required while at the same time minimizing the total power consumption under the premise that forbidden zones should be taken into account. An optimization problem was mathematically formulated to provide the optimal thrust allocation by introducing the corresponding design variables, objective function, and constraints. A hybrid optimization algorithm consisting of a genetic algorithm and a sequential quadratic programming (SQP algorithm was selected and used to solve this problem. The proposed method was evaluated by applying it to a thrust allocation problem for a semisubmersible drilling rig. The results indicate that the proposed method can be used as part of a cost-effective strategy for thrust allocation of the rig.
Mohamed, Ahmed F; Elarini, Mahdi M; Othman, Ahmed M
2014-05-01
One of the most recent optimization techniques applied to the optimal design of photovoltaic system to supply an isolated load demand is the Artificial Bee Colony Algorithm (ABC). The proposed methodology is applied to optimize the cost of the PV system including photovoltaic, a battery bank, a battery charger controller, and inverter. Two objective functions are proposed: the first one is the PV module output power which is to be maximized and the second one is the life cycle cost (LCC) which is to be minimized. The analysis is performed based on measured solar radiation and ambient temperature measured at Helwan city, Egypt. A comparison between ABC algorithm and Genetic Algorithm (GA) optimal results is done. Another location is selected which is Zagazig city to check the validity of ABC algorithm in any location. The ABC is more optimal than GA. The results encouraged the use of the PV systems to electrify the rural sites of Egypt. PMID:25685507
Directory of Open Access Journals (Sweden)
Ahmed F. Mohamed
2014-05-01
Full Text Available One of the most recent optimization techniques applied to the optimal design of photovoltaic system to supply an isolated load demand is the Artificial Bee Colony Algorithm (ABC. The proposed methodology is applied to optimize the cost of the PV system including photovoltaic, a battery bank, a battery charger controller, and inverter. Two objective functions are proposed: the first one is the PV module output power which is to be maximized and the second one is the life cycle cost (LCC which is to be minimized. The analysis is performed based on measured solar radiation and ambient temperature measured at Helwan city, Egypt. A comparison between ABC algorithm and Genetic Algorithm (GA optimal results is done. Another location is selected which is Zagazig city to check the validity of ABC algorithm in any location. The ABC is more optimal than GA. The results encouraged the use of the PV systems to electrify the rural sites of Egypt.
Institute of Scientific and Technical Information of China (English)
ZHANG Xinhua
2006-01-01
Aim to the manufacturing supply chain optimization problem with time windows, presents an improved orthogonal genetic algorithm to solve it. At first, we decompose this problem into two sub-problems (distribution and routing) plus an interface mechanism to allow the two algorithms to collaborate in a master-slave fashion, with the distribution algorithm driving the routing algorithm. At second, we describe the proposed improved orthogonal genetic algorithm for solving giving problem detailedly. Finally, the examples suggest that this proposed approach is feasible, correct and valid.
FREQUENCY-CODED OPTIMIZATION OF HOPPED-FREQUENCY PULSE SIGNAL BASED ON GENETIC ALGORITHM
Institute of Scientific and Technical Information of China (English)
Liu Zheng; Mu Xuehua
2005-01-01
The Frequency-Coded Pulse (FCP) signal has good performance of range and Doppler resolution. This paper first gives the mathematical expression of the ambiguity function for FCP signals, and then presents a coding rule for optimizing FCP signal. The genetic algorithm is presented to solve this kind of problem for optimizing codes. Finally, an example for optimizing calculation is illustrated and the optimized frequency coding results are given with the code length N=64 and N=128 respectively.
Proton Exchange Membrane Fuel Cell Modeling Based on Seeker Optimization Algorithm
Institute of Scientific and Technical Information of China (English)
LI Qi; DAI Chao-hua; Chen Wei-rong; JIA Jun-bo; HAN Ming
2008-01-01
Seeker optimization algorithm (SOA) has applications in continuous space of swarm intelligence. In the fields of proton ex-change membrane fuel cell (PEMFC) modeling, SOA was proposed to research a set of optimized parameters in PEMFC polariza-tion curve model. Experimental result showed that the mean square error of the optimization modeling strategy was only 6.9 × 10-23. Hence, the optimization model could fit the experiment data with high precision.
Optimization of wavelet- and curvelet-based denoising algorithms by multivariate SURE and GCV
Mortezanejad, R.; Gholami, A.
2016-06-01
One of the most crucial challenges in seismic data processing is the reduction of noise in the data or improving the signal-to-noise ratio (SNR). Wavelet- and curvelet-based denoising algorithms have become popular to address random noise attenuation for seismic sections. Wavelet basis, thresholding function, and threshold value are three key factors of such algorithms, having a profound effect on the quality of the denoised section. Therefore, given a signal, it is necessary to optimize the denoising operator over these factors to achieve the best performance. In this paper a general denoising algorithm is developed as a multi-variant (variable) filter which performs in multi-scale transform domains (e.g. wavelet and curvelet). In the wavelet domain this general filter is a function of the type of wavelet, characterized by its smoothness, thresholding rule, and threshold value, while in the curvelet domain it is only a function of thresholding rule and threshold value. Also, two methods, Stein’s unbiased risk estimate (SURE) and generalized cross validation (GCV), evaluated using a Monte Carlo technique, are utilized to optimize the algorithm in both wavelet and curvelet domains for a given seismic signal. The best wavelet function is selected from a family of fractional B-spline wavelets. The optimum thresholding rule is selected from general thresholding functions which contain the most well known thresholding functions, and the threshold value is chosen from a set of possible values. The results obtained from numerical tests show high performance of the proposed method in both wavelet and curvelet domains in comparison to conventional methods when denoising seismic data.
Directory of Open Access Journals (Sweden)
He Wang
2015-04-01
Full Text Available Demand prediction of supply chain is an important content and the first premise in supply management of different enterprises and has become one of the difficulties and hot research fields for the researchers related. The paper takes fresh food demand prediction for example and presents a new algorithm for predicting demand of fresh food supply chain. First, the working principle and the root causes of the defects of particle swarm optimization algorithm are analyzed in the study; Second, the study designs a new cloud particle swarm optimization algorithm to guarantee the effectiveness of particles in later searching phase and redesigns its cloud global optimization searching method and crossover operation; Finally, a certain fresh food supply chain is taken for example to illustrate the validity and feasibility of the improved algorithm and the experimental results show that the improved algorithm can improve prediction accuracy and calculation efficiency when used for demand prediction of fresh food supply chain.
HYBRID OPTIMIZING GRIFFON-VULTURE ALGORITHM BASED ON SWARM INTELLIGENCE MECHANISMS
Chastikova V. A.; Ostapov D. S.
2014-01-01
Griffon-vultures with input parameters minimal value for compound functions optimization that change during the time searching hybrid algorithm offered in this article. Researches of its efficiency and comparing analysis with some other systems have been performed
Directory of Open Access Journals (Sweden)
Sharvani G S
2012-10-01
Full Text Available Designing an effective load balancing algorithm is difficult due to Dynamic topology of MANET. Toaddress the problem, a load balancing routing algorithm namely Modified Termite Algorithm (MTA hasbeen developed based on ant’s food foraging behavior. Stability of the link is determined based on nodestability factor ‘’. The stability factor “ “of the node is the ratio defined between the “hello sent” and“hello replied” by a node to its neighbors. This also indicates the link stability in relation to other pathstowards the destination. A higher ratio of “” indicates that the neighbor node is more stable. Using thisconcept pheromone evaporation for the stable node is fine tuned such that if the ratio “” is more, theevaporation is slow and if “” is less the evaporation is faster. This leads to decreasing of the pheromonecontent in an optimal path which may result in congestion. These paths can be avoided using efficientevaporation technique. The MTA developed by adopting efficient pheromone evaporation technique willaddress the load balancing problems and expected to enhance the performance of the network in terms ofthroughput, and reduces End-to-end delay and Routing overheads
Directory of Open Access Journals (Sweden)
Zhibo Zhai
2015-01-01
Full Text Available Cutting parameter optimization dramatically affects the production time, cost, profit rate, and the quality of the final products, in milling operations. Aiming to select the optimum machining parameters in multitool milling operations such as corner milling, face milling, pocket milling, and slot milling, this paper presents a novel version of TLBO, TLBO with dynamic assignment learning strategy (DATLBO, in which all the learners are divided into three categories based on their results in “Learner Phase”: good learners, moderate learners, and poor ones. Good learners are self-motivated and try to learn by themselves; each moderate learner uses a probabilistic approach to select one of good learners to learn; each poor learner also uses a probabilistic approach to select several moderate learners to learn. The CEC2005 contest benchmark problems are first used to illustrate the effectiveness of the proposed algorithm. Finally, the DATLBO algorithm is applied to a multitool milling process based on maximum profit rate criterion with five practical technological constraints. The unit time, unit cost, and profit rate from the Handbook (HB, Feasible Direction (FD method, Genetic Algorithm (GA method, five other TLBO variants, and DATLBO are compared, illustrating that the proposed approach is more effective than HB, FD, GA, and five other TLBO variants.
Microgenetic optimization algorithm for optimal wavefront shaping
Anderson, Benjamin R; Gunawidjaja, Ray; Eilers, Hergen
2015-01-01
One of the main limitations of utilizing optimal wavefront shaping in imaging and authentication applications is the slow speed of the optimization algorithms currently being used. To address this problem we develop a micro-genetic optimization algorithm ($\\mu$GA) for optimal wavefront shaping. We test the abilities of the $\\mu$GA and make comparisons to previous algorithms (iterative and simple-genetic) by using each algorithm to optimize transmission through an opaque medium. From our experiments we find that the $\\mu$GA is faster than both the iterative and simple-genetic algorithms and that both genetic algorithms are more resistant to noise and sample decoherence than the iterative algorithm.
Georgieva, A.; Jordanov, Ivan
2009-01-01
In this paper a new heuristic hybrid technique for bound-constrained global optimization is proposed. We developed iterative algorithm called GLPτS that uses genetic algorithms, LPτ low-discrepancy sequences of points and heuristic rules to find regions of attraction when searching a global minimum of an objective function. Subsequently Nelder-Mead Simplex local search technique is used to refine the solution. The combination of the three techniques (Genetic algorithms, LPτO Low-discrepancy s...
An Aircraft Navigation System Fault Diagnosis Method Based on Optimized Neural Network Algorithm
Institute of Scientific and Technical Information of China (English)
Jean-dedieu Weyepe
2014-01-01
Air data and inertial reference system (ADIRS) is one of the complex sub-system in the aircraft navigation system and it plays an important role into the flight safety of the aircraft. This paper propose an optimize neural network algorithm which is a combination of neural network and ant colony algorithm to improve efficiency of maintenance engineer job task.
Optimization design of wind turbine drive train based on Matlab genetic algorithm toolbox
Li, R. N.; Liu, X.; Liu, S. J.
2013-12-01
In order to ensure the high efficiency of the whole flexible drive train of the front-end speed adjusting wind turbine, the working principle of the main part of the drive train is analyzed. As critical parameters, rotating speed ratios of three planetary gear trains are selected as the research subject. The mathematical model of the torque converter speed ratio is established based on these three critical variable quantity, and the effect of key parameters on the efficiency of hydraulic mechanical transmission is analyzed. Based on the torque balance and the energy balance, refer to hydraulic mechanical transmission characteristics, the transmission efficiency expression of the whole drive train is established. The fitness function and constraint functions are established respectively based on the drive train transmission efficiency and the torque converter rotating speed ratio range. And the optimization calculation is carried out by using MATLAB genetic algorithm toolbox. The optimization method and results provide an optimization program for exact match of wind turbine rotor, gearbox, hydraulic mechanical transmission, hydraulic torque converter and synchronous generator, ensure that the drive train work with a high efficiency, and give a reference for the selection of the torque converter and hydraulic mechanical transmission.
Directory of Open Access Journals (Sweden)
Wei Zhang
2012-01-01
Full Text Available This paper presents the model and algorithms for traffic flow data monitoring and optimal traffic light control based on wireless sensor networks. Given the scenario that sensor nodes are sparsely deployed along the segments between signalized intersections, an analytical model is built using continuum traffic equation and develops the method to estimate traffic parameter with the scattered sensor data. Based on the traffic data and principle of traffic congestion formation, we introduce the congestion factor which can be used to evaluate the real-time traffic congestion status along the segment and to predict the subcritical state of traffic jams. The result is expected to support the timing phase optimization of traffic light control for the purpose of avoiding traffic congestion before its formation. We simulate the traffic monitoring based on the Mobile Century dataset and analyze the performance of traffic light control on VISSIM platform when congestion factor is introduced into the signal timing optimization model. The simulation result shows that this method can improve the spatial-temporal resolution of traffic data monitoring and evaluate traffic congestion status with high precision. It is helpful to remarkably alleviate urban traffic congestion and decrease the average traffic delays and maximum queue length.
Optimization design of wind turbine drive train based on Matlab genetic algorithm toolbox
International Nuclear Information System (INIS)
In order to ensure the high efficiency of the whole flexible drive train of the front-end speed adjusting wind turbine, the working principle of the main part of the drive train is analyzed. As critical parameters, rotating speed ratios of three planetary gear trains are selected as the research subject. The mathematical model of the torque converter speed ratio is established based on these three critical variable quantity, and the effect of key parameters on the efficiency of hydraulic mechanical transmission is analyzed. Based on the torque balance and the energy balance, refer to hydraulic mechanical transmission characteristics, the transmission efficiency expression of the whole drive train is established. The fitness function and constraint functions are established respectively based on the drive train transmission efficiency and the torque converter rotating speed ratio range. And the optimization calculation is carried out by using MATLAB genetic algorithm toolbox. The optimization method and results provide an optimization program for exact match of wind turbine rotor, gearbox, hydraulic mechanical transmission, hydraulic torque converter and synchronous generator, ensure that the drive train work with a high efficiency, and give a reference for the selection of the torque converter and hydraulic mechanical transmission
Optimization design of drilling string by screw coal miner based on ant colony algorithm
Institute of Scientific and Technical Information of China (English)
ZHANG Qiang; MAO Jun; DING Fei
2008-01-01
It took that the weight minimum and drive efficiency maximal were as double optimizing target, the optimization model had built the drilling string, and the optimization solution was used of the ant colony algorithm to find in progress. Adopted a two-layer search of the continuous space ant colony algorithm with overlapping or variation global ant search operation strategy and conjugated gradient partial ant search operation strat-egy. The experiment indicates that the spiral drill weight reduces 16.77% and transports the efficiency enhance 7.05% through the optimization design, the ant colony algorithm application on the spiral drill optimized design has provided the basis for the system re-search screw coal mine machine.
Optimization design of drilling string by screw coal miner based on ant colony algorithm
Institute of Scientific and Technical Information of China (English)
ZHANG Qiang; MAO Jun; DING Fei
2008-01-01
It took that the weight minimum and drive efficiency maximal were as double optimizing target,the optimization model had built the drilling string,and the optimization solution was used of the ant colony algorithm to find in progress.Adopted a two-layer search of the continuous space ant colony algorithm with overlapping or variation global ant search operation strategy and conjugated gradient partial ant search operation strategy.The experiment indicates that the spiral drill weight reduces 16.77% and transports the efficiency enhance 7.05% through the optimization design,the ant colony algorithm application on the spiral drill optimized design has provided the basis for the system research screw coal mine machine.
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
A global optimization approach to turbine blade design based on hierarchical fair competition genetic algorithms with dynamic niche (HFCDN-GAs) coupled with Reynolds-averaged Navier-Stokes (RANS) equation is presented. In order to meet the search theory of GAs and the aerodynamic performances of turbine, Bezier curve is adopted to parameterize the turbine blade profile, and a fitness function pertaining to optimization is designed. The design variables are the control points' ordinates of characteristic polygon of Bezier curve representing the turbine blade profile. The object function is the maximum lift-drag ratio of the turbine blade. The constraint conditions take into account the leading and trailing edge metal angle, and the strength and aerodynamic performances of turbine blade. And the treatment method of the constraint conditions is the flexible penalty function. The convergence history of test function indicates that HFCDN-GAs can locate the global optimum within a few search steps and have high robustness. The lift-drag ratio of the optimized blade is 8.3% higher than that of the original one. The results show that the proposed global optimization approach is effective for turbine blade.
Mingming Gao; Jingchang Nan; Surina Wang
2014-01-01
In order to design and optimize high-linearity power amplifier (PA), which with nonlinear and memory effect, it is very important to build power amplifier behavior modeling accurately. This paper proposes a power amplifier behavior modeling based on RBF neural network with improved chaos particle swarm optimization algorithm. To make the particles evenly distribute in the problem search space, a novel Chaos Particle Swarm Optimization (CPSO) is proposed based on the analysis of the ergodicity...
Directory of Open Access Journals (Sweden)
Xu Yang
2013-01-01
Full Text Available Based on the hot rolling process, a load distribution optimization model is established, which includes rolling force model, thickness distribution model, and temperature model. The rolling force ratio distribution and good strip shape are integrated as two indicators of objective function in the optimization model. Then, the evolutionary algorithm for complex-process optimization (EACOP is introduced in the following optimization algorithm. Due to its flexible framework structure on search mechanism, the EACOP is improved within differential evolutionary strategy, for better coverage speed and search efficiency. At last, the experimental and simulation result shows that evolutionary algorithm for complex-process optimization based on differential evolutionary strategy (DEACOP is the organism including local search and global search. The comparison with experience distribution and EACOP shows that DEACOP is able to use fewer adjustable parameters and more efficient population differential strategy during solution searching; meanwhile it still can get feasible mathematical solution for actual load distribution problems in hot rolling process.
An extension theory-based maximum power tracker using a particle swarm optimization algorithm
International Nuclear Information System (INIS)
Highlights: • We propose an adaptive maximum power point tracking (MPPT) approach for PV systems. • Transient and steady state performances in tracking process are improved. • The proposed MPPT can automatically tune tracking step size along a P–V curve. • A PSO algorithm is used to determine the weighting values of extension theory. - Abstract: The aim of this work is to present an adaptive maximum power point tracking (MPPT) approach for photovoltaic (PV) power generation system. Integrating the extension theory as well as the conventional perturb and observe method, an maximum power point (MPP) tracker is made able to automatically tune tracking step size by way of the category recognition along a P–V characteristic curve. Accordingly, the transient and steady state performances in tracking process are improved. Furthermore, an optimization approach is proposed on the basis of a particle swarm optimization (PSO) algorithm for the complexity reduction in the determination of weighting values. At the end of this work, a simulated improvement in the tracking performance is experimentally validated by an MPP tracker with a programmable system-on-chip (PSoC) based controller
DEFF Research Database (Denmark)
Wang, Yong; Cai, Zixing; Zhou, Yuren; Fan, Zhun
2009-01-01
A novel approach to deal with numerical and engineering constrained optimization problems, which incorporates a hybrid evolutionary algorithm and an adaptive constraint-handling technique, is presented in this paper. The hybrid evolutionary algorithm simultaneously uses simplex crossover and two...... four well-known constrained design problems verify the effectiveness and efficiency of the proposed method. The experimental results show that integrating the hybrid evolutionary algorithm with the adaptive constraint-handling technique is beneficial, and the proposed method achieves competitive...... performance with respect to some other state-of-the-art approaches in constrained evolutionary optimization....
Optimal congestion control algorithm for ad hoc networks: Penalty function-based approach
Institute of Scientific and Technical Information of China (English)
XU Wet-qiang; WU Tie-jun
2006-01-01
In this paper, based on the inherent characteristic of the contention relation between flows in ad hoc networks, we introduce the notion of the link's interference set, extend the utility maximization problem representing congestion control in wireline networks to ad hoc networks, apply the penalty function approach and the subgradient method to solve this problem, and propose the congestion control algorithm Penalty function-based Optical Congestion Control (POCC) which is implemented in NS2 simulator. Specifically, each link transmits periodically the information on its congestion state to its interference set; the session at each source adjusts the transmission rate based on the optimal tradeoffbetween the utility value and the congestion level which the interference set of the links that this session goes though suffers from. MATLAB-based simulation results showed that POCC can approach the globally optimal solution. The NS2-based simulation results showed that POCC outperforms default TCP and ATCP to achieve efficient and fair resource allocation in ad hoc networks.
Directory of Open Access Journals (Sweden)
Miaomiao Bai
2014-11-01
Full Text Available In underwater blasting experiment, the layout of the sensor has always been highly concerned. From the perspective of reconstruction with explosion overpressure field, the paper presents four indicators, which can obtain the optimal sensor layout scheme and guide sensor layout in practical experiment, combining with the genetic algorithm with global search. Then, a multi-scale model in every subregion of underwater blasting field was established to be used simulation experiments. By Matlab, the variation of these four indicators with different sensor layout, and reconstruction accuracy are analyzed and discussed. Finally, a conclusion has been raised through the analysis and comparison of simulation results, that the program can get a better sensor layout. It requires fewer number of sensors to be able to get good results with high accuracy. In the actual test explosions, we can refer to this scheme laid sensors.
Institute of Scientific and Technical Information of China (English)
Yu-xiang LI; Yin-liang ZHAO‡; Bin LIU; Shuo JI
2015-01-01
Thread partition plays an important role in speculative multithreading (SpMT) for automatic parallelization of ir-regular programs. Using unified values of partition parameters to partition different applications leads to the fact that every ap-plication cannot own its optimal partition scheme. In this paper, five parameters affecting thread partition are extracted from heuristic rules. They are the dependence threshold (DT), lower limit of thread size (TSL), upper limit of thread size (TSU), lower limit of spawning distance (SDL), and upper limit of spawning distance (SDU). Their ranges are determined in accordance with heuristic rules, and their step-sizes are set empirically. Under the condition of setting speedup as an objective function, all com-binations of five threshold values form the solution space, and our aim is to search for the best combination to obtain the best thread granularity, thread dependence, and spawning distance, so that every application has its best partition scheme. The issue can be attributed to a single objective optimization problem. We use the artificial immune algorithm (AIA) to search for the optimal solution. On Prophet, which is a generic SpMT processor to evaluate the performance of multithreaded programs, Olden bench-marks are used to implement the process. Experiments show that we can obtain the optimal parameter values for every benchmark, and Olden benchmarks partitioned with the optimized parameter values deliver a performance improvement of 3.00%on a 4-core platform compared with a machine learning based approach, and 8.92%compared with a heuristics-based approach.
Note: Ultrasonic gas flowmeter based on optimized time-of-flight algorithms
Wang, X. F.; Tang, Z. A.
2011-04-01
A new digital signal processor based single path ultrasonic gas flowmeter is designed, constructed, and experimentally tested. To achieve high accuracy measurements, an optimized ultrasound driven method of incorporation of the amplitude modulation and the phase modulation of the transmit-receive technique is used to stimulate the transmitter. Based on the regularities among the received envelope zero-crossings, different received signal's signal-to-noise ratio situations are discriminated and optional time-of-flight algorithms are applied to take flow rate calculations. Experimental results from the dry calibration indicate that the designed flowmeter prototype can meet the zero-flow verification test requirements of the American Gas Association Report No. 9. Furthermore, the results derived from the flow calibration prove that the proposed flowmeter prototype can measure flow rate accurately in the practical experiments, and the nominal accuracies after FWME adjustment are lower than 0.8% throughout the calibration range.
Note: Ultrasonic gas flowmeter based on optimized time-of-flight algorithms
Energy Technology Data Exchange (ETDEWEB)
Wang, X. F.; Tang, Z. A. [Department of Electronic Science and Technology, Dalian University of Technology, Dalian, 116023 (China)
2011-04-15
A new digital signal processor based single path ultrasonic gas flowmeter is designed, constructed, and experimentally tested. To achieve high accuracy measurements, an optimized ultrasound driven method of incorporation of the amplitude modulation and the phase modulation of the transmit-receive technique is used to stimulate the transmitter. Based on the regularities among the received envelope zero-crossings, different received signal's signal-to-noise ratio situations are discriminated and optional time-of-flight algorithms are applied to take flow rate calculations. Experimental results from the dry calibration indicate that the designed flowmeter prototype can meet the zero-flow verification test requirements of the American Gas Association Report No. 9. Furthermore, the results derived from the flow calibration prove that the proposed flowmeter prototype can measure flow rate accurately in the practical experiments, and the nominal accuracies after FWME adjustment are lower than 0.8% throughout the calibration range.
A Bell-Curved Based Algorithm for Mixed Continuous and Discrete Structural Optimization
Kincaid, Rex K.; Weber, Michael; Sobieszczanski-Sobieski, Jaroslaw
2001-01-01
An evolutionary based strategy utilizing two normal distributions to generate children is developed to solve mixed integer nonlinear programming problems. This Bell-Curve Based (BCB) evolutionary algorithm is similar in spirit to (mu + mu) evolutionary strategies and evolutionary programs but with fewer parameters to adjust and no mechanism for self adaptation. First, a new version of BCB to solve purely discrete optimization problems is described and its performance tested against a tabu search code for an actuator placement problem. Next, the performance of a combined version of discrete and continuous BCB is tested on 2-dimensional shape problems and on a minimum weight hub design problem. In the latter case the discrete portion is the choice of the underlying beam shape (I, triangular, circular, rectangular, or U).
International Nuclear Information System (INIS)
Genetic algorithms are biologically motivated adaptive systems which have been used, with good results, for function optimization. The purpose of this work is to introduce a new parallelization method to be applied to the Population-Based Incremental Learning (PBIL) algorithm. PBIL combines standard genetic algorithm mechanisms with simple competitive learning and has ben successfully used in combinatorial optimization problems. The development of this algorithm aims its application to the reload optimization of PWR nuclear reactors. Tests have been performed with combinatorial optimization problems similar to the reload problem. Results are compared to the serial PBIL ones, showing the new method's superiority and its viability as a tool for the nuclear core reload problem solution. (author)
A nonvoxel-based dose convolution/superposition algorithm optimized for scalable GPU architectures
International Nuclear Information System (INIS)
, respectively. Accuracy was investigated using three distinct phantoms with varied geometries and heterogeneities and on a series of 14 segmented lung CT data sets. Performance gains were calculated using three 256 mm cube homogenous water phantoms, with isotropic voxel dimensions of 1, 2, and 4 mm. Results: The nonvoxel-based GPU algorithm was independent of the data size and provided significant computational gains over the CPU algorithm for large CT data sizes. The parameter search analysis also showed that the ray combination of 8 zenithal and 8 azimuthal angles along with 1 mm radial sampling and 2 mm parallel ray spacing maintained dose accuracy with greater than 99% of voxels passing the γ test. Combining the acceleration obtained from GPU parallelization with the sampling optimization, the authors achieved a total performance improvement factor of >175 000 when compared to our voxel-based ground truth CPU benchmark and a factor of 20 compared with a voxel-based GPU dose convolution method. Conclusions: The nonvoxel-based convolution method yielded substantial performance improvements over a generic GPU implementation, while maintaining accuracy as compared to a CPU computed ground truth dose distribution. Such an algorithm can be a key contribution toward developing tools for adaptive radiation therapy systems
Event Space-Correlation Analysis Algorithm Based on Ant Colony Optimization
Directory of Open Access Journals (Sweden)
Mingsheng Hu
2013-03-01
Full Text Available Historical disaster events are taken as a case for space-correlation analysis, three-dimensional disasters space-time network are modeled and chain relationship of disaster nodes are mined by looking for similar space vector in network. Then transformed the vector discover problem into a path optimization problem and solved by using ant colony algorithm, where the pheromone parameter in the process of optimal-path finding is concerned as the algorithm result, in order to solve the problem of path competition which existed when only to solve the optimal path. Experimental results of MATLAB show that this method has high accuracy and practicality.
A Genetic Algorithm Optimized Decision Tree-SVM based Stock Market Trend Prediction System
Directory of Open Access Journals (Sweden)
Binoy B. Nair
2010-12-01
Full Text Available Prediction of stock market trends has been an area of great interest both to researchers attempting to uncover the information hidden in the stock market data and for those who wish to profit by trading stocks. The extremely nonlinear nature of the stock market data makes it very difficult to design a system that can predict the future direction of the stock market with sufficient accuracy. This work presents a data mining based stock market trend prediction system, which produces highly accurate stock market forecasts. The proposed system is a genetic algorithm optimized decision tree-support vector machine (SVM hybrid, which can predict one-day-ahead trends in stockmarkets. The uniqueness of the proposed system lies in the use ofthe hybrid system which can adapt itself to the changing market conditions and in the fact that while most of the attempts at stockmarket trend prediction have approached it as a regression problem, present study converts the trend prediction task into a classification problem, thus improving the prediction accuracysignificantly. Performance of the proposed hybrid system isvalidated on the historical time series data from the Bombaystock exchange sensitive index (BSE-Sensex. The system performance is then compared to that of an artificial neural network (ANN based system and a naïve Bayes based system. It is found that the trend prediction accuracy is highest for the hybrid system and the genetic algorithm optimized decision tree- SVM hybrid system outperforms both the artificial neural network and the naïve bayes based trend prediction systems.
An efficient algorithm for function optimization: modified stem cells algorithm
Taherdangkoo, Mohammad; Paziresh, Mahsa; Yazdi, Mehran; Bagheri, Mohammad
2013-03-01
In this paper, we propose an optimization algorithm based on the intelligent behavior of stem cell swarms in reproduction and self-organization. Optimization algorithms, such as the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, Ant Colony Optimization (ACO) algorithm and Artificial Bee Colony (ABC) algorithm, can give solutions to linear and non-linear problems near to the optimum for many applications; however, in some case, they can suffer from becoming trapped in local optima. The Stem Cells Algorithm (SCA) is an optimization algorithm inspired by the natural behavior of stem cells in evolving themselves into new and improved cells. The SCA avoids the local optima problem successfully. In this paper, we have made small changes in the implementation of this algorithm to obtain improved performance over previous versions. Using a series of benchmark functions, we assess the performance of the proposed algorithm and compare it with that of the other aforementioned optimization algorithms. The obtained results prove the superiority of the Modified Stem Cells Algorithm (MSCA).
Optimal sensor placement for time-domain identification using a wavelet-based genetic algorithm
Mahdavi, Seyed Hossein; Razak, Hashim Abdul
2016-06-01
This paper presents a wavelet-based genetic algorithm strategy for optimal sensor placement (OSP) effective for time-domain structural identification. Initially, the GA-based fitness evaluation is significantly improved by using adaptive wavelet functions. Later, a multi-species decimal GA coding system is modified to be suitable for an efficient search around the local optima. In this regard, a local operation of mutation is introduced in addition with regeneration and reintroduction operators. It is concluded that different characteristics of applied force influence the features of structural responses, and therefore the accuracy of time-domain structural identification is directly affected. Thus, the reliable OSP strategy prior to the time-domain identification will be achieved by those methods dealing with minimizing the distance of simulated responses for the entire system and condensed system considering the force effects. The numerical and experimental verification on the effectiveness of the proposed strategy demonstrates the considerably high computational performance of the proposed OSP strategy, in terms of computational cost and the accuracy of identification. It is deduced that the robustness of the proposed OSP algorithm lies in the precise and fast fitness evaluation at larger sampling rates which result in the optimum evaluation of the GA-based exploration and exploitation phases towards the global optimum solution.
A Modified Particle Swarm Optimization Algorithm
Jie He; Hui Guo
2013-01-01
In optimizing the particle swarm optimization (PSO) that inevitable existence problem of prematurity and the local convergence, this paper base on this aspects is put forward a kind of modified particle swarm optimization algorithm, take the gradient descent method (BP algorithm) as a particle swarm operator embedded in particle swarm algorithm, and at the same time use to attenuation wall (Damping) approach to make fly off the search area of the particles of size remain unchanged and avoid t...
Hashemi-Dezaki, Hamed; Mohammadalizadeh-Shabestary, Masoud; Askarian-Abyaneh, Hossein; Rezaei-Jegarluei, Mohammad
2014-01-01
In electrical distribution systems, a great amount of power are wasting across the lines, also nowadays power factors, voltage profiles and total harmonic distortions (THDs) of most loads are not as would be desired. So these important parameters of a system play highly important role in wasting money and energy, and besides both consumers and sources are suffering from a high rate of distortions and even instabilities. Active power filters (APFs) are innovative ideas for solving of this adversity which have recently used instantaneous reactive power theory. In this paper, a novel method is proposed to optimize the allocation of APFs. The introduced method is based on the instantaneous reactive power theory in vectorial representation. By use of this representation, it is possible to asses different compensation strategies. Also, APFs proper placement in the system plays a crucial role in either reducing the losses costs and power quality improvement. To optimize the APFs placement, a new objective function has been defined on the basis of five terms: total losses, power factor, voltage profile, THD and cost. Genetic algorithm has been used to solve the optimization problem. The results of applying this method to a distribution network illustrate the method advantages.
Centrifugal compressor blade optimization based on uniform design and genetic algorithms
Institute of Scientific and Technical Information of China (English)
Xinwei SHU; Chuangang GU; Jun XIAO; Chuang GAO
2008-01-01
An optimization approach to centrifugal com-pressor blade design, incorporating uniform design method (UDM), computational fluid dynamics (CFD) analysis technique, regression analysis method and gen-etic algorithms (GA), is presented. UDM is employed to generate the geometric information of trial samples whose performance is evaluated by CFD technique. Then, func-tion approximation of sample information is performed by regression analysis method. Finally, global optimiza-tion of the approximative function is obtained by genetic algorithms. Taking maximum isentropic efficiency as objective function, this optimization approach has been applied to the optimum design of a certain centrifugal compressor blades. The results, compared with those of the original one, show that isentropic efficiency of the optimized impeller has been improved which indicates the effectiveness of the proposed optimization approach.
Directory of Open Access Journals (Sweden)
Jie-Sheng Wang
2015-06-01
Full Text Available In order to improve the accuracy and real-time of all kinds of information in the cash business, and solve the problem which accuracy and stability is not high of the data linkage between cash inventory forecasting and cash management information in the commercial bank, a hybrid learning algorithm is proposed based on adaptive population activity particle swarm optimization (APAPSO algorithm combined with the least squares method (LMS to optimize the adaptive network-based fuzzy inference system (ANFIS model parameters. Through the introduction of metric function of population diversity to ensure the diversity of population and adaptive changes in inertia weight and learning factors, the optimization ability of the particle swarm optimization (PSO algorithm is improved, which avoids the premature convergence problem of the PSO algorithm. The simulation comparison experiments are carried out with BP-LMS algorithm and standard PSO-LMS by adopting real commercial banks’ cash flow data to verify the effectiveness of the proposed time series prediction of bank cash flow based on improved PSO-ANFIS optimization method. Simulation results show that the optimization speed is faster and the prediction accuracy is higher.
Optimal operation of water supply systems with tanks based on genetic algorithm
Institute of Scientific and Technical Information of China (English)
YU Ting-chao; ZHANG Tu-qiao; LI Xun
2005-01-01
In view of the poor water supply system's network properties, the system's complicated network hydraulic equations were replaced by macroscopic nodal pressure model and the model of relationship between supply flow and water source head. By using pump-station pressure head and initial tank water levels as decision variables, the model of optimal allocation of water supply between pump-sources was developed. Genetic algorithm was introduced to deal with the model of optimal allocation of water supply. Methods for handling each constraint condition were put forward, and overcome the shortcoming such as premature convergence of genetic algorithm;a solving method was brought forward in which genetic algorithm was combined with simulated annealing technology and self-adaptive crossover and mutation probabilities were adopted. An application example showed the feasibility of this algorithm.
An Image Filter Based on Shearlet Transformation and Particle Swarm Optimization Algorithm
Directory of Open Access Journals (Sweden)
Kai Hu
2015-01-01
Full Text Available Digital image is always polluted by noise and made data postprocessing difficult. To remove noise and preserve detail of image as much as possible, this paper proposed image filter algorithm which combined the merits of Shearlet transformation and particle swarm optimization (PSO algorithm. Firstly, we use classical Shearlet transform to decompose noised image into many subwavelets under multiscale and multiorientation. Secondly, we gave weighted factor to those subwavelets obtained. Then, using classical Shearlet inverse transform, we obtained a composite image which is composed of those weighted subwavelets. After that, we designed fast and rough evaluation method to evaluate noise level of the new image; by using this method as fitness, we adopted PSO to find the optimal weighted factor we added; after lots of iterations, by the optimal factors and Shearlet inverse transform, we got the best denoised image. Experimental results have shown that proposed algorithm eliminates noise effectively and yields good peak signal noise ratio (PSNR.
Zabbah, Iman
2012-01-01
Electro Discharge Machine (EDM) is the commonest untraditional method of production for forming metals and the Non-Oxide ceramics. The increase of smoothness, the increase of the remove of filings, and also the decrease of proportional erosion tool has an important role in this machining. That is directly related to the choosing of input parameters.The complicated and non-linear nature of EDM has made the process impossible with usual and classic method. So far, some methods have been used based on intelligence to optimize this process. At the top of them we can mention artificial neural network that has modelled the process as a black box. The problem of this kind of machining is seen when a workpiece is composited of the collection of carbon-based materials such as silicon carbide. In this article, besides using the new method of mono-pulse technical of EDM, we design a fuzzy neural network and model it. Then the genetic algorithm is used to find the optimal inputs of machine. In our research, workpiece is a Non-Oxide metal called silicon carbide. That makes the control process more difficult. At last, the results are compared with the previous methods.
The Convergence Speed of Single- And Multi-Objective Immune Algorithm Based Optimization Problems
Prof. M. Abo-Zahhad; Sabah M. Ahmed; Nabil Sabor; Ahmad F. Al-Ajlouni
2010-01-01
Despite the considerable amount of research related to immune algorithms and its applications in numerical optimization, digital filters design, and data mining, there is still little work related to issues as important as sensitivity analysis, [1]-[4]. Other aspects, such as convergence speed and parameters adaptation, have been practically disregarded in the current specialized literature [7]-[8]. The convergence speed of the immune algorithm heavily depends on its main control parameters: ...
Optimization of PID controller based on The Bees Algorithm for one leg of a quadruped robot
Directory of Open Access Journals (Sweden)
Bakırcıoğlu Veli
2016-01-01
Full Text Available In this paper, we apply The Bees Algorithm to find optimal PID controller gains to control angular positions of robot leg joints with the minimum position error. In order to present more realistic simulation, system modelled in MATLAB/Simulink environment which is close to experimental set up. Solid model of system, which has two degrees of freedom, drawn by using a CAD software. Required physical specifications of robot leg for MATLAB/Simulink modelling is obtained from this CAD model. Controller of the system is designed in MATLAB/Simulink interface. Simulation results derived to show effectiveness of The Bees Algorithm to find optimal PID controller gains.
Discrete PSO algorithm based optimization of transmission lines loading in TNEP problem
International Nuclear Information System (INIS)
Transmission network expansion planning (TNEP) is a basic part of power system planning that determines where, when and how many new transmission lines should be added to the network. Up till now, various methods have been presented to solve the static transmission network expansion planning (STNEP) problem. But in all of these methods, lines adequacy rate has not been considered at the end of planning horizon, i.e. expanded network misses adequacy after some times and needs to be expanded again. In this paper, expansion planning has been implemented by merging lines loading parameter in the STNEP and inserting investment cost into the fitness function constraints using discrete particle swarm optimization (DPSO) algorithm. Expanded network will possess a maximum adequacy to provide load demand and also the transmission lines overloaded later. The proposed idea has been tested on the Garvers network and an actual transmission network of the Azerbaijan regional electric company, Iran, and the results are compared with the decimal codification genetic algorithm (DCGA) technique. The results evaluation shows that the network will possess maximum efficiency economically. Also, it is shown that precision and convergence speed of the proposed DPSO based method for the solution of the STNEP problem is superior to DCGA approach.
International Nuclear Information System (INIS)
A chaotic sequence based differential evolution (DE) approach for solving the dynamic economic dispatch problem (DEDP) with valve-point effect is presented in this paper. The proposed method combines the DE algorithm with the local search technique to improve the performance of the algorithm. DE is the main optimizer, while an approximated model for local search is applied to fine tune in the solution of the DE run. To accelerate convergence of DE, a series of constraints handling rules are adopted. An initial population obtained by using chaotic sequence exerts optimal performance of the proposed algorithm. The combined algorithm is validated for two test systems consisting of 10 and 13 thermal units whose incremental fuel cost function takes into account the valve-point loading effects. The proposed combined method outperforms other algorithms reported in literatures for DEDP considering valve-point effects.
Directory of Open Access Journals (Sweden)
Dębski Roman
2014-09-01
Full Text Available Effective, simulation-based trajectory optimization algorithms adapted to heterogeneous computers are studied with reference to the problem taken from alpine ski racing (the presented solution is probably the most general one published so far. The key idea behind these algorithms is to use a grid-based discretization scheme to transform the continuous optimization problem into a search problem over a specially constructed finite graph, and then to apply dynamic programming to find an approximation of the global solution. In the analyzed example it is the minimum-time ski line, represented as a piecewise-linear function (a method of elimination of unfeasible solutions is proposed. Serial and parallel versions of the basic optimization algorithm are presented in detail (pseudo-code, time and memory complexity. Possible extensions of the basic algorithm are also described. The implementation of these algorithms is based on OpenCL. The included experimental results show that contemporary heterogeneous computers can be treated as μ-HPC platforms-they offer high performance (the best speedup was equal to 128 while remaining energy and cost efficient (which is crucial in embedded systems, e.g., trajectory planners of autonomous robots. The presented algorithms can be applied to many trajectory optimization problems, including those having a black-box represented performance measure
Bat algorithm optimized fuzzy PD based speed controller for brushless direct current motor
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K. Premkumar
2016-06-01
Full Text Available In this paper, design of fuzzy proportional derivative controller and fuzzy proportional derivative integral controller for speed control of brushless direct current drive has been presented. Optimization of the above controllers design is carried out using nature inspired optimization algorithms such as particle swarm, cuckoo search, and bat algorithms. Time domain specifications such as overshoot, undershoot, settling time, recovery time, and steady state error and performance indices such as root mean squared error, integral of absolute error, integral of time multiplied absolute error and integral of squared error are measured and compared for the above controllers under different operating conditions such as varying set speed and load disturbance conditions. The precise investigation through simulation is performed using simulink toolbox. From the simulation test results, it is evident that bat optimized fuzzy proportional derivative controller has superior performance than the other controllers considered. Experimental test results have also been taken and analyzed for the optimal controller identified through simulation.
A Schedule Optimization Model on Multirunway Based on Ant Colony Algorithm
Yu Jiang; Zhaolong Xu; Xinxing Xu; Zhihua Liao; Yuxiao Luo
2014-01-01
In order to make full use of the slot of runway, reduce flight delay, and ensure fairness among airlines, a schedule optimization model for arrival-departure flights is established in the paper. The total delay cost and fairness among airlines are two objective functions. The ant colony algorithm is adopted to solve this problem and the result is more efficient and reasonable when compared with FCFS (first come first served) strategy. Optimization results show that the flight delay and fair d...
Reduction of Transmission Losses based on Optimal Power Flow using Genetic Algorithm
Dinesh Birla; Yagvalkya Sharma; Jinendra Rahul
2012-01-01
This paper presents the application of GeneticAlgorithm (GA) for solving optimal power flow problems. It is animportant tool for performance analysis of many power systemsproblems. Optimal power flow (OPF) is of very muchsignificance in power system operation analysis underderegulated environment of electricity industry. The OPFoptimizes a power system operating objective function, whilesatisfying a set of system operating constraints. The basic OPFsolution is obtained with production cost mi...
Ghiasi, Mohammad Sadegh; Arjmand, Navid; Boroushaki, Mehrdad; Farahmand, Farzam
2016-03-01
A six-degree-of-freedom musculoskeletal model of the lumbar spine was developed to predict the activity of trunk muscles during light, moderate and heavy lifting tasks in standing posture. The model was formulated into a multi-objective optimization problem, minimizing the sum of the cubed muscle stresses and maximizing the spinal stability index. Two intelligent optimization algorithms, i.e., the vector evaluated particle swarm optimization (VEPSO) and nondominated sorting genetic algorithm (NSGA), were employed to solve the optimization problem. The optimal solution for each task was then found in the way that the corresponding in vivo intradiscal pressure could be reproduced. Results indicated that both algorithms predicted co-activity in the antagonistic abdominal muscles, as well as an increase in the stability index when going from the light to the heavy task. For all of the light, moderate and heavy tasks, the muscles' activities predictions of the VEPSO and the NSGA were generally consistent and in the same order of the in vivo electromyography data. The proposed methodology is thought to provide improved estimations for muscle activities by considering the spinal stability and incorporating the in vivo intradiscal pressure data. PMID:26088358
Astuty; Haryono, T.
2016-04-01
Transmission expansion planning (TEP) is one of the issue that have to be faced caused by addition of large scale power generation into the existing power system. Optimization need to be conducted to get optimal solution technically and economically. Several mathematic methods have been applied to provide optimal allocation of new transmission line such us genetic algorithm, particle swarm optimization and tabu search. This paper proposed novel binary particle swarm optimization (NBPSO) to determine which transmission line should be added to the existing power system. There are two scenerios in this simulation. First, considering transmission power losses and the second is regardless transmission power losses. NBPSO method successfully obtain optimal solution in short computation time. Compare to the first scenario, the number of new line in second scenario which regardless power losses is less but produces high power losses that cause the cost becoming extremely expensive.
Optimized features selection for gender classification using optimization algorithms
KHAN, Sajid Ali; Nazir, Muhammad; RIAZ, Naveed
2013-01-01
Optimized feature selection is an important task in gender classification. The optimized features not only reduce the dimensions, but also reduce the error rate. In this paper, we have proposed a technique for the extraction of facial features using both appearance-based and geometric-based feature extraction methods. The extracted features are then optimized using particle swarm optimization (PSO) and the bee algorithm. The geometric-based features are optimized by PSO with ensem...
Directory of Open Access Journals (Sweden)
Debkalpa Goswami
2014-01-01
Full Text Available Electrochemical micromachining (EMM appears to be a very promising micromachining process for having higher machining rate, better precision and control, reliability, flexibility, environmental acceptability, and capability of machining a wide range of materials. It permits machining of chemically resistant materials, like titanium, copper alloys, super alloys and stainless steel to be used in biomedical, electronic, micro-electromechanical system and nano-electromechanical system applications. Therefore, the optimal use of an EMM process for achieving enhanced machining rate and improved profile accuracy demands selection of its various machining parameters. Various optimization tools, primarily Derringer’s desirability function approach have been employed by the past researchers for deriving the best parametric settings of EMM processes, which inherently lead to sub-optimal or near optimal solutions. In this paper, an attempt is made to apply an almost new optimization tool, i.e. differential search algorithm (DSA for parametric optimization of three EMM processes. A comparative study of optimization performance between DSA, genetic algorithm and desirability function approach proves the wide acceptability of DSA as a global optimization tool.
Cao, Jianfang; Cui, Hongyan; Shi, Hao; Jiao, Lijuan
2016-01-01
A back-propagation (BP) neural network can solve complicated random nonlinear mapping problems; therefore, it can be applied to a wide range of problems. However, as the sample size increases, the time required to train BP neural networks becomes lengthy. Moreover, the classification accuracy decreases as well. To improve the classification accuracy and runtime efficiency of the BP neural network algorithm, we proposed a parallel design and realization method for a particle swarm optimization (PSO)-optimized BP neural network based on MapReduce on the Hadoop platform using both the PSO algorithm and a parallel design. The PSO algorithm was used to optimize the BP neural network's initial weights and thresholds and improve the accuracy of the classification algorithm. The MapReduce parallel programming model was utilized to achieve parallel processing of the BP algorithm, thereby solving the problems of hardware and communication overhead when the BP neural network addresses big data. Datasets on 5 different scales were constructed using the scene image library from the SUN Database. The classification accuracy of the parallel PSO-BP neural network algorithm is approximately 92%, and the system efficiency is approximately 0.85, which presents obvious advantages when processing big data. The algorithm proposed in this study demonstrated both higher classification accuracy and improved time efficiency, which represents a significant improvement obtained from applying parallel processing to an intelligent algorithm on big data. PMID:27304987
Institute of Scientific and Technical Information of China (English)
杨璐鸿; 刘顺安; 张冠宇; 王春雪
2015-01-01
To improve the operational efficiency of global optimization in engineering, Kriging model was established to simplify the mathematical model for calculations. Ducted coaxial-rotors aircraft was taken as an example and Fluent software was applied to the virtual prototype simulations. Through simulation sample points, the total lift of the ducted coaxial-rotors aircraft was obtained. The Kriging model was then constructed, and the function was fitted. Improved particle swarm optimization (PSO) was also utilized for the global optimization of the Kriging model of the ducted coaxial-rotors aircraft for the determination of optimized global coordinates. Finally, the optimized results were simulated by Fluent. The results show that the Kriging model and the improved PSO algorithm significantly improve the lift performance of ducted coaxial-rotors aircraft and computer operational efficiency.
An optimal-estimation-based aerosol retrieval algorithm using OMI near-UV observations
Jeong, U.; Kim, J.; Ahn, C.; Torres, O.; Liu, X.; Bhartia, P. K.; Spurr, R. J. D.; Haffner, D.; Chance, K.; Holben, B. N.
2016-01-01
An optimal-estimation(OE)-based aerosol retrieval algorithm using the OMI (Ozone Monitoring Instrument) near-ultraviolet observation was developed in this study. The OE-based algorithm has the merit of providing useful estimates of errors simultaneously with the inversion products. Furthermore, instead of using the traditional look-up tables for inversion, it performs online radiative transfer calculations with the VLIDORT (linearized pseudo-spherical vector discrete ordinate radiative transfer code) to eliminate interpolation errors and improve stability. The measurements and inversion products of the Distributed Regional Aerosol Gridded Observation Network campaign in northeast Asia (DRAGON NE-Asia 2012) were used to validate the retrieved aerosol optical thickness (AOT) and single scattering albedo (SSA). The retrieved AOT and SSA at 388 nm have a correlation with the Aerosol Robotic Network (AERONET) products that is comparable to or better than the correlation with the operational product during the campaign. The OE-based estimated error represented the variance of actual biases of AOT at 388 nm between the retrieval and AERONET measurements better than the operational error estimates. The forward model parameter errors were analyzed separately for both AOT and SSA retrievals. The surface reflectance at 388 nm, the imaginary part of the refractive index at 354 nm, and the number fine-mode fraction (FMF) were found to be the most important parameters affecting the retrieval accuracy of AOT, while FMF was the most important parameter for the SSA retrieval. The additional information provided with the retrievals, including the estimated error and degrees of freedom, is expected to be valuable for relevant studies. Detailed advantages of using the OE method were described and discussed in this paper.
HYBRID OPTIMIZING GRIFFON-VULTURE ALGORITHM BASED ON SWARM INTELLIGENCE MECHANISMS
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Chastikova V. A.
2014-06-01
Full Text Available Griffon-vultures with input parameters minimal value for compound functions optimization that change during the time searching hybrid algorithm offered in this article. Researches of its efficiency and comparing analysis with some other systems have been performed
Zhang, Hong; Sun, Yanfeng; Zhai, Bing; Wang, Yiding
2013-07-01
This paper studies on the image registration of the medical images. Wavelet transform is adopted to decompose the medical images because the resolution of the medical image is high and the computational amount of the registration is large. Firstly, the low frequency sub-images are matched. Then source images are matched. The image registration was fulfilled by the ant colony optimization algorithm to search the extremum of the mutual information. The experiment result demonstrates the proposed approach can not only reduce calculation amount, but also skip from the local extremum during optimization process, and search the optimization value.
Optimization of Wireless Optical Communication System Based on Augmented Lagrange Algorithm
International Nuclear Information System (INIS)
The optimal model for wireless optical communication system with Gaussian pointing loss factor is studied, in which the value of bit error probability (BEP) is prespecified and the optimal system parameters is to be found. For the superiority of augmented Lagrange method, the model considered is solved by using a classical quadratic augmented Lagrange algorithm. The detailed numerical results are reported. Accordingly, the optimal system parameters such as transmitter power, transmitter wavelength, transmitter telescope gain and receiver telescope gain can be established, which provide a scheme for efficient operation of the wireless optical communication system.
STRUCTURED PARAMETRIC OPTIMIZATION OF MULTIVARIABLE ROBUST CONTROL BASED ON GENETIC ALGORITHMS
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Anatoliy A. Tunik
2008-02-01
Full Text Available This paper is devoted to the parametric robust - optimization of the LQG- controller designed for multivariable flight control system. At the 1St stage LQG- regulator is designed using the separation theorem. At the 2nd stage this controller was parametrically optimized on the basis of -criterion using genetic algorithm to find the trade-off between the performance and robustness. The sensitivity theory is applied to reduce the number of parameters involved in the optimization procedure.
de Lamare, R C; Fa, R
2012-01-01
This paper presents reduced-rank linearly constrained minimum variance (LCMV) beamforming algorithms based on joint iterative optimization of filters. The proposed reduced-rank scheme is based on a constrained joint iterative optimization of filters according to the minimum variance criterion. The proposed optimization procedure adjusts the parameters of a projection matrix and an adaptive reducedrank filter that operates at the output of the bank of filters. We describe LCMV expressions for the design of the projection matrix and the reduced-rank filter. We then describe stochastic gradient and develop recursive least-squares adaptive algorithms for their efficient implementation along with automatic rank selection techniques. An analysis of the stability and the convergence properties of the proposed algorithms is presented and semi-analytical expressions are derived for predicting their mean squared error (MSE) performance. Simulations for a beamforming application show that the proposed scheme and algorit...
Directory of Open Access Journals (Sweden)
Jian Liu
2016-01-01
Full Text Available The feasibility design method with multidisciplinary and multiobjective optimization is applied in the research of lightweight design and NVH performances of crankshaft in high-power marine reciprocating compressor. Opt-LHD is explored to obtain the experimental scheme and perform data sampling. The elliptical basis function neural network (EBFNN model considering modal frequency, static strength, torsional vibration angular displacement, and lightweight design of crankshaft is built. Deterministic optimization and reliability optimization for lightweight design of crankshaft are operated separately. Multi-island genetic algorithm (MIGA combined with multidisciplinary cooptimization method is used to carry out the multiobjective optimization of crankshaft structure. Pareto optimal set is obtained. Optimization results demonstrate that the reliability optimization which considers the uncertainties of production process can ensure product stability compared with deterministic optimization. The coupling and decoupling of structure mechanical properties, NVH, and lightweight design are considered during the multiobjective optimization of crankshaft structure. Designers can choose the optimization results according to their demands, which means the production development cycle and the costs can be significantly reduced.
An Optimal-Estimation-Based Aerosol Retrieval Algorithm Using OMI Near-UV Observations
Jeong, U; Kim, J.; Ahn, C.; Torres, O.; Liu, X.; Bhartia, P. K.; Spurr, R. J. D.; Haffner, D.; Chance, K.; Holben, B. N.
2016-01-01
An optimal-estimation(OE)-based aerosol retrieval algorithm using the OMI (Ozone Monitoring Instrument) near-ultraviolet observation was developed in this study. The OE-based algorithm has the merit of providing useful estimates of errors simultaneously with the inversion products. Furthermore, instead of using the traditional lookup tables for inversion, it performs online radiative transfer calculations with the VLIDORT (linearized pseudo-spherical vector discrete ordinate radiative transfer code) to eliminate interpolation errors and improve stability. The measurements and inversion products of the Distributed Regional Aerosol Gridded Observation Network campaign in northeast Asia (DRAGON NE-Asia 2012) were used to validate the retrieved aerosol optical thickness (AOT) and single scattering albedo (SSA). The retrieved AOT and SSA at 388 nm have a correlation with the Aerosol Robotic Network (AERONET) products that is comparable to or better than the correlation with the operational product during the campaign. The OEbased estimated error represented the variance of actual biases of AOT at 388 nm between the retrieval and AERONET measurements better than the operational error estimates. The forward model parameter errors were analyzed separately for both AOT and SSA retrievals. The surface reflectance at 388 nm, the imaginary part of the refractive index at 354 nm, and the number fine-mode fraction (FMF) were found to be the most important parameters affecting the retrieval accuracy of AOT, while FMF was the most important parameter for the SSA retrieval. The additional information provided with the retrievals, including the estimated error and degrees of freedom, is expected to be valuable for relevant studies. Detailed advantages of using the OE method were described and discussed in this paper.
International Nuclear Information System (INIS)
Multiple objectives must be considered in anatomy-based dose optimization for high-dose-rate brachytherapy and a large number of parameters must be optimized to satisfy often competing objectives. For objectives expressed solely in terms of dose variances, deterministic gradient-based algorithms can be applied and a weighted sum approach is able to produce a representative set of non-dominated solutions. As the number of objectives increases, or non-convex objectives are used, local minima can be present and deterministic or stochastic algorithms such as simulated annealing either cannot be used or are not efficient. In this case we employ a modified hybrid version of the multi-objective optimization algorithm NSGA-II. This, in combination with the deterministic optimization algorithm, produces a representative sample of the Pareto set. This algorithm can be used with any kind of objectives, including non-convex, and does not require artificial importance factors. A representation of the trade-off surface can be obtained with more than 1000 non-dominated solutions in 2-5 min. An analysis of the solutions provides information on the possibilities available using these objectives. Simple decision making tools allow the selection of a solution that provides a best fit for the clinical goals. We show an example with a prostate implant and compare results obtained by variance and dose-volume histogram (DVH) based objectives
Optimizing Artificial Neural Networks using Cat Swarm Optimization Algorithm
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John Paul T. Yusiong
2012-12-01
Full Text Available An Artificial Neural Network (ANN is an abstract representation of the biological nervous system which has the ability to solve many complex problems. The interesting attributes it exhibits makes an ANN capable of “learning”. ANN learning is achieved by training the neural network using a training algorithm. Aside from choosing a training algorithm to train ANNs, the ANN structure can also be optimized by applying certain pruning techniques to reduce network complexity. The Cat Swarm Optimization (CSO algorithm, a swarm intelligence-based optimization algorithm mimics the behavior of cats, is used as the training algorithm and the Optimal Brain Damage (OBD method as the pruning algorithm. This study suggests an approach to ANN training through the simultaneous optimization of the connection weights and ANN structure. Experiments performed on benchmark datasets taken from the UCI machine learning repository show that the proposed CSONN-OBD is an effective tool for training neural networks.
Shape, sizing optimization and material selection based on mixed variables and genetic algorithm
Tang, X.; Bassir, D.H.; Zhang, W.
2010-01-01
In this work, we explore simultaneous designs of materials selection and structural optimization. As the material selection turns out to be a discrete process that finds the optimal distribution of materials over the design domain, it cannot be performed with common gradient-based optimization metho
Mahdi M. M. El-Arini; Othman, Ahmed M.; Ahmed Fathy
2013-01-01
In recent years, the solar energy has become one of the most important alternative sources of electric energy, so it is important to operate photovoltaic (PV) panel at the optimal point to obtain the possible maximum efficiency. This paper presents a new optimization approach to maximize the electrical power of a PV panel. The technique which is based on objective function represents the output power of the PV panel and constraints, equality and inequality. First the dummy variables that have...
基于粒子群算法的布谷鸟搜索算法%Cuckoo search algorithm based on particle swarm optimization algorithm
Institute of Scientific and Technical Information of China (English)
李娜; 贺兴时
2014-01-01
为进一步提高布谷鸟搜索算法（Cuckoo Search ）的收敛速度和计算精度，将PSO算法用于CS算法的位置更新过程，提出了基于PSO算法的布谷鸟搜索算法（CSPSO ）。最后，通过6个典型测试函数进行仿真实验。结果表明，CSPSO 算法比CS算法和自适应步长布谷鸟搜索算法（ASCS）具有更快的收敛速度，更高的收敛精度和稳定性。%In order to make further improvement on the convergence speed and computational accuracy of cuckoo search algorithm ,a new cuckoo search algorithm based on particle swarm optimization algorithm is propased ,which uses particle swarm optimization instead of the original Levy flight mechanism into the location update process of CS algorithm .The simulation results show that the CSPSO can search for global optimization more quickly ,precisely and stably than original CS algorithm and self-adaptive step cuckoo search algorithm .
Huang, Yin; Chen, Jianhua; Xiong, Shaojun
2009-07-01
Mobile-Learning (M-learning) makes many learners get the advantages of both traditional learning and E-learning. Currently, Web-based Mobile-Learning Systems have created many new ways and defined new relationships between educators and learners. Association rule mining is one of the most important fields in data mining and knowledge discovery in databases. Rules explosion is a serious problem which causes great concerns, as conventional mining algorithms often produce too many rules for decision makers to digest. Since Web-based Mobile-Learning System collects vast amounts of student profile data, data mining and knowledge discovery techniques can be applied to find interesting relationships between attributes of learners, assessments, the solution strategies adopted by learners and so on. Therefore ,this paper focus on a new data-mining algorithm, combined with the advantages of genetic algorithm and simulated annealing algorithm , called ARGSA(Association rules based on an improved Genetic Simulated Annealing Algorithm), to mine the association rules. This paper first takes advantage of the Parallel Genetic Algorithm and Simulated Algorithm designed specifically for discovering association rules. Moreover, the analysis and experiment are also made to show the proposed method is superior to the Apriori algorithm in this Mobile-Learning system.
The Convergence Speed of Single- And Multi-Objective Immune Algorithm Based Optimization Problems
Directory of Open Access Journals (Sweden)
Prof. M. Abo-Zahhad
2010-12-01
Full Text Available Despite the considerable amount of research related to immune algorithms and its applications in numerical optimization, digital filters design, and data mining, there is still little work related to issues as important as sensitivity analysis, [1]-[4]. Other aspects, such as convergence speed and parameters adaptation, have been practically disregarded in the current specialized literature [7]-[8]. The convergence speed of the immune algorithm heavily depends on its main control parameters: population size, replication rate, mutation rate, clonal rate and hyper-mutation rate. In this paper we investigate the effect of control parameters variation on the convergence speed for single- and multi-objective optimization problems. Three examples are a devoted for this purpose; namely the design of 2-D recursive digital filter, minimization of simple function, and banana function. The effect of each parameter on the convergence speed of the IA is studied considering the other parameters with fix values and taking the average of 100 times independent runs. Then, the concluded rules are applied on some examples introduced in [2] and [3]. Computational results show how to select the immune algorithm parameters to speedup the algorithm convergence and to obtain the optimal solution.
International Nuclear Information System (INIS)
This study aims to evaluate the performance of a new algorithm for optimization of beam weights in anatomy-based intensity modulated radiotherapy (IMRT). The algorithm uses a numerical technique called Gaussian-Elimination that derives the optimum beam weights in an exact or non-iterative way. The distinct feature of the algorithm is that it takes only fraction of a second to optimize the beam weights, irrespective of the complexity of the given case. The algorithm has been implemented using MATLAB with a Graphical User Interface (GUI) option for convenient specification of dose constraints and penalties to different structures. We have tested the numerical and clinical capabilities of the proposed algorithm in several patient cases in comparison with KonRad inverse planning system. The comparative analysis shows that the algorithm can generate anatomy-based IMRT plans with about 50% reduction in number of MUs and 60% reduction in number of apertures, while producing dose distribution comparable to that of beamlet-based IMRT plans. Hence, it is clearly evident from the study that the proposed algorithm can be effectively used for clinical applications. (author)
Novel Genetic Algorithm Based Solutions for Optimal Power Flow under Contingency Conditions
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S. V. Durga Bhavani,
2014-06-01
Full Text Available Power system throughout the world is undergoing tremendous changes and developments due to rapid Restructuring, Deregulation and Open-access policies. Greater liberalization, larger market and increasing dependency on the electricity lead to the system operators to work on limited spinning reserve and to operate on vicinities to maximize the economy compromising on the reliability and security of the system for greater profits, which lead to establishment of a monitoring authority and accurate electronic system to prevent any untoward incidents like Blackouts. In any power system, unexpected outages of lines or transformers occur due to faults or other disturbances. These events may cause significant overloading of transmission lines or transformers, which in turn may lead to a viability crisis of the power system. The main role of power system control is to maintain a secure system state, i.e., to prevent the power system, moving from secure state into emergency state over the widest range of operating conditions. Security Constrained Optimal Power Flow (SCOPF is major tool used to improve the security of the system. In this work, Genetic algorithm has been used to solve the OPF and SCOPF problems. As initial effort conventional GA (binary coded based OPF and SCOPF is going to be attempted. The difficulties of binary coded GA in handling continuous search space lead to the evolution of real coded GA‟s. Solutions obtained using both the algorithms are compared. Case studies are made on the IEEE30 bus test system to demonstrate the ability of real coded GA in solving the OPF and SCOPF problems.
Directory of Open Access Journals (Sweden)
Jian Fang
2014-01-01
Full Text Available The dynamics model is established in view of the self-designed, two-wheeled, and self-balancing robot. This paper uses the particle swarm algorithm to optimize the parameter matrix of LQR controller based on the LQR control method to make the two-wheeled and self-balancing robot realize the stable control and reduce the overshoot amount and the oscillation frequency of the system at the same time. The simulation experiments prove that the LQR controller improves the system stability, obtains the good control effect, and has higher application value through using the particle swarm optimization algorithm.
Directory of Open Access Journals (Sweden)
Chang Luo
2015-01-01
Full Text Available The many-objective optimization performance of the Kriging-surrogate-based evolutionary algorithm (EA, which maximizes expected hypervolume improvement (EHVI for updating the Kriging model, is investigated and compared with those using expected improvement (EI and estimation (EST updating criteria in this paper. Numerical experiments are conducted in 3- to 15-objective DTLZ1-7 problems. In the experiments, an exact hypervolume calculating algorithm is used for the problems with less than six objectives. On the other hand, an approximate hypervolume calculating algorithm based on Monte Carlo sampling is adopted for the problems with more objectives. The results indicate that, in the nonconstrained case, EHVI is a highly competitive updating criterion for the Kriging model and EA based many-objective optimization, especially when the test problem is complex and the number of objectives or design variables is large.
Asymptotically Optimal Algorithm for Short-Term Trading Based on the Method of Calibration
V'yugin, Vladimir
2012-01-01
A trading strategy based on a natural learning process, which asymptotically outperforms any trading strategy from RKHS (Reproduced Kernel Hilbert Space), is presented. In this process, the trader rationally chooses his gambles using predictions made by a randomized well calibrated algorithm. Our strategy is based on Dawid's notion of calibration with more general changing checking rules and on some modification of Kakade and Foster's randomized algorithm for computing calibrated forecasts. We use also Vovk's method of defensive forecasting in RKHS.
距离寻优中Dijkstra算法的优化%A OPTIMIZATION ALGORITHM BASED ON DIJKSTRA'S ALGORITHM IN SEARCH OF SHORTCUT
Institute of Scientific and Technical Information of China (English)
鲍培明
2001-01-01
When shortcut between two nodes is searched with Dijkstra'salgorithm, a lot of nodes away from the shortcut are involved.So efficiency of Dijkstra's algorithm is low. A optimization algorithm is presented in this paper based on Dijkstra's algorithm in search of shortcut. Based on the idea of beeline distance, the formula for the disposition of nodes is changed in the optimization algorithm. In the course of the optimization algorithm running, only these nodes in the shortcut or close to the shortcut are processed, and those nodes away from the shortcut are not processed. So the number of processed nodes is largely reduced in the optimization algorithm. Efficiency of the optimization algorithm is improved. Validity of this algorithm is proved. Practicablity and efficiency about this algorithm are disscused. This algorithm has been applied in practical task.%Dijkstra算法在求解两指定顶点间最短距离时，对两顶点之间最短路径以外的大量顶点进行了计算，而影响了算法的速度.在对Dijkstra算法分析的基础上，结合网络模型的特点，对Dijkstra算法进行了优化.优化算法基于两点之间直线最短的思想，改变了对顶点处理顺序的规则.在算法流程中只对最短路径上及其附近的顶点做了处理，而与最短路径相距较远的顶点基本不涉及.因此，在优化算法中计算的顶点数量大幅减少，提高了算法的速度.给出了优化算法的正确性证明，对优化算法的实用性和效率加以讨论.优化算法在实际中已经得到应用.
Directory of Open Access Journals (Sweden)
Hongjian Zhang
2015-09-01
Full Text Available Aiming to effectively recognize train center plate bolt loss faults, this paper presents an improved fault detection method. A multi-scale local binary pattern operator containing the local texture information of different radii is designed to extract more efficient discrimination information. An improved teaching-learning-based optimization algorithm is established to optimize the classification results in the decision level. Two new phases including the worst recombination phase and the cuckoo search phase are incorporated to improve the diversity of the population and enhance the exploration. In the worst recombination phase, the worst solution is updated by a crossover recombination operation to prevent the premature convergence. The cuckoo search phase is adopted to escape the local optima. Experimental results indicate that the recognition accuracy is up to 98.9% which strongly demonstrates the effectiveness and reliability of the proposed detection method.
An optimal structural design algorithm using optimality criteria
Taylor, J. E.; Rossow, M. P.
1976-01-01
An algorithm for optimal design is given which incorporates several of the desirable features of both mathematical programming and optimality criteria, while avoiding some of the undesirable features. The algorithm proceeds by approaching the optimal solution through the solutions of an associated set of constrained optimal design problems. The solutions of the constrained problems are recognized at each stage through the application of optimality criteria based on energy concepts. Two examples are described in which the optimal member size and layout of a truss is predicted, given the joint locations and loads.
Genetic algorithm based optimization on modeling and design of hybrid renewable energy systems
International Nuclear Information System (INIS)
Highlights: • Solar data was analyzed in the location under consideration. • A program was developed to simulate operation of the PV hybrid system. • Genetic algorithm was used to optimize the sizes of the hybrid system components. • The costs of the pollutant emissions were considered in the optimization. • It is cost effective to power houses in remote areas with such hybrid systems. - Abstract: A sizing optimization of a hybrid system consisting of photovoltaic (PV) panels, a backup source (microturbine or diesel), and a battery system minimizes the cost of energy production (COE), and a complete design of this optimized system supplying a small community with power in the Palestinian Territories is presented in this paper. A scenario that depends on a standalone PV, and another one that depends on a backup source alone were analyzed in this study. The optimization was achieved via the usage of genetic algorithm. The objective function minimizes the COE while covering the load demand with a specified value for the loss of load probability (LLP). The global warming emissions costs have been taken into account in this optimization analysis. Solar radiation data is firstly analyzed, and the tilt angle of the PV panels is then optimized. It was discovered that powering a small rural community using this hybrid system is cost-effective and extremely beneficial when compared to extending the utility grid to supply these remote areas, or just using conventional sources for this purpose. This hybrid system decreases both operating costs and the emission of pollutants. The hybrid system that realized these optimization purposes is the one constructed from a combination of these sources
Directory of Open Access Journals (Sweden)
Weihua Jin
2013-01-01
Full Text Available This paper proposes a genetic-algorithms-based approach as an all-purpose problem-solving method for operation programming problems under uncertainty. The proposed method was applied for management of a municipal solid waste treatment system. Compared to the traditional interactive binary analysis, this approach has fewer limitations and is able to reduce the complexity in solving the inexact linear programming problems and inexact quadratic programming problems. The implementation of this approach was performed using the Genetic Algorithm Solver of MATLAB (trademark of MathWorks. The paper explains the genetic-algorithms-based method and presents details on the computation procedures for each type of inexact operation programming problems. A comparison of the results generated by the proposed method based on genetic algorithms with those produced by the traditional interactive binary analysis method is also presented.
Directory of Open Access Journals (Sweden)
Yukai Yao
2015-01-01
Full Text Available We propose an optimized Support Vector Machine classifier, named PMSVM, in which System Normalization, PCA, and Multilevel Grid Search methods are comprehensively considered for data preprocessing and parameters optimization, respectively. The main goals of this study are to improve the classification efficiency and accuracy of SVM. Sensitivity, Specificity, Precision, and ROC curve, and so forth, are adopted to appraise the performances of PMSVM. Experimental results show that PMSVM has relatively better accuracy and remarkable higher efficiency compared with traditional SVM algorithms.
Institute of Scientific and Technical Information of China (English)
Lihui CEN; Yugeng XI
2008-01-01
By considering the flow control of urban sewer networks to minimize the electricity consumption of pumping stations.a decomposition-coordination strategy for energy savings based on network community division is developed in this paper. A mathematical model characterizing the smady-state flow of urball sewer networks is first constructed,consisting of a set of algebraic equations with the structure transportation capacities captured as constraints.Since the sewer networks have no apparent natural hierarchical structure in general.it is very difficult to identify the clustered groups.A fast network division approach through calculating the betweenness of each edge is successfully applied to identify the groups and a sewer network with arbitrary configuration could be then decomposed into subnetworks.By integrating the coupling constraints of the subnetworks.the original problem is separated into N optimization subproblems in accordance with the network decomposition.Each subproblem is solved locally and the solutions to the subproblems are coordinated to form an appropriate global solution.Finally,an application to a specified large-scale sewer network is also investigated to demonstrate the validity of the proposed algorithm.
Institute of Scientific and Technical Information of China (English)
HUO Hai-bo; ZHU Xin-jian; CAO Guang-yi
2007-01-01
A kind of new design method for two-degree-of-freedom (2DOF) PID regulator was presented, in which, a new global search heuristic--improved generalized extremal optimization (GEO) algorithm is applied to the parameter optimization design of 2DOF PID regulator. The simulated results show that very good dynamic response performance of both command tracking and disturbance rejection characteristics can be achieved simultaneously. At the same time, the comparisons of simulation results with the improved GA, the basic GEO and the improved GEO were given. From the comparisons, it is shown that the improved GEO algorithm is competitive in performance with the GA and basic GEO and is an attractive tool to be used in the design of two-degree-of-freedom PID regulator.
Qingsong Jiang; Wei Xing; Ruihuan Hou; Baoping Zhou
2015-01-01
In order to investigate the inventory optimization of circulation enterprises, demand analysis was carried out firstly considering supply-demand balance. Then, it was assumed that the demand process complied with mutually independent compound Poisson process. Based on this assumption, an optimization model for inventory control of circulation enterprises was established with the goal of minimizing the average total costs in unit time of inventory system. In addition, the optimal computing alg...
Optimization of PID controller based on The Bees Algorithm for one leg of a quadruped robot
Bakırcıoğlu Veli; Arif Şen M.; Kalyoncu Mete
2016-01-01
In this paper, we apply The Bees Algorithm to find optimal PID controller gains to control angular positions of robot leg joints with the minimum position error. In order to present more realistic simulation, system modelled in MATLAB/Simulink environment which is close to experimental set up. Solid model of system, which has two degrees of freedom, drawn by using a CAD software. Required physical specifications of robot leg for MATLAB/Simulink modelling is obtained from this CAD model. Contr...
Zhang, Yan-jun; Liu, Wen-zhe; Fu, Xing-hu; Bi, Wei-hong
2015-10-01
According to the high precision extracting characteristics of scattering spectrum in Brillouin optical time domain reflection optical fiber sensing system, this paper proposes a new algorithm based on flies optimization algorithm with adaptive mutation and generalized regression neural network. The method takes advantages of the generalized regression neural network which has the ability of the approximation ability, learning speed and generalization of the model. Moreover, by using the strong search ability of flies optimization algorithm with adaptive mutation, it can enhance the learning ability of the neural network. Thus the fitting degree of Brillouin scattering spectrum and the extraction accuracy of frequency shift is improved. Model of actual Brillouin spectrum are constructed by Gaussian white noise on theoretical spectrum, whose center frequency is 11.213 GHz and the linewidths are 40-50, 30-60 and 20-70 MHz, respectively. Comparing the algorithm with the Levenberg-Marquardt fitting method based on finite element analysis, hybrid algorithm particle swarm optimization, Levenberg-Marquardt and the least square method, the maximum frequency shift error of the new algorithm is 0.4 MHz, the fitting degree is 0.991 2 and the root mean square error is 0.024 1. The simulation results show that the proposed algorithm has good fitting degree and minimum absolute error. Therefore, the algorithm can be used on distributed optical fiber sensing system based on Brillouin optical time domain reflection, which can improve the fitting of Brillouin scattering spectrum and the precision of frequency shift extraction effectively. PMID:26904844
International Nuclear Information System (INIS)
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)
International Nuclear Information System (INIS)
Highlights: • Solar cell and PEM fuel cell parameter estimations are investigated in the paper. • A new biogeography-based method (BBO-M) is proposed for cell parameter estimations. • In BBO-M, two mutation operators are designed to enhance optimization performance. • BBO-M provides a competitive alternative in cell parameter estimation problems. - Abstract: Mathematical models are useful tools for simulation, evaluation, optimal operation and control of solar cells and proton exchange membrane fuel cells (PEMFCs). To identify the model parameters of these two type of cells efficiently, a biogeography-based optimization algorithm with mutation strategies (BBO-M) is proposed. The BBO-M uses the structure of biogeography-based optimization algorithm (BBO), and both the mutation motivated from the differential evolution (DE) algorithm and the chaos theory are incorporated into the BBO structure for improving the global searching capability of the algorithm. Numerical experiments have been conducted on ten benchmark functions with 50 dimensions, and the results show that BBO-M can produce solutions of high quality and has fast convergence rate. Then, the proposed BBO-M is applied to the model parameter estimation of the two type of cells. The experimental results clearly demonstrate the power of the proposed BBO-M in estimating model parameters of both solar and fuel cells
Database query optimization based on Bat Algorithm and Cuckoo Search Algorithm%基于BACS算法的数据库查询优化
Institute of Scientific and Technical Information of China (English)
王磊
2015-01-01
针对布谷鸟算法局部搜索能力弱、寻优精度低等缺陷，提出一种蝙蝠算法和布谷鸟算法相融合的数据库查询优化算法（BACS）。按照布谷鸟优化算法对鸟巢位置进行更新，利用蝙蝠算法的动态转换策略对鸟巢位置进一步更新，避免算法陷入局部最优；最后将BACS应用于数据库查询优化问题求解，并通过仿真实验对BACS的性能进行测试。实验结果表明，BACS加快了数据库查询优化求解的收敛速度，获得了质量更高的查询优化方案。%In order to solve the problems of bat algorithm which has low optimizing accuracy and weak local search ability, a novel query optimization method of database is proposed based on Bat Algorithm and Cuckoo Search Algorithm (BACS). Firstly, nest location is updated according to the cuckoo search optimization algorithm, and then cuckoo nest location is further replaced according to the dynamic conversion strategy in the bat algorithm and avoids falling into local optimum, finally it is applied to solve the query optimization problem of database, and the performance of BACS is tested by simulation experiments. The results show that, BACS accelerates the convergence speed of database query optimiza-tion and can obtain higher quality query optimization scheme.
Mengjun Tong; Yangli Chen; Fangxiang Chen; Xiaoping Wu; Guozhong Shou
2015-01-01
An energy-efficient ACO-based multipath routing algorithm (EAMR) is proposed for energy-constrained wireless sensor networks. EAMR is a hybrid multipath algorithm, which is reactive in path discovery and proactive in route maintenance. EAMR has improvement and innovation in the ant packet structure, pheromone update formula, pheromone update mode, and the mechanism of multipath. Average energy consumption and congestion of path make pheromone update formula more reasonable. Incremental pherom...
Fruit fly optimization algorithm based high efficiency and low NOx combustion modeling for a boiler
Institute of Scientific and Technical Information of China (English)
ZHANG Zhenxing∗; SUN Baomin; XIN Jing
2014-01-01
In order to control NOx emissions and enhance boiler efficiency in coal-fired boilers,the thermal operating data from an ultra-supercritical 1 000 MW unit boiler were analyzed.On the basis of the support vector regression machine (SVM),the fruit fly optimization algorithm (FOA)was applied to optimize the penalty parameter C,ker-nel parameter g and insensitive loss coefficient of the model.Then,the FOA-SVM model was established to predict the NOx emissions and boiler efficiency,and the performance of this model was compared with that of the GA-SVM model optimized by genetic algorithm (GA).The results show the FOA-SVM model has better prediction accuracy and generalization capability,of which the maximum average relative error of testing set lies in the NOx emissions model,which is only 3 .5 9%.The above models can predict the NOx emissions and boiler efficiency accurately,so they are very suitable for on-line modeling prediction,which provides a good model foundation for further optimiza-tion operation of large capacity boilers.
Institute of Scientific and Technical Information of China (English)
贺建军; 喻寿益; 钟掘
2003-01-01
A new searching algorithm named the annealing-genetic algorithm(AGA) was proposed by skillfully merging GA with SAA. It draws on merits of both GA and SAA ,and offsets their shortcomings. The difference from GA is that AGA takes objective function as adaptability function directly, so it cuts down some unnecessary time expense because of float-point calculation of function conversion. The difference from SAA is that AGA need not execute a very long Markov chain iteration at each point of temperature, so it speeds up the convergence of solution and makes no assumption on the search space,so it is simple and easy to be implemented. It can be applied to a wide class of problems. The optimizing principle and the implementing steps of AGA were expounded. The example of the parameter optimization of a typical complex electromechanical system named temper mill shows that AGA is effective and superior to the conventional GA and SAA. The control system of temper mill optimized by AGA has the optimal performance in the adjustable ranges of its parameters.
Optimization modeling of single-chain antibody against hepatoma based on similarity algorithm.
Zhao, Zhi-Jun; Chen, Jing-Tao; Yuan, Jia-Ying; Yin, Xiao-Xiang; Song, Hua-Yong; Wang, Xin-Chun
2015-01-01
The purposes was to establish optimal modeling of single-chain antibody molecules based on similarity algorithm and seek the connecting peptides that had the minimal effect on the structure and bioactivity of the variable region of heavy chain (VH) and that of light chain (VL) in a single-chain antibody against liver cancer. After the Linker with different lengths (n=0~7) had been added into single chain fragment variable (ScFv), modeling of the overall sequences of VH, VL and ScFv were conducted respectively. Meanwhile, the peptide chain structure of (Gly4Ser)n was adopted for the connecting peptide. Then the spatial spherical shell layer alignment algorithm based on spherical polar coordinates was utilized for comparing the structural similarity of VH and VL before and after adding connecting peptide. Equally, in order to determine the stability of VH and VL, MATLAB was applied for analysis of the fore and aft distances and the diffusion radius. Indirect ELISA method was used to detect single-chain antibody immunological activity of Linker with different lengths. The MTT assay was utilized for the examination of the inhibition rate of single-chain antibody with different lengths of Linker to liver cancer cell. When n=4, the structural similarity between VH together with VL and their original ones was the highest. When n=3, the influence of connecting peptide on the stability of VH and VL was minimum. When n>3, the fore and aft distances changed little due to the increase and fold of the length of peptide chain. The results of ELISA detection showed that when n=4, affinity of single chain antibody to liver cancer cells was much higher. The MTT test also indicated that when n=4, the inhibition rate of the connecting peptide on hepatoma carcinoma cell reached the highest, and that came second when n=3. When n=4, the structural stability and biological functions of anti-hepatoma single-chain antibody were both favorable. This study has provided a basis for the design
Ramyachitra, D; Sofia, M; Manikandan, P
2015-09-01
Microarray technology allows simultaneous measurement of the expression levels of thousands of genes within a biological tissue sample. The fundamental power of microarrays lies within the ability to conduct parallel surveys of gene expression using microarray data. The classification of tissue samples based on gene expression data is an important problem in medical diagnosis of diseases such as cancer. In gene expression data, the number of genes is usually very high compared to the number of data samples. Thus the difficulty that lies with data are of high dimensionality and the sample size is small. This research work addresses the problem by classifying resultant dataset using the existing algorithms such as Support Vector Machine (SVM), K-nearest neighbor (KNN), Interval Valued Classification (IVC) and the improvised Interval Value based Particle Swarm Optimization (IVPSO) algorithm. Thus the results show that the IVPSO algorithm outperformed compared with other algorithms under several performance evaluation functions. PMID:26484222
Directory of Open Access Journals (Sweden)
D. Ramyachitra
2015-09-01
Full Text Available Microarray technology allows simultaneous measurement of the expression levels of thousands of genes within a biological tissue sample. The fundamental power of microarrays lies within the ability to conduct parallel surveys of gene expression using microarray data. The classification of tissue samples based on gene expression data is an important problem in medical diagnosis of diseases such as cancer. In gene expression data, the number of genes is usually very high compared to the number of data samples. Thus the difficulty that lies with data are of high dimensionality and the sample size is small. This research work addresses the problem by classifying resultant dataset using the existing algorithms such as Support Vector Machine (SVM, K-nearest neighbor (KNN, Interval Valued Classification (IVC and the improvised Interval Value based Particle Swarm Optimization (IVPSO algorithm. Thus the results show that the IVPSO algorithm outperformed compared with other algorithms under several performance evaluation functions.
Directory of Open Access Journals (Sweden)
Rabindra Kumar Sahu
2016-03-01
Full Text Available This paper presents the design and analysis of Proportional-Integral-Double Derivative (PIDD controller for Automatic Generation Control (AGC of multi-area power systems with diverse energy sources using Teaching Learning Based Optimization (TLBO algorithm. At first, a two-area reheat thermal power system with appropriate Generation Rate Constraint (GRC is considered. The design problem is formulated as an optimization problem and TLBO is employed to optimize the parameters of the PIDD controller. The superiority of the proposed TLBO based PIDD controller has been demonstrated by comparing the results with recently published optimization technique such as hybrid Firefly Algorithm and Pattern Search (hFA-PS, Firefly Algorithm (FA, Bacteria Foraging Optimization Algorithm (BFOA, Genetic Algorithm (GA and conventional Ziegler Nichols (ZN for the same interconnected power system. Also, the proposed approach has been extended to two-area power system with diverse sources of generation like thermal, hydro, wind and diesel units. The system model includes boiler dynamics, GRC and Governor Dead Band (GDB non-linearity. It is observed from simulation results that the performance of the proposed approach provides better dynamic responses by comparing the results with recently published in the literature. Further, the study is extended to a three unequal-area thermal power system with different controllers in each area and the results are compared with published FA optimized PID controller for the same system under study. Finally, sensitivity analysis is performed by varying the system parameters and operating load conditions in the range of ±25% from their nominal values to test the robustness.
International Nuclear Information System (INIS)
We have further improved our procedure for the optimization of multilayer semiconductor nanostructures, based upon diluted magnetic semiconductors, developed previously with the goal of maximizing their spin-filtering properties. The new optimization scheme relies on the application of a modern class of evolutionary algorithms for global optimization, specifically the genetic algorithm. Its fitness function is set to select the best possible spin-polarization properties within the chosen range of bias voltages and with a fixed value of external magnetic field. Numerical calculations are presented for the ZnSe/Zn1-xMnxSe based semiconductor system, and the obtained results predict an enhanced spin-diode performance over the existing designs
International Nuclear Information System (INIS)
In multi-slice helical CT, the single-tilted-plane-based reconstruction algorithm has been proposed to combat helical and cone beam artifacts by tilting a reconstruction plane to fit a helical source trajectory optimally. Furthermore, to improve the noise characteristics or dose efficiency of the single-tilted-plane-based reconstruction algorithm, the multi-tilted-plane-based reconstruction algorithm has been proposed, in which the reconstruction plane deviates from the pose globally optimized due to an extra rotation along the 3rd axis. As a result, the capability of suppressing helical and cone beam artifacts in the multi-tilted-plane-based reconstruction algorithm is compromised. A matched view weighting approach is proposed here to optimize the capability of suppressing helical and cone beam artifacts and noise characteristics. A helical body phantom is employed to quantitatively evaluate the imaging performance of the matched view weighting approach, showing that the matched view weighting improves both the helical artifact suppression and noise characteristics or dose efficiency significantly in comparison to the case in which nonmatched view weighting is applied. The matched view weighting approach is of practical importance in the development of multi-slice helical CT, because it maintains the computational structure of fan beam filtered backprojection and demands no extra computational resources
Directory of Open Access Journals (Sweden)
Dębski Roman
2016-06-01
Full Text Available A new dynamic programming based parallel algorithm adapted to on-board heterogeneous computers for simulation based trajectory optimization is studied in the context of “high-performance sailing”. The algorithm uses a new discrete space of continuously differentiable functions called the multi-splines as its search space representation. A basic version of the algorithm is presented in detail (pseudo-code, time and space complexity, search space auto-adaptation properties. Possible extensions of the basic algorithm are also described. The presented experimental results show that contemporary heterogeneous on-board computers can be effectively used for solving simulation based trajectory optimization problems. These computers can be considered micro high performance computing (HPC platforms-they offer high performance while remaining energy and cost efficient. The simulation based approach can potentially give highly accurate results since the mathematical model that the simulator is built upon may be as complex as required. The approach described is applicable to many trajectory optimization problems due to its black-box represented performance measure and use of OpenCL.
An optimization algorithm of collaborative indoor locating
Directory of Open Access Journals (Sweden)
SHI Ying
2014-06-01
Full Text Available Based on triangular centroid locating algorithm,this paper will use the idea of collaboration to indoor locating system.On account of the condition which has two nodes to be located in the test environment,we have designed a circular type optimization algorithm.Verified simulation results show that the circular type optimization algorithm,compared with the triangular centroid locating algorithm,can decrease the average error by 11.62%,decrease the maximum error by 7.74% and decrease the minimum error by 22.66%.The maximum value of the optimize degree of the circular type optimization algorithm is 28.63%,and the minimum value of that is 0.05%.
A Hybrid Intelligent Optimization Algorithm to Assess the NSS Based on FNN Trained by HPS
Directory of Open Access Journals (Sweden)
Zhibin Liu
2010-09-01
Full Text Available Along with the widespread application of computer network, the network security problem becomes more and more important, and the security problem research also becomes an important topic to the network development. Because the different networks request the different security level, therefore, it is helpful to understand the network security for the customers to assess the network security situation (NSS scientifically, thus takes the corresponding measure, enhances the network security performance and the overall economic benefit. This paper proposed a novel hybrid intelligent optimization algorithm to assess the network security situation, the algorithm unified the fuzzy neural network (FNN and hybrid particle swarm (HPS optimization algorithm, which not only can centralize the FNN’s advantages of learning, association, identification, adaptation and fuzzy information processing, and improve the learning and expression ability, but also can balance the search capability between the global account and partial account using HPS, and have the faster velocity of convergence. The NSS situation of 20 samples in Hebei province shows that the results given by this hybrid model are reliable, and this method to assessment the NSS is feasible.
Institute of Scientific and Technical Information of China (English)
范勤勤; 王循华; 颜学峰
2015-01-01
A modified harmony search algorithm with co-evolutional control parameters (DEHS), applied through differential evolution optimization, is proposed. In DEHS, two control parameters, i.e., harmony memory considering rate and pitch adjusting rate, are encoded as a symbiotic individual of an original individual (i.e., harmony vector). Harmony search operators are applied to evolving the original population. DE is applied to co-evolving the symbiotic population based on feedback information from the original population. Thus, with the evolution of the original population in DEHS, the symbiotic population is dynamically and self-adaptively adjusted, and real-time optimum control parameters are obtained. The proposed DEHS algorithm has been applied to various benchmark functions and two typical dynamic optimization problems. The experimental results show that the performance of the proposed algorithm is better than that of other HS variants. Satisfactory results are obtained in the application.
Directory of Open Access Journals (Sweden)
Anna Syberfeldt
2015-10-01
Full Text Available This paper presents a real-world case study of optimizing waste collection in Sweden. The problem, involving approximately 17,000 garbage bins served by three bin lorries, is approached as a travelling salesman problem and solved using simulation-based optimization and an evolutionary algorithm. To improve the performance of the evolutionary algorithm, it is enhanced with a repair function that adjusts its genome values so that shorter routes are found more quickly. The algorithm is tested using two crossover operators, i.e., the order crossover and heuristic crossover, combined with different mutation rates. The results indicate that the order crossover is superior to the heuristics crossover, but that the driving force of the search process is the mutation operator combined with the repair function.
Trajectory Optimization Algorithm Studies
Gandhi, Manan
2015-01-01
In complex engineered systems, completing an objective is sometimes not enough. The system must be able to reach a set performance characteristic, such as an unmanned aerial vehicle flying from point A to point B, \\textit{under 10 seconds}. This introduces the notion of optimality, what is the most efficient, the fastest, the cheapest way to complete a task. This report explores the two pillars of optimal control, Bellman's Dynamic Programming and Pontryagin's Maximum Principle, and compares ...
Wang, Li; Jia, Pengfei; Huang, Tailai; Duan, Shukai; Yan, Jia; Wang, Lidan
2016-01-01
An electronic nose (E-nose) is an intelligent system that we will use in this paper to distinguish three indoor pollutant gases (benzene (C₆H₆), toluene (C₇H₈), formaldehyde (CH₂O)) and carbon monoxide (CO). The algorithm is a key part of an E-nose system mainly composed of data processing and pattern recognition. In this paper, we employ support vector machine (SVM) to distinguish indoor pollutant gases and two of its parameters need to be optimized, so in order to improve the performance of SVM, in other words, to get a higher gas recognition rate, an effective enhanced krill herd algorithm (EKH) based on a novel decision weighting factor computing method is proposed to optimize the two SVM parameters. Krill herd (KH) is an effective method in practice, however, on occasion, it cannot avoid the influence of some local best solutions so it cannot always find the global optimization value. In addition its search ability relies fully on randomness, so it cannot always converge rapidly. To address these issues we propose an enhanced KH (EKH) to improve the global searching and convergence speed performance of KH. To obtain a more accurate model of the krill behavior, an updated crossover operator is added to the approach. We can guarantee the krill group are diversiform at the early stage of iterations, and have a good performance in local searching ability at the later stage of iterations. The recognition results of EKH are compared with those of other optimization algorithms (including KH, chaotic KH (CKH), quantum-behaved particle swarm optimization (QPSO), particle swarm optimization (PSO) and genetic algorithm (GA)), and we can find that EKH is better than the other considered methods. The research results verify that EKH not only significantly improves the performance of our E-nose system, but also provides a good beginning and theoretical basis for further study about other improved krill algorithms' applications in all E-nose application areas. PMID
Optimized Angular a Star Algorithm for Global Path Search Based on Neighbor Node Evaluation
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Ankit Bhadoria
2014-07-01
Full Text Available Any electromechanical device can be termed as Robot, which imitates human actions and in some of the situation can be used as a replacement for human. These days Robots are the integral part of our life and can be applied in several applications and tasks by giving respective commands. The research in robotics domain is to make it as autonomous and as much independent as it can be. The problem that arises is of controlling a mobile robot with the energy constraint. A lot of energy is wasted, if it takes wrong trajectory motion, this motion depends upon the robot knowledge which indeed in not constant. The variation in the environment results in making difficult for the robot to take precise and accurate measurements to reach the destination without much of the energy loss. An autonomous robot is expected to take decision according to the situation. For this precise decisions of robot path planning there are algorithms like A*, Dijkstra, D* etc. In this paper we have done analysis on partially known environment situation. Optimal path is planned by new heuristic approach over the A star algorithm, robot moving at an appropriate angle cuts down the unnecessary cost of path planning. Experimental results show that the proposed algorithm is much effective for more than 8% than the conventional A* algorithm in the same map environment.
Algorithms for optimizing drug therapy
Directory of Open Access Journals (Sweden)
Martin Lene
2004-07-01
Full Text Available Abstract Background Drug therapy has become increasingly efficient, with more drugs available for treatment of an ever-growing number of conditions. Yet, drug use is reported to be sub optimal in several aspects, such as dosage, patient's adherence and outcome of therapy. The aim of the current study was to investigate the possibility to optimize drug therapy using computer programs, available on the Internet. Methods One hundred and ten officially endorsed text documents, published between 1996 and 2004, containing guidelines for drug therapy in 246 disorders, were analyzed with regard to information about patient-, disease- and drug-related factors and relationships between these factors. This information was used to construct algorithms for identifying optimum treatment in each of the studied disorders. These algorithms were categorized in order to define as few models as possible that still could accommodate the identified factors and the relationships between them. The resulting program prototypes were implemented in HTML (user interface and JavaScript (program logic. Results Three types of algorithms were sufficient for the intended purpose. The simplest type is a list of factors, each of which implies that the particular patient should or should not receive treatment. This is adequate in situations where only one treatment exists. The second type, a more elaborate model, is required when treatment can by provided using drugs from different pharmacological classes and the selection of drug class is dependent on patient characteristics. An easily implemented set of if-then statements was able to manage the identified information in such instances. The third type was needed in the few situations where the selection and dosage of drugs were depending on the degree to which one or more patient-specific factors were present. In these cases the implementation of an established decision model based on fuzzy sets was required. Computer programs
International Nuclear Information System (INIS)
In this work, a Particle Swarm Optimization Algorithm (PSO) is developed for preventive maintenance optimization. The proposed methodology, which allows the use flexible intervals between maintenance interventions, instead of considering fixed periods (as usual), allows a better adaptation of scheduling in order to deal with the failure rates of components under aging. Moreover, because of this flexibility, the planning of preventive maintenance becomes a difficult task. Motivated by the fact that the PSO has proved to be very competitive compared to other optimization tools, this work investigates the use of PSO as an alternative tool of optimization. Considering that PSO works in a real and continuous space, it is a challenge to use it for discrete optimization, in which scheduling may comprise variable number of maintenance interventions. The PSO model developed in this work overcome such difficulty. The proposed PSO searches for the best policy for maintaining and considers several aspects, such as: probability of needing repair (corrective maintenance), the cost of such repairs, typical outage times, costs of preventive maintenance, the impact of maintaining the reliability of systems as a whole, and the probability of imperfect maintenance. To evaluate the proposed methodology, we investigate an electro-mechanical system consisting of three pumps and four valves, High Pressure Injection System (HPIS) of a PWR. Results show that PSO is quite efficient in finding the optimum preventive maintenance policies for the HPIS. (author)
Poultangari, Iman; Shahnazi, Reza; Sheikhan, Mansour
2012-09-01
In order to control the pitch angle of blades in wind turbines, commonly the proportional and integral (PI) controller due to its simplicity and industrial usability is employed. The neural networks and evolutionary algorithms are tools that provide a suitable ground to determine the optimal PI gains. In this paper, a radial basis function (RBF) neural network based PI controller is proposed for collective pitch control (CPC) of a 5-MW wind turbine. In order to provide an optimal dataset to train the RBF neural network, particle swarm optimization (PSO) evolutionary algorithm is used. The proposed method does not need the complexities, nonlinearities and uncertainties of the system under control. The simulation results show that the proposed controller has satisfactory performance. PMID:22738782
Wang, Zhaocai; Pu, Jun; Cao, Liling; Tan, Jian
2015-01-01
The unbalanced assignment problem (UAP) is to optimally resolve the problem of assigning n jobs to m individuals (m parallel DNA algorithm for solving the unbalanced assignment problem using DNA molecular operations. We reasonably design flexible-length DNA strands representing different jobs and individuals, take appropriate steps, and get the solutions of the UAP in the proper length range and O(mn) time. We extend the application of DNA molecular operations and simultaneity to simplify the complexity of the computation. PMID:26512650
Optimal Design of Broadband Loaded Wire Antennas Based on Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
Xu Peng; Xiao Bo-xun; Zhu Guo-qiang; Ke Heng-yu
2003-01-01
This paper, using the frequency bandwidth, where both the gain and the VSWR (Voltage Standing Wave Ratio) of a monopole can satisfy the design requirement, as object function, mainly describes the process, in which the load locations, the matching network topology and their component values are optimized by the AGA (Adaptive Genetic Algorithm), to achieve a gain more than -2 dB in horizontal direction and a VSWR less than 3 in bandwidth as wide as possible. Moreover the design results are presented for monopoles with two concentrated loadings. It shows that the AGA is an effective method for designing wideband antennas.
Optimal Design of Broadband Loaded Wire Antennas Based on Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
Xu; Peng; Xiao; Bo-xun; 等
2003-01-01
This paper, using the frequency bandwidth, where both the gain and the VSWR(Voltage Standing Wave Ratio) of a monopole can satisfy the design requirement, as object function, mainly describes the process, in which the load locations, the matching network topology and their component values are optimized by the AGA(Adaptive Genetic Algorithm), to achieve a gain more than -2dB in horizontal direction and a VSWR less than 3 in bandwidth as wide as possible. Moreover the design results are presented for monopoles with two concentrated loadings. It shows that the AGA is an effective method for designing wideband antennas.
Directory of Open Access Journals (Sweden)
Umut Okkan
2011-07-01
Full Text Available Recently, Artificial Neural Networks (ANN, which is mathematical modelingtools inspired by the properties of the biological neural system, has been typically used inthe studies of hydrological time series modeling. These modeling studies generally includethe standart feed forward backpropagation (FFBP algorithms such as gradient-descent,gradient-descent with momentum rate and, conjugate gradient etc. As the standart FFBPalgorithms have some disadvantages relating to the time requirement and slowconvergency in training, Newton and Levenberg-Marquardt algorithms, which arealternative approaches to standart FFBP algorithms, were improved and also used in theapplications. In this study, an application of Levenberg-Marquardt algorithm based ANN(LM-ANN for the modeling of monthly inflows of Demirkopru Dam, which is located inthe Gediz basin, was presented. The LM-ANN results were also compared with gradientdescentwith momentum rate algorithm based FFBP model (GDM-ANN. When thestatistics of the long-term and also seasonal-term outputs are compared, it can be seen thatthe LM-ANN model that has been developed, is more sensitive for prediction of theinflows. In addition, LM-ANN approach can be used for modeling of other hydrologicalcomponents in terms of a rapid assessment and its robustness.
Algorithms for Weighted Boolean Optimization
Manquinho, Vasco; Marques-Silva, Joao; Planes Cid, Jordi
2009-01-01
The Pseudo-Boolean Optimization (PBO) and Maximum Satisfiability (MaxSAT) problems are natural optimization extensions of Boolean Satisfiability (SAT). In the recent past, different algorithms have been proposed for PBO and for MaxSAT, despite the existence of straightforward mappings from PBO to MaxSAT and viceversa. This papers proposes Weighted Boolean Optimization (WBO), a new uni- fied framework that aggregates and extends PBO and MaxSAT. In addition, the paper proposes...
Advances in metaheuristic algorithms for optimal design of structures
Kaveh, A
2014-01-01
This book presents efficient metaheuristic algorithms for optimal design of structures. Many of these algorithms are developed by the author and his colleagues, consisting of Democratic Particle Swarm Optimization, Charged System Search, Magnetic Charged System Search, Field of Forces Optimization, Dolphin Echolocation Optimization, Colliding Bodies Optimization, Ray Optimization. These are presented together with algorithms which were developed by other authors and have been successfully applied to various optimization problems. These consist of Particle Swarm Optimization, Big Bang-Big Crunch Algorithm, Cuckoo Search Optimization, Imperialist Competitive Algorithm, and Chaos Embedded Metaheuristic Algorithms. Finally a multi-objective optimization method is presented to solve large-scale structural problems based on the Charged System Search algorithm. The concepts and algorithms presented in this book are not only applicable to optimization of skeletal structures and finite element models, but can equally ...
International Nuclear Information System (INIS)
Highlights: • Systematic approach is proposed to control optimally the automotive power systems. • Energy management algorithms are based on Pontryagin’s Minimum Principle. • Both single and hybrid energy storage systems are investigated. • State constraints on the supercapacitor are considered in the control design. • Real-time control strategies are directly derived from offline optimal algorithms. - Abstract: The objective of this work is to show how to control the electric power systems of a vehicle in such a manner that their power flows should be optimized in the sense of energy efficiency. As will be seen, the control problem considered in this work can be formulated as an optimization problem in the presence of several constraints. A systematic approach based on optimal control will be adopted to design the energy management strategies. Then, by means of these strategies, the electric energy will be generated and stored in the most appropriate manner so that the overall energy consumption and eventually the pollutant emissions can be minimized for a given driving cycle. To this end, both non-causal optimization method using the knowledge of the entire driving cycle and causal one are developed for two case studies with different structures of energy storage system. These strategies are then evaluated in an advanced simulation environment to point out their effectiveness
Izzuan Jaafar, Hazriq; Mohd Ali, Nursabillilah; Mohamed, Z.; Asmiza Selamat, Nur; Faiz Zainal Abidin, Amar; Jamian, J. J.; Kassim, Anuar Mohamed
2013-12-01
This paper presents development of an optimal PID and PD controllers for controlling the nonlinear gantry crane system. The proposed Binary Particle Swarm Optimization (BPSO) algorithm that uses Priority-based Fitness Scheme is adopted in obtaining five optimal controller gains. The optimal gains are tested on a control structure that combines PID and PD controllers to examine system responses including trolley displacement and payload oscillation. The dynamic model of gantry crane system is derived using Lagrange equation. Simulation is conducted within Matlab environment to verify the performance of system in terms of settling time (Ts), steady state error (SSE) and overshoot (OS). This proposed technique demonstrates that implementation of Priority-based Fitness Scheme in BPSO is effective and able to move the trolley as fast as possible to the various desired position.
International Nuclear Information System (INIS)
This paper presents development of an optimal PID and PD controllers for controlling the nonlinear gantry crane system. The proposed Binary Particle Swarm Optimization (BPSO) algorithm that uses Priority-based Fitness Scheme is adopted in obtaining five optimal controller gains. The optimal gains are tested on a control structure that combines PID and PD controllers to examine system responses including trolley displacement and payload oscillation. The dynamic model of gantry crane system is derived using Lagrange equation. Simulation is conducted within Matlab environment to verify the performance of system in terms of settling time (Ts), steady state error (SSE) and overshoot (OS). This proposed technique demonstrates that implementation of Priority-based Fitness Scheme in BPSO is effective and able to move the trolley as fast as possible to the various desired position
Genetic Algorithm Based Optimal Testing Effort Allocation Problem for Modular Software
Directory of Open Access Journals (Sweden)
Gurjeet Kaur
2012-01-01
Full Text Available Software reliability growth models (SRGM are used to assess modular software quantitatively and predict the reliability of each of the modules during module testing phase. In the last few decades various SRGM’s have been proposed in literature. However, it is difficult to select the best model from a plethora of models available. To reduce this difficulty, unified modeling approaches have been proposed by many researchers. In this paper we present a generalized framework for software reliability growth modeling with respect to testing effort expenditure and incorporate the faults of different severity. We have used different standard probability distribution functions for representing failure observation and fault detection/ correction times. The faults in the software are labeled as simple, hard and complex faults. Developing reliable modular software is necessary. But, at the same time the testing effort available during the testing time is limited. Consequently, it is important for the project manager to allocate these limited resources among the modules optimally during the testing process. In this paper we have formulated an optimization problem in which the total number of faults removed from modular software is (which include simple, hard and complex faults maximized subject to budgetary and reliability constraints. To solve the optimization problem we have used genetic algorithm. One numerical example has been discussed to illustrate the solution of the formulated optimal effort allocation problem.
Directory of Open Access Journals (Sweden)
Meysam Gheisarnezhad
2015-01-01
Full Text Available Fractional-order PID (FOPID controller is a generalization of standard PID controller using fractional calculus. Compared with the Standard PID controller, two adjustable variables “differential order” and “integral order” are added to the PID controller.Three tank system is a nonlinear multivariable process that is a good prototype of chemical industrial processes. Cuckoo Optimization Algorithm (COA, that was recently introduced has shown its good performance in optimization problems. In this study, Improved Cuckoo Optimization Algorithm (ICOA has been presented. The aim of the paper is to compare different controllers tuned with a Improved Cuckoo Optimization Algorithm (ICOA for Three Tank System. In order to compare the performance of the optimized FOPID controller with other controllers, Genetic Algorithm(GA, Particle swarm optimization (PSO, Cuckoo Optimization Algorithm (COA and Imperialist Competitive Algorithm (ICA.
Directory of Open Access Journals (Sweden)
Mohamed Abu ElSoud
2016-04-01
Full Text Available Feature selection is an importance step in classification phase and directly affects the classification performance. Feature selection algorithm explores the data to eliminate noisy, redundant, irrelevant data, and optimize the classification performance. This paper addresses a new subset feature selection performed by a new Social Spider Optimizer algorithm (SSOA to find optimal regions of the complex search space through the interaction of individuals in the population. SSOA is a new natural meta-heuristic computation algorithm which mimics the behavior of cooperative social-spiders based on the biological laws of the cooperative colony. Different combinatorial set of feature extraction is obtained from different methods in order to keep and achieve optimal accuracy. Normalization function is applied to smooth features between [0,1] and decrease gap between features. SSOA based on feature selection and reduction compared with other methods over CT liver tumor dataset, the proposed approach proves better performance in both feature size reduction and classification accuracy. Improvements are observed consistently among 4 classification methods. A theoretical analysis that models the number of correctly classified data is proposed using Confusion Matrix, Precision, Recall, and Accuracy. The achieved accuracy is 99.27%, precision is 99.37%, and recall is 99.19%. The results show that, the mechanism of SSOA provides very good exploration, exploitation and local minima avoidance.
Directory of Open Access Journals (Sweden)
Kuan-Cheng Lin
2015-01-01
Full Text Available Rapid advances in information and communication technology have made ubiquitous computing and the Internet of Things popular and practicable. These applications create enormous volumes of data, which are available for analysis and classification as an aid to decision-making. Among the classification methods used to deal with big data, feature selection has proven particularly effective. One common approach involves searching through a subset of the features that are the most relevant to the topic or represent the most accurate description of the dataset. Unfortunately, searching through this kind of subset is a combinatorial problem that can be very time consuming. Meaheuristic algorithms are commonly used to facilitate the selection of features. The artificial fish swarm algorithm (AFSA employs the intelligence underlying fish swarming behavior as a means to overcome optimization of combinatorial problems. AFSA has proven highly successful in a diversity of applications; however, there remain shortcomings, such as the likelihood of falling into a local optimum and a lack of multiplicity. This study proposes a modified AFSA (MAFSA to improve feature selection and parameter optimization for support vector machine classifiers. Experiment results demonstrate the superiority of MAFSA in classification accuracy using subsets with fewer features for given UCI datasets, compared to the original FASA.
A Novel Rough Set Reduct Algorithm for Medical Domain Based on Bee Colony Optimization
Suguna, N.; k. Thanushkodi
2010-01-01
Feature selection refers to the problem of selecting relevant features which produce the most predictive outcome. In particular, feature selection task is involved in datasets containing huge number of features. Rough set theory has been one of the most successful methods used for feature selection. However, this method is still not able to find optimal subsets. This paper proposes a new feature selection method based on Rough set theory hybrid with Bee Colony Optimization (BCO) in an attempt...
2015-01-01
The wireless sensor network (WSN) is composed of a set of sensor nodes. It is deemed suitable for deploying with large-scale in the environment for variety of applications. Recent advances in WSN have led to many new protocols specifically for reducing the power consumption of sensor nodes. A new scheme for predetermining the optimized routing path is proposed based on the enhanced parallel cat swarm optimization (EPCSO) in this paper. This is the first leading precedent that the EPCSO is emp...
Energy transmission modes based on Tabu search and particle swarm hybrid optimization algorithm
Institute of Scientific and Technical Information of China (English)
LI xiang; CUI Ji-feng; QI Jian-xun; YANG Shang-dong
2007-01-01
In China, economic centers are far from energy storage bases, so it is significant to select a proper energy transferring mode to improve the efficiency of energy usage, To solve this problem, an optimal allocation model based on energy transfer mode was proposed after objective function for optimizing energy using efficiency Was established, and then, a new Tabu search and power transmission was gained.Based on the above discussion, some proposals were put forward for optimal allocation of energy transfer modes in China. By comparing other three traditional methodsthat are based on regional price differences. freight rates and annual cost witll the proposed method, the result indicates that the economic efficiency of the energy transfer Can be enhanced by 3.14%, 5.78% and 6.01%, respectively.
Spaceborne SAR Imaging Algorithm for Coherence Optimized.
Directory of Open Access Journals (Sweden)
Zhiwei Qiu
Full Text Available This paper proposes SAR imaging algorithm with largest coherence based on the existing SAR imaging algorithm. The basic idea of SAR imaging algorithm in imaging processing is that output signal can have maximum signal-to-noise ratio (SNR by using the optimal imaging parameters. Traditional imaging algorithm can acquire the best focusing effect, but would bring the decoherence phenomenon in subsequent interference process. Algorithm proposed in this paper is that SAR echo adopts consistent imaging parameters in focusing processing. Although the SNR of the output signal is reduced slightly, their coherence is ensured greatly, and finally the interferogram with high quality is obtained. In this paper, two scenes of Envisat ASAR data in Zhangbei are employed to conduct experiment for this algorithm. Compared with the interferogram from the traditional algorithm, the results show that this algorithm is more suitable for SAR interferometry (InSAR research and application.
Novel multi-objective optimization algorithm
Institute of Scientific and Technical Information of China (English)
Jie Zeng; Wei Nie
2014-01-01
Many multi-objective evolutionary algorithms (MOEAs) can converge to the Pareto optimal front and work wel on two or three objectives, but they deteriorate when faced with many-objective problems. Indicator-based MOEAs, which adopt various indicators to evaluate the fitness values (instead of the Pareto-dominance relation to select candidate solutions), have been regarded as promising schemes that yield more satisfactory re-sults than wel-known algorithms, such as non-dominated sort-ing genetic algorithm (NSGA-II) and strength Pareto evolution-ary algorithm (SPEA2). However, they can suffer from having a slow convergence speed. This paper proposes a new indicator-based multi-objective optimization algorithm, namely, the multi-objective shuffled frog leaping algorithm based on the ε indicator (ε-MOSFLA). This algorithm adopts a memetic meta-heuristic, namely, the SFLA, which is characterized by the powerful capa-bility of global search and quick convergence as an evolutionary strategy and a simple and effective ε-indicator as a fitness as-signment scheme to conduct the search procedure. Experimental results, in comparison with other representative indicator-based MOEAs and traditional Pareto-based MOEAs on several standard test problems with up to 50 objectives, show thatε-MOSFLA is the best algorithm for solving many-objective optimization problems in terms of the solution quality as wel as the speed of convergence.
Abedini, Mohammad; Moradi, Mohammad H; Hosseinian, S M
2016-03-01
This paper proposes a novel method to address reliability and technical problems of microgrids (MGs) based on designing a number of self-adequate autonomous sub-MGs via adopting MGs clustering thinking. In doing so, a multi-objective optimization problem is developed where power losses reduction, voltage profile improvement and reliability enhancement are considered as the objective functions. To solve the optimization problem a hybrid algorithm, named HS-GA, is provided, based on genetic and harmony search algorithms, and a load flow method is given to model different types of DGs as droop controller. The performance of the proposed method is evaluated in two case studies. The results provide support for the performance of the proposed method. PMID:26767800
Aerodynamic Shape Optimization using an Evolutionary Algorithm
Holst, Terry L.; Pulliam, Thomas H.; Kwak, Dochan (Technical Monitor)
2003-01-01
A method for aerodynamic shape optimization based on an evolutionary algorithm approach is presented and demonstrated. Results are presented for a number of model problems to access the effect of algorithm parameters on convergence efficiency and reliability. A transonic viscous airfoil optimization problem, both single and two-objective variations, is used as the basis for a preliminary comparison with an adjoint-gradient optimizer. The evolutionary algorithm is coupled with a transonic full potential flow solver and is used to optimize the inviscid flow about transonic wings including multi-objective and multi-discipline solutions that lead to the generation of pareto fronts. The results indicate that the evolutionary algorithm approach is easy to implement, flexible in application and extremely reliable.
Iglesias Rey, Pedro Luís; Martínez-Solano, F. Javier; Mora Melia, Daniel; MARTINEZ SOLANO, PEDRO DANIEL
2014-01-01
The paper presents a solution to the problem of the Battle of Background Leakage Assessment for Water Networks (BBLAWN) using a methodology that combines the use of Best Management Practices (BMPs) and an optimization model based on a Pseudo- Genetic Algorithm (PGA) as described in [1]. In a first stage, an analysis of marginal costs of pipes whose replacement would be potentially recommended was performed. Next, a network topological analysis to study the pipes that could potentiall...
OPTIMIZATION OF GRID RESOURCE SCHEDULING USING PARTICLE SWARM OPTIMIZATION ALGORITHM
Directory of Open Access Journals (Sweden)
S. Selvakrishnan
2010-10-01
Full Text Available Job allocation process is one of the big issues in grid environment and it is one of the research areas in Grid Computing. Hence a new area of research is developed to design optimal methods. It focuses on new heuristic techniques that provide an optimal or near optimal solution for large grids. By learning grid resource scheduling and PSO (Particle Swarm Optimization algorithm, this proposed scheduler allocates an application to a host from a pool of available hosts and applications by selecting the best match. PSO-based algorithm is more effective in grid resources scheduling with the favor of reducing the executing time and completing time.
Directory of Open Access Journals (Sweden)
Yong Wang
2014-01-01
Full Text Available In order to increase the driving range and improve the overall performance of all-electric vehicles, a new dual-motor hybrid driving system with two power sources was proposed. This system achieved torque-speed coupling between the two power sources and greatly improved the high performance working range of the motors; at the same time, continuously variable transmission (CVT was achieved to efficiently increase the driving range. The power system parameters were determined using the “global optimization method”; thus, the vehicle’s dynamics and economy were used as the optimization indexes. Based on preliminary matches, quantum genetic algorithm was introduced to optimize the matching in the dual-motor hybrid power system. Backward simulation was performed on the combined simulation platform of Matlab/Simulink and AVL-Cruise to optimize, simulate, and verify the system parameters of the transmission system. Results showed that quantum genetic algorithms exhibited good global optimization capability and convergence in dealing with multiobjective and multiparameter optimization. The dual-motor hybrid-driving system for electric cars satisfied the dynamic performance and economy requirements of design, efficiently increasing the driving range of the car, having high performance, and reducing energy consumption of 15.6% compared with the conventional electric vehicle with single-speed reducers.
Directory of Open Access Journals (Sweden)
Khaled Loukhaoukha
2013-01-01
Full Text Available We present a new optimal watermarking scheme based on discrete wavelet transform (DWT and singular value decomposition (SVD using multiobjective ant colony optimization (MOACO. A binary watermark is decomposed using a singular value decomposition. Then, the singular values are embedded in a detailed subband of host image. The trade-off between watermark transparency and robustness is controlled by multiple scaling factors (MSFs instead of a single scaling factor (SSF. Determining the optimal values of the multiple scaling factors (MSFs is a difficult problem. However, a multiobjective ant colony optimization is used to determine these values. Experimental results show much improved performances of the proposed scheme in terms of transparency and robustness compared to other watermarking schemes. Furthermore, it does not suffer from the problem of high probability of false positive detection of the watermarks.
Optimization of Consumed Power in Two Different DC Motors Coupled Based on Genetic Algorithm
Directory of Open Access Journals (Sweden)
Mehrdad Jafarboland
2011-01-01
Full Text Available A single DC motor can be substituted by two different couple DC motors in submarines. By this way, by varying the speed of submarine, the power of propellant and subsequently the mechanical power of these motors would vary. One important promlem in controlling the mechanical coupling of these motors is the power sharing between them. In the previous reports the mechanical power was shared between them in nonoptimized manner. In this paper an optimized cantroller is indroduced that optimize the efficiency of the system. The power sharing between these motors would vary according to their speed. The proposed controller is based on Genetic Algoritm and is able to share the mechanical power between the motors in an optimized manner at different speeds. The simutation results shows the well behavior of system and also the optimize power sharing.
Energy Technology Data Exchange (ETDEWEB)
Moeini, R.; Afshar, M.H.
2011-07-15
Hydropower is currently the number one source of electricity production in the world. For the design and construction of such systems, mathematical modelling is often use for reservoir operations. As conventional methods present some shortcomings in solving reservoir operation problems, a new method is presented here. It consists in an arc-based formulation of hydropower reservoir operation problems which can be applied to ant colony optimization algorithms. This paper first described this formulation and then applied it to solve two hydropower reservoir operation problems. The results showed that this formulation can optimally solve large-scale hydropower reservoir operation problems while offering a clear definition of heuristic information.
SVM-Based Synthetic Fingerprint Discrimination Algorithm and Quantitative Optimization Strategy
Chen, Suhang; Chang, Sheng; Huang, Qijun; He, Jin; Wang, Hao; Huang, Qiangui
2014-01-01
Synthetic fingerprints are a potential threat to automatic fingerprint identification systems (AFISs). In this paper, we propose an algorithm to discriminate synthetic fingerprints from real ones. First, four typical characteristic factors—the ridge distance features, global gray features, frequency feature and Harris Corner feature—are extracted. Then, a support vector machine (SVM) is used to distinguish synthetic fingerprints from real fingerprints. The experiments demonstrate that this method can achieve a recognition accuracy rate of over 98% for two discrete synthetic fingerprint databases as well as a mixed database. Furthermore, a performance factor that can evaluate the SVM's accuracy and efficiency is presented, and a quantitative optimization strategy is established for the first time. After the optimization of our synthetic fingerprint discrimination task, the polynomial kernel with a training sample proportion of 5% is the optimized value when the minimum accuracy requirement is 95%. The radial basis function (RBF) kernel with a training sample proportion of 15% is a more suitable choice when the minimum accuracy requirement is 98%. PMID:25347063
Energy Technology Data Exchange (ETDEWEB)
Mellit, A. [Medea Univ., Medea (Algeria). Inst. of Science Engineering, Dept. of Electronics
2007-07-01
Stand-alone photovoltaic (PV) power supply systems are regarded as reliable and economical sources of electricity in rural remote areas, particularly in developing countries. However, the sizing of stand-alone photovoltaic (PV) systems is an important part of the system design. Choosing the optimal number of solar cell panels and the size of the storage battery to be used for a certain application at a particular site is an important economical problem. In this paper, a genetic algorithm (GA) and an adaptive neuro-fuzzy inference scheme (ANFIS) were proposed as a means for determining the optimal size of PV system, particularly, in isolated areas. The GA-ANFIS model was shown to be suitable for modelling the optimal sizing parameters of PVS systems. The GA was used to determine the PV-array capacity and the storage capacity for 60 sites. From this database, 56 pairs relative to 56 sites were used for training the network. Four pairs were used for testing and validating the ANFIS model. A correlation of 99 per cent was achieved when complete unknown data parameters were presented to the model. The proposed technique provided more accurate results than the alternative artificial neural network (ANN) with GA. The advantage of this model was that it could estimate the PV-array area and the useful capacity of the battery from only geographical coordinates. Although the technique was applied and tested in Algeria, it can be generalized for any location in the world. 15 refs., 4 tabs., 8 figs.
Long Cheng; Yan Wang; Shuai Li
2015-01-01
With the development of wireless communication and sensor techniques, source localization based on sensor network is getting more attention. However, fewer works investigate the multiple source localization for binary sensor network. In this paper, a self-adaptive particle swarm optimization based multiple source localization method is proposed. A detection model based on Neyman-Pearson criterion is introduced. Then the maximum likelihood estimator is employed to establish the objective funct...
Staged optimization algorithms based MAC dynamic bandwidth allocation for OFDMA-PON
Liu, Yafan; Qian, Chen; Cao, Bingyao; Dun, Han; Shi, Yan; Zou, Junni; Lin, Rujian; Wang, Min
2016-06-01
Orthogonal frequency division multiple access passive optical network (OFDMA-PON) has being considered as a promising solution for next generation PONs due to its high spectral efficiency and flexible bandwidth allocation scheme. In order to take full advantage of these merits of OFDMA-PON, a high-efficiency medium access control (MAC) dynamic bandwidth allocation (DBA) scheme is needed. In this paper, we propose two DBA algorithms which can act on two different stages of a resource allocation process. To achieve higher bandwidth utilization and ensure the equity of ONUs, we propose a DBA algorithm based on frame structure for the stage of physical layer mapping. Targeting the global quality of service (QoS) of OFDMA-PON, we propose a full-range DBA algorithm with service level agreement (SLA) and class of service (CoS) for the stage of bandwidth allocation arbitration. The performance of the proposed MAC DBA scheme containing these two algorithms is evaluated using numerical simulations. Simulations of a 15 Gbps network with 1024 sub-carriers and 32 ONUs demonstrate the maximum network throughput of 14.87 Gbps and the maximum packet delay of 1.45 ms for the highest priority CoS under high load condition.
Belief Propagation Algorithm for Portfolio Optimization Problems.
Shinzato, Takashi; Yasuda, Muneki
2015-01-01
The typical behavior of optimal solutions to portfolio optimization problems with absolute deviation and expected shortfall models using replica analysis was pioneeringly estimated by S. Ciliberti et al. [Eur. Phys. B. 57, 175 (2007)]; however, they have not yet developed an approximate derivation method for finding the optimal portfolio with respect to a given return set. In this study, an approximation algorithm based on belief propagation for the portfolio optimization problem is presented using the Bethe free energy formalism, and the consistency of the numerical experimental results of the proposed algorithm with those of replica analysis is confirmed. Furthermore, the conjecture of H. Konno and H. Yamazaki, that the optimal solutions with the absolute deviation model and with the mean-variance model have the same typical behavior, is verified using replica analysis and the belief propagation algorithm. PMID:26305462
Enhanced Bee Colony Algorithm for Complex Optimization Problems
S.Suriya; R. Deepalakshmi; S.Suresh kannan; Dr.S.P.SHANTHARAJAH
2012-01-01
Optimization problems are considered to be one kind of NP hard problems. Usually heuristic approaches are found to provide solutions for NP hard problems. There are a plenty of heuristic algorithmsavailable to solve optimization problems namely: Ant Colony Optimization, Particle Swarm Optimization, Bee Colony Optimization, etc. The basic Bee Colony algorithm, a population based search algorithm, is analyzed to be a novel tool for complex optimization problems. The algorithm mimics the food fo...
Combinatorial optimization theory and algorithms
Korte, Bernhard
2002-01-01
Combinatorial optimization is one of the youngest and most active areas of discrete mathematics, and is probably its driving force today. This book describes the most important ideas, theoretical results, and algorithms of this field. It is conceived as an advanced graduate text, and it can also be used as an up-to-date reference work for current research. The book includes the essential fundamentals of graph theory, linear and integer programming, and complexity theory. It covers classical topics in combinatorial optimization as well as very recent ones. The emphasis is on theoretical results and algorithms with provably good performance. Some applications and heuristics are mentioned, too.
Genetic Algorithm: A Versatile Optimization Tool
Directory of Open Access Journals (Sweden)
Dr. Madhavi Sinha
2009-07-01
Full Text Available Genetic Algorithms are a powerful search technique based onthe mechanics of natural selection and natural genetics thatare used successfully to solve problems in many differentdisciplines.The good robustness of these algorithms on problems of highcomplexity has led to an increasing number of applications inthe fields of artificial intelligence, numeric and combinatorialoptimization, business, management, medicine, computerscience, engineering etc.In this paper we present a cross section of current geneticalgorithm applications from diverse fields and lay a specialemphasis on use of genetic algorithms in one of the mostimportant optimization problems in computer science, databasequery optimization.
Zhang, De-Jia
2009-07-01
With the fast development of Internet, many systems have emerged in e-commerce applications to support the product recommendation. Collaborative filtering is one of the most promising techniques in recommender systems, providing personalized recommendations to users based on their previously expressed preferences in the form of ratings and those of other similar users. In practice, with the adding of user and item scales, user-item ratings are becoming extremely sparsity and recommender systems utilizing traditional collaborative filtering are facing serious challenges. To address the issue, this paper presents an approach to compute item genre similarity, through mapping each item with a corresponding descriptive genre, and computing similarity between genres as similarity, then make basic predictions according to those similarities to lower sparsity of the user-item ratings. After that, item-based collaborative filtering steps are taken to generate predictions. Compared with previous methods, the presented collaborative filtering employs the item genre similarity can alleviate the sparsity issue in the recommender systems, and can improve accuracy of recommendation.
Directory of Open Access Journals (Sweden)
Zhaocai Wang
2015-10-01
Full Text Available The unbalanced assignment problem (UAP is to optimally resolve the problem of assigning n jobs to m individuals (m < n, such that minimum cost or maximum profit obtained. It is a vitally important Non-deterministic Polynomial (NP complete problem in operation management and applied mathematics, having numerous real life applications. In this paper, we present a new parallel DNA algorithm for solving the unbalanced assignment problem using DNA molecular operations. We reasonably design flexible-length DNA strands representing different jobs and individuals, take appropriate steps, and get the solutions of the UAP in the proper length range and O(mn time. We extend the application of DNA molecular operations and simultaneity to simplify the complexity of the computation.
Constrained Multiobjective Biogeography Optimization Algorithm
Directory of Open Access Journals (Sweden)
Hongwei Mo
2014-01-01
Full Text Available Multiobjective optimization involves minimizing or maximizing multiple objective functions subject to a set of constraints. In this study, a novel constrained multiobjective biogeography optimization algorithm (CMBOA is proposed. It is the first biogeography optimization algorithm for constrained multiobjective optimization. In CMBOA, a disturbance migration operator is designed to generate diverse feasible individuals in order to promote the diversity of individuals on Pareto front. Infeasible individuals nearby feasible region are evolved to feasibility by recombining with their nearest nondominated feasible individuals. The convergence of CMBOA is proved by using probability theory. The performance of CMBOA is evaluated on a set of 6 benchmark problems and experimental results show that the CMBOA performs better than or similar to the classical NSGA-II and IS-MOEA.
Directory of Open Access Journals (Sweden)
Mingming Gao
2014-05-01
Full Text Available In order to design and optimize high-linearity power amplifier (PA, which with nonlinear and memory effect, it is very important to build power amplifier behavior modeling accurately. This paper proposes a power amplifier behavior modeling based on RBF neural network with improved chaos particle swarm optimization algorithm. To make the particles evenly distribute in the problem search space, a novel Chaos Particle Swarm Optimization (CPSO is proposed based on the analysis of the ergodicity of chaos and inertia weight of Particle Swarm Optimization (PSO. Based on circle model, the new model is introduced to avoid PSO from getting into local optimum. This paper uses free scale semiconductor chip MRF6S21140 to carry on amplifier circuit design in the ADS and the MATLAB fitting simulation of the extracted data, by improved CPSO-RBF algorithm. Its accuracy is assessed by comparing RBF modeling with voltage RMS error (RMSE, epochs, and fitting time. The result shows that improved CPSO-RBF has better fitting function.
Lu, Lin; Chang, Yunlong; Li, Yingmin; Lu, Ming
2013-05-01
An orthogonal experiment was conducted by the means of multivariate nonlinear regression equation to adjust the influence of external transverse magnetic field and Ar flow rate on welding quality in the process of welding condenser pipe by high-speed argon tungsten-arc welding (TIG for short). The magnetic induction and flow rate of Ar gas were used as optimum variables, and tensile strength of weld was set to objective function on the base of genetic algorithm theory, and then an optimal design was conducted. According to the request of physical production, the optimum variables were restrained. The genetic algorithm in the MATLAB was used for computing. A comparison between optimum results and experiment parameters was made. The results showed that the optimum technologic parameters could be chosen by the means of genetic algorithm with the conditions of excessive optimum variables in the process of high-speed welding. And optimum technologic parameters of welding coincided with experiment results.
Distributed Hybrid Genetic Algorithms for Structural Optimization
International Nuclear Information System (INIS)
The great advantages on the Genetic Algorithms (GAs) are ease of implementation, and robustness in solving a wide variety of problems, several GAs based optimization models for solving complex structural problems were proposed. However, there are two major disadvantages in GAs. Firstly, implementation of GAs-based optimization is computationally too expensive for practical use in the field of structural optimization, particularly for large-scale problems. The second problem is too difficult to find proper parameters required in GAs for a particular problem. Therefore, in the paper, a distributed hybrid genetic algorithms (DHGAs) is developed for structural optimization on a cluster of personal computers. The algorithm is applied to the minimum weight design of steel structures
Space mapping optimization algorithms for engineering design
DEFF Research Database (Denmark)
Koziel, Slawomir; Bandler, John W.; Madsen, Kaj
2006-01-01
A simple, efficient optimization algorithm based on space mapping (SM) is presented. It utilizes input SM to reduce the misalignment between the coarse and fine models of the optimized object over a region of interest, and output space mapping (OSM) to ensure matching of response and first-order ...... benchmark problem. In comparison with SMIS, the models presented are simple and have a small number of parameters that need to be extracted. The new algorithm is applied to the optimization of coupled-line band-pass filter....
Structure Design of the 3-D Braided Composite Based on a Hybrid Optimization Algorithm
Zhang, Ke
Three-dimensional braided composite has the better designable characteristic. Whereas wide application of hollow-rectangular-section three-dimensional braided composite in engineering, optimization design of the three-dimensional braided composite made by 4-step method were introduced. Firstly, the stiffness and damping characteristic analysis of the composite is presented. Then, the mathematical models for structure design of the three-dimensional braided composite were established. The objective functions are based on the specific damping capacity and stiffness of the composite. The design variables are the braiding parameters of the composites and sectional geometrical size of the composite. The optimization problem is solved by using ant colony optimization (ACO), contenting the determinate restriction. The results of numeral examples show that the better damping and stiffness characteristic could be obtained. The method proposed here is useful for the structure design of the kind of member and its engineering application.
An optimization algorithm for simulation-based planning of low-income housing projects
Directory of Open Access Journals (Sweden)
Mohamed M. Marzouk
2010-10-01
Full Text Available Construction of low-income housing projects is a replicated process and is associated with uncertainties that arise from the unavailability of resources. Government agencies and/or contractors have to select a construction system that meets low-income housing projects constraints including project conditions, technical, financial and time constraints. This research presents a framework, using computer simulation, which aids government authorities and contractors in the planning of low-income housing projects. The proposed framework estimates the time and cost required for the construction of low-income housing using pre-cast hollow core with hollow blocks bearing walls. Five main components constitute the proposed framework: a network builder module, a construction alternative selection module, a simulation module, an optimization module and a reporting module. An optimization module utilizing a genetic algorithm enables the defining of different options and ranges of parameters associated with low-income housing projects that influence the duration and total cost of the pre-cast hollow core with hollow blocks bearing walls method. A computer prototype, named LIHouse_Sim, was developed in MS Visual Basic 6.0 as proof of concept for the proposed framework. A numerical example is presented to demonstrate the use of the developed framework and to illustrate its essential features.
An Approach In Optimization Of Ad-Hoc Routing Algorithms
Directory of Open Access Journals (Sweden)
Sarvesh Kumar Sharma
2012-06-01
Full Text Available In this paper different optimization of Ad-hoc routing algorithm is surveyed and a new method using training based optimization algorithm for reducing the complexity of routing algorithms is suggested. A binary matrix is assigned to each node in the network and gets updated after each data transfer using the protocols. The use of optimization algorithm in routing algorithm can reduce the complexity of routing to the least amount possible.
Optimized Ant Colony Algorithm by Local Pheromone Update
Hui Yu
2013-01-01
Ant colony algorithm, a heuristic simulated algorithm, provides better solutions for non-convex, non-linear and discontinuous optimization problems. For ant colony algorithm, it is frequently to be trapped into local optimum, which might lead to stagnation. This article presents the city-select strategy, local pheromone update strategy, optimum solution prediction strategy and local optimization strategy to optimize ant colony algorithm, provides ant colony algorithm based on local pheromone...
Loading pattern optimization using ant colony algorithm
Energy Technology Data Exchange (ETDEWEB)
Hoareau, Fabrice [EDF R and D, Clamart (France)
2008-07-01
Electricite de France (EDF) operates 58 nuclear power plants (NPP), of the Pressurized Water Reactor type. The loading pattern optimization of these NPP is currently done by EDF expert engineers. Within this framework, EDF R and D has developed automatic optimization tools that assist the experts. LOOP is an industrial tool, developed by EDF R and D and based on a simulated annealing algorithm. In order to improve the results of such automatic tools, new optimization methods have to be tested. Ant Colony Optimization (ACO) algorithms are recent methods that have given very good results on combinatorial optimization problems. In order to evaluate the performance of such methods on loading pattern optimization, direct comparisons between LOOP and a mock-up based on the Max-Min Ant System algorithm (a particular variant of ACO algorithms) were made on realistic test-cases. It is shown that the results obtained by the ACO mock-up are very similar to those of LOOP. Future research will consist in improving these encouraging results by using parallelization and by hybridizing the ACO algorithm with local search procedures. (author)
Loading pattern optimization using ant colony algorithm
International Nuclear Information System (INIS)
Electricite de France (EDF) operates 58 nuclear power plants (NPP), of the Pressurized Water Reactor type. The loading pattern optimization of these NPP is currently done by EDF expert engineers. Within this framework, EDF R and D has developed automatic optimization tools that assist the experts. LOOP is an industrial tool, developed by EDF R and D and based on a simulated annealing algorithm. In order to improve the results of such automatic tools, new optimization methods have to be tested. Ant Colony Optimization (ACO) algorithms are recent methods that have given very good results on combinatorial optimization problems. In order to evaluate the performance of such methods on loading pattern optimization, direct comparisons between LOOP and a mock-up based on the Max-Min Ant System algorithm (a particular variant of ACO algorithms) were made on realistic test-cases. It is shown that the results obtained by the ACO mock-up are very similar to those of LOOP. Future research will consist in improving these encouraging results by using parallelization and by hybridizing the ACO algorithm with local search procedures. (author)
International Nuclear Information System (INIS)
Highlights: ► PSO and ACO algorithms are hybridized for forecasting energy demands of Turkey. ► Linear and quadratic forms are developed to meet the fluctuations of indicators. ► GDP, population, export and import have significant impacts on energy demand. ► Quadratic form provides better fit solution than linear form. ► Proposed approach gives lower estimation error than ACO and PSO, separately. - Abstract: This paper proposes a new hybrid method (HAP) for estimating energy demand of Turkey using Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). Proposed energy demand model (HAPE) is the first model which integrates two mentioned meta-heuristic techniques. While, PSO, developed for solving continuous optimization problems, is a population based stochastic technique; ACO, simulating behaviors between nest and food source of real ants, is generally used for discrete optimizations. Hybrid method based PSO and ACO is developed to estimate energy demand using gross domestic product (GDP), population, import and export. HAPE is developed in two forms which are linear (HAPEL) and quadratic (HAPEQ). The future energy demand is estimated under different scenarios. In order to show the accuracy of the algorithm, a comparison is made with ACO and PSO which are developed for the same problem. According to obtained results, relative estimation errors of the HAPE model are the lowest of them and quadratic form (HAPEQ) provides better-fit solutions due to fluctuations of the socio-economic indicators.
A novel bee swarm optimization algorithm for numerical function optimization
Akbari, Reza; Mohammadi, Alireza; Ziarati, Koorush
2010-10-01
The optimization algorithms which are inspired from intelligent behavior of honey bees are among the most recently introduced population based techniques. In this paper, a novel algorithm called bee swarm optimization, or BSO, and its two extensions for improving its performance are presented. The BSO is a population based optimization technique which is inspired from foraging behavior of honey bees. The proposed approach provides different patterns which are used by the bees to adjust their flying trajectories. As the first extension, the BSO algorithm introduces different approaches such as repulsion factor and penalizing fitness (RP) to mitigate the stagnation problem. Second, to maintain efficiently the balance between exploration and exploitation, time-varying weights (TVW) are introduced into the BSO algorithm. The proposed algorithm (BSO) and its two extensions (BSO-RP and BSO-RPTVW) are compared with existing algorithms which are based on intelligent behavior of honey bees, on a set of well known numerical test functions. The experimental results show that the BSO algorithms are effective and robust; produce excellent results, and outperform other algorithms investigated in this consideration.
Directory of Open Access Journals (Sweden)
Yinggao Yue
2016-01-01
Full Text Available Data collection is a fundamental operation in various mobile wireless sensor networks (MWSN applications. The energy of nodes around the Sink can be untimely depleted owing to the fact that sensor nodes must transmit vast amounts of data, readily forming a bottleneck in energy consumption; mobile wireless sensor networks have been designed to address this issue. In this study, we focused on a large-scale and intensive MWSN which allows a certain amount of data latency by investigating mobile Sink balance from three aspects: data collection maximization, mobile path length minimization, and network reliability optimization. We also derived a corresponding formula to represent the MWSN and proved that it represents an NP-hard problem. Traditional data collection methods only focus on increasing the amount data collection or reducing the overall network energy consumption, which is why we designed the proposed heuristic algorithm to jointly consider cluster head selection, the routing path from ordinary nodes to the cluster head node, and mobile Sink path planning optimization. The proposed data collection algorithm for mobile Sinks is, in effect, based on artificial bee colony. Simulation results show that, in comparison with other algorithms, the proposed algorithm can effectively reduce data transmission, save energy, improve network data collection efficiency and reliability, and extend the network lifetime.
Institute of Scientific and Technical Information of China (English)
WangWenjun; YuLongjiang; HePu; ZhouPengpeng
2004-01-01
Genetic algorithms (GA) based on the principle of mimicing Darwinian evolution and survival of the fittest in a natural environment was used to optimize the medium for astaxanthin production by the mutant strain W6-8 of Xantho-phyllomyces dendrorhous. The 50 concentration levels of 6 medium components were optimized within 50 experiments (full experimental plan: 506 experiments). The results showed that GA could be applied in the medium optimization and better results were obtained. By employing optimized medium components (glucose 39.8 g l-1, yeast extract 4.08 g l-1,(NH4)2SO4 7.36 g l-1, MgSO4 2 g l-1, K2HPO4 2.04 g l-1 and KH2PO4 3.48 g l-1), the highest astaxanthin production was 9.855 mg l-1, approximately 31% higher than that under the initial conditions, and was approximately 15.46% higher than that by orthogonal array but only slightly higher than that by response surface methodology. In the sequent scale-up experiments, the astaxanthin yield was obtained approximately 14.753 mg l-1, employing the optimal medium. The results indicated that GA, as an euiicient method for medium optimization, was superior to other optimal means such as orthogonal array.
Institute of Scientific and Technical Information of China (English)
刘洪
2004-01-01
A multiple-objective evolutionary algorithm (MOEA) with a new Decision Making (DM) scheme for MOD of conceptual missile shapes was presented, which is contrived to determine suitable tradeoffs from Pareto optimal set using interactive preference articulation. There are two objective functions, to maximize ratio of lift to drag and to minimize radar cross-section (RCS) value. 3D computational electromagnetic solver was used to evaluate RCS, electromagnetic performance. 3D Navier-Stokes flow solver was adopted to evaluate aerodynamic performance. A flight mechanics solver was used to analyze the stability of the missile. Based on the MOEA, a synergetic optimization of missile shapes for aerodynamic and radar cross-section performance is completed. The results show that the proposed approach can be used in more complex optimization case of flight vehicles.
International Nuclear Information System (INIS)
The design of an ultrasonic array and its settings for a specific inspection involves multiple parameters and is the result of a compromise between different requirements and limitations. Parametric studies based on simulation tools are often performed to ensure that the transducer has suitable performances in terms of detection of the defects sought. An automatic optimization tool, based on an evolutionary algorithm driving CIVA's simulation data, is proposed and tested in realistic applications to design inspection parameters (designing, positioning and/or setting the transducer). (authors)
An effective docking strategy for virtual screening based on multi-objective optimization algorithm
Directory of Open Access Journals (Sweden)
Kang Ling
2009-02-01
Full Text Available Abstract Background Development of a fast and accurate scoring function in virtual screening remains a hot issue in current computer-aided drug research. Different scoring functions focus on diverse aspects of ligand binding, and no single scoring can satisfy the peculiarities of each target system. Therefore, the idea of a consensus score strategy was put forward. Integrating several scoring functions, consensus score re-assesses the docked conformations using a primary scoring function. However, it is not really robust and efficient from the perspective of optimization. Furthermore, to date, the majority of available methods are still based on single objective optimization design. Results In this paper, two multi-objective optimization methods, called MOSFOM, were developed for virtual screening, which simultaneously consider both the energy score and the contact score. Results suggest that MOSFOM can effectively enhance enrichment and performance compared with a single score. For three different kinds of binding sites, MOSFOM displays an excellent ability to differentiate active compounds through energy and shape complementarity. EFMOGA performed particularly well in the top 2% of database for all three cases, whereas MOEA_Nrg and MOEA_Cnt performed better than the corresponding individual scoring functions if the appropriate type of binding site was selected. Conclusion The multi-objective optimization method was successfully applied in virtual screening with two different scoring functions that can yield reasonable binding poses and can furthermore, be ranked with the potentially compromised conformations of each compound, abandoning those conformations that can not satisfy overall objective functions.
Zhang, Ying; Liang, Jixing; Jiang, Shengming; Chen, Wei
2016-01-01
Due to their special environment, Underwater Wireless Sensor Networks (UWSNs) are usually deployed over a large sea area and the nodes are usually floating. This results in a lower beacon node distribution density, a longer time for localization, and more energy consumption. Currently most of the localization algorithms in this field do not pay enough consideration on the mobility of the nodes. In this paper, by analyzing the mobility patterns of water near the seashore, a localization method for UWSNs based on a Mobility Prediction and a Particle Swarm Optimization algorithm (MP-PSO) is proposed. In this method, the range-based PSO algorithm is used to locate the beacon nodes, and their velocities can be calculated. The velocity of an unknown node is calculated by using the spatial correlation of underwater object's mobility, and then their locations can be predicted. The range-based PSO algorithm may cause considerable energy consumption and its computation complexity is a little bit high, nevertheless the number of beacon nodes is relatively smaller, so the calculation for the large number of unknown nodes is succinct, and this method can obviously decrease the energy consumption and time cost of localizing these mobile nodes. The simulation results indicate that this method has higher localization accuracy and better localization coverage rate compared with some other widely used localization methods in this field. PMID:26861348
Zhang, Ying; Liang, Jixing; Jiang, Shengming; Chen, Wei
2016-01-01
Due to their special environment, Underwater Wireless Sensor Networks (UWSNs) are usually deployed over a large sea area and the nodes are usually floating. This results in a lower beacon node distribution density, a longer time for localization, and more energy consumption. Currently most of the localization algorithms in this field do not pay enough consideration on the mobility of the nodes. In this paper, by analyzing the mobility patterns of water near the seashore, a localization method for UWSNs based on a Mobility Prediction and a Particle Swarm Optimization algorithm (MP-PSO) is proposed. In this method, the range-based PSO algorithm is used to locate the beacon nodes, and their velocities can be calculated. The velocity of an unknown node is calculated by using the spatial correlation of underwater object’s mobility, and then their locations can be predicted. The range-based PSO algorithm may cause considerable energy consumption and its computation complexity is a little bit high, nevertheless the number of beacon nodes is relatively smaller, so the calculation for the large number of unknown nodes is succinct, and this method can obviously decrease the energy consumption and time cost of localizing these mobile nodes. The simulation results indicate that this method has higher localization accuracy and better localization coverage rate compared with some other widely used localization methods in this field. PMID:26861348
Tang, Qiuhua; Li, Zixiang; Zhang, Liping; Floudas, C. A.; Cao, Xiaojun
2015-09-01
Due to the NP-hardness of the two-sided assembly line balancing (TALB) problem, multiple constraints existing in real applications are less studied, especially when one task is involved with several constraints. In this paper, an effective hybrid algorithm is proposed to address the TALB problem with multiple constraints (TALB-MC). Considering the discrete attribute of TALB-MC and the continuous attribute of the standard teaching-learning-based optimization (TLBO) algorithm, the random-keys method is hired in task permutation representation, for the purpose of bridging the gap between them. Subsequently, a special mechanism for handling multiple constraints is developed. In the mechanism, the directions constraint of each task is ensured by the direction check and adjustment. The zoning constraints and the synchronism constraints are satisfied by teasing out the hidden correlations among constraints. The positional constraint is allowed to be violated to some extent in decoding and punished in cost function. Finally, with the TLBO seeking for the global optimum, the variable neighborhood search (VNS) is further hybridized to extend the local search space. The experimental results show that the proposed hybrid algorithm outperforms the late acceptance hill-climbing algorithm (LAHC) for TALB-MC in most cases, especially for large-size problems with multiple constraints, and demonstrates well balance between the exploration and the exploitation. This research proposes an effective and efficient algorithm for solving TALB-MC problem by hybridizing the TLBO and VNS.
Directory of Open Access Journals (Sweden)
Mahesh S. Narkhede
2015-01-01
Full Text Available An attempt has been made in this article to compare the performances of two multiobjective evolutionary algorithms namely ev-MOGA and GODLIKE. The performances of both are evaluated on risk based optimal power scheduling of virtual power plant. The risk based scheduling is proposed as a conflicting bi objective optimization problem with increased number of durations of day. Both the algorithms are elaborated in detail. Results based on the performance analysis are depicted at the end.
Research on a Distribution Center Location Model Based on a Particle Swarm Optimization Algorithm
Institute of Scientific and Technical Information of China (English)
WANG Fei; HU Xin-bu; JIA Tao
2009-01-01
Logistics is supposed to be the important source of profits for the enterprises besides reducing material consumption and improving labor productivity.Transportation costs,distribution center construction costs,ordering costs,safe inventory costs and inventory holding costs are the important parts of the total logistics costs.In this paper,based on the research results of LMRP( location model of risk pooling) location with fixed construction cost,the LMRPVCC (location model of risk pooling based on variable construction cost) will be introduced.Applying particle swarm optimization to several computational instances,the authors find the suboptimum solution of the model.
Yi, Pengxing; Dong, Lijian; Shi, Tielin
2014-12-01
To improve the dynamic performance and reduce the weight of the planet carrier in wind turbine gearbox, a multi-objective optimization method, which is driven by the maximum deformation, the maximum stress and the minimum mass of the studied part, is proposed by combining the response surface method and genetic algorithms in this paper. Firstly, the design points' distribution for the design variables of the planet carrier is established with the central composite design (CCD) method. Then, based on the computing results of finite element analysis (FEA), the response surface analysis is conducted to find out the proper sets of design variable values. And a multi-objective genetic algorithm (MOGA) is applied to determine the direction of optimization. As well, this method is applied to design and optimize the planet carrier in a 1.5MW wind turbine gearbox, the results of which are validated by an experimental modal test. Compared with the original design, the mass and the stress of the optimized planet carrier are respectively reduced by 9.3% and 40%. Consequently, the cost of planet carrier is greatly reduced and its stability is also improved.
A data mining approach to optimize pellets manufacturing process based on a decision tree algorithm.
Ronowicz, Joanna; Thommes, Markus; Kleinebudde, Peter; Krysiński, Jerzy
2015-06-20
The present study is focused on the thorough analysis of cause-effect relationships between pellet formulation characteristics (pellet composition as well as process parameters) and the selected quality attribute of the final product. The shape using the aspect ratio value expressed the quality of pellets. A data matrix for chemometric analysis consisted of 224 pellet formulations performed by means of eight different active pharmaceutical ingredients and several various excipients, using different extrusion/spheronization process conditions. The data set contained 14 input variables (both formulation and process variables) and one output variable (pellet aspect ratio). A tree regression algorithm consistent with the Quality by Design concept was applied to obtain deeper understanding and knowledge of formulation and process parameters affecting the final pellet sphericity. The clear interpretable set of decision rules were generated. The spehronization speed, spheronization time, number of holes and water content of extrudate have been recognized as the key factors influencing pellet aspect ratio. The most spherical pellets were achieved by using a large number of holes during extrusion, a high spheronizer speed and longer time of spheronization. The described data mining approach enhances knowledge about pelletization process and simultaneously facilitates searching for the optimal process conditions which are necessary to achieve ideal spherical pellets, resulting in good flow characteristics. This data mining approach can be taken into consideration by industrial formulation scientists to support rational decision making in the field of pellets technology. PMID:25835791
Knowledge-based function optimization using fuzzy cultural algorithms with evolutionary programming.
Reynolds, R G; Zhu, S
2001-01-01
In this paper, the advantages of a fuzzy representation in problem solving and search is investigated using the framework of Cultural algorithms (CAs). Since all natural languages contain a fuzzy component, the natural question is "Does this fuzzy representation facilitate the problem-solving process, within these systems". In order to investigate this question we use the CA framework of Reynolds (1996), CAs are a computational model of cultural evolution derived from and used to express basic anthropological models of culture and its development. A mathematical model of a full fuzzy CA is developed there. In it, the problem solving knowledge is represented using a fuzzy framework. Several theoretical results concerning its properties are presented. The model is then applied to the solution of a set of 12 difficult, benchmark problems in nonlinear real-valued function optimization. The performance of the full fuzzy model is compared with 8 other fuzzy and crisp architectures. The results suggest that a fuzzy approach can produce a statistically significant improvement in search efficiency over nonfuzzy versions for the entire set of functions, the then investigate the class of performance functions for which the full fuzzy system exhibits the greatest improvements over nonfuzzy systems. In general, these are functions which require some preliminary investigation in order to embark on an effective search. PMID:18244764
A moments-based algorithm for optimizing the information mined in post-processing spray images
Tan, Z. P.; Zinn, B. T.; Lubarsky, E.; Bibik, O.; Shcherbik, D.; Shen, L.
2016-02-01
The Moments-algorithm was developed to post-process images of sprays with the aim of characterizing the sprays' complex features (e.g., trajectory, dispersions and dynamics) in terms of simple curves, which can be used for developing correlation models and design tools. To achieve this objective, the algorithm calculates the first moments of pixel intensity values in instantaneous images of the spray to determine its center-of-gravity ( CG) trajectory (i.e., the spray density-weighted centerline trajectory). Thereafter, the second moments (i.e., standard-deviations, σ) of intensities are calculated to describe the dispersion of spray materials around the CG. After the instantaneous CG's and σ's for the instantaneous images have been obtained, they are arithmetically averaged to produce the average spray trajectories and dispersions. Additionally, the second moments of instantaneous CG's are used to characterize the spray's fluctuation magnitude. The Moments-algorithm has three main advantages over threshold-based edge-tracking and other conventional post-processing approaches: (1) It simultaneously describes the spray's instantaneous and average trajectories, dispersions and fluctuations, instead of just the outer/inner-edges, (2) the use of moments to define these spray characteristics is more physically meaningful because they reflect the statistical distribution of droplets within the spray plume instead of relying on an artificially interpreted "edge", and (3) the use of moments decreases the uncertainties of the post-processed results because moments are mathematically defined and do not depend upon user-adjustments/interpretations.
FACE RECOGNITION BASED ON CUCKOO SEARCH ALGORITHM
VIPINKUMAR TIWARI
2012-01-01
Feature Selection is a optimization technique used in face recognition technology. Feature selection removes the irrelevant, noisy and redundant data thus leading to the more accurate recognition of face from the database.Cuckko Algorithm is one of the recent optimization algorithm in the league of nature based algorithm. Its optimization results are better than the PSO and ACO optimization algorithms. The proposal of applying the Cuckoo algorithm for feature selection in the process of face ...
Genetic Algorithm for Optimization: Preprocessor and Algorithm
Sen, S. K.; Shaykhian, Gholam A.
2006-01-01
Genetic algorithm (GA) inspired by Darwin's theory of evolution and employed to solve optimization problems - unconstrained or constrained - uses an evolutionary process. A GA has several parameters such the population size, search space, crossover and mutation probabilities, and fitness criterion. These parameters are not universally known/determined a priori for all problems. Depending on the problem at hand, these parameters need to be decided such that the resulting GA performs the best. We present here a preprocessor that achieves just that, i.e., it determines, for a specified problem, the foregoing parameters so that the consequent GA is a best for the problem. We stress also the need for such a preprocessor both for quality (error) and for cost (complexity) to produce the solution. The preprocessor includes, as its first step, making use of all the information such as that of nature/character of the function/system, search space, physical/laboratory experimentation (if already done/available), and the physical environment. It also includes the information that can be generated through any means - deterministic/nondeterministic/graphics. Instead of attempting a solution of the problem straightway through a GA without having/using the information/knowledge of the character of the system, we would do consciously a much better job of producing a solution by using the information generated/created in the very first step of the preprocessor. We, therefore, unstintingly advocate the use of a preprocessor to solve a real-world optimization problem including NP-complete ones before using the statistically most appropriate GA. We also include such a GA for unconstrained function optimization problems.
International Nuclear Information System (INIS)
Highlights: • A detailed data processing will make more accurate results prediction. • Taking a full account of more load factors to improve the prediction precision. • Improved BP network obtains higher learning convergence. • Genetic algorithm optimized by chaotic cat map enhances the global search ability. • The combined GA–BP model improved by modified additional momentum factor is superior to others. - Abstract: This paper proposes an appropriate combinational approach which is based on improved BP neural network for short-term gas load forecasting, and the network is optimized by the real-coded genetic algorithm. Firstly, several kinds of modifications are carried out on the standard neural network to accelerate the convergence speed of network, including improved additional momentum factor, improved self-adaptive learning rate and improved momentum and self-adaptive learning rate. Then, it is available to use the global search capability of optimized genetic algorithm to determine the initial weights and thresholds of BP neural network to avoid being trapped in local minima. The ability of GA is enhanced by cat chaotic mapping. In light of the characteristic of natural gas load for Shanghai, a series of data preprocessing methods are adopted and more comprehensive load factors are taken into account to improve the prediction accuracy. Such improvements facilitate forecasting efficiency and exert maximum performance of the model. As a result, the integration model improved by modified additional momentum factor gets more ideal solutions for short-term gas load forecasting, through analyses and comparisons of the above several different combinational algorithms
Mahmoodabadi, M. J.; Bagheri, A.; Nariman-zadeh, N.; Jamali, A.
2012-10-01
Particle swarm optimization (PSO) is a randomized and population-based optimization method that was inspired by the flocking behaviour of birds and human social interactions. In this work, multi-objective PSO is modified in two stages. In the first stage, PSO is combined with convergence and divergence operators. Here, this method is named CDPSO. In the second stage, to produce a set of Pareto optimal solutions which has good convergence, diversity and distribution, two mechanisms are used. In the first mechanism, a new leader selection method is defined, which uses the periodic iteration and the concept of the particle's neighbour number. This method is named periodic multi-objective algorithm. In the second mechanism, an adaptive elimination method is employed to limit the number of non-dominated solutions in the archive, which has influences on computational time, convergence and diversity of solution. Single-objective results show that CDPSO performs very well on the complex test functions in terms of solution accuracy and convergence speed. Furthermore, some benchmark functions are used to evaluate the performance of periodic multi-objective CDPSO. This analysis demonstrates that the proposed algorithm operates better in three metrics through comparison with three well-known elitist multi-objective evolutionary algorithms. Finally, the algorithm is used for Pareto optimal design of a two-degree of freedom vehicle vibration model. The conflicting objective functions are sprung mass acceleration and relative displacement between sprung mass and tyre. The feasibility and efficiency of periodic multi-objective CDPSO are assessed in comparison with multi-objective modified NSGAII.
Optimal Detection Range of RFID Tag for RFID-based Positioning System Using the k-NN Algorithm
Directory of Open Access Journals (Sweden)
Joon Heo
2009-06-01
Full Text Available Positioning technology to track a moving object is an important and essential component of ubiquitous computing environments and applications. An RFID-based positioning system using the k-nearest neighbor (k-NN algorithm can determine the position of a moving reader from observed reference data. In this study, the optimal detection range of an RFID-based positioning system was determined on the principle that tag spacing can be derived from the detection range. It was assumed that reference tags without signal strength information are regularly distributed in 1-, 2- and 3-dimensional spaces. The optimal detection range was determined, through analytical and numerical approaches, to be 125% of the tag-spacing distance in 1-dimensional space. Through numerical approaches, the range was 134% in 2-dimensional space, 143% in 3-dimensional space.
Optimal Detection Range of RFID Tag for RFID-based Positioning System Using the k-NN Algorithm
Han, Soohee; Kim, Junghwan; Park, Choung-Hwan; Yoon, Hee-Cheon; Heo, Joon
2009-01-01
Positioning technology to track a moving object is an important and essential component of ubiquitous computing environments and applications. An RFID-based positioning system using the k-nearest neighbor (k-NN) algorithm can determine the position of a moving reader from observed reference data. In this study, the optimal detection range of an RFID-based positioning system was determined on the principle that tag spacing can be derived from the detection range. It was assumed that reference tags without signal strength information are regularly distributed in 1-, 2- and 3-dimensional spaces. The optimal detection range was determined, through analytical and numerical approaches, to be 125% of the tag-spacing distance in 1-dimensional space. Through numerical approaches, the range was 134% in 2-dimensional space, 143% in 3-dimensional space. PMID:22408540
An Efficient Chemical Reaction Optimization Algorithm for Multiobjective Optimization.
Bechikh, Slim; Chaabani, Abir; Ben Said, Lamjed
2015-10-01
Recently, a new metaheuristic called chemical reaction optimization was proposed. This search algorithm, inspired by chemical reactions launched during collisions, inherits several features from other metaheuristics such as simulated annealing and particle swarm optimization. This fact has made it, nowadays, one of the most powerful search algorithms in solving mono-objective optimization problems. In this paper, we propose a multiobjective variant of chemical reaction optimization, called nondominated sorting chemical reaction optimization, in an attempt to exploit chemical reaction optimization features in tackling problems involving multiple conflicting criteria. Since our approach is based on nondominated sorting, one of the main contributions of this paper is the proposal of a new quasi-linear average time complexity quick nondominated sorting algorithm; thereby making our multiobjective algorithm efficient from a computational cost viewpoint. The experimental comparisons against several other multiobjective algorithms on a variety of benchmark problems involving various difficulties show the effectiveness and the efficiency of this multiobjective version in providing a well-converged and well-diversified approximation of the Pareto front. PMID:25373137
Jian Fang
2014-01-01
The dynamics model is established in view of the self-designed, two-wheeled, and self-balancing robot. This paper uses the particle swarm algorithm to optimize the parameter matrix of LQR controller based on the LQR control method to make the two-wheeled and self-balancing robot realize the stable control and reduce the overshoot amount and the oscillation frequency of the system at the same time. The simulation experiments prove that the LQR controller improves the system stability, obtains ...
International Nuclear Information System (INIS)
In the present paper an improved genetic algorithm (GA) based linear quadratic regulator (LQR) control scheme has been proposed for active vibration control of smart fiber reinforced polymer (FRP) composite shell structures under combined mechanical and thermal loading. A layered shell finite element formulation has been done to obtain the electro-thermo-mechanical response of fiber reinforced polymer (FRP) composite shell structures bonded with piezoelectric patches. Based on the responses obtained from finite element analysis, a real coded GA based improved LQR control scheme has been incorporated, which maximizes the closed loop damping while keeping the actuator voltages within limit. It has been observed that the developed FE code can be used for determination of the accurate response of smart FRP shell structures for the simulation of active vibration control of such structures. The proposed GA based LQR control scheme could control both dynamic oscillation due to mechanical load as well as the static displacement due to a thermal gradient, which was not possible with conventional LQR control scheme
Yang, Qidong; Zuo, Hongchao; Li, Weidong
2016-01-01
Improving the capability of land-surface process models to simulate soil moisture assists in better understanding the atmosphere-land interaction. In semi-arid regions, due to limited near-surface observational data and large errors in large-scale parameters obtained by the remote sensing method, there exist uncertainties in land surface parameters, which can cause large offsets between the simulated results of land-surface process models and the observational data for the soil moisture. In this study, observational data from the Semi-Arid Climate Observatory and Laboratory (SACOL) station in the semi-arid loess plateau of China were divided into three datasets: summer, autumn, and summer-autumn. By combing the particle swarm optimization (PSO) algorithm and the land-surface process model SHAW (Simultaneous Heat and Water), the soil and vegetation parameters that are related to the soil moisture but difficult to obtain by observations are optimized using three datasets. On this basis, the SHAW model was run with the optimized parameters to simulate the characteristics of the land-surface process in the semi-arid loess plateau. Simultaneously, the default SHAW model was run with the same atmospheric forcing as a comparison test. Simulation results revealed the following: parameters optimized by the particle swarm optimization algorithm in all simulation tests improved simulations of the soil moisture and latent heat flux; differences between simulated results and observational data are clearly reduced, but simulation tests involving the adoption of optimized parameters cannot simultaneously improve the simulation results for the net radiation, sensible heat flux, and soil temperature. Optimized soil and vegetation parameters based on different datasets have the same order of magnitude but are not identical; soil parameters only vary to a small degree, but the variation range of vegetation parameters is large. PMID:26991786
Directory of Open Access Journals (Sweden)
Zhan Wenting
2012-09-01
Full Text Available The study on the impacts of human activities on natural resources is of critical importance in constructing effective management strategies in rafting trips. The Camping Schedule Intelligent Generator (CSIG, the computer program developed in the study, which successfully models the complex, dynamic human-environment interactions in the rafting river. This generator includes two parts: artificial intelligence simulation and BSGA-based Optimization. It employs artificial intelligence in creating an individual-based modeling system. With the help of BSGA, this simulation system successfully models the recreatinal rafting behavior and captures the decision making of rafting trips as they responsively seek to optimize their functions. After modeling, the paper applys CSIG to the Colorado River, which is a famous rafting river and find that: the numbers of short motor-trips (6-8 day and long-oar trips (15-18 day are obviously larger than the other two. Finally, the study analyzes the sensitivity of the model and finds that when the water velocity varies in the actual range.
Asgarali Bouyer; Abdolreza Hatamlou; Abdul Hanan Abdullah
2010-01-01
The Kohonen self organizing map is an efficient tool in exploratory phase of data mining and pattern recognition. The SOM is a popular tool that maps high dimensional space into a small number of dimensions by placing similar elements close together, forming clusters. Recently, most of the researchers found that to take the uncertainty concerned in cluster analysis, using the crisp boundaries in some clustering operations is not necessary. In this paper, an optimized two-level clustering algo...
Simulated Annealing-Based Ant Colony Algorithm for Tugboat Scheduling Optimization
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Qi Xu
2012-01-01
Full Text Available As the “first service station” for ships in the whole port logistics system, the tugboat operation system is one of the most important systems in port logistics. This paper formulated the tugboat scheduling problem as a multiprocessor task scheduling problem (MTSP after analyzing the characteristics of tugboat operation. The model considers factors of multianchorage bases, different operation modes, and three stages of operations (berthing/shifting-berth/unberthing. The objective is to minimize the total operation times for all tugboats in a port. A hybrid simulated annealing-based ant colony algorithm is proposed to solve the addressed problem. By the numerical experiments without the shifting-berth operation, the effectiveness was verified, and the fact that more effective sailing may be possible if tugboats return to the anchorage base timely was pointed out; by the experiments with the shifting-berth operation, one can see that the objective is most sensitive to the proportion of the shifting-berth operation, influenced slightly by the tugboat deployment scheme, and not sensitive to the handling operation times.
International Nuclear Information System (INIS)
This paper presents an optimization framework for improving case-based computer-aided decision (CB-CAD) systems. The underlying hypothesis of the study is that each example in the knowledge database of a medical decision support system has different importance in the decision making process. A new decision algorithm incorporating an importance weight for each example is proposed to account for these differences. The search for the best set of importance weights is defined as an optimization problem and a genetic algorithm is employed to solve it. The optimization process is tailored to maximize the system's performance according to clinically relevant evaluation criteria. The study was performed using a CAD system developed for the classification of regions of interests (ROIs) in mammograms as depicting masses or normal tissue. The system was constructed and evaluated using a dataset of ROIs extracted from the Digital Database for Screening Mammography (DDSM). Experimental results show that, according to receiver operator characteristic (ROC) analysis, the proposed method significantly improves the overall performance of the CAD system as well as its average specificity for high breast mass detection rates
Wang, Jie-Sheng; Han, Shuang
2015-01-01
For predicting the key technology indicators (concentrate grade and tailings recovery rate) of flotation process, a feed-forward neural network (FNN) based soft-sensor model optimized by the hybrid algorithm combining particle swarm optimization (PSO) algorithm and gravitational search algorithm (GSA) is proposed. Although GSA has better optimization capability, it has slow convergence velocity and is easy to fall into local optimum. So in this paper, the velocity vector and position vector of GSA are adjusted by PSO algorithm in order to improve its convergence speed and prediction accuracy. Finally, the proposed hybrid algorithm is adopted to optimize the parameters of FNN soft-sensor model. Simulation results show that the model has better generalization and prediction accuracy for the concentrate grade and tailings recovery rate to meet the online soft-sensor requirements of the real-time control in the flotation process. PMID:26583034
Optimization of HMM Parameters Based on Chaos and Genetic Algorithm for Hand Gesture Recognition
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
In order to prevent standard genetic algorithm (SGA) from being premature, chaos is introduced into GA,thus forming chaotic anneal genetic algorithm (CAGA). Chaos' ergodicity is used to initialize the population, and chaoticanneal mutation operator is used as the substitute for the mutation operator in SGA. CAGA is a unified framework of theexisting chaotic mutation methods. To validate the proposed algorithm, three algorithms, i.e. Baum-Welch, SGA andCAGA, are compared on training hidden Markov model (HMM) to recognize the hand gestures. Experiments on twenty-six alphabetical gestures show the CAGA's validity.
Optimized Bayesian dynamic advising theory and algorithms
Karny, Miroslav
2006-01-01
Written by one of the world''s leading groups in the area of Bayesian identification, control, and decision making, this book provides the theoretical and algorithmic basis of optimized probabilistic advising. It is accompanied by a CD that contains a specialized Matlab-based Mixtools toolbox, and examples illustrating the important areas.
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Hong-Hsu Yen
2009-06-01
Full Text Available In wireless sensor networks, data aggregation routing could reduce the number of data transmissions so as to achieve energy efficient transmission. However, data aggregation introduces data retransmission that is caused by co-channel interference from neighboring sensor nodes. This kind of co-channel interference could result in extra energy consumption and significant latency from retransmission. This will jeopardize the benefits of data aggregation. One possible solution to circumvent data retransmission caused by co-channel interference is to assign different channels to every sensor node that is within each other’s interference range on the data aggregation tree. By associating each radio with a different channel, a sensor node could receive data from all the children nodes on the data aggregation tree simultaneously. This could reduce the latency from the data source nodes back to the sink so as to meet the user’s delay QoS. Since the number of radios on each sensor node and the number of non-overlapping channels are all limited resources in wireless sensor networks, a challenging question here is to minimize the total transmission cost under limited number of non-overlapping channels in multi-radio wireless sensor networks. This channel constrained data aggregation routing problem in multi-radio wireless sensor networks is an NP-hard problem. I first model this problem as a mixed integer and linear programming problem where the objective is to minimize the total transmission subject to the data aggregation routing, channel and radio resources constraints. The solution approach is based on the Lagrangean relaxation technique to relax some constraints into the objective function and then to derive a set of independent subproblems. By optimally solving these subproblems, it can not only calculate the lower bound of the original primal problem but also provide useful information to get the primal feasible solutions. By incorporating these
International Nuclear Information System (INIS)
A methodology is proposed for designing a low-energy consuming ternary-valued full adder based on a quantum dot (QD) electrostatically coupled with a single electron transistor operating as a charge sensor. The methodology is based on design optimization: the values of the physical parameters of the system required for implementing the logic operations are optimized using a multiobjective genetic algorithm. The searching space is determined by elements of the capacitance matrix describing the electrostatic couplings in the entire device. The objective functions are defined as the maximal absolute error over actual device logic outputs relative to the ideal truth tables for the sum and the carry-out in base 3. The logic units are implemented on the same device: a single dual-gate quantum dot and a charge sensor. Their physical parameters are optimized to compute either the sum or the carry out outputs and are compatible with current experimental capabilities. The outputs are encoded in the value of the electric current passing through the charge sensor, while the logic inputs are supplied by the voltage levels on the two gate electrodes attached to the QD. The complex logic ternary operations are directly implemented on an extremely simple device, characterized by small sizes and low-energy consumption compared to devices based on switching single-electron transistors. The design methodology is general and provides a rational approach for realizing non-switching logic operations on QD devices.
A Comprehensive Review of Swarm Optimization Algorithms
Ab Wahab, Mohd Nadhir; Nefti-Meziani, Samia; Atyabi, Adham
2015-01-01
Many swarm optimization algorithms have been introduced since the early 60’s, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known optimization algorithms. Selected algorithms are briefly explained and compared with each other comprehensively through experiments conducted using thirty well-known benchmark functions. Their advantage...
Near optimal power allocation algorithm for OFDM-based cognitive using adaptive relaying strategy
Soury, Hamza
2012-01-01
Relayed transmission increases the coverage and achievable capacity of communication systems. Adaptive relaying scheme is a relaying technique by which the benefits of the amplifying or decode and forward techniques can be achieved by switching the forwarding technique according to the quality of the signal. A cognitive Orthogonal Frequency-Division Multiplexing (OFDM) based adaptive relaying protocol is considered in this paper. The objective is to maximize the capacity of the cognitive radio system while ensuring that the interference introduced to the primary user is below the tolerated limit. A Near optimal power allocation in the source and the relay is presented for two pairing techniques such that the matching and random pairing. The simulation results confirm the efficiency of the proposed adaptive relaying protocol, and the consequence of choice of pairing technique. © 2012 ICST.
FPGA Based Optimized Discontinuous SVPWM Algorithm for Three Phase VSI in AC Drives
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Tole Sutikno
2013-02-01
Full Text Available The discontinuous space vector pulse width modulation (SVPWM has well-known that can reduce switching losses. By simplifying the thermal management issues, the discontinuous SVPWM can potentially reduce the inverter size and cost. However, using the modulation due to different time interval equations for each sector can introduce glitches at the points when the sector is changed. The more main problem, it can increase unwanted harmonic content and current ripple. Consider the decrease in switching losses associated with discontinuous modulation allows the system to utilize a higher switching frequency, this paper present high frequency switching of optimized discontinuous SVPWM based on FPGA to overcome the problems above. The proposed SVPWM has been successfully implemented by using APEX20KE Altera FPGA to drive on a three phase inverter system with 1.5 kW induction machine as load. The results have proved that the method can reduce harmonic content and current ripple without glitches.
Kostrzewa, Daniel; Josiński, Henryk
2016-06-01
The expanded Invasive Weed Optimization algorithm (exIWO) is an optimization metaheuristic modelled on the original IWO version inspired by dynamic growth of weeds colony. The authors of the present paper have modified the exIWO algorithm introducing a set of both deterministic and non-deterministic strategies of individuals' selection. The goal of the project was to evaluate the modified exIWO by testing its usefulness for multidimensional numerical functions optimization. The optimized functions: Griewank, Rastrigin, and Rosenbrock are frequently used as benchmarks because of their characteristics.
Naeem, Huma; Hussain, Mukhtar; Khan, Shoab A
2009-01-01
This paper presents a novel two-stage flexible dynamic decision support based optimal threat evaluation and defensive resource scheduling algorithm for multi-target air-borne threats. The algorithm provides flexibility and optimality by swapping between two objective functions, i.e. the preferential and subtractive defense strategies as and when required. To further enhance the solution quality, it outlines and divides the critical parameters used in Threat Evaluation and Weapon Assignment (TEWA) into three broad categories (Triggering, Scheduling and Ranking parameters). Proposed algorithm uses a variant of many-to-many Stable Marriage Algorithm (SMA) to solve Threat Evaluation (TE) and Weapon Assignment (WA) problem. In TE stage, Threat Ranking and Threat-Asset pairing is done. Stage two is based on a new flexible dynamic weapon scheduling algorithm, allowing multiple engagements using shoot-look-shoot strategy, to compute near-optimal solution for a range of scenarios. Analysis part of this paper presents ...
Search tree-based approach for the p-median problem using the ant colony optimization algorithm
Gabriel Bodnariuc; Sergiu Cataranciuc
2014-01-01
In this paper we present an approximation algorithm for the $p$-median problem that uses the principles of ant colony optimization technique. We introduce a search tree that keeps the partial solutions during the solution process of the $p$-median problem. An adaptation is proposed that allows ant colony optimization algorithm to perform on this tree and obtain good results in short time.
2014-01-01
This paper proposed a novel radial basis function (RBF) neural network model optimized by exponential decreasing inertia weight particle swarm optimization (EDIW-PSO). Based on the inertia weight decreasing strategy, we propose a new Exponential Decreasing Inertia Weight (EDIW) to improve the PSO algorithm. We use the modified EDIW-PSO algorithm to determine the centers, widths, and connection weights of RBF neural network. To assess the performance of the proposed EDIW-PSO-RBF model, we choo...
Naeem, Huma; Masood, Asif; Hussain, Mukhtar; Shoab A. Khan
2009-01-01
This paper presents a novel two-stage flexible dynamic decision support based optimal threat evaluation and defensive resource scheduling algorithm for multi-target air-borne threats. The algorithm provides flexibility and optimality by swapping between two objective functions, i.e. the preferential and subtractive defense strategies as and when required. To further enhance the solution quality, it outlines and divides the critical parameters used in Threat Evaluation and Weapon Assignment (T...
Tang, Zhenxing; Pei, Yuanji; Pang, Jian
2015-08-01
In this paper, we present an optimal design based on a genetic algorithm for a compact standing-wave (SW) accelerating tube with an operating frequency of 2998 MHz for industrial and medical applications. It consists of bi-periodic structures with a nose cone whose inter-cavity coupling is achieved through electric coupling rather than magnetic coupling. A mathematical model is established to optimize the arc at the cavity wall to reduce the microwave power loss and to optimize the nose cone to increase the electric field along the axis to achieve a high shunt impedance. The simulation results indicate that with the proper nose cone and arc, the shunt impedance of the cavity can be as high as 114 MΩ / m. Afterward, we present the tuning of the tube using SUPERFISH and the calculation of the beam dynamics using ASTRA and Parmela. The total length of the optimal tube is only 30.175 cm. Finally, a coupler is designed with a small-aperture coupling using CST MICROWAVE STUDIO.
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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.
Enhanced Bee Colony Algorithm for Complex Optimization Problems
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S.Suriya
2012-01-01
Full Text Available Optimization problems are considered to be one kind of NP hard problems. Usually heuristic approaches are found to provide solutions for NP hard problems. There are a plenty of heuristic algorithmsavailable to solve optimization problems namely: Ant Colony Optimization, Particle Swarm Optimization, Bee Colony Optimization, etc. The basic Bee Colony algorithm, a population based search algorithm, is analyzed to be a novel tool for complex optimization problems. The algorithm mimics the food foraging behavior of swarmsof honey bees. This paper deals with a modified fitness function of Bee Colony algorithm. The effect of problem dimensionality on the performance of the algorithms will be investigated. This enhanced Bee Colony Optimization will be evaluated based on the well-known benchmark problems. The testing functions like Rastrigin, Rosenbrock, Ackley, Griewank and Sphere are used to evaluavate the performance of the enhanced Bee Colony algorithm. The simulation will be developed on MATLAB.
Wang, Wei; Chen, Xiyuan
2016-08-10
Modeling and compensation of temperature drift is an important method for improving the precision of fiber-optic gyroscopes (FOGs). In this paper, a new method of modeling and compensation for FOGs based on improved particle swarm optimization (PSO) and support vector machine (SVM) algorithms is proposed. The convergence speed and reliability of PSO are improved by introducing a dynamic inertia factor. The regression accuracy of SVM is improved by introducing a combined kernel function with four parameters and piecewise regression with fixed steps. The steps are as follows. First, the parameters of the combined kernel functions are optimized by the improved PSO algorithm. Second, the proposed kernel function of SVM is used to carry out piecewise regression, and the regression model is also obtained. Third, the temperature drift is compensated for by the regression data. The regression accuracy of the proposed method (in the case of mean square percentage error indicators) increased by 83.81% compared to the traditional SVM. PMID:27534465
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Šime Ukić
2013-01-01
Full Text Available Gradient ion chromatography was used for the separation of eight sugars: arabitol, cellobiose, fructose, fucose, lactulose, melibiose, N-acetyl-D-glucosamine, and raffinose. The separation method was optimized using a combination of simplex or genetic algorithm with the isocratic-to-gradient retention modeling. Both the simplex and genetic algorithms provided well separated chromatograms in a similar analysis time. However, the simplex methodology showed severe drawbacks when dealing with local minima. Thus the genetic algorithm methodology proved as a method of choice for gradient optimization in this case. All the calculated/predicted chromatograms were compared with the real sample data, showing more than a satisfactory agreement.
NEW HMM ALGORITHM FOR TOPOLOGY OPTIMIZATION
Institute of Scientific and Technical Information of China (English)
Zuo Kongtian; Zhao Yudong; Chen Liping; Zhong Yifang; Huang Yuying
2005-01-01
A new hybrid MMA-MGCMMA (HMM) algorithm for solving topology optimization problems is presented. This algorithm combines the method of moving asymptotes (MMA) algorithm and the modified globally convergent version of the method of moving asymptotes (MGCMMA) algorithm in the optimization process. This algorithm preserves the advantages of both MMA and MGCMMA. The optimizer is switched from MMA to MGCMMA automatically, depending on the numerical oscillation value existing in the calculation. This algorithm can improve calculation efficiency and accelerate convergence compared with simplex MMA or MGCMMA algorithms, which is proven with an example.
Yang, Yun; Kimura, Shinji
This paper proposes an efficient design algorithm for power/ground (P/G) network synthesis with dynamic signal consideration, which is mainly caused by Ldi/dt noise and Cdv/dt decoupling capacitance (DECAP) current in the distribution network. To deal with the nonlinear global optimization under synthesis constraints directly, the genetic algorithm (GA) is introduced. The proposed GA-based synthesis method can avoid the linear transformation loss and the restraint condition complexity in current SLP, SQP, ICG, and random-walk methods. In the proposed Hybrid Grid Synthesis algorithm, the dynamic signal is simulated in the gene disturbance process, and Trapezoidal Modified Euler (TME) method is introduced to realize the precise dynamic time step process. We also use a hybrid-SLP method to reduce the genetic execute time and increase the network synthesis efficiency. Experimental results on given power distribution network show the reduction on layout area and execution time compared with current P/G network synthesis methods.
International Nuclear Information System (INIS)
This paper presents some results of the implementation of several optimization algorithms based on ant colonies, applied to the fuel reload design in a Boiling Water Reactor. The system called Azcaxalli is constructed with the following algorithms: Ant Colony System, Ant System, Best-Worst Ant System and MAX-MIN Ant System. Azcaxalli starts with a random fuel reload. Ants move into reactor core channels according to the State Transition Rule in order to select two fuel assemblies into a 1/8 part of the reactor core and change positions between them. This rule takes into account pheromone trails and acquired knowledge. Acquired knowledge is obtained from load cycle values of fuel assemblies. Azcaxalli claim is to work in order to maximize the cycle length taking into account several safety parameters. Azcaxalli's objective function involves thermal limits at the end of the cycle, cold shutdown margin at the beginning of the cycle and the neutron effective multiplication factor for a given cycle exposure. Those parameters are calculated by CM-PRESTO code. Through the Haling Principle is possible to calculate the end of the cycle. This system was applied to an equilibrium cycle of 18 months of Laguna Verde Nuclear Power Plant in Mexico. The results show that the system obtains fuel reloads with higher cycle lengths than the original fuel reload. Azcaxalli results are compared with genetic algorithms, tabu search and neural networks results.
Energy Technology Data Exchange (ETDEWEB)
Esquivel-Estrada, Jaime, E-mail: jaime.esquivel@fi.uaemex.m [Facultad de Ingenieria, Universidad Autonoma del Estado de Mexico, Cerro de Coatepec S/N, Toluca de Lerdo, Estado de Mexico 50000 (Mexico); Instituto Nacional de Investigaciones Nucleares, Carr. Mexico Toluca S/N, Ocoyoacac, Estado de Mexico 52750 (Mexico); Ortiz-Servin, Juan Jose, E-mail: juanjose.ortiz@inin.gob.m [Instituto Nacional de Investigaciones Nucleares, Carr. Mexico Toluca S/N, Ocoyoacac, Estado de Mexico 52750 (Mexico); Castillo, Jose Alejandro; Perusquia, Raul [Instituto Nacional de Investigaciones Nucleares, Carr. Mexico Toluca S/N, Ocoyoacac, Estado de Mexico 52750 (Mexico)
2011-01-15
This paper presents some results of the implementation of several optimization algorithms based on ant colonies, applied to the fuel reload design in a Boiling Water Reactor. The system called Azcaxalli is constructed with the following algorithms: Ant Colony System, Ant System, Best-Worst Ant System and MAX-MIN Ant System. Azcaxalli starts with a random fuel reload. Ants move into reactor core channels according to the State Transition Rule in order to select two fuel assemblies into a 1/8 part of the reactor core and change positions between them. This rule takes into account pheromone trails and acquired knowledge. Acquired knowledge is obtained from load cycle values of fuel assemblies. Azcaxalli claim is to work in order to maximize the cycle length taking into account several safety parameters. Azcaxalli's objective function involves thermal limits at the end of the cycle, cold shutdown margin at the beginning of the cycle and the neutron effective multiplication factor for a given cycle exposure. Those parameters are calculated by CM-PRESTO code. Through the Haling Principle is possible to calculate the end of the cycle. This system was applied to an equilibrium cycle of 18 months of Laguna Verde Nuclear Power Plant in Mexico. The results show that the system obtains fuel reloads with higher cycle lengths than the original fuel reload. Azcaxalli results are compared with genetic algorithms, tabu search and neural networks results.
Optimal Genetic View Selection Algorithm for Data Warehouse
Institute of Scientific and Technical Information of China (English)
Wang Ziqiang; Feng Boqin
2005-01-01
To efficiently solve the materialized view selection problem, an optimal genetic algorithm of how to select a set of views to be materialized is proposed so as to achieve both good query performance and low view maintenance cost under a storage space constraint. First, a pre-processing algorithm based on the maximum benefit per unit space is used to generate initial solutions. Then, the initial solutions are improved by the genetic algorithm having the mixture of optimal strategies. Furthermore, the generated infeasible solutions during the evolution process are repaired by loss function. The experimental results show that the proposed algorithm outperforms the heuristic algorithm and canonical genetic algorithm in finding optimal solutions.
A reliable algorithm for optimal control synthesis
Vansteenwyk, Brett; Ly, Uy-Loi
1992-01-01
In recent years, powerful design tools for linear time-invariant multivariable control systems have been developed based on direct parameter optimization. In this report, an algorithm for reliable optimal control synthesis using parameter optimization is presented. Specifically, a robust numerical algorithm is developed for the evaluation of the H(sup 2)-like cost functional and its gradients with respect to the controller design parameters. The method is specifically designed to handle defective degenerate systems and is based on the well-known Pade series approximation of the matrix exponential. Numerical test problems in control synthesis for simple mechanical systems and for a flexible structure with densely packed modes illustrate positively the reliability of this method when compared to a method based on diagonalization. Several types of cost functions have been considered: a cost function for robust control consisting of a linear combination of quadratic objectives for deterministic and random disturbances, and one representing an upper bound on the quadratic objective for worst case initial conditions. Finally, a framework for multivariable control synthesis has been developed combining the concept of closed-loop transfer recovery with numerical parameter optimization. The procedure enables designers to synthesize not only observer-based controllers but also controllers of arbitrary order and structure. Numerical design solutions rely heavily on the robust algorithm due to the high order of the synthesis model and the presence of near-overlapping modes. The design approach is successfully applied to the design of a high-bandwidth control system for a rotorcraft.
A novel metaheuristic for continuous optimization problems: Virus optimization algorithm
Liang, Yun-Chia; Rodolfo Cuevas Juarez, Josue
2016-01-01
A novel metaheuristic for continuous optimization problems, named the virus optimization algorithm (VOA), is introduced and investigated. VOA is an iteratively population-based method that imitates the behaviour of viruses attacking a living cell. The number of viruses grows at each replication and is controlled by an immune system (a so-called 'antivirus') to prevent the explosive growth of the virus population. The viruses are divided into two classes (strong and common) to balance the exploitation and exploration effects. The performance of the VOA is validated through a set of eight benchmark functions, which are also subject to rotation and shifting effects to test its robustness. Extensive comparisons were conducted with over 40 well-known metaheuristic algorithms and their variations, such as artificial bee colony, artificial immune system, differential evolution, evolutionary programming, evolutionary strategy, genetic algorithm, harmony search, invasive weed optimization, memetic algorithm, particle swarm optimization and simulated annealing. The results showed that the VOA is a viable solution for continuous optimization.
A Hybrid Algorithm for Optimizing Multi- Modal Functions
Institute of Scientific and Technical Information of China (English)
Li Qinghua; Yang Shida; Ruan Youlin
2006-01-01
A new genetic algorithm is presented based on the musical performance. The novelty of this algorithm is that a new genetic algorithm, mimicking the musical process of searching for a perfect state of harmony, which increases the robustness of it greatly and gives a new meaning of it in the meantime, has been developed. Combining the advantages of the new genetic algorithm, simplex algorithm and tabu search, a hybrid algorithm is proposed. In order to verify the effectiveness of the hybrid algorithm, it is applied to solving some typical numerical function optimization problems which are poorly solved by traditional genetic algorithms. The experimental results show that the hybrid algorithm is fast and reliable.
Worst-case Optimal Join Algorithms
Ngo, Hung Q; Ré, Christopher; Rudra, Atri
2012-01-01
Efficient join processing is one of the most fundamental and well-studied tasks in database research. In this work, we examine algorithms for natural join queries over many relations and describe a novel algorithm to process these queries optimally in terms of worst-case data complexity. Our result builds on recent work by Atserias, Grohe, and Marx, who gave bounds on the size of a full conjunctive query in terms of the sizes of the individual relations in the body of the query. These bounds, however, are not constructive: they rely on Shearer's entropy inequality which is information-theoretic. Thus, the previous results leave open the question of whether there exist algorithms whose running time achieve these optimal bounds. An answer to this question may be interesting to database practice, as it is known that any algorithm based on the traditional select-project-join style plans typically employed in an RDBMS are asymptotically slower than the optimal for some queries. We construct an algorithm whose runn...
Fuzzy C Means (FCM) Clustering Based Hybrid Swarm Intelligence Algorithm for Test Case Optimization
Abraham Kiran Joseph; G. Radhamani
2014-01-01
The main objective of an operative testing strategy is the delivery of a reliable and quality oriented software product to the end user. Testing an application entirely from end to end is a time consuming and laborious process. Exhaustive testing utilizes a good chunk of the resources in a project for meticulous scrutiny to identify even a minor bug. A need to optimize the existing suite is highly recommended, with minimum resources and a shorter time span. To achieve this optimization in tes...
Multiobjective Optimization of Classifiers by Means of 3-D Convex Hull Based Evolutionary Algorithm
Zhao, Jiaqi; Fernandes, Vitor Basto; Jiao, Licheng; Yevseyeva, Iryna; Maulana, Asep; Li, Rui; Bäck, Thomas; Emmerich, Michael T. M.
2014-01-01
Finding a good classifier is a multiobjective optimization problem with different error rates and the costs to be minimized. The receiver operating characteristic is widely used in the machine learning community to analyze the performance of parametric classifiers or sets of Pareto optimal classifiers. In order to directly compare two sets of classifiers the area (or volume) under the convex hull can be used as a scalar indicator for the performance of a set of classifiers in receiver operati...
Optimal configuration algorithm of a satellite transponder
Sukhodoev, M. S.; Savenko, I. I.; Martynov, Y. A.; Savina, N. I.; Asmolovskiy, V. V.
2016-04-01
This paper describes the algorithm of determining the optimal transponder configuration of the communication satellite while in service. This method uses a mathematical model of the pay load scheme based on the finite-state machine. The repeater scheme is shown as a weighted oriented graph that is represented as plexus in the program view. This paper considers an algorithm example for application with a typical transparent repeater scheme. In addition, the complexity of the current algorithm has been calculated. The main peculiarity of this algorithm is that it takes into account the functionality and state of devices, reserved equipment and input-output ports ranged in accordance with their priority. All described limitations allow a significant decrease in possible payload commutation variants and enable a satellite operator to make reconfiguration solutions operatively.
A Cuckoo Search Algorithm for Multimodal Optimization
Erik Cuevas; Adolfo Reyna-Orta
2014-01-01
Interest in multimodal optimization is expanding rapidly, since many practical engineering problems demand the localization of multiple optima within a search space. On the other hand, the cuckoo search (CS) algorithm is a simple and effective global optimization algorithm which can not be directly applied to solve multimodal optimization problems. This paper proposes a new multimodal optimization algorithm called the multimodal cuckoo search (MCS). Under MCS, the original CS is enhanced with...
Directory of Open Access Journals (Sweden)
Sheng-Qiang Li
2010-10-01
Full Text Available In many wireless sensor network applications, the possibility of exceptions occurring is relatively small, so in a normal situation, data obtained at sequential time points by the same node are time correlated, while, spatial correlation may exist in data obtained at the same time by adjacent nodes. A great deal of node energy will be wasted if data which include time and space correlation is transmitted. Therefore, this paper proposes a data compression algorithm for wireless sensor networks based on optimal order estimation and distributed coding. Sinks can obtain correlation parameters based on optimal order estimation by exploring time and space redundancy included in data which is obtained by sensors. Then the sink restores all data based on time and space correlation parameters and only a little necessary data needs to be transmitted by nodes. Because of the decrease of redundancy, the average energy cost per node will be reduced and the life of the wireless sensor network will obviously be extended as a result.
Genetic Algorithm Based Incremental Learning For Optimal Weight and Classifier Selection
Hulley, Gregory; Marwala, Tshilidzi
2007-11-01
The ability of a classifier to take on new information and classes by evolving the classifier without it having to be fully retrained is known as incremental learning. Incremental learning has been successfully applied to many classification problems, where the data is changing and is not all available at once. In this paper there is a comparison between Learn++, which is one of the most recent incremental learning algorithms, and the new proposed method of Incremental Learning Using Genetic Algorithm (ILUGA). Learn++ has shown good incremental learning capabilities on benchmark datasets on which the new ILUGA method has been tested. ILUGA has also shown good incremental learning ability using only a few classifiers and does not suffer from catastrophic forgetting. The results obtained for ILUGA on the Optical Character Recognition (OCR) and Wine datasets are good, with an overall accuracy of 93% and 94% respectively showing a 4% improvement over Learn++.MT for the difficult multi-class OCR dataset.
OPTIMAL ALGORITHM FOR PREDICTING STUDENTS’ ACADEMIC PERFORMANCE
Directory of Open Access Journals (Sweden)
Afolashade Kuyoro
2013-01-01
Full Text Available Machine learning has been successfully applied to numerous domains such as pattern recognition, image recognition, fraud detection, medical diagnosis, banking, bioinformatics, commodity trading, computer games and various control applications. Recently, this paradigm is been employed to enhance and evaluate higher education tasks. The focus of this work is on identifying the optimal algorithm suitable for predicting first-year tertiary students academic performance based on their family background factors and previous academic achievement. One thousand five hundred (1,500 enrolment records of students admitted into computer science programme Babcock University, Nigeria between 2001 and 2010 was used. The students’ first year academic performance was measured by Cumulative Grade Point Average (CGPA at the end of the first session and the previous academic achievement was measured by SSCE grade score and UME score. Waikato Environment for Knowledge Analysis (WEKA was used to generate 10 classification models( five decision tree algorithms -Random forest, Random tree, J48, Decision stump and REPTree and five rule induction algorithms –JRip, OneR, ZeroR, PART, and Decision table and a multilayer perceptron, an artificial neural network function. These algorithms were compared using 10-fold cross validation and hold-out method considering accuracy level, confusion matrices and CPU time to determine the optimal model. This work will be taken further by designing a framework of predictive system based on the rules generated from the optimal model.
Umut Okkan
2011-01-01
Recently, Artificial Neural Networks (ANN), which is mathematical modelingtools inspired by the properties of the biological neural system, has been typically used inthe studies of hydrological time series modeling. These modeling studies generally includethe standart feed forward backpropagation (FFBP) algorithms such as gradient-descent,gradient-descent with momentum rate and, conjugate gradient etc. As the standart FFBPalgorithms have some disadvantages relating to the time requirement and...
Multi-objective exergy based optimization of a polygeneration system using an evolutionary algorithm
Energy Technology Data Exchange (ETDEWEB)
Ahmadi, Pouria; Rosen, Marc A.; Dincer, Ibrahim [Faculty of Engineering and Applied Science, University of Ontario Institute of Technology (Canada)], email: Pouria.Ahmadi@uoit.ca, email: Marc.Rosen@uoit.ca, email: Ibrahim.Dincer@uoit.ca
2011-07-01
World-wide, many applications are significant users of energy and with the depletion of fossil fuels and climate change, it is important that energy be used more efficiently and sustainably. Exergy analysis uses the second law of thermodynamics to identify and understand sustainable energy options. The aim of this paper is to present the thermodynamic modeling and optimization of a polygeneration energy system. A multi-objective optimization method was used to find the best design parameters of the polygeneration energy system and 2 objective functions were used to minimize the total cost rate and maximize the system exergy efficiency. In addition, the impacts of different parameters on the exergy efficiency and CO2 emission were studied. This study provided a better understanding of the performance of polygeneration energy systems and provided a closed form equation to help designers in optimizing polygeneration plants.
Parasuraman, Ramviyas; Molinari, Luca; Kershaw, Keith; Di Castro, Mario; Masi, Alessandro; Ferre, Manuel
2014-01-01
The reliability of wireless communication in a network of mobile wireless robot nodes depends on the received radio signal strength (RSS). When the robot nodes are deployed in hostile environments with ionizing radiations (such as in some scientific facilities), there is a possibility that some electronic components may fail randomly (due to radiation effects), which causes problems in wireless connectivity. The objective of this paper is to maximize robot mission capabilities by maximizing the wireless network capacity and to reduce the risk of communication failure. Thus, in this paper, we consider a multi-node wireless tethering structure called the “server-relay-client” framework that uses (multiple) relay nodes in between a server and a client node. We propose a robust stochastic optimization (RSO) algorithm using a multi-sensor-based RSS sampling method at the relay nodes to efficiently improve and balance the RSS between the source and client nodes to improve the network capacity and to provide red...
Particle Swarm Optimization and Genetic Algorithms
Elisa Valentina Oneţ
2009-01-01
This paper presents two evolutionary computation techniques: particle swarm optimization – part of swarm intelligence and genetic algorithms – part of the evolutionary algorithms. The basic algorithm for each is reviewed, in case of optimization problems in asearch space. It is presented how each evolutionary computation technique works, and the way in which features from one can be included into the other.
Genetic algorithm and particle swarm optimization combined with Powell method
Bento, David; Pinho, Diana; Pereira, Ana I.; Lima, Rui
2013-10-01
In recent years, the population algorithms are becoming increasingly robust and easy to use, based on Darwin's Theory of Evolution, perform a search for the best solution around a population that will progress according to several generations. This paper present variants of hybrid genetic algorithm - Genetic Algorithm and a bio-inspired hybrid algorithm - Particle Swarm Optimization, both combined with the local method - Powell Method. The developed methods were tested with twelve test functions from unconstrained optimization context.
Optimization of Association Rule Mining Apriori Algorithm Using ACO
Badri Patel; Vijay K Chaudhari; Rajneesh K Karan; YK Rana
2011-01-01
Association rule mining is an important topic in data mining field. In a given large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. Apriori algorithm that generates all significant association rules between items in the database. On the basis of the association rule mining and Apriori algorithm, this paper proposes an improved algorithm based on the Ant Colony Optimization algorithm. We can optimize the result generated by Apriori al...
Sizing Optimization of Truss Structures using a Hybridized Genetic Algorithm
Asl, Reza Najian; Aslani, Mohamad; Panahi, Masoud Shariat
2013-01-01
This paper presents a genetic-based hybrid algorithm that combines the exploration power of Genetic Algorithm (GA) with the exploitation capacity of a phenotypical probabilistic local search algorithm. Though not limited to a certain class of optimization problems, the proposed algorithm has been \\?ne tuned" to work particularly e?ciently on the optimal design of planar and space structures, a class of problems characterized by the large number of design variables and constraints, high degree...
Directory of Open Access Journals (Sweden)
Subbaraj Potti
2011-01-01
Full Text Available Problem statement: A new multi-objective approach, Strength Pareto Evolutionary Algorithm (SPEA, is presented in this paper to solve the shortest path routing problem. The routing problem is formulated as a multi-objective mathematical programming problem which attempts to minimize both cost and delay objectives simultaneously. Approach: SPEA handles the shortest path routing problem as a true multi-objective optimization problem with competing and noncommensurable objectives. Results: SPEA combines several features of previous multi-objective evolutionary algorithms in a unique manner. SPEA stores nondominated solutions externally in another continuously-updated population and uses a hierarchical clustering algorithm to provide the decision maker with a manageable pareto-optimal set. SPEA is applied to a 20 node network as well as to large size networks ranging from 50-200 nodes. Conclusion: The results demonstrate the capabilities of the proposed approach to generate true and well distributed pareto-optimal nondominated solutions.
Institute of Scientific and Technical Information of China (English)
Hai-tao Bo; Xiao-feng Jia; Xiao-rui Wang
2009-01-01
As in the building of deep buried long tunnels, there are complicated conditions such as great deformation, high stress, multi-variables, high non-linearity and so on, the algorithm for structure optimization and its application in tunnel engineering are still in the starting stage. Along with the rapid development of highways across the country, It has become a very urgent task to be tackled to carry out the optimization design of the structure of the section of the tunnel to lessen excavation workload and to reinforce the support. Artificial intelligence demonstrates an extremely strong capability of identifying, expressing and disposing such kind of multiple variables and complicated non- linear relations. In this paper, a comprehensive consideration of the strategy of the selection and updating of the concentration and adaptability of the immune algorithm is made to replace the selection mode in the original genetic algorithm which depends simply on the adaptability value. Such an algorithm has the advantages of both the immune algorithm and the genetic algorithm, thus serving the purpose of not only enhancing the individual adaptability but maintaining the individual diversity as well. By use of the identifying function of the antigen memory, the global search capability of the immune genetic algorithm is raised, thereby avoiding the occurrence of the premature phenomenon. By optimizing the structure of the section of the Huayuan tunnel, the current excavation area and support design are adjusted. A conclusion with applicable value is arrived at. At a higher computational speed and a higher efficiency, the current method is verified to have advantages in the optimization computation of the tunnel project. This also suggests that the application of the immune genetic algorithm has a practical significance to the stability assessment and informationlzation design of the wall rock of the tunnel.
An Improved Point-track Optimal Assignment Algorithm
Zhonglei Zhang; Weihua Zhang; Li Zhou
2013-01-01
In order to improve the accuracy of data association of the Optimal Assignment (OA) algorithm based on dynamic information, an improved Point-Track Optimal Assignment (IPTOA) algorithm based on multi-source information is proposed. The improved algorithm gets valid 3-tuple of measurement set by solving 3-Dimensional (3-D) assignment problem which is based on dynamic information. Then fuses multi-source information by combination rule of D-S evidence theory and constructs the point-track corre...
Multi-objective optimal design of lithium-ion battery packs based on evolutionary algorithms
Severino, Bernardo; Gana, Felipe; Palma-Behnke, Rodrigo; Estévez, Pablo A.; Calderón-Muñoz, Williams R.; Orchard, Marcos E.; Reyes, Jorge; Cortés, Marcelo
2014-12-01
Lithium-battery energy storage systems (LiBESS) are increasingly being used on electric mobility and stationary applications. Despite its increasing use and improvements of the technology there are still challenges associated with cost reduction, increasing lifetime and capacity, and higher safety. A correct battery thermal management system (BTMS) design is critical to achieve these goals. In this paper, a general framework for obtaining optimal BTMS designs is proposed. Due to the trade-off between the BTMS's design goals and the complex modeling of thermal response inside the battery pack, this paper proposes to solve this problem using a novel Multi-Objective Particle Swarm Optimization (MOPSO) approach. A theoretical case of a module with 6 cells and a real case of a pack used in a Solar Race Car are presented. The results show the capabilities of the proposal methodology, in which improved designs for battery packs are obtained.
Genetic Algorithm Based Charge Optimization of Lithium-Ion Batteries in Small Satellites
Jain, Saurabh; Simon, Dan
2005-01-01
Small spacecraft that are powered by solar energy have limitations because of the size of their solar panels. With the limitations on the solar panel size, it is generally hard to comply with the demands from all the satellite subsystems, payloads and batteries at the same time. To overcome these problems we have developed and adopted a power management optimization scheme that runs in real time in the satellite. The proposed power management scheme primarily involves scheduling of loads (var...