An appropriate mathematical model can help researchers to simulate,evaluate,and control a proton exchange membrane fuel cell (PEMFC) stack system.Because a PEMFC is a nonlinear and strongly coupled system,many assumpt...An appropriate mathematical model can help researchers to simulate,evaluate,and control a proton exchange membrane fuel cell (PEMFC) stack system.Because a PEMFC is a nonlinear and strongly coupled system,many assumptions and approximations are considered during modeling.Therefore,some differences are found between model results and the real performance of PEMFCs.To increase the precision of the models so that they can describe better the actual performance,opti-mization of PEMFC model parameters is essential.In this paper,an artificial bee swarm optimization algorithm,called ABSO,is proposed for optimizing the parameters of a steady-state PEMFC stack model suitable for electrical engineering applications.For studying the usefulness of the proposed algorithm,ABSO-based results are compared with the results from a genetic algo-rithm (GA) and particle swarm optimization (PSO).The results show that the ABSO algorithm outperforms the other algorithms.展开更多
In the paper, a new selection probability inspired by artificial bee colony algorithm is introduced into standard particle swarm optimization by improving the global extremum updating condition to enhance the capabili...In the paper, a new selection probability inspired by artificial bee colony algorithm is introduced into standard particle swarm optimization by improving the global extremum updating condition to enhance the capability of its overall situation search. The experiment result shows that the new scheme is more valuable and effective than other schemes in the convergence of codebook design and the performance of codebook, and it can avoid the premature phenomenon of the particles.展开更多
This paper presents the first application of the bees algorithm to the optimisation of parameters of a two-dimensional （2D） recursive digital filter. The algorithm employs a search technique inspired by the foraging...This paper presents the first application of the bees algorithm to the optimisation of parameters of a two-dimensional （2D） recursive digital filter. The algorithm employs a search technique inspired by the foraging behaviour of honey bees. The results obtained show clear improvement compared to those produced by the widely adopted genetic algorithm （GA）.展开更多
The recently invented artificial bee colony （ABC） al- gorithm is an optimization algorithm based on swarm intelligence that has been used to solve many kinds of numerical function optimization problems. It performs ...The recently invented artificial bee colony （ABC） al- gorithm is an optimization algorithm based on swarm intelligence that has been used to solve many kinds of numerical function optimization problems. It performs well in most cases, however, there still exists an insufficiency in the ABC algorithm that ignores the fitness of related pairs of individuals in the mechanism of find- ing a neighboring food source. This paper presents an improved ABC algorithm with mutual learning （MutualABC） that adjusts the produced candidate food source with the higher fitness between two individuals selected by a mutual learning factor. The perfor- mance of the improved MutualABC algorithm is tested on a set of benchmark functions and compared with the basic ABC algo- rithm and some classical versions of improved ABC algorithms. The experimental results show that the MutualABC algorithm with appropriate parameters outperforms other ABC algorithms in most experiments.展开更多
针对求解多目标优化问题(MOP:Multi-Objective Problem)时,人工蜂群算法(ABC:Artificial Bee Colony)存在难以收敛和候选解多样性难以保持的问题,对其各部分求解策略进行了改进。基于ABC算法框架,设计了一种基于自适应求解策略的多目标...针对求解多目标优化问题(MOP:Multi-Objective Problem)时,人工蜂群算法(ABC:Artificial Bee Colony)存在难以收敛和候选解多样性难以保持的问题,对其各部分求解策略进行了改进。基于ABC算法框架,设计了一种基于自适应求解策略的多目标ABC算法,并在机电执行器设计的实际应用工程设计问题中,将所提出的改进多目标ABC与其他典型的群智能算法进行优化性能比较。通过实验验证可知,所提出的MOABC/DD(Multi-Objective Artificial Bee Colony Based on Dominance and Decomposition)算法在求解机电执行器设计问题基准测试用例时,与典型算法相比,具有较好的问题求解精度。并且MOABC/DD的实验结果较为稳定,从而证明了MOABC/DD具有较高的求解稳定性和健壮性。展开更多
基金supported by the Renewable Energy Organization of Iran (SANA)
文摘An appropriate mathematical model can help researchers to simulate,evaluate,and control a proton exchange membrane fuel cell (PEMFC) stack system.Because a PEMFC is a nonlinear and strongly coupled system,many assumptions and approximations are considered during modeling.Therefore,some differences are found between model results and the real performance of PEMFCs.To increase the precision of the models so that they can describe better the actual performance,opti-mization of PEMFC model parameters is essential.In this paper,an artificial bee swarm optimization algorithm,called ABSO,is proposed for optimizing the parameters of a steady-state PEMFC stack model suitable for electrical engineering applications.For studying the usefulness of the proposed algorithm,ABSO-based results are compared with the results from a genetic algo-rithm (GA) and particle swarm optimization (PSO).The results show that the ABSO algorithm outperforms the other algorithms.
基金Sponsored by the Qing Lan Project of Jiangsu Province
文摘In the paper, a new selection probability inspired by artificial bee colony algorithm is introduced into standard particle swarm optimization by improving the global extremum updating condition to enhance the capability of its overall situation search. The experiment result shows that the new scheme is more valuable and effective than other schemes in the convergence of codebook design and the performance of codebook, and it can avoid the premature phenomenon of the particles.
基金supported by the ERDF (Objective One) project"Supporting Innovative Product Engineering and Responsive Manufacturing" (SUPERMAN)the EC-funded Network of Excellence"Innovative Production Machines and Systems" (I*PROMS)
文摘This paper presents the first application of the bees algorithm to the optimisation of parameters of a two-dimensional （2D） recursive digital filter. The algorithm employs a search technique inspired by the foraging behaviour of honey bees. The results obtained show clear improvement compared to those produced by the widely adopted genetic algorithm （GA）.
基金supported by the National Natural Science Foundation of China (60803074)the Fundamental Research Funds for the Central Universities (DUT10JR06)
文摘The recently invented artificial bee colony （ABC） al- gorithm is an optimization algorithm based on swarm intelligence that has been used to solve many kinds of numerical function optimization problems. It performs well in most cases, however, there still exists an insufficiency in the ABC algorithm that ignores the fitness of related pairs of individuals in the mechanism of find- ing a neighboring food source. This paper presents an improved ABC algorithm with mutual learning （MutualABC） that adjusts the produced candidate food source with the higher fitness between two individuals selected by a mutual learning factor. The perfor- mance of the improved MutualABC algorithm is tested on a set of benchmark functions and compared with the basic ABC algo- rithm and some classical versions of improved ABC algorithms. The experimental results show that the MutualABC algorithm with appropriate parameters outperforms other ABC algorithms in most experiments.
文摘针对求解多目标优化问题(MOP:Multi-Objective Problem)时,人工蜂群算法(ABC:Artificial Bee Colony)存在难以收敛和候选解多样性难以保持的问题,对其各部分求解策略进行了改进。基于ABC算法框架,设计了一种基于自适应求解策略的多目标ABC算法,并在机电执行器设计的实际应用工程设计问题中,将所提出的改进多目标ABC与其他典型的群智能算法进行优化性能比较。通过实验验证可知,所提出的MOABC/DD(Multi-Objective Artificial Bee Colony Based on Dominance and Decomposition)算法在求解机电执行器设计问题基准测试用例时,与典型算法相比,具有较好的问题求解精度。并且MOABC/DD的实验结果较为稳定,从而证明了MOABC/DD具有较高的求解稳定性和健壮性。
