期刊文献+

基于爆炸冲击波模型的粒子群优化算法

New particle swarm optimization based on blast wave model
下载PDF
导出
摘要 针对基本粒子群优化(PSO)算法在解决复杂多峰问题时易于陷入局部最优解的问题,提出一种基于爆炸冲击波模型的PSO算法(简称BW-PSO算法)。该算法通过加入种群多样性监督条件,使得当种群数量缩小至给定阈值时,触发粒子冲击波过程:最优粒子与次优粒子进行交叉变异,处于爆炸半径内的粒子受到牵引力,加速收敛至当前极值;处于爆炸半径外的粒子受到冲击力向外扩散,增加了找到全局最优值的可能性。BW-PSO算法不仅能够通过最优粒子变异操作提升当前解的精度,而且通过粒子冲击波过程,增加了种群多样性,提升了粒子对全局空间开发的能力。实验结果表明,基于爆炸冲击波模型的PSO算法在求解多峰问题表现优于变异PSO算法与带电PSO算法。 A new Particle Swarm Optimization (PSO) algorithm based on the blast wave model (referred to as BW-PSO algorithm) was proposed aiming at the problem that the basic PSO algorithm when solving complex muhimodal problems is easy to fall into local optimal solution. The supervision conditions of population diversity were added to the basic PSO algorithm so that the process of particle shock was triggered when the population decreased to a given threshold value. Crossover and mutation occurred between optimal and suboptimal particles so that the particles within the blast radius by the traction were subjected to accelerate convergence to the current extreme and the particles outside the blast radius were subjected to spread out, which increased the possibility of finding the global optimum value. BW-PSO algorithm not only improved the accuracy of the current solution by the mutation between optimal and suboptimal particles, but also increased the population diversity with the shock wave process of the particles and enhances the ability of the global space development of the particles. Compared with the mutative PSO and charged PSO, the results indicate that the BW-PSO algorithm has a better performance to solve multi-modal optimization problem.
出处 《计算机应用》 CSCD 北大核心 2014年第7期2085-2089,共5页 journal of Computer Applications
关键词 粒子群优化算法 爆炸冲击波 种群多样性 交叉变异 多峰函数 Particle Swarm Optimization (PSO) algorithm blast wave population diversity crossover and mutation multi-modal function
  • 相关文献

参考文献12

  • 1KENNEDY J,EBERHART C.Particle swarm optimization[C]//Proceedings of the 1995 IEEE International Conference on Neural Networks.Piscataway:IEEE,1995:1942-1948.
  • 2刘衍民,牛奔.新型粒子群算法理论与实践[M].北京:科学出版社,2013.
  • 3van den BERGH F.An analysis of particle swarm optimizers[D].Pretoria:University of Pretoria,2002.
  • 4QIN J,YIN Y,BAN X.A hybrid of particle swarm optimization and local search for multimodal functions[C]// Advances in Swarm Intelligence-First International Conference.Bedim Springer,2010:589-596.
  • 5JI C,LIU F,ZHANG X.Particle swarm optimization based on catfish effect for flood optimal operation of reservoir[C]//Proceedings of the 7th International Conference on Natural Computation.Piseataway:IEEE,2011:1197-1201.
  • 6ROBINSON J,SINTON S,RAHMAT-SAMII Y.Particle swarms,genetic algorithm,and their hybrids; optimization of a profiled corrugated horn antenna[C]// Proceedings of the 2002 IEEE Intemational Symposium on Antennas and Propagation Society.Piseataway:IEEE,2002:314-317.
  • 7LOVBERG M,RASMUSSEN T K,KRINK T.Hybrid particle swarm optimizer with breeding and subpopulations[C]// Proceedings of the Genetic and Evolutionary Computation Conference.New York:ACM,2001:469-476.
  • 8BLACKWELL T M,BENTLEY P J.Dynamic search with charged swarms[C]//Proceedings of the 2002 Genetic and Evolutionary Computation Conference.New York:Elsevier Science and Technology Books,2002:19-26.
  • 9乔登江.空中爆炸冲击波(Ⅰ):基本理论[J].爆炸与冲击,1985,10(5):79-80.
  • 10VESTEROM J S,RIGET J,KRINK T.Division of labor in particle swarm optimization[C]//Proceedings of the 2002 IEEE Congress on Evolutionary Computation.Piscataway:IEEE,2002:1570-1575.

共引文献1

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部