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提出一种Memetic框架下的混合粒子群优化算法(HM-PSO).针对粒子群算法的搜索结果,该算法采用基于拉马克学习的局部搜索策略帮助具有一定改进能力的个体提高收敛速度,同时利用禁忌策略帮助可能陷入局部最优的个体跳出局部最优点.HM-PSO算法在加速个体收敛的同时提高算法搜索的多样性,避免陷入局部最优.实验结果表明,改进拉马克学习策略有效可行,HM-PSO算法具有良好的全局寻优性能.
A hybrid particle swarm optimization (HM-PSO) algorithm based on Memetic framework is proposed.Aiming at the search results of particle swarm optimization algorithm, this algorithm uses the local search strategy based on Lamarck’s learning to help individuals with some improvement ability to improve the convergence speed, Taboo strategy helps local individuals who may fall into the local optimum to jump out of the local optimal point.HM-PSO algorithm can accelerate the individual convergence and improve the diversity of algorithm search, to avoid falling into the local optimum.The experimental results show that improving the Lamarck’s learning strategy is effective and feasible, HM-PSO algorithm has good global optimization performance.