基于Memetic框架的混合粒子群算法  

Hybrid PSO Algorithm Based on Memetic Framework

作　　者：魏臻[1] 吴雷[1] 葛方振[1] 王强[1] 

机构地区：[1]合肥工业大学计算机与信息学院,合肥230009

出　　处：《模式识别与人工智能》2012年第2期213-219,共7页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金(No.60873195;61070220);高等学校博士学科点专项科研基金(No.20090111110002)资助项目

摘　　要：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 PSO algorithm based on memetic framework （itM-PSO） is proposed. It helps the parucles which have certain leaning capacity accelerate convergence rate by Lamarckian Learning based local search strategy and helps the particles which fall into the local optimum escape from local optimum by Tabu search. HM-PSO avoids falling into the local optimum by enhancing the diversity of swarm with accelerating convergence rate. The experimental results show that the improved Lamarckian Learning strategy is effective and feasible and HM-PSO is an effective optimization algorithm with better global search performance.

关 键 词：混合粒子群 禁忌搜索 拉马克学习 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]