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中文摘要：
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．
英文摘要：
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.
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