粒子群与多种群元胞遗传混合优化算法  

A Hybrid Particle Swarm and Multi-Population Cellular Genetic Algorithm

作　　者：黎明[1] 揭丽琳[1] 鲁宇明[1] 

机构地区：[1]南昌航空大学信息工程学院,南昌330063

出　　处：《模式识别与人工智能》2012年第4期610-616,共7页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金(No.60963002);江西省自然科学基金(No.2009GZS0090)资助项目

摘　　要：元胞遗传算法通过限定个体之间的相互作用邻域提高算法的全局收敛率,但在一定程度降低搜索效率.文中提出一种粒子群与多种群元胞遗传混合优化算法.首先将群体分割成多个相互之间没有邻域关系的元胞子种群,适度降低算法的选择压力,从而更好地保持种群的多样性.算法的变异操作被粒子群算法替代,使得局部搜索能力明显提高.元胞群体分割和粒子群变异较好地均衡全局探索和局部寻优之间的关系.分析混合算法的选择压力和多样性变化规律.实验结果表明,该算法在保证搜索效率较高的同时还显著提高元胞遗传算法的全局收敛率且稳定性得到明显改善.Cellular genetic algorithm （CGA） enhances global convergence rate via constraining individual interaction in its neighbor. However, it results in of low search efficiency. An algorithm, called hybrid particle swarm and multi-population cellular genetic algorithm （HPCGA）, is proposed. Firstly, the whole population is divided into some sub-populations, the individuals in different sub-populations do not interact each other. Nevertheless different sub-populations can communicate with each other via immigrant and share the evolutionary information. Division of the population appropriately reduces the selection pressure, and thus the individual diversity is maintained more effectively. The mutation of CGA is replaced by particle swarm optimization to improve the ability of local search. The above two improvements balance the trade-off between global exploration and local exploitation. Selection pressure and individual diversity of the proposed HPCGA are also studied. Optimization of six typical functions is carried out by using the proposed HPCGA and CGA. The experimental results show that the performance of the proposed HPCGA is obviously superior to that of CGA in global convergence rate, convergence speed and stability.

关 键 词：元胞遗传算法 粒子群算法 种群分割 选择压力 多样性 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]