Abstract：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.
黎明，揭丽琳，鲁宇明. 粒子群与多种群元胞遗传混合优化算法[J]. 模式识别与人工智能, 2012, 25(4): 610-616.
LI Ming, JIE Li-Lin, LU Yu-Ming. A Hybrid Particle Swarm and Multi-Population Cellular Genetic Algorithm. , 2012, 25(4): 610-616.
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2012, Vol. 25 
