针对粒子群算法存在容易陷入局部最优的问题，提出一种改进杂交粒子群算法以强化全局搜索的能力。将粒子群优化与分布估计算法相结合，利用分布估计模型提取全局信息，用以指导种群大小的自适应变化。在全局信息变化显著时扩充种群，来加强算法在未知区域的探索；在种群大小达到上限时，利用全局信息更新较差个体，引导种群向最优区域集中；在全局信息或最优解均基本不变时，对种群进行重构，降低计算代价并防止陷入局部最优。7组标准函数的测试结果表明，改进算法优于其余几种与分布估计模型结合的杂交算法，在全部5组多模态函数的测试中其结果是最好的，其中在理论最小值未知的函数 F7上，所得最优值比其它算法提升了9.5～13.6。%For the easily falling into local optimum problem of the particle swarm optimization ,an advanced hybrid version was proposed to enhance the global searching ability .The particle swarm optimization was combined with the estimation of the distri-bution algorithm to obtain the global information ,and the information was used to guide the self-adaptation of the population size .The population was expanded to enhance the exploration in unknown regions when the global information varied obviously . If the population reached the upper limit size ,the worse particles were replaced so that all the particles moved to the promising regions .If the global information or the best value rarely changed ,the population was compressed and reconstructed to reduce the computational cost and to avoid falling into the local optimum .Results of experiments on seven benchmarks show that the proposed algorithm works better than other hybrid algorithms based on the estimation of the distribution model .Especially ,the proposed algorithm obtains the best results on five multimodal benchmarks ,and for the benchmark F7 with the unknown global optimum ,the best value is improved by 9.5-13.6 compared to others .
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