摘要： 针对粒子群算法(PSO)收敛速度慢、求解精度不高以及易陷入局部最优的缺点，结合云遗传算法(CGA)和粒子群优化算法，提出一种新型的双种群混合算法(CGA-PSO)。将整个种群平均分成2个子群，分别采用云遗传算法和加入自调整惯性权值策略的粒子群优化算法完成进化。通过引入一种新型的信息交流机制：两子群子代间信息交流以及子代与父代间信息交流，共享最优个体，淘汰最劣个体，实现共同进化，适时对粒子群适应度较差的个体进行云变异操作，该操作是基于云模型的随机性和稳定性，利用全局最优位置和最劣位置实现对部分粒子位置的变异过程。对5个经典测试函数进行测试，并与CGA和PSO算法及其优化算法进行比较，结果表明，CGA-PSO算法具有较高的搜索效率、求解精度和较快的收敛速度，鲁棒性也较强。

关键词: 云遗传算法, 粒子群优化算法, 双种群混合算法, 自调整惯性权值策略, 信息交流机制, 云变异操作

Abstract: Considering the problem including slow convergence rates, low solving precisions and easy to trap in local optimum of Particle Swarm Optimization(PSO) algorithm, a novel dual population hybrid algorithm named CGA-PSO is presented, which is based on Cloud Genetic Algorithm(CGA) and PSO algorithm. In this algorithm, the whole population is divided into two equal populations. CGA and PSO with self-adjusting inertia weight strategy are used in the process of evolution of two populations. Two populations share the best individual and eliminate the worst individual by exchanging information between the two groups of offspring as well as offspring and parent to complete the evolution, and a timely cloud mutation operation is given on poor fitness of individuals. Cloud mutation operation is based on stable tendency and randomness property of cloud model. The global best position and the global worst position are used to complete mutation on the part of the particle’s position. By testing five classical functions and comparing CGA-PSO with CGA, PSO and their optimization algorithms, the results show that the proposed algorithm has higher search efficiency, accuracy and rapid convergence speed, and stronger robustness.

Key words: Cloud Genetic Algorithm(CGA), Particle Swarm Optimization(PSO) algorithm, dual populations hybrid algorithm, self- adjusting inertia weight strategy, information exchange mechanism, cloud mutation operation

中图分类号: 

• TP18