基于粒子群和人工蜂群算法的混合优化算法

提出一种基于粒子群(PSO)和人工蜂群算法(ABC)相结合的新型混合优化算法—PSOABC。该算法基于一种双种群进化策略，一个种群中的个体由粒子群算法进化而来，另一种群的个体由人工蜂群算法进化而来，并且在人工蜂群算法中按轮盘赌的方式选择个体进化所需的随机个体。此外，算法采用一种信息分享机制，使两个种群中的个体可以实现协同进化。对4个基准函数进行仿真实验并与ABC进行比较，表明本文提出的算法能有效地改善寻优性能，增强摆脱局部极值的能力。

Hybrid Optimization Algorithm based on Particle Swarm Optimization and Artificial Bee Colony Algorithm

A new hybrid global optimization algorithm PSOABC is presented, which is based on the combination of the particle swarm optimization (PSO) and artificial bee colony algorithm (ABC). PSOABC is based on a two population evolution scheme, in which the individuals of one population are evolved by PSO and the individuals of the other population are evolved by ABC. Random individuals in which evolution of individual required are selected by roulette in ABC. The individuals both in PSO and ABC are coevolved by employing an information sharing mechanism. Four benchmark functions are tested, and the performance of the proposed PSOABC algorithm is compared with ABC. Which demonstrate that PSOABC can improve optimizing performance effectively, and it can avoid getting struck at local optima effectively.