一种基于粒子群优化算法和差分进化算法的新型混合全局优化算法

A Novel Hybrid Global Optimization Algorithm Based on Particle Swarm Optimization and Differential Evolution

提出一种基于粒子群算法(PSO)和差分进化算法(DE)相结合的新型混合全局优化算法——PSODE.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,另一种群的个体由差分操作进化而来.此外,通过采用一种信息分享机制,在算法执行过程中两个种群中的个体可以实现协同进化.为了进一步提高PSODE算法的性能,摆脱陷入局部最优点,还采用了一种变异机制.通过4个标准测试函数的测试并与PSO和DE算法进行比较,证明本文提出的PSODE算法是一种收敛速度快、求解精度高、鲁棒性较强的全局优化算法.

A new hybrid global optimization algorithm - PSODE is presented, which is based on the combination of the particle swarm optimization （PSO） and differential evolution （DE）. PSODE 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 DE. The individuals both in PSO and DE are co-evolved during the algorithm execution by employing an information sharing mechanism. To further improve the proposed PSODE, a mutation mechanism is also introduced when it is strapped into a local minima. In test, four benchmark functions are used, and the performance of the proposed PSODE algorithm is compared with PSO and DE, which demonstrate that it＇s a powerful global optimization algorithm with rapid convergence rate, high solution quality and algorithm robustness.