关键词: 动态优化问题, 骨干粒子群算法, 过时记忆, 多样性丧失, 多种群

Abstract: To solve the challenges of outdated memory and diversity loss in Dynamic Optimization Problem（DOP）, this paper proposes an improved Multi-swarms Bare Bones Particle Swarm Optimization（MBBPSO）. First of all, the particles of environment survey are set to detect timely the change of environment in MBBPSO, which avoids incorrect information guiding the direction of swarms’evolution. After the change of environment, MBBPSO reinitialize all swarms by using the information which every swarm explores in last environment which enhances fast tracking ability of the excellent solution to the current environment. When the swarm falls into a standstill, MBBPSO designs newly methods to enhance particles’activation and use the multi-swarms measure to maintain the whole swarm’s diversity. The simulation experiment results show that MBBPSO has stronger competitiveness in dynamic environment.

Key words: dynamic optimization problem, Bare Bones Particle Swarm Optimization（BBPSO）, outdated memory, diversity loss, multi-swarms