Abstract: To solve the challenges of outdated memory and diversity loss in Dynamic Optimization Problem（DOP）, this paper proposes an improved Multi-swarms Bare Bones Particle Swarm Optimization（MBBPSO）.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Key words: dynamic optimization problem, Bare Bones Particle Swarm Optimization（BBPSO）, outdated memory, diversity loss, multi-swarms

摘要： 针对动态优化问题（Dynamic Optimization Problem，DOP）中所面临的过时记忆和多样性丧失的挑战，提出了一种改进的多种群骨干粒子群优化算法（Multi-swarms Bare Bones Particle Swarm Optimization，MBBPSO）。通过设置环境勘探粒子及时检测环境的变化，避免了错误信息误导种群的进化方向；环境改变后，利用上一个环境搜索的信息初始化新的种群，提高MBBPSO快速追踪到当前环境的优秀解的能力；当种群陷入停滞时，采用新的进化方程以加强粒子的活性和多种群策略维持群体的多样性。仿真实验表明，MBBPSO在解决动态环境问题中具有较强的竞争力。

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