计算机工程与应用2017，Vol.53Issue(19)：45-50,108,7.DOI:10.3778/j.issn.1002-8331.1606-0030
求解动态优化问题的多种群骨干粒子群算法
Multi-swarms Bare Bones Particle Swarms Optimization of solving dynamic optimization problems
摘要
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To solve the challenges of outdated memory and diversity loss in Dynamic Optimization Problem(DOP), this paper proposes an improved Multi-swarms Bare Bones Particle Swarm Optimization(MBBPSO).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 experi-ment results show that MBBPSO has stronger competitiveness in dynamic environment.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Key words
dynamic optimization problem/Bare Bones Particle Swarm Optimization(BBPSO)/outdated memory/diversity loss/multi-swarms分类
信息技术与安全科学引用本文复制引用
陈健,申元霞,纪滨..求解动态优化问题的多种群骨干粒子群算法[J].计算机工程与应用,2017,53(19):45-50,108,7.基金项目
国家自然科学基金(No.61300059,No.61502010). （No.61300059,No.61502010）
