摘要
在求解多目标优化问题时,针对粒子群优化算法容易陷入局部极值的现象,提出了一种组合粒子群和差分进化的多目标优化算法,使用粒子群优化算法和差分进化算法共同产生新粒子,通过一个判断因子控制两种算法的使用比例,并对粒子群优化算法的速度更新公式进行了改变,以提高搜索效率。通过三个测试函数进行了仿真,并同NSGA-Ⅱ、MOPSO-CD进行了比较。实验结果表明改进算法求得的Pareto解集收敛性和多样性好,并且算法稳定性高,运行速度快。
To deal with the phenomenon of particle swarm optimization algorithm being often trapped in local opti- ma for multi - objective optimization problems, a multi - objective optimization algorithm composed of particle swarm optimization and differential evolution was proposed. Both particle swarm optimization algorithm and differential evo- lution algorithm were used to create new particles. A controlling factor was used to control the proportion of the use of two algorithms. The velocity updating formula of particle swarm optimization algorithm was changed to improve the search efficiency. Three test functions were used to evaluate the performance of the proposed algorithm, and the pro- posed algorithm was compared with NSGA - II and MOPSO - CD. The experimental results show that the Pareto sets obtained by the proposed algorithm have good convergence and diversity performance, and the proposed algorithm is stable and fast.
出处
《计算机仿真》
CSCD
北大核心
2013年第4期313-316,共4页
Computer Simulation
基金
国家自然科学基金(61074076)
中国博士后科学基金(20090450119)
中国博士点新教师基金(20092304120017)
关键词
多目标优化
粒子群优化
差分进化
Multi - objective optimization
Particle swarm optimization （PSO）
Differential evolution
