[1]王越,吕光宏.改进的粒子群求解多目标优化算法[J].计算机技术与发展,2014,24(02):42-45.
　WANG Yue,Lü Guang-hong.Modified Particle Swarm Optimization Algorithm Solving Multi-objective[J].,2014,24(02):42-45.
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改进的粒子群求解多目标优化算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
24
期数:
2014年02期
页码:
42-45
栏目:
智能、算法、系统工程
出版日期:
2014-02-28

文章信息/Info

Title:
Modified Particle Swarm Optimization Algorithm Solving Multi-objective
文章编号:
1673-629X（2014）02-0042-04
作者:
王越; 吕光宏
四川大学 计算机学院
Author(s):
WANG Yue; Lü Guang-hong
关键词:
粒子群算法; 多目标优化; Pareto最优解; 全局最优值; 个体最优值
Keywords:
particle swarm optimization algorithm; multi-objective optimization; Pareto optimal; global best; personal best
分类号:
TP301.6
文献标志码:
A
摘要:
根据粒子群算法求解多目标问题的特点，个体极值和全局极值的选择不同会对实验结果产生很大影响。目前普遍的选择方法仅仅根据简单的支配关系，但是会存在两个解之间没有支配关系而导致不去更新个体最优值(PB)和全局最优值(GB)，这样会导致更好的个体极值和全局极值的遗漏从而降低收敛时间。文中提出一种新的个体极值和全局极值的选择策略。使用这种策略，可以加快收敛，提高准确性，防止非劣解的遗漏。通过几个测试函数的实验仿真，所得解集的分步性和多样性都有显著的提高。
Abstract:
According to the characteristics of particle swarm optimization algorithm for solving multi-objective problems,the choice of personal best and global best will affect the result greatly. The current selection method is only based on their dominant relationship,but if there is no dominant relationship between two solutions,PB and GB will not be updated. This will miss the better PB and GB and ex-tend the convergence time. A new selection strategy for personal best and global best is presented. Using this strategy can accelerate con-vergence,improve accuracy,avoid non-dominated solution discard. The performance of this strategy is evaluated on several test function. The results show that the diversity and the distribution of the non-dominated solution is highly raised compared with other PSO algo-rithm.

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更新日期/Last Update: 1900-01-01