Multi-Objective PSO Based on Evolutionary Programming

被引：0
|
作者：
Shao, Zengzhen [1 ]
Liu, Yanmin [2 ]
Dong, Shuxia [3 ]
机构：
[1] Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250014, Peoples R China
[2] Zunyi Normal Coll, Dept Math, Zunyi 563002, Peoples R China
[3] ShanDong Womens Univ, Sch Informat & Technol, Jinan 250002, Peoples R China
来源：
ADVANCED INTELLIGENT COMPUTING THEORIES AND APPLICATIONS | 2010年 / 6215卷
关键词：
Particle swarm optimizer; Multiobjective optimization; Evolutionary; Programming;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Multi Objective Particle Swarm Optimizers (MOPSOs) easily converge to a false Pareto front In this paper, we proposed a hybrid algorithm of MOPSO with evolutionary programming (denoted as EPMOPSO) for solving MOPs In EPMOPSO the neighborhood of each particle is dynamically constructed and the velocity of each particle is adjusted by all particles in its neighborhood including itself, the best performing particle in the swarm and the elite group that is evolved using evolutionary programming Simulation results show that EPMOPSO is able to find a much better spread of solutions and has faster convergence to true Pareto-optimal front compared with five state-of-the art MOPSOs
引用
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页码：602 / +
页数：2
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