Modified Particle Swarm Optimization with Switching Update Strategy

被引:0
作者
Kundu, Rupam [1 ]
Mukherjee, Rohan [1 ]
Das, Swagatam [2 ]
机构
[1] Jadavpur Univ, Dept Elect & Telecommun Engn, Kolkata 700032, W Bengal, India
[2] Indian Stat Inst, Elect & Commun Sci Unit, Kolkata 700108, India
来源
SWARM, EVOLUTIONARY, AND MEMETIC COMPUTING, (SEMCCO 2012) | 2012年 / 7677卷
关键词
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
This article aims at improving the Particle Swarm Optimization, by uniquely reshaping its update strategy for generating new solutions with a switching strategy that transits between exploration and convergence, a time-varying inertia weight to control particles' movement and an aging mechanism to avoid stagnation in local basins of attraction. The algorithm addressed as MPSO-SUS has been compared with eight other state-of-artEAs on a standard benchmark of sixteen functions. The results of such comparison indicate that MPSO-SUS clearly and statistically outperform the other well-known approaches, justifying its distinctive feature which makes it a successful optimizer.
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页码:644 / 652
页数:9
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