Title
An expanded particle swarm optimization based on multi-exemplar and forgetting ability.
Abstract
There are two phenomena in human society and biological systems. One is that people prefer to extract knowledge from multiple exemplars to obtain better learning ability. The other one is the forgetting ability that helps the encoding and consolidation of new information by removing unused or unwanted memories. Inspired by these phenomena, this paper transplants the multi-exemplar and forgetting ability to particle swarm optimization (PSO), and proposes an eXpanded PSO, called XPSO. Firstly, XPSO expands the “social-learning” part of each particle from one exemplar to two exemplars, learning from both the locally and the globally best exemplars. Secondly, XPSO assigns different forgetting abilities to different particles, simulating the forgetting phenomenon in the human society. Under the multi-exemplar learning model with forgetting ability, XPSO further adopts an adaptive scheme to update the acceleration coefficients and selects a reselection mechanism to update the population topology. The effectiveness of these additional proposed strategies is verified by extensive experiments. Moreover, comparison results among XPSO and other 9 popular PSO as well as 3 non-PSO algorithms on CEC’13 test suite suggest that XPSO attains a very promising performance for solving different types of functions, contributing to both higher solution accuracy and faster convergence speed.
Year
DOI
Venue
2020
10.1016/j.ins.2019.08.065
Information Sciences
Keywords
Field
DocType
Particle swarm optimization,Global optimization,Multi-exemplar,Forgetting ability,Adaptive adjustment
Test suite,Convergence (routing),Particle swarm optimization,Forgetting,Population,Artificial intelligence,Acceleration,Machine learning,Mathematics,Encoding (memory)
Journal
Volume
ISSN
Citations 
508
0020-0255
6
PageRank 
References 
Authors
0.40
0
8
Name
Order
Citations
PageRank
Xuewen Xia1736.87
Ling Gui2475.18
Guoliang He37512.73
bo wei45814.91
Yinglong Zhang5162.53
Fei Yu660.74
Hongrun Wu7262.64
Zhi-hui Zhan8178986.72