NDLI: Critical considerations on angle modulated particle swarm optimisers

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Critical considerations on angle modulated particle swarm optimisers

Content Provider	Springer Nature Link
Author	Leonard, Barend J. Engelbrecht, Andries P. Cleghorn, Christopher W.
Copyright Year	2015
Abstract	This article investigates various aspects of angle modulated particle swarm optimisers (AMPSO). Previous attempts at improving the algorithm have only been able to produce better results in a handful of test cases. With no clear understanding of when and why the algorithm fails, improving the algorithm’s performance has proved to be a difficult and sometimes blind undertaking. Therefore, the aim of this study is to identify the circumstances under which the algorithm might fail, and to understand and provide evidence for such cases. It is shown that the general assumption that good solutions are grouped together in the search space does not hold for the standard AMPSO algorithm or any of its existing variants. The problem is explained by specific characteristics of the generating function used in AMPSO. Furthermore, it is shown that the generating function also prevents particle velocities from decreasing, hindering the algorithm’s ability to exploit the binary solution space. Methods are proposed to both confirm and potentially solve the problems found in this study. In particular, this study addresses the problem of finding suitable generating functions for the first time. It is shown that the potential of a generating function to solve arbitrary binary optimisation problems can be quantified. It is further shown that a novel generating function with a single coefficient is able to generate solutions to binary optimisation problems with fewer than four dimensions. The use of ensemble generating functions is proposed as a method to solve binary optimisation problems with more than 16 dimensions.
Starting Page	291
Ending Page	314
Page Count	24
File Format	PDF
ISSN	19353812
Journal	Swarm Intelligence
Volume Number	9
Issue Number	4
e-ISSN	19353820
Language	English
Publisher	Springer US
Publisher Date	2015-10-07
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Swarm intelligence Particle swarm optimisation Angle modulation Discrete optimisation Binary optimisation Artificial Intelligence (incl. Robotics) Computer Systems Organization and Communication Networks ApplicationMathematics/Computational Methods of Engineering Communications Engineering, Networks Computer Communication Networks
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence

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