NDLI: Characterising the searchability of continuous optimisation problems for PSO

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Characterising the searchability of continuous optimisation problems for PSO

Content Provider	Springer Nature Link
Author	Malan, K. M. Engelbrecht, A. P.
Copyright Year	2014
Abstract	The focus of research in swarm intelligence has been largely on the algorithmic side with relatively little attention being paid to the study of problems and the behaviour of algorithms in relation to problems. When a new algorithm or variation on an existing algorithm is proposed in the literature, there is seldom any discussion or analysis of algorithm weaknesses and on what kinds of problems the algorithm is expected to fail. Fitness landscape analysis is an approach that can be used to analyse optimisation problems. By characterising problems in terms of fitness landscape features, the link between problem types and algorithm performance can be studied. This article investigates a number of measures for analysing the ability of a search process to improve fitness on a particular problem (called evolvability in literature but referred to as searchability in this study to broaden the scope to non-evolutionary-based search techniques). A number of existing fitness landscape analysis techniques originally proposed for discrete problems are adapted to work in continuous search spaces. For a range of benchmark problems, the proposed searchability measures are viewed alongside performance measures for a traditional global best particle swarm optimisation (PSO) algorithm. Empirical results show that no single measure can be used as a predictor of PSO performance, but that multiple measures of different fitness landscape features can be used together to predict PSO failure.
Starting Page	275
Ending Page	302
Page Count	28
File Format	PDF
ISSN	19353812
Journal	Swarm Intelligence
Volume Number	8
Issue Number	4
e-ISSN	19353820
Language	English
Publisher	Springer US
Publisher Date	2014-10-28
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Fitness landscape analysis Evolvability Particle swarm 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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