Characterising the searchability of continuous optimisation problems for PSO

Show simple item record Malan, Katherine Mary Engelbrecht, Andries P. 2015-06-24T07:26:05Z 2015-06-24T07:26:05Z 2014-12
dc.description.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 towork 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. en_ZA
dc.description.embargo 2015-12-31 en_ZA
dc.description.librarian hb2015 en_ZA
dc.description.uri en_ZA
dc.identifier.citation Malan, KM & Engelbrecht, AP 2014, 'Characterising the searchability of continuous optimisation problems for PSO', Swarm Intelligence, vol. 8, pp. 275-302. en_ZA
dc.identifier.issn 1935-3812 (print)
dc.identifier.issn 1935-3820 (online)
dc.identifier.other 10.1007/s11721-014-0099-x
dc.language.iso en en_ZA
dc.publisher Springer en_ZA
dc.rights © Springer Science+Business Media New York 2014. The original publication is available at : http://link.springer.comjournal/11721 en_ZA
dc.subject Fitness landscape analysis en_ZA
dc.subject Evolvability en_ZA
dc.subject Particle swarm optimization (PSO) en_ZA
dc.title Characterising the searchability of continuous optimisation problems for PSO en_ZA
dc.type Postprint Article en_ZA

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