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.
