Journal
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
Volume 46, Issue 9, Pages 1572-1599Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/00207721.2013.823526
Keywords
ant colony optimisation; artificial immune systems; differential evolution; particle swarm optimisation; genetic algorithms; estimation of distribution algorithms
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In this work we present an overview of the most prominent population-based algorithms and the methodologies used to extend them to multiple objective problems. Although not exact in the mathematical sense, it has long been recognised that population-based multi-objective optimisation techniques for real-world applications are immensely valuable and versatile. These techniques are usually employed when exact optimisation methods are not easily applicable or simply when, due to sheer complexity, such techniques could potentially be very costly. Another advantage is that since a population of decision vectors is considered in each generation these algorithms are implicitly parallelisable and can generate an approximation of the entire Pareto front at each iteration. A critique of their capabilities is also provided.
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