Abstract:
Extremal optimisation (EO) is a relatively recent nature-inspired heuristic whose search method is especially suitable to solve combinatorial optimisation problems. To da...Show MoreMetadata
Abstract:
Extremal optimisation (EO) is a relatively recent nature-inspired heuristic whose search method is especially suitable to solve combinatorial optimisation problems. To date, most of the research in EO has been applied for solving single-objective problems and only a relatively small number of attempts to extend EO toward multi-objective problems. This paper presents a hybrid multi-objective version of EO (HMEO) to solve multi-objective combinatorial problems. This new approach consists of a multi-objective EO framework, for the coarse-grain search, which contains a novel multi-objective combinatorial local search framework for the fine-grain search. The chosen problems to test the proposed method are the multi-objective knapsack problem and the multi-objective quadratic assignment problem. The results show that the new algorithm is able to obtain competitive results to SPEA2 and NSGA-II. The non-dominated points found are well-distributed and similar or very close to the Pareto-front found by previous works.
Published in: IEEE Congress on Evolutionary Computation
Date of Conference: 18-23 July 2010
Date Added to IEEE Xplore: 27 September 2010
ISBN Information: