Distributed multi-objective evolutionary optimization using island-based selective operator application

Abstract

Parallel island-model co-evolutionary algorithms are well-known methods, suitable for dealing with large multi-objective optimization problems. This paper proposes a version of these algorithms where each island modifies a fragment of the chromosome that encodes a possible solution to the problem. The objective of this paper is to demonstrate that automatically setting the size of the overlapping fragments depending on the number of islands obtains better results than using a fixed overlapping size. This method has been compared to other parallel evolutionary techniques considering a different number of islands, chromosome sizes and benchmarks. The analysis of the obtained experimental results, by using different metrics, shows that our approach can provide statistically significant improvements with respect to the base algorithm in high-dimensional, un-decomposable, multi-objective problems. This opens a very promising line to automatically adapt the overlapping sizes in this kind of algorithms.