The Multi-objective Gene-pool Optimal Mixing Evolutionary Algorithm (MO-GOMEA) has been shown to be a promising solver for multi-objective combinatorial optimization problems, obtaining an excellent scalability on both standard benchmarks and real-world applications. To attain optimal performance, MO-GOMEA requires its two parameters, namely the population size and the number of clusters, to be set properly with respect to the problem instance at hand, which is a non-trivial task for any EA practitioner. In this article, we present a new version of MO-GOMEA in combination with the so-called Interleaved Multi-start Scheme (IMS) for the multi-objective domain that eliminates the manual setting of these two parameters. The new MO-GOMEA is then evaluated on multiple benchmark problems in comparison with two well-known multi-objective evolutionary algorithms (MOEAs): Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D). Experiments suggest that MO-GOMEA with the IMS is an easy-to-use MOEA that retains the excellent performance of the original MO-GOMEA.

Evolutionary algorithms, Linkage learning, Multi-objective optimization, Optimal mixing, Parameter settings, Scalability
Swarm and Evolutionary Computation
Computational Capacity Planning in Electricity Networks
Centrum Wiskunde & Informatica, Amsterdam, The Netherlands

Luong, N.H, La Poutré, J.A, & Bosman, P.A.N. (2018). Multi-objective Gene-pool Optimal Mixing Evolutionary Algorithm with the interleaved multi-start scheme. Swarm and Evolutionary Computation. doi:10.1016/j.swevo.2018.02.005