Animorphic ensemble optimization: a large-scale island model

Abstract

Abstract In this paper, a flexible large-scale ensemble-based optimization algorithm is presented for complex optimization problems. According to the no free lunch theorem, no single optimization algorithm demonstrates superior performance across all optimization problems. Therefore, with the animorphic ensemble optimization (AEO) algorithm presented here, a set of algorithms can be used as an ensemble which demonstrate stronger performance across a wider range of optimization problems than any standalone algorithm. AEO is a high-level ensemble designed to handle large ensembles using a well-defined stochastic migration process. The high-level nature of AEO allows for an arbitrary number of diverse standalone algorithms to interface with one another through an island model interface strategy, where various populations change size according to the performance of the algorithm associated with each population. In this study, AEO is demonstrated using ensembles of both evolutionary and swarm algorithms such as differential evolution, particle swarm, gray wolf optimization, moth-flame optimization, and more, and strong performance is observed. Quantitative diagnostics metrics to describe the migration of individuals across populations are also presented and observed with application to some test problems. In the end, AEO demonstrated strong consistent performance across more than 150 benchmark functions of 10–50 dimensions.