Niching Multimodal Landscapes Faster Yet Effectively: VMO and HillVallEA Benefit Together

Ricardo Navarro and Chyon Hae Kim
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Ricardo Navarro: Faculty of Science and Engineering, Iwate University, Ueda 4-3-5, Morioka, Iwate 020-0066, Japan
Chyon Hae Kim: Faculty of Science and Engineering, Iwate University, Ueda 4-3-5, Morioka, Iwate 020-0066, Japan

Mathematics, 2020, vol. 8, issue 5, 1-37

Abstract: Variable Mesh Optimization with Niching (VMO-N) is a framework for multimodal problems (those with multiple optima at several search subspaces). Its only two instances are restricted though. Being a potent multimodal optimizer, the Hill-Valley Evolutionary Algorithm (HillVallEA) uses large populations that prolong its execution. This study strives to revise VMO-N, to contrast it with related approaches, to instantiate it effectively, to get HillVallEA faster, and to indicate methods (previous or new) for practical use. We hypothesize that extra pre-niching search in HillVallEA may reduce the overall population, and that if such a diminution is substantial, it runs more rapidly but effective. After refining VMO-N, we bring out a new case of it, dubbed Hill-Valley-Clustering-based VMO (HVcMO), which also extends HillVallEA. Results show it as the first competitive variant of VMO-N, also on top of the VMO-based niching strategies. Regarding the number of optima found, HVcMO performs statistically similar to the last HillVallEA version. However, it comes with a pivotal benefit for HillVallEA: a severe reduction of the population, which leads to an estimated drastic speed-up when the volume of the search space is in a certain range.

Keywords: AMaLGaM; clustering; estimation of distribution; evolutionary algorithm; framework; heuristic; hill-valley; multimodal optimization; niching; variable mesh optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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