Multiobjective variable mesh optimization

Yamisleydi Salgueiro (), Jorge L. Toro, Rafael Bello and Rafael Falcon
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Yamisleydi Salgueiro: University of Las Tunas
Jorge L. Toro: University of Las Tunas
Rafael Bello: Central University of Las Villas
Rafael Falcon: University of Ottawa

Annals of Operations Research, 2017, vol. 258, issue 2, No 30, 869-893

Abstract: Abstract In this article we introduce a new multiobjective optimizer based on a recently proposed metaheuristic algorithm named Variable Mesh Optimization (VMO). Our proposal (multiobjective VMO, MOVMO) combines typical concepts from the multiobjective optimization arena such as Pareto dominance, density estimation and external archive storage. MOVMO also features a crossover operator between local and global optima as well as dynamic population replacement. We evaluated MOVMO using a suite of four well-known benchmark function families, and against seven state-of-the-art optimizers: NSGA-II, SPEA2, MOCell, AbYSS, SMPSO, MOEA/D and MOEA/D.DRA. The statistically validated results across the additive epsilon, spread and hypervolume quality indicators confirm that MOVMO is indeed a competitive and effective method for multiobjective optimization of numerical spaces.

Keywords: Multi-objective optimization; Evolutionary computation; Variable mesh optimization; Meta-heuristic optimization (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10479-016-2221-5

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