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An Evolutionary Sequential Sampling Algorithm for Multi-Objective Optimization

Aristotelis E. Thanos

Industrial Engineering, The University of Miami, Coral Gables, FL, USA

E-mail Address: mthanos1986@gmail.com

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Nurcin Celik

Industrial Engineering, The University of Miami, Coral Gables, FL, USA

E-mail Address: celik@miami.edu

Corresponding author.

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Juan P. Sáenz

Industrial Engineering, The University of Miami, Coral Gables, FL, USA

E-mail Address: j.saenz4@miami.edu

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https://doi.org/10.1142/S0217595916500068Cited by:0 (Source: Crossref)

Abstract

In this paper, we present a novel sequential sampling methodology for solving multi-objective optimization problems. Random sequential sampling is performed using the information from within the non-dominated solution set generated by the algorithm, while resampling is performed using the extreme points of the non-dominated solution set. The proposed approach has been benchmarked against well-known multi-objective optimization algorithms that exist in the literature through a series of problem instances. The proposed algorithm has been demonstrated to perform at least as good as the alternatives found in the literature in problems where the Pareto front presents convexity, nonconvexity, or discontinuity; while producing very promising results in problem instances where there is multi-modality or nonuniform distribution of the solutions along the Pareto front.

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