An Evolutionary Sequential Sampling Algorithm for Multi-Objective Optimization

Aristotelis E. Thanos, Nurcin Celik, Juan P. Sáenz

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article number1650006
JournalAsia-Pacific Journal of Operational Research
Issue number1
StatePublished - Feb 1 2016


  • evolutionary algorithms
  • Multi-criterion decision-making
  • multi-objective optimization
  • Pareto optimality
  • sequential sampling

ASJC Scopus subject areas

  • Management Science and Operations Research


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