Sequential Monte Carlo-based multi-objective optimization

Juan Pablo Sáenz, Nurcin Celik

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Multi-objective optimization problems are often found in everyday life, such as in the tradeoff between the cost and quality of a product. As the simultaneous multiple objectives may conflict with each other, the optimization problem is very challenging, and there rarely is a single global optimum. There are two common approaches for multi-objective optimization problems. The first focuses on transforming the problem into a single objective problem through the use of normalization or combination techniques, whereas the second uses multi-objective evolutionary algorithms that construct a non-dominated solution set. As an alternative, in this paper we present a sequential Monte Carlo algorithm with two-stage sampling for multi-objective optimization problems. In the first stage, sampling is executed from within the non-dominated solution set generated by the algorithm; while in the second stage, sampling is performed from within the extreme points of the non-dominated solution set and some of the closest extreme points of the search space. The proposed approach has been benchmarked against well-known multi-objective optimization algorithms. It has performed better than the alternatives in problems where the Pareto front presents convexity, or multimodality; while producing promising results in instances with discontinuity along the Pareto front.

Original languageEnglish
Title of host publicationIIE Annual Conference and Expo 2013
PublisherInstitute of Industrial Engineers
Number of pages10
StatePublished - Jan 1 2013
EventIIE Annual Conference and Expo 2013 - San Juan, Puerto Rico
Duration: May 18 2013May 22 2013


OtherIIE Annual Conference and Expo 2013
Country/TerritoryPuerto Rico
CitySan Juan


  • Multi-objective optimization
  • Pareto optimality
  • Particle filtering
  • Sequential Monte Carlo

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering


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