An Evolutionary Sequential Sampling Algorithm for Multi-Objective Optimization

Aristotelis E. Thanos (), Nurcin Celik and Juan P. Sáenz ()
Additional contact information
Aristotelis E. Thanos: Industrial Engineering, The University of Miami, Coral Gables, FL, USA
Nurcin Celik: Industrial Engineering, The University of Miami, Coral Gables, FL, USA
Juan P. Sáenz: Industrial Engineering, The University of Miami, Coral Gables, FL, USA

Asia-Pacific Journal of Operational Research (APJOR), 2016, vol. 33, issue 01, 1-21

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.

Keywords: Multi-criterion decision-making; multi-objective optimization; evolutionary algorithms; Pareto optimality; sequential sampling (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0217595916500068
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:apjorx:v:33:y:2016:i:01:n:s0217595916500068

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0217595916500068

Access Statistics for this article

Asia-Pacific Journal of Operational Research (APJOR) is currently edited by Gongyun Zhao

More articles in Asia-Pacific Journal of Operational Research (APJOR) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().