C-PSA: Constrained Pareto simulated annealing for constrained multi-objective optimization

  • Authors:

  • Hemant Kumar Singh;Tapabrata Ray;Warren Smith

  • Affiliations:

  • School of Engineering and Information Technology (SEIT), University of New South Wales at Australian Defence Academy (UNSW@ADFA), Northcott Drive, Canberra ACT 2600, Australia;School of Engineering and Information Technology (SEIT), University of New South Wales at Australian Defence Academy (UNSW@ADFA), Northcott Drive, Canberra ACT 2600, Australia;School of Engineering and Information Technology (SEIT), University of New South Wales at Australian Defence Academy (UNSW@ADFA), Northcott Drive, Canberra ACT 2600, Australia

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2010

Quantified Score

Hi-index 0.07

Visualization

Abstract

In recent years, evolutionary algorithms (EAs) have been extensively developed and utilized to solve multi-objective optimization problems. However, some previous studies have shown that for certain problems, an approach which allows for non-greedy or uphill moves (unlike EAs), can be more beneficial. One such approach is simulated annealing (SA). SA is a proven heuristic for solving numerical optimization problems. But owing to its point-to-point nature of search, limited efforts has been made to explore its potential for solving multi-objective problems. The focus of the presented work is to develop a simulated annealing algorithm for constrained multi-objective problems. The performance of the proposed algorithm is reported on a number of difficult constrained benchmark problems. A comparison with other established multi-objective optimization algorithms, such as infeasibility driven evolutionary algorithm (IDEA), Non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective Scatter search II (MOSS-II) has been included to highlight the benefits of the proposed approach.