1. ¹University of Minnesota, Minneapolis, MN, USA
2. ²North Carolina State University, Raleigh, NC, USA

Correspondence: B Suman, Department of Chemical Engineering and Materials Science, Mailbox # 30, 151 Amundson Hall, 421 Washington Avenue SE, University of Minnesota, Minneapolis, MN 55455, USA. E-mail: suman@cems.umn.edu

Received December 2004; Accepted July 2005; Published online 19 October 2005.

Abstract

This paper presents a comprehensive review of simulated annealing (SA)-based optimization algorithms. SA-based algorithms solve single and multiobjective optimization problems, where a desired global minimum/maximum is hidden among many local minima/maxima. Three single objective optimization algorithms (SA, SA with tabu search and CSA) and five multiobjective optimization algorithms (SMOSA, UMOSA, PSA, WDMOSA and PDMOSA) based on SA have been presented. The algorithms are briefly discussed and are compared. The key step of SA is probability calculation, which involves building the annealing schedule. Annealing schedule is discussed briefly. Computational results and suggestions to improve the performance of SA-based multiobjective algorithms are presented. Finally, future research in the area of SA is suggested.