Title: Differential evolution and threshold accepting hybrid algorithm for unconstrained optimisation

Authors: Nikunj Chauhan, V. Ravi

Addresses: IBM India Private Limited, EGL, C-block, 2nd floor, Koramangla-560071, Bangalore, India. ' Institute for Development and Research in Banking Technology (IDRBT), Castle Hills Road #1, Masab Tank, Hyderabad-500 057 (AP), India

Abstract: This paper develops a hybrid global optimisation metaheuristic methodology for solving unconstrained optimisation problems. The hybrid, to be called DETA, comprises two global optimisation algorithms viz. differential evolution (DE) and threshold accepting (TA) in tandem. While working with DE on benchmark problems, we noticed that it slows down before convergence is achieved. After analysing the possible reason for this shortcoming, we propose DETA to address it. DETA works in two phases: Phase 1 implements the original DE with relaxed convergence criterion. Then, a switch over is made from Phase 1 to Phase 2, where TA is used to quickly guide the search to global optimum. Performance of DETA is compared with that of DE on 26 unconstrained benchmark problems. The results obtained indicate that DETA hybrid is much superior to DE in terms of speed for the same level of accuracy.

Keywords: global optimisation; differential evolution; threshold accepting; hybrid metaheuristics; unconstrained optimisation.

DOI: 10.1504/IJBIC.2010.033086

International Journal of Bio-Inspired Computation, 2010 Vol.2 No.3/4, pp.169 - 182

Published online: 07 May 2010 *

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