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Improving differential evolution algorithm by synergizing different improvement mechanisms

Published:30 July 2012Publication History
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Abstract

Differential Evolution (DE) is a well-known Evolutionary Algorithm (EA) for solving global optimization problems. Practical experiences, however, show that DE is vulnerable to problems like slow and/or premature convergence. In this article we propose a simple and modified DE framework, called MDE, which is a fusion of three recent modifications in DE: (1) Opposition-Based Learning (OBL); (2) tournament method for mutation; and (3) single population structure. These features have a specific role which helps in improving the performance of DE. While OBL helps in giving a good initial start to DE, the use of the tournament best base vector in the mutation phase helps in preserving the diversity. Finally the single population structure helps in faster convergence. Their synergized effect balances the exploitation and exploration capabilities of DE without compromising with the solution quality or the convergence rate. The proposed MDE is validated on a set of 25 standard benchmark problems, 7 nontraditional shifted benchmark functions proposed at the special session of CEC2008, and three engineering design problems. Numerical results and statistical analysis show that the proposed MDE is better than or at least comparable to the basic DE and several other state-of-the art DE variants.

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        • Published in

          cover image ACM Transactions on Autonomous and Adaptive Systems
          ACM Transactions on Autonomous and Adaptive Systems  Volume 7, Issue 2
          July 2012
          275 pages
          ISSN:1556-4665
          EISSN:1556-4703
          DOI:10.1145/2240166
          Issue’s Table of Contents

          Copyright © 2012 ACM

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 30 July 2012
          • Accepted: 1 March 2011
          • Revised: 1 November 2010
          • Received: 1 June 2010
          Published in taas Volume 7, Issue 2

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