Coupled eagle strategy and differential evolution for unconstrained and constrained global optimization

  • Authors:

  • Amir Hossein Gandomi;Xin-She Yang;Siamak Talatahari;Suash Deb

  • Affiliations:

  • Young Researchers Club, Central Tehran Branch, Islamic Azad University, Tehran, Iran;Mathematics and Scientific Computing, National Physical Laboratory, Teddington TW11 0LW, UK;Marand Faculty of Engineering, University of Tabriz, Tabriz, Iran;Department of Computer Science & Engineering, C. V. Raman College of Engineering, Bidyanagar, Mahura, Janla, Bhubaneswar 752054, India

  • Venue:
  • Computers & Mathematics with Applications
  • Year:
  • 2012

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Abstract

The performance of an optimization tool is largely determined by the efficiency of the search algorithm used in the process. The fundamental nature of a search algorithm will essentially determine its search efficiency and thus the types of problems it can solve. Modern metaheuristic algorithms are generally more suitable for global optimization. This paper carries out extensive global optimization of unconstrained and constrained problems using the recently developed eagle strategy by Yang and Deb in combination with the efficient differential evolution. After a detailed formulation and explanation of its implementation, the proposed algorithm is first verified using twenty unconstrained optimization problems or benchmarks. For the validation against constrained problems, this algorithm is subsequently applied to thirteen classical benchmarks and three benchmark engineering problems reported in the engineering literature. The performance of the proposed algorithm is further compared with various, state-of-the-art algorithms in the area. The optimal solutions obtained in this study are better than the best solutions obtained by the existing methods. The unique search features used in the proposed algorithm are analyzed, and their implications for future research are also discussed in detail.