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Soft Computing

, Volume 13, Issue 8–9, pp 741–762 | Cite as

AMA: a new approach for solving constrained real-valued optimization problems

  • Abu S. S. M. Barkat Ullah
  • Ruhul Sarker
  • David Cornforth
  • Chris Lokan
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Abstract

Memetic algorithms (MA) have recently been applied successfully to solve decision and optimization problems. However, selecting a suitable local search technique remains a critical issue of MA, as this significantly affects the performance of the algorithms. This paper presents a new agent based memetic algorithm (AMA) for solving constrained real-valued optimization problems, where the agents have the ability to independently select a suitable local search technique (LST) from our designed set. Each agent represents a candidate solution of the optimization problem and tries to improve its solution through co-operation with other agents. Evolutionary operators consist of only crossover and one of the self-adaptively selected LSTs. The performance of the proposed algorithm is tested on five new benchmark problems along with 13 existing well-known problems, and the experimental results show convincing performance.

Keywords

Memetic algorithms Evolutionary algorithms Genetic algorithms Agent-based systems Constrained optimization 

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Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Abu S. S. M. Barkat Ullah
    • 1
  • Ruhul Sarker
    • 1
  • David Cornforth
    • 1
  • Chris Lokan
    • 1
  1. 1.School of Information Technology and Electrical EngineeringUniversity of New South Wales at the Australian Defence Force AcademyCanberraAustralia

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