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Handling equality constraints in evolutionary optimization

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  • Barkat Ullah, Abu S.S.M.
  • Sarker, Ruhul
  • Lokan, Chris

Abstract

Over the last few decades several methods have been proposed for handling functional constraints while solving optimization problems using evolutionary algorithms (EAs). However, the presence of equality constraints makes the feasible space very small compared to the entire search space. As a consequence, the handling of equality constraints has long been a difficult issue for evolutionary optimization methods. This paper presents a Hybrid Evolutionary Algorithm (HEA) for solving optimization problems with both equality and inequality constraints. In HEA, we propose a new local search technique with special emphasis on equality constraints. The basic concept of the new technique is to reach a point on the equality constraint from the current position of an individual solution, and then explore on the constraint landscape. We believe this new concept will influence the future research direction for constrained optimization using population based algorithms. The proposed algorithm is tested on a set of standard benchmark problems. The results show that the proposed technique works very well on those benchmark problems.

Suggested Citation

  • Barkat Ullah, Abu S.S.M. & Sarker, Ruhul & Lokan, Chris, 2012. "Handling equality constraints in evolutionary optimization," European Journal of Operational Research, Elsevier, vol. 221(3), pages 480-490.
    • Handle: RePEc:eee:ejores:v:221:y:2012:i:3:p:480-490
    DOI: 10.1016/j.ejor.2012.01.047
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    References listed on IDEAS

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    1. Kimbrough, Steven Orla & Koehler, Gary J. & Lu, Ming & Wood, David Harlan, 2008. "On a Feasible-Infeasible Two-Population (FI-2Pop) genetic algorithm for constrained optimization: Distance tracing and no free lunch," European Journal of Operational Research, Elsevier, vol. 190(2), pages 310-327, October.
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    Cited by:

    1. Oliver Cuate & Antonin Ponsich & Lourdes Uribe & Saúl Zapotecas-Martínez & Adriana Lara & Oliver Schütze, 2019. "A New Hybrid Evolutionary Algorithm for the Treatment of Equality Constrained MOPs," Mathematics, MDPI, vol. 8(1), pages 1-25, December.

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