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Infeasibility Driven Evolutionary Algorithm for Constrained Optimization

  • Chapter
Constraint-Handling in Evolutionary Optimization

Part of the book series: Studies in Computational Intelligence ((SCI,volume 198))

Abstract

Real life optimization problems often involve one or more constraints and in most cases, the optimal solutions to such problems lie on constraint boundaries. The performance of an optimization algorithm is known to be largely dependent on the underlying mechanism of constraint handling. Most population based stochastic optimization methods prefer a feasible solution over an infeasible solution during their course of search. Such a preference drives the population to feasibility first before improving its objective function value which effectively means that the solutions approach the constraint boundaries from the feasible side of the search space. In this chapter, we introduce an evolutionary algorithm that explicitly maintains a small percentage of infeasible solutions close to the constraint boundaries during its course of evolution. The presence of marginally infeasible solutions in the population allows the algorithm to approach the constraint boundary from the infeasible side of the search space in addition to its approach from the feasible side of the search space via evolution of feasible solutions. Furthermore, “good” infeasible solutions are ranked higher than the feasible solutions, thereby focusing the search for the optimal solutions near the constraint boundaries. The performance of the proposed algorithm is compared with Non-dominated Sorting Genetic Algorithm II (NSGA-II) on a set of single and multi-objective test problems. The results clearly indicate that the rate of convergence of the proposed algorithm is better than NSGA-II on the studied test problems. Additionally, the algorithm provides a set of marginally infeasible solutions which are of great use in trade-off studies.

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Authors and Affiliations

  1. Australian Defence Force Academy, University of New South Wales, Northcott Drive, Canberra, ACT 2600, Australia

    Tapabrata Ray, Hemant Kumar Singh, Amitay Isaacs & Warren Smith

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  1. Tapabrata Ray
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  2. Hemant Kumar Singh
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  3. Amitay Isaacs
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  4. Warren Smith
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  1. National Laboratory on Advanced Informatics (LANIA A.C.) Rébsamen 80, CENTRO, 91000, Xalapa, Veracruz, Mexico

    Efrén Mezura-Montes

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Ray, T., Singh, H.K., Isaacs, A., Smith, W. (2009). Infeasibility Driven Evolutionary Algorithm for Constrained Optimization. In: Mezura-Montes, E. (eds) Constraint-Handling in Evolutionary Optimization. Studies in Computational Intelligence, vol 198. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00619-7_7

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  • DOI: https://doi.org/10.1007/978-3-642-00619-7_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-00618-0

  • Online ISBN: 978-3-642-00619-7

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