Abstract
Many optimization problems that involve practical applications have functional constraints, and some of these constraints are active, meaning that they prevent any solution from improving the objective function value beyond the constraint limits. Therefore, the optimal solution usually lies on the boundary of the feasible region. In order to converge faster when solving such problems, a new ranking and selection scheme is introduced which exploits this feature of constrained problems. In conjunction with selection, a new crossover method is also presented based on three parents. When comparing the results of this new algorithm with four other evolutionary based methods, using nine benchmark problems from the relevant literature, it shows very encouraging performance.
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Elfeky, E.Z., Sarker, R.A., Essam, D.L. (2006). A Simple Ranking and Selection for Constrained Evolutionary Optimization. In: Wang, TD., et al. Simulated Evolution and Learning. SEAL 2006. Lecture Notes in Computer Science, vol 4247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11903697_68
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DOI: https://doi.org/10.1007/11903697_68
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-47331-2
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