Feasibility preserving constraint-handling strategies for real parameter evolutionary optimization
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| Publicat a: | Computational Optimization and Applications vol. 62, no. 3 (Dec 2015), p. 851 |
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| Autor principal: | |
| Altres autors: | , |
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Springer Nature B.V.
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| Accés en línia: | Citation/Abstract Full Text Full Text - PDF |
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| Resum: | (ProQuest: ... denotes formulae and/or non-USASCII text omitted; see image) Evolutionary algorithms (EAs) are being routinely applied for a variety of optimization tasks, and real-parameter optimization in the presence of constraints is one such important area. During constrained optimization EAs often create solutions that fall outside the feasible region; hence a viable constraint-handling strategy is needed. This paper focuses on the class of constraint-handling strategies that repair infeasible solutions by bringing them back into the search space and explicitly preserve feasibility of the solutions. Several existing constraint-handling strategies are studied, and two new single parameter constraint-handling methodologies based on parent-centric and inverse parabolic probability (IP) distribution are proposed. The existing and newly proposed constraint-handling methods are first studied with PSO, DE, GAs, and simulation results on four scalable test-problems under different location settings of the optimum are presented. The newly proposed constraint-handling methods exhibit robustness in terms of performance and also succeed on search spaces comprising up-to ...... variables while locating the optimum within an error of ....... The working principle of the IP based methods is also demonstrated on (i) some generic constrained optimization problems, and (ii) a classic 'Weld' problem from structural design and mechanics. The successful performance of the proposed methods clearly exhibits their efficacy as a generic constrained-handling strategy for a wide range of applications. |
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| ISSN: | 0926-6003 1573-2894 |
| DOI: | 10.1007/s10589-015-9752-6 |
| Font: | ABI/INFORM Global |