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...
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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.
Surrogate models are effective in reducing the computational time required for solving optimization problems. However, there have been a lukewarm interest in finding multiple trade-off solutions for multi-objective op...
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ISBN:
(纸本)9781450342063
Surrogate models are effective in reducing the computational time required for solving optimization problems. However, there have been a lukewarm interest in finding multiple trade-off solutions for multi-objective optimization problems using surrogate models. The literature on surrogate modeling for constrained optimization problems is also rare. The difficulty lies in the requirement of building and solving multiple surrogate models, one for each Pareto-optimal solution. In this paper, we first provide a brief introduction of the past studies and suggest a computationally fast, Kriging-based, and generative procedure for finding multiple near Pareto-optimal solutions in a systematic manner. The expected improvement metric is maximized using a real-parametergenetic algorithm for finding new solutions for high-fidelity evaluations. The approach is computationally fast due to the interlinking of building multiple surrogate models and in its systematic sequencing methodology for assisting one model with another. In standard two and three-objective test problems with and without constraints, our proposed methodology takes only a few hundreds of high-fidelity solution evaluations to find a widely distributed near Pareto-optimal solutions compared to the standard EMO methods requiring tens of thousands of high-fidelity solution evaluations. The framework is generic and can be extended to utilize other surrogate modeling methods easily.
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