| While research on constrained optimization using evolutionary algorithms has been actively pursued, it has had to face the problems that the ability to solve multi-modal problems, which have many local solutions within a feasible region, is insufficient, that the ability to solve problems with equality constraints is inadequate, and that the stability and efficiency of searches is low. In this study, we propose a new constrained optimization algorithm εDE, defined by applying the ε constrained method to a differential evolution (DE). DE is a simple, fast and stable population based search algorithm that is robust to multi-modal problems. Also, a new and simple way of controlling relaxation of constraints is proposed for theε constrained method to solve problems with equality constraints. The εDE realizes stable and efficient searches that can solve multi-modal problems and those with equality constraints. The advantage of the εDE is shown by applying it to thirteen constrained problems of various types and comparing the results to those obtained by other methods. |
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