A probability collectives approach with a feasibility-based rule for constrained optimization
Applied Computational Intelligence and Soft Computing
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In this paper, a modified Differential Evolution (MDE) is proposed for solving the Integer Programming problems. In order to increase the probability of each parent to generate a better offspring, each solution is allowed to generate more than one offspring through six different mutation operators. A migration operator is designed to overcome premature convergence of DE. In practical applications, most optimization problems have complex constraints. Three criteria based on feasibility are used to deal with the constraints of the problem. Numerical examples are given to illustrate the effectiveness of the proposed algorithm. The comparison results demonstrate that the proposed algorithm is superior to the other methods compared in terms of convergence speed and solution quality. More importantly, it can solve high dimensional problems as well as constrained problems.