Enhanced competitive differential evolution for constrained optimization

The constrained optimization with differential evolution (DE) is addressed. A novel variant of competitive differential evolution with a hybridized search of feasibility region is proposed, where opposition-based optimization and adaptive controlled random search are combined. Various variants of the algorithm are experimentally compared on the benchmark set developed for the special session of IEEE Congress of Evolutionary Computation (CEC) 2010. The results of the enhanced competitive DE show effective search of feasible solutions, in difficult problems significantly better than the competitive DE variant presented at CEC 2010.

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