An effective improved differential evolution algorithm to solve constrained optimization problems

An effective extended differential evolution algorithm is proposed to deal with constrained optimization problems. The proposed algorithm adopts a new mechanism to cope with constrained problems by transforming the equality into inequality first. Then, two kinds of offspring generation approaches are applied to balance the diversity and the convergence speed of the population during evolution, and seven criteria are designed to compare feasible solution over infeasible solution. The performance of the novel algorithm is evaluated on a set of well-known constrained problems from CEC2006. The experimental results are quite competitive when comparing the proposed algorithm against state-of-the-art optimization algorithms.

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