Differential Evolution with automatic population injection scheme for constrained problems

Over the last few years, Differential Evolution (DE) algorithms have shown brilliant performance in solving a wide variety of complex optimization problems. However, there is no guarantee that these algorithms will not be trapped in local optima for some problems. In this paper, a DE algorithm is proposed that uses a new mechanism to escape from local optima, during the evolution process by injecting new individuals, when the algorithm gets stuck in local optima. The performance of the algorithm is analyzed by solving a well-known set of constrained optimization problems. The algorithm shows consistent performance, and is superior to several state-of-the-art algorithms.

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