The Distributed Differential Evolution (dDE) algorithm is a natural extension of the Differential Evolution (DE) algorithm, which is a recent addition to the Evolutionary Algorithms (EAs) pool, in the Evolutionary Computing (EC) field of computer science. The algorithmic novelty of the dDE algorithm is well evident in the literature. However, the theoretical studies on the performance of the dDE algorithms are scarcely reported. This paper is an attempt to analyze the performance of the dDE algorithm with a theoretical study. A theoretical equation, to measure the population diversity of the sub-population of the dDE algorithm, after migration, is derived and the validity of the same is verified with a simple distributed framework of dDE with two sub-population.
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R. Raghu and Dr. Jeyakumar G., “Empirical analysis on the population diversity of the sub-population in distributed differential evolution algorithm”, In Proceedings of Springer International Conference on Soft Computing Systems, International Journal of Control Theory and Applications, vol. 8, no. 5, pp. 1809-1816, 2016.