Abstract
In this paper we have proposed a DE variant, abbreviated by ADE_CM, to improve optimization performance of DE by imposing controlled mutation strategy. We also incorporated the concept of selective pressure to choose the random vectors in the selection of donor vector for each population. Basically we used DE/rand/1 and DE/target-to-best/1 schemes (with modifications using selective pressure) in the selection of donor using controlled mutation. The control parameter for mutation, linearly decreasing with generation, is the complement of the probability of selecting DE/target-to-best/1 in each generation. The algorithm is basically a trade-off between diversity and greediness. To improve diversity scaling factor is made adaptive and also a worst p% scheme is used in the difference vector of donor. ADE_CM is tested on 25 benchmark functions of CEC 2005 in 50 and 100 dimensions. Experimental results show that this algorithm outperforms many popular DE variants on most of the functions.
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Roy, S.B., Dan, M., Mitra, P. (2012). Improving Adaptive Differential Evolution with Controlled Mutation Strategy. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Nanda, P.K. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2012. Lecture Notes in Computer Science, vol 7677. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35380-2_74
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DOI: https://doi.org/10.1007/978-3-642-35380-2_74
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