Abstract
Nature-inspired optimization is the field of study for planning, simulation, and execution of problems using scientific methodologies. In this paper, a novel mutation-based modified differential evolution (DE) algorithm has been proposed. Enhance-based adaption mutation operator helps in avoiding the local optimum problem. The proposed approach is named as enhance-based adaption (EBA) in the existing mutation vector to provide more diversity for selecting effective mutant solutions. The proposed approach provides more promising solutions to guide the evolution and helps DE escaping the situation of the local optimum problem. Comparisons with other DE variants such as CPI-DE, TSDE, ToPDE, MPEDE, and JADEcr establish that the proposed Environment adaption-based operator is able to improve the performance of differential evolution algorithms.
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Singh, S.P., Singh, D.K. (2020). Differential Evolution Algorithm Using Enhance-Based Adaption Mutant Vector. In: Kolhe, M., Tiwari, S., Trivedi, M., Mishra, K. (eds) Advances in Data and Information Sciences. Lecture Notes in Networks and Systems, vol 94. Springer, Singapore. https://doi.org/10.1007/978-981-15-0694-9_22
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DOI: https://doi.org/10.1007/978-981-15-0694-9_22
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