Title: A novel differential evolution approach for constraint optimisation

Authors: Pooja; Praveena Chaturvedi; Pravesh Kumar; Amit Tomar

Addresses: Department of Computer Science, Gurukula Kangri Vishwavidyalaya, Haridwar, India ' Department of Computer Science, Gurukula Kangri Vishwavidyalaya, Haridwar, India ' Department of Mathematics, Rajkiya Engineering College, Bijnor, India ' Department of Mathematics, Amity University, Noida, India

Abstract: In the present study, a modified DE framework is proposed, which is a fusion of two modifications in the parent DE: 1) self-adaptive control parameter; 2) single population structure. Both the concepts are used to modify the parent DE that improves the convergence rate without compromising on quality of the solution. While self-adaptive control parameters are used to get a good quality solution, the single population structure helps in faster convergence as reducing the memory and computational efforts. The resultant algorithm, named NDE, found by application of these concepts balances the exploration and exploitation of the parent DE algorithm. The validation of the performance of the proposed NDE algorithm is drawn on a set of benchmark test functions and is compared to several other state-of-the-arts of DE variants. Numerical results pointed out that the proposed NDE algorithm is better than or at least comparable to the parent DE algorithm.

Keywords: differential evolution; control parameters; population structure; constrained test problems.

DOI: 10.1504/IJBIC.2018.096459

International Journal of Bio-Inspired Computation, 2018 Vol.12 No.4, pp.254 - 265

Accepted: 27 Dec 2017
Published online: 29 Nov 2018 *

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