A hybridization of differential evolution and monarch butterfly optimization for solving systems of nonlinear equations. Issue 3 (25th October 2018)

• Record Type: Journal Article
• Authors: Ibrahim, Abdelmonem M.; Tawhid, Mohamed A.
• Is Part Of: Journal of computational design and engineering. Volume 6:Issue 3(2019)
• Page Start: 354
• Page End: 367
• Publication Date: 2018-10-25
• Subjects: Hybrid algorithm -- Metaheuristics -- Nonlinear system of equations -- Differential evolution -- Monarch butterfly optimization; Engineering -- Data processing -- Periodicals; Computer-aided design -- Periodicals; Computer-aided design; Engineering -- Data processing; Electronic journals; Electronic journals; Periodicals; 620.0042
• DOI: 10.1016/j.jcde.2018.10.006
• Languages: English
• ISSNs: 2288-4300

Abstract

Abstract: In this study, we propose a new hybrid algorithm consisting of two meta-heuristic algorithms; Differential Evolution (DE) and the Monarch Butterfly Optimization (MBO). This hybrid is called DEMBO. Both of the meta-heuristic algorithms are typically used to solve nonlinear systems and unconstrained optimization problems. DE is a common metaheuristic algorithm that searches large areas of candidate space. Unfortunately, it often requires more significant numbers of function evaluations to get the optimal solution. As for MBO, it is known for its time-consuming fitness functions, but it traps at the local minima. In order to overcome all of these disadvantages, we combine the DE with MBO and propose DEMBO which can obtain the optimal solutions for the majority of nonlinear systems as well as unconstrained optimization problems. We apply our proposed algorithm, DEMBO, on nine different, unconstrained optimization problems and eight well-known nonlinear systems. Our results, when compared with other existing algorithms in the literature, demonstrate that DEMBO gives the best results for the majority of the nonlinear systems and unconstrained optimization problems. As such, the experimental results demonstrate the efficiency of our hybrid algorithm in comparison to the known algorithms.