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A hybrid shuffled complex evolution approach based on differential evolution for unconstrained optimization |
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| Authors: | Viviana Cocco Mariani Luiz Guilherme Justi Luvizotto Leandro dos Santos Coelho |
| Affiliation: | a Department of Mechanical Engineering, PPGEM, Pontifical Catholic University of Paraná, PUCPR, Imaculada Conceição, 1155, 80215-901 Curitiba, Paraná, Brazil b Industrial and Systems Engineering Graduate Program, PPGEPS, Pontifical Catholic University of Paraná, PUCPR, Imaculada Conceição, 1155, 80215-901 Curitiba, Paraná, Brazil c Department of Electrical Engineering, PPGEE Federal University of Paraná, UFPR, Polytechnic Center, 81531-970 Curitiba, Paraná, Brazil d Institute of Technology for Development (LACTEC), Eletroelectronics Department (DPEE), Electrical System Division (DVSE), Federal University of Paraná, UFPR, BR 116, Km 98, Jardim das Américas, 81531-980 Curitiba, Paraná, Brazil |
| Abstract: | Numerous optimization methods have been proposed for the solution of the unconstrained optimization problems, such as mathematical programming methods, stochastic global optimization approaches, and metaheuristics. In this paper, a metaheuristic algorithm called Modified Shuffled Complex Evolution (MSCE) is proposed, where an adaptation of the Downhill Simplex search strategy combined with the differential evolution method is proposed. The efficiency of the new method is analyzed in terms of the mean performance and computational time, in comparison with the genetic algorithm using floating-point representation (GAF) and the classical shuffled complex evolution (SCE-UA) algorithm using six benchmark optimization functions. Simulation results and the comparisons with SCE-UA and GAF indicate that the MSCE improves the search performance on the five benchmark functions of six tested functions. |
| Keywords: | Shuffled complex evolution algorithm Genetic algorithm Differential evolution Unconstrained optimization Evolutionary algorithms Benchmark functions |
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