A hybrid shuffled complex evolution approach based on differential evolution for unconstrained optimization

作者：Mariani Viviana Cocco^*; Justi Luvizotto Luiz Guilherme; Guerra Fabio Alessandro; Coelho Leandro dos Santos

来源：Applied Mathematics and Computation, 2011, 217(12): 5822-5829.

DOI：10.1016/j.amc.2010.12.064

• Shuffled complex evolution algorithm
• Genetic algorithm
• Differential evolution
• Unconstrained optimization
• Evolutionary algorithms
• Benchmark functions

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.

• 出版日期2011-2-15