Over the last two decades, many different evolutionary algorithms (EAs) have been proposed for solving optimization problems. However, no single EA has consistently been the best for solving a wide range of them. In the literature, this drawback has been tackled by using multiple EAs in a single framework. In this paper, a new multi-method based EA that utilizes the search ability of multi-operator differential evolution algorithm (MODE) and covariance matrix adaptation evolution strategy CMA-ES algorithm in a single framework, has been presented, with the orthogonal experimental design (OED) and factor analysis (FA) used to select the proper combination of mutation strategies, control parameters adaptation strategies, and crossover operators. To judge the performance of this algorithm, 30 problems are solved from the CEC2017 competition and their results are analyzed.
|Number of pages||8|
|Publication status||Published - 5 Jul 2017|
|Event||2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Donostia-San Sebastian, Spain|
Duration: 5 Jun 2017 → 8 Jun 2017
|Conference||2017 IEEE Congress on Evolutionary Computation, CEC 2017|
|Period||5/06/17 → 8/06/17|