Balancing the Influence of Evolutionary Operators for Global optimization
Diego Oliva, Erick Rodríguez-Esparza, Marcella Scoczynski Ribeiro Martins, Mohamed Abd Elaziz, Salvador Hinojosa, Ahmed A. Ewees, Songfeng Lu
Abstract
The proper use of evolutionary operators is crucial to find optimal solutions in a search space. Moreover, the diversity of the population affects the performance of Evolutionary Algorithms (EAs). This article introduces an EA called BWEAD which balances the influence of the operators. The proposal also performs a statistical analysis of the population when the diversity is low and decides which solutions might be replaced. Then BWEAD is able to explore the search space and exploit the prominent regions. The BWEAD has been tested over the CEC2014 set of benchmark functions. The experiments provide competitive results showing an improvement of 30% in 30-dimensional and 50-dimensional functions in comparison with state-of-the-art algorithms, overcoming some addressed instances and providing evidence of its capabilities on complex optimization problems.