Algoritmo memético cultural para otimização de problemas de variáveis reais

Abstract

Technology has made great strides in recent years, but computing resources for certain applications need optimization so that the costs involved in solving some problems are not high. There is a very broad area of research for the development of efficient algorithms for multimodal optimization problems. In the last two decades the use of evolutionary algorithms in multimodal optimization has been shown to be a success. Among these evolutionary algorithms, which are global search algorithms, one can cite the use of Cultural Algorithms. A natural enhancement of the Cultural Algorithm is its hybridization with some other local search algorithm, so as to have the advantages of global search combined with local search. However, the local search Cultural Algorithms used for multimodal optimization are not always evaluated by efficient statistical tests. The objective of this work is to analyze the behavior of the Cultural Algorithm, with populations evolved by the Genetic Algorithm, when the local search heuristics are used: Tabu Search, Beam Search, Climbing and Simulated Annealing. One of the contributions of this work was the updating of the topographic knowledge of the cultural algorithm by the use of the triangular area defined by the best results found in the local search. To perform the analysis, a memetic algorithm was developed by hybridizing the cultural algorithm with the local search heuristics mentioned, being selected one at a time. Real world problems usually have multimodal characteristics, so the evaluations were performed using multimodal benchmark functions, which had their results evaluated by non-parametric tests. In addition, the memetic algorithm was tested on real optimization problems with constraints in the engineering areas. In the evaluations carried out, the developed Cultural Algorithm presented better results when compared to the available results of the researched scientific literature.