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. This thesis analyzes the behavior of the Cultural Algorithm, with populations evolved by the Genetic Algorithm, when 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. For the analysis, a Memetic Algorithm was developed by hybridizing the Cultural Algorithm with the local search heuristics mentioned, applied one at a time. The evaluations were carried out using multimodal benchmark functions and real constrained optimization problems in engineering areas. The results showed that the developed Cultural Memetic Algorithm presented better results compared to those available in the researched scientific literature.