Aplicação de parâmetros autoadaptativos de ajuste de domínio na otimização de funções contínuas utilizando colônia de formigas

• Ano de defesa: 2021
• Autor(a) principal: Freitas, Jairo Gervásio de
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Uberlândia; Brasil; Programa de Pós-graduação em Engenharia Elétrica
• Palavras-chave em Português: otimização por colônia de formigas; ant colony optimization; otimização de funções contínuas; continuous functions optimization; problemas de otimização; optimization problems; computação evolutiva; evolutionary computation; engenharia elétrica; electrical engineering; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO; Engenharia Elétrica; Computação; Algoritmos; Funções contínuas - Teses
• Link de acesso: https://repositorio.ufu.br/handle/123456789/33368; http://doi.org/10.14393/ufu.te.2021.596

There is a wide variety of computational methods used for solving optimization problems. Among these, there are various strategies that are derived from the concept of ant colony optimization (ACO). However, the great majority of these methods are limited-range-search algorithms, that is, they find the optimal solution, as long as the domain provided contains this solution. This becomes a limitation, due to the fact that it does not allow these algorithms to be applied successfully to real-world problems, as in the real world, it is not always possible to determine with certainty the correct domain. The search proposes the use of a broad-range search algorithm, that is, that seeks the optimal solution, with success most of the time, even if the initial domain provided does not contain this solution, as the initial domain provided will be adjusted until it finds a domain that contains the solution. This algorithm called ARACO, derived from RACO, makes for the obtaining of better results possible, through strategies for accelerating the parameters responsible for adjusting the supplied domain at opportune moments and, in case there is a stagnation of the algorithm, expansion of the domain around the best solution found to prevent the algorithm becoming trapped in a local minimum. Through these strategies, ARACO obtains better results than its predecessors, in relation to the number of function evaluations necessary to find the optimal solution, in addition to its one hundred percent success rate in practically all the tested functions, thus demonstrating itself as being a high performance and reliable algorithm. The algorithm has been tested on some classic benchmark functions and also on the benchmark functions of the IEEE Congress of Evolutionary Computation Benchmark Test Functions (CEC 2019 100-Digit Challenge).