IVF/NSGA-III: Uma Metaheurística Evolucionária Many-Objective com Busca Guiada por Balizas e Fertilização In Vitro

• Ano de defesa: 2024
• Autor(a) principal: Sampaio, Sávio Menezes
• Orientador(a): Camilo Junior, Celso Gonçalves , Camilo Junior
• Banca de defesa: Camilo Junior, Celso Gonçalves, Lima Neto, Fernando Buarque de, Leite, Karla Tereza Figueiredo, Rodrigues, Vagner José do Sacramento, Oliveira, Sávio Salvarino Teles de
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• dARK ID: ark:/38995/001300000fn4t
• Idioma: por
• Instituição de defesa: Universidade Federal de Goiás
• Programa de Pós-Graduação: Programa de Pós-graduação em Ciência da Computação (INF)
• Departamento: Instituto de Informática - INF (RMG)
• País: Brasil
• Palavras-chave em Português: Busca guiada por balizas; In Vitro Fertilization Method; Algoritmos Meméticos multi-objetivo e Many-Objective; Algoritmos evolucionários multi-objetivo e ManyObjective; Algoritmos genéticos; Evolução diferencial; NSGA-III
• Palavras-chave em Inglês: Beacon-guided search; In Vitro Fertilization Method; Multi-objective and Many-objective Memetic Algorithms; Multi-Objective and Many-Objective Evolutionary Algorithms; Genetic algorithms; Differential evolution; NSGA-III
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://repositorio.bc.ufg.br/tede/handle/tede/13650

Sampaio, Sávio Menezes. The In Vitro Fertilization Genetic Algorithm (IVF/GA) demonstrates robust applicability to single-objective optimization problems, particularly those that are complex and multimodal. This work proposes the expansion of the IVF method to many-objective optimization, which deals with more than three simultaneous objectives. The study introduces new activation criteria, selection, assisted exploration, and transfer mechanisms, consolidating innovation through the integration of the IVF method with NSGA-III, here referred to as IVF/NSGA-III. This approach incorporates the Beacon-Guided Search strategy in a Steady State configuration, aiming to overcome the inherent challenges of many-objective optimization. It focuses on dynamic convergence to promising regions of the solution space and adopts an adaptive scale factor within the context of Differential Evolution, providing an alternative methodology to conventional intensification methods. Experiments conducted with the many-objective benchmarks DTLZ, MaF, WFG show that IVF/NSGA-III significantly enhances performance compared to the standard NSGA-III algorithm across various tested problems, validating its potential as a valuable contribution to the field of Many-Objective Evolutionary Algorithms (MOEAs). The study suggests new directions for the development of many-objective memetic strategies and offers significant insights for researchers seeking more effective and adaptable optimization methods.. Goiânia-GO, 2024. 220p. PhD. Thesis Relatório de Graduação. Instituto de Informática, Universidade Federal de Goiás.