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

Abstract

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.