A New Dynamic Multi-objective Evolutionary Algorithm without Change Detector
Elaine Guerrero-Peña, A.F.R. Araujo · 2019
A Dynamic Multi-Objective Evolutionary Algorithm (DMOEA) usually detects a change in an environment and responds to its dynamics, which can lead to new optimal solutions over time. However, in some real problems, correct change detection cannot be guaranteed. The existing methods can miss changes when there is noise in the landscape, or they can yield false positives, demanding an algorithm to respond to a nonexistent new scenario. To handle DMOPs without such detection, a new DMOEA was proposed in which diversity is inserted into the population by a Gaussian Mixture Model-based Local Search (GMM-LS) strategy depending on a new condition based on the HyperVolume metric, triggered independently of occurrences of changes. The parameters of the GMM are determined using Variational Inference. The experiments were performed on FDA1-5 and dMOPl -2, and in a real-world problem. The experimental results suggest the efficacy of the method.