Adaptive ε-Constraint Multi-Objective Evolutionary Algorithm Based on Decomposition and Differential Evolution

Abstract

To improve distribution and convergence of the obtained solution set in constrained multi-objective optimization problems, this paper presents an adaptive $\varepsilon $ -constraint multi-objective evolutionary algorithm based on decomposition and differential evolution ( $\varepsilon $ -MOEA/D-DE). First, an adaptive $\varepsilon $ -constraint strategy based on both evolution generation and constraint violation is designed to make better use of excellent evolution individuals and improve population diversity. Then, an adaptive differential evolution (DE) mutation strategy with full utilization of infeasible individuals is proposed to increase search efficiency and avoid falling into the local optimum. Finally, a replacement mechanism is suggested to take advantage of the infeasible individuals in the population with better objective function values and constraint violation degree, and thus both diversity and convergence are well coordinated. A comparative experiment with four other excellent constrained multi-objective algorithms was implemented on standard constrained multi-objective optimization problems (CF series), and the results showed that the diversity and convergence of our algorithm were both improved.