A Multiple Reference Point-based evolutionary algorithm for dynamic multi-objective optimization with undetectable changes

Abstract

Dynamic multi-objective optimization problems involve the simultaneous optimization of several competing objectives where the objective functions and/or constraints may change over time. Evolutionary algorithms have been considered as popular approaches to solve such problems. Despite the considerable number of studies reported in evolutionary optimization in dynamic environments, most of them are restricted to the single objective case. Moreover, the majority of dynamic multi-objective optimization algorithms are based on the use of some techniques to detect or predict changes which is sometimes difficult or even impossible. In this work, we address the problem of dynamic multi-objective optimization with undetectable changes. To achieve this task, we propose a new algorithm called Multiple Reference Point-based Multi-Objective Evolutionary Algorithm (MRP-MOEA) which does not need to detect changes. Our algorithm uses a new reference point-based dominance relation ensuring the guidance of the search towards the Pareto optimal front. The performance of our proposed method is assessed using various benchmark problems. Furthermore, the comparative experiments show that MRP-MOEA outperforms serveral dynamic multi-objective optimization algorithms not only in tracking the Pareto front but also in maintainig diversity over time albeit the changes are undetectable.