A Mahalanobis Distance-Based Approach for Dynamic Multiobjective Optimization With Stochastic Changes
针对现有算法难以处理随机环境变化的动态多目标优化问题，提出基于马氏距离的方法，通过评估历史环境与新环境的关系来加速收敛并保持种群多样性，实验表明该方法优于最新算法。
In recent years, researchers have made significant progress in handling dynamic multiobjective optimization problems (DMOPs), particularly for environmental changes with predictable characteristics. However, little attention has been paid to DMOPs with stochastic changes. It may be difficult for existing dynamic multiobjective evolutionary algorithms (DMOEAs) to effectively handle this kind of DMOPs because most DMOEAs assume that environmental changes follow regular patterns and consecutive environments are similar. This article presents a Mahalanobis distance-based approach (MDA) to deal with DMOPs with stochastic changes. Specifically, we make an all-sided assessment of search environments via Mahalanobis distance on saved information to learn the relationship between the new environment and historical ones. Afterward, a change response strategy applies the learning to the new environment to accelerate the convergence and maintain the diversity of the population. Besides, the change degree is considered for all decision variables to alleviate the impact of stochastic changes on the evolving population. An MDA has been tested on stochastic DMOPs with two to four objectives. The results show that MDA performs significantly better than the other latest algorithms in this article, suggesting that MDA is effective for DMOPs with stochastic changes.