A Preliminary Study of Adaptive Indicator Based Evolutionary Algorithm for Dynamic Multiobjective Optimization via Autoencoding

Wei Zhou, Liang Feng, Siwei Jiang, Shu Zhang, Yaqing Hou, Yew-Soon Ong, Zexuan Zhu, Kai Liu

Abstract: Dynamic multi-objective optimization problem (D-MOP) is widely existed in many real-world applications. Over the years, DMOP has attracted many research attentions in the literature. The adaptive indicator-based evolutionary algorithm (IBEA2) is a recently proposed multi-objective evolutionary algorithm (MOEA). It has demonstrated strong search capability on commonly used multi-objective benchmarks over state-of-the-art MOEAs. However, as the adaptation of parameter $k$ is based on the selected solutions with maximum hypervolume, this mechanism will be inappropriate if the problem changes over time. The reason is that the solutions with high hypervolume at one particular time instance may not be with high hypervolume at another if the problem changed. Keeping this in mind, inspired by the recent autoencoding evolutionary search, which is able to transfer the past search experiences to improve the evolutionary search on unseen problems, in this paper, we propose to extend the IBEA2 by adapting k with transferred high hypervolume solutions obtained before the dynamic change occurs, for solving DMOP. To evaluate the proposed method, empirical comparisons on the commonly used Farina-Deb-Amato (FDA) DMOP benchmarks, against both the IBEA2 and one recently proposed dynamic MOEA, are presented.