Handling dynamic multiobjective optimization problems with variable environmental change via classification prediction and dynamic mutation

Abstract

This paper proposes an adaptive dynamic multiobjective optimization algorithm for handling dynamic multiobjective optimization problems with variable environmental change types. Most of the existing dynamic multiobjective optimization problems (DMOPs) only deal with a single change type in the environment. Therefore, we design a set of DMOPs that has variable and mixed change types. Next, this paper proposes an adaptive dynamic multiobjective optimization algorithm (DMOA) focusing on the change types, to solve DMOPs with variable change types. It can detect the different types of environmental changes. The main purpose of a DMOA is to find the Pareto-optimal set (PS) of each environment. Therefore, the change types of DMOPs mainly contain two categories: PS changes over time and PS remains constant. After detecting the change type, an adaptive response strategy is activated to react to environmental changes. If PS changes over time, a classification prediction (CP) strategy is active to respond to environmental changes. If PS remains constant, a dynamic mutation (DM) strategy works to react to environmental changes. The proposed algorithm is extensively studied through comparison with several advanced DMOAs, thereby demonstrating its effectiveness in working out complex DMOPs with variable change types and on the parameter-tuning problem of PID controllers for dynamic systems.