Tutorial: Evolutionary Computation for Dynamic Multi-Objective Optimization Problems

Many real-world optimization problems involve multiple conflicting objectives to be optimized and are subject to dynamic environments, where changes may occur over time regarding optimization objectives, decision variables, and/or constraint conditions. Such dynamic multi-objective optimization problems (DMOPs) are challenging problems due to their nature of difficulty. Yet, they are important problems that researchers and practitioners in decision-making in many domains need to face and solve. Evolutionary computation (EC) encapsulates a class of stochastic optimization methods that mimic principles from natural evolution to solve optimization and search problems. EC methods are good tools to address DMOPs due to their inspiration from natural and biological evolution, which has always been subject to changing environments. EC for DMOPs has attracted a lot of research effort during the last two decades with some promising results. However, this research area is still quite young and far away from well-understood. This tutorial provides an introduction to the research area of EC for DMOPs and carry out an in-depth description of the state-of-the-art of research in the field. The purpose is to (i) provide detailed description and classification of DMOP benchmark problems and performance measures; (ii) review current EC approaches and provide detailed explanations on how they work for DMOPs; (iii) present current applications in the area of EC for DMOPs; (iv) analyse current gaps and challenges in EC for DMOPs; and (v) point out future research directions in EC for DMOPs.