Dynamic Multi-objective Optimization Using Computational Intelligence Algorithms

Abstract

Multi-objective optimization problems (MOPs) have multiple, often conflicting objectives where an improvement in one objective leads to the worsening of at least one other objective. The goal of a multi-objective algorithm (MOA) is to find a set of optimal trade-off solutions that is both accurate and diverse. However, many real-world problems are dynamic in nature where at least one objective and/or constraint changes over time. A dynamic multi-objective algorithm (DMOA) must therefore be able to track the changing set of optimal trade-off solutions over time. This chapter highlights issues that have to be addressed when evaluating the performance of DMOAs. It discusses areas that require further research, including decision making and analyzing the behavior of DMOAs. Emerging areas, and how they can impact on research in the field of dynamic multi-objective optimization (DMOO), are also highlighted.