Challenges of Dynamic Multi-objective Optimisation

Dynamic multi-objective optimisation (DMOO) entails solving optimisation problems with more than one objective, where at least one objective changes over time. Normally at least two of the objectives are in conflict with one another. Therefore, a single solution does not exist and the goal of an algorithm is to find for each environment a set of solutions that are both diverse and as close as possible to the optimal trade-off solution set. Solving dynamic multi-objective optimisation problems (DMOOPs) is not a trivial task, since the field of DMOO has many challenges. This paper highlights these challenges, namely the selection of benchmark functions and performance measures, the analyses of obtained results and selecting a preferred solution from the set of trade-off solutions. In addition, this paper discusses emerging research areas within computational intelligence (CI), such as hyper-heuristics, constrained optimisation, many-objective optimisation, self-adapting algorithms and formal analysis of fitness landscapes, highlighting research areas within the field of DMOO that should be addressed in future work.