Cooperative particle swarm optimization with reference-point-based prediction strategy for dynamic multiobjective optimization

Abstract

Dynamic multiobjective optimization problems (DMOPs) have received increasing attention in the evolutionary community in recent years. The problem environment of a DMOP dynamically changes over time, causing the movement of the Pareto front (PF). It is critical but challenging to find the new PF in a new environment by reusing historical information of past environments since the successive environments are often relevant. Thus, we propose a new cooperative particle swarm optimization with a reference-point-based prediction strategy to solve DMOPs. In the proposed method, multiple swarms cooperate to approximate the whole PF with a new learning strategy in dynamic environments. Specially, when the environment is changed, the outdated particles are relocated based on the PF subparts they belong to using the novel reference-point-based prediction strategy. The proposed algorithm has been evaluated on the very recent scalable dynamic problem test suite with different numbers of objectives and different change severity. Experimental results show that the proposed algorithm is competitive to other typical state-of-the-art dynamic multiobjective algorithms and can find well-diversified and well-converged solution sets in dynamic environments.