A Decomposition-based Large-scale Multi-modal Multi-objective Optimization Algorithm
This work addresses the challenge of solution diversity for researchers and practitioners in optimization, but it is incremental as it builds on the widely used MOEA/D algorithm.
The paper tackles the problem of preserving diverse Pareto optimal solutions in large-scale multi-modal multi-objective optimization by proposing an algorithm based on MOEA/D with sub-populations, clearing mechanisms, and greedy removal strategies, resulting in effective diversity preservation in the decision space.
A multi-modal multi-objective optimization problem is a special kind of multi-objective optimization problem with multiple Pareto subsets. In this paper, we propose an efficient multi-modal multi-objective optimization algorithm based on the widely used MOEA/D algorithm. In our proposed algorithm, each weight vector has its own sub-population. With a clearing mechanism and a greedy removal strategy, our proposed algorithm can effectively preserve equivalent Pareto optimal solutions (i.e., different Pareto optimal solutions with same objective values). Experimental results show that our proposed algorithm can effectively preserve the diversity of solutions in the decision space when handling large-scale multi-modal multi-objective optimization problems.