  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.
