Multimodal multi-objective problems (MMOPs) commonly arise in the real world where
distant solutions in decision space correspond to very similar objective values. To obtain …

Multimodal multiobjective optimization aims to find all Pareto optimal solutions, including
overlap** solutions in the objective space. Multimodal multiobjective optimization has …

Multi-modal multi-objective optimization problem (MMOPs) has attracted more and more
attention in evolutionary computing recently. It is not easy to solve these problems using the …

This paper presents a new particle swarm optimizer for solving multimodal multiobjective
optimization problems which may have more than one Pareto-optimal solution …

Multimodal multiobjective problems (MMOPs) commonly arise in real-world situations where
distant solutions in decision space share a very similar objective value. Traditional …

In multiobjective optimization, the relationship between decision space and objective space
is generally assumed to be a one-to-one map**, but it is not always the case. In some …

As an effective evolutionary algorithm, particle swarm optimization (PSO) has been widely
used to solve single or multi-objective optimization problems. However, the performance of …

There have been few researches on solving multimodal multiobjective optimization
problems, whereas they are commonly seen in real-world applications but difficult for the …

Multimodal multiobjective problems (MMOPs) arise frequently in the real world, in which
multiple Pareto-optimal solution (PS) sets correspond to the same point on the Pareto front …

This article suggests a multimodal multiobjective evolutionary algorithm with dual clustering
in decision and objective spaces. One clustering is run in decision space to gather nearby …