In recent years, swarm intelligence algorithms have received extensive attention and
research. Swarm intelligence algorithms are a biological heuristic method, which is widely …

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 …

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 …

Feature selection has been considered as an effective method to solve imbalanced
classification problems. It can be formulated as a multiobjective optimization problem (MOP) …

Abstract Multimodal Multi-objective Optimization Problems (MMOPs) refer to the problems
that have multiple Pareto-optimal solution sets in decision space corresponding to the same …

In the multimodal multi-objective optimization problems (MMOPs), there may exist two or
multiple equivalent Pareto optimal sets (PS) with the same Pareto Front (PF). The difficulty of …

Recently, multimodal multi-objective problem (MMOP) has become a popular research field
in multi-objective optimization problems. Multimodal multi-objective optimization problem …

This paper proposes a self-organized speciation based multi-objective particle swarm
optimizer (SS-MOPSO) to locate multiple Pareto optimal solutions for solving multimodal …

In the multimodal multi-objective optimization problems (MMOPs), there exists more than
one Pareto optimal solutions in the decision space corresponding to the same location on …