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 …

In real world applications, there are many multi-objective optimization problems. Most
existing multi-objective optimization algorithms focus on improving the diversity, spread and …

This paper proposes a multimodal multiobjective Differential Evolution optimization
algorithm (MMODE). The technique is conceived for deployment on problems with a Pareto …

We propose an extension of Newton's method for unconstrained multiobjective optimization
(multicriteria optimization). This method does not use a priori chosen weighting factors or …

There may exist more than one Pareto optimal solution with the same objective vector to a
multimodal multiobjective optimization problem (MMOP). The difficulties in finding such …

Most existing multiobjective evolutionary algorithms aim at approximating the Pareto front
(PF), which is the distribution of the Pareto-optimal solutions in the objective space. In many …

Evolutionary multimodal multiobjective optimization aims to search for a set of Pareto
optimal solutions that are well distributed in both the objective and decision spaces. In …

This paper proposes a novel decomposition-based evolutionary algorithm for multi-modal
multi-objective optimization, which is the problem of locating as many as possible (almost) …

Multimodal multiobjective optimization problems (MMOPs) are commonly seen in real-world
applications. Many evolutionary algorithms have been proposed to solve continuous …