Multi-objective evolutionary algorithms (MOEAs) have shown promising performance in
solving various optimization problems, but their performance may deteriorate drastically …

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 …

In this paper, a novel swarm-based metaheuristic algorithm inspired from the behaviors of
beluga whales, called beluga whale optimization (BWO), is presented to solve optimization …

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 …

Handling constrained multiobjective optimization problems (CMOPs) is extremely
challenging, since multiple conflicting objectives subject to various constraints require to be …

When addressing constrained multiobjective optimization problems (CMOPs) via
evolutionary algorithms, various constraints and multiple objectives need to be satisfied and …

Hypervolume is widely used as a performance indicator in the field of evolutionary
multiobjective optimization (EMO). It is used not only for performance evaluation of EMO …

The ongoing advancements in network architecture design have led to remarkable
achievements in deep learning across various challenging computer vision tasks …

Inthis study, a flexible job shop scheduling problem with time-of-use electricity price
constraint is considered. The problem includes machine processing speed, setup time, idle …

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