In order to effectively schedule railway train delay, an adaptive cooperative co-evolutionary
differential evolution with dynamic hybrid mechanism of the quantum evolutionary algorithm …

Differential evolution (DE) is one of the most efficient evolutionary algorithms for solving
numerical optimization problems; however, it still suffers from premature convergence and …

As optimization problems become more and more complex in real-world scenarios, the
effectiveness of many existing differential evolution (DE) methods is critically challenged. To …

In this study, a selection strategy based on discarded trial vectors was constructed to
improve the performance of the differential evolution (DE) algorithm. The proposed strategy …

Nowadays, the differential evolution (DE) achieved noticeable progress and solved a wide
range of non-linear/non-convex engineering optimization issues. As a strong optimizer, DE …

Differential evolution (DE) algorithms tend to be limited to local optimization when solving
complex optimization problems. Different iteration schemes lead to different convergence …

In recent years, differential evolution algorithms based on archives have achieved significant
success because archives can increase population diversity and balance the exploration …

Differential evolution (DE) is an efficient global optimization algorithm. However, due to its
random properties, some individuals may mutate in the direction of deviating from the …

This study addresses the bi-objective traffic light scheduling problem (TLSP), which aims to
minimize the network-wise delay time of all vehicles and pedestrians within a predefined …

Abstract Several distinctive Pareto Sets (PSs) with an identical Pareto Front (PF) and local
PSs with acceptable quality are comprised in multimodal multi-objective optimization …