Multimodal optimization problems (MMOPs) are common problems with multiple optimal
solutions. In this article, a novel method of population division, called nearest-better …

Multimodal optimization problems (MMOPs) require the algorithms to find multiple global or
local optima in a single run, which is considered a difficult task. In recent years, niching …

Multimodal optimization problems (MMOPs), in which multiple optimal solutions need to be
found for decision-makers, are common in real-world applications. Finding as many peaks …

Evolutionary algorithms rely on the population initialization strategy to determine a set of
candidate solutions, which provide the preliminary knowledge of the problem landscape for …

Evolutionary algorithms (EAs) that integrate niching techniques are among the most effective
methods for multimodal optimization problems. However, most algorithmic contributions are …

Multimodal optimization problems, which involve multiple global optima, are common in real-
world applications. So far, plenty of multimodal evolutionary algorithms (MMEAs) have been …

Environmental variability often degrades the performance of algorithms designed to capture
the global convergence of a given search space. Several approaches have been developed …

There are some natural similarities among fitness landscapes of different modals involved in
a multimodal optimization problem (MMOP). However, existing multimodal evolutionary …

Evolutionary algorithms (EAs) are successfully employed to solve complex optimization
problems such as network design problems, path finding problems, scheduling problems …

This study explores groupage delivery optimization through a two-stage GA framework. The
goal of optimization is to define locations of regional branch warehouses of Logistics …