The whale optimization algorithm (WOA) tends to suffer from slow convergence speed and
quickly falling into the local optimum. In this work, a WOA variant is proposed based on multi …

The problem of maximizing diversity or dispersion deals with selecting a subset of elements
from a given set in such a way that the distance among the selected elements is maximized …

Many fields such as data science, data mining suffered from the rapid growth of data volume
and high data dimensionality. The main problems which are faced by these fields include …

This work addresses a soft-clustered vehicle routing problem that extends the classical
capacitated vehicle routing problem with one additional constraint, that is, customers are …

Opposition-based learning (OBL) is an effective approach to improve the performance of
metaheuristic optimization algorithms, which are commonly used for solving complex …

The application of metaheuristic algorithms is one of the most promising approaches for
solving real-world problems. The marine predators algorithm (MPA) is a recently proposed …

Equilibrium Optimizer (EO) is a newly developed physics-based metaheuristic algorithm that
is based on control volume mass balance models, and has shown competitive performance …

Recently, evolutionary multi-tasking (EMT) has surfaced as a new search paradigm in the
field of evolutionary computation to solve two or more tasks simultaneously. EMT algorithms …

Moth flame optimization (MFO) algorithm proves to be an excellent choice for numerical
optimization. However, for some complex objectives, MFO may get trapped in local optima or …

Given a weighted graph with a capacity associated to each node (element), the capacitated
dispersion problem (CDP) consists in selecting a subset of elements satisfying a capacity …