Numerous optimization problems can be addressed using metaheuristics instead of
deterministic and heuristic approaches. This study proposes a novel population-based …

Due to the global progress, the optimization problems are becoming more and more
complex. Hence, deterministic and heuristic approaches are no longer adequate for dealing …

The “curse of dimensionality” and the high computational cost have still limited the
application of the evolutionary algorithm in high-dimensional feature selection (FS) …

Learning and optimization are the two essential abilities of human beings for problem
solving. Similarly, computer scientists have made great efforts to design artificial neural …

In the mid 1990s two landmark metaheuristics have been proposed: Particle Swarm
Optimization and Differential Evolution. Their initial versions were very simple, but rapidly …

As a powerful optimization technique, multi-objective particle swarm optimization algorithms
have been widely used in various fields. However, performing well in terms of convergence …

Particle swarm optimization (PSO) is one of the most well-regard metaheuristics with
remarkable performance when solving diverse optimization problems. However, PSO faces …

Evolutionary multitask optimization is an emerging research topic that aims to solve multiple
tasks simultaneously. A general challenge in solving multitask optimization problems …

Large-scale optimization has become a significant and challenging research topic in the
evolutionary computation (EC) community. Although many improved EC algorithms have …

Particle swarm optimization (PSO) is a very simple and effective metaheuristic algorithm.
Search operators with similar behavior may lead to the loss of diversity in the search space …