Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm
motivated by intelligent collective behavior of some animals such as flocks of birds or …

This paper presents an overview of the research progress in Particle Swarm Optimization
(PSO) during 1995–2017. Fifty two papers have been reviewed. They have been …

A large number of intelligent algorithms based on social intelligent behavior have been
extensively researched in the past few decades, through the study of natural creatures, and …

Many evolutionary computation (EC) methods have been used to solve feature selection
problems and they perform well on most small-scale feature selection problems. However …

Abstract Grey Wolf Optimizer (GWO) algorithm is a relatively new algorithm in the field of
swarm intelligence for solving continuous optimization problems as well as real world …

Differential evolution (DE) is one of the most popular and efficient evolutionary algorithms for
numerical optimization and it has gained much success in a series of academic benchmark …

Social learning in particle swarm optimization (PSO) helps collective efficiency, whereas
individual reproduction in genetic algorithm (GA) facilitates global effectiveness. This …

In population-based optimization algorithms (POAs), given an optimization problem, the
quality of the solutions depends heavily on the selection of algorithms, strategies and …

Optimization techniques have been indispensable for designing high-performance meta-
devices targeted to a wide range of applications. In fact, today optimization is no longer an …

Many optimization problems in reality have become more and more complex, which promote
the research on the improvement of different optimization algorithms. The particle swarm …