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 …

Optimization in computationally expensive numerical problems with limited function
evaluations provides computational advantages over constraints based on runtime …

The improvement of enterprise competitiveness depends on the ability to match segmented
customers in a competitive market. In this study, we propose a customer segmentation …

Recently, optimization makes an important role in our day-to-day life. Evolutionary and
population-based optimization algorithms are widely employed in several of engineering …

There are two common challenges in particle swarm optimization (PSO) research, that is,
selecting proper exemplars and designing an efficient learning model for a particle. In this …

Abstract Particle Swarm Optimization (PSO) algorithm is a nature-inspired meta-heuristic
that has been utilized as a powerful optimization tool in a wide range of applications since its …

This paper proposes a multi-swarm particle swarm optimization (MSPSO) that consists of
three novel strategies to balance the exploration and exploitation abilities. The new …

In the paper, we proposed a novel multi-swarm particle swarm optimization with dynamic
learning strategy (PSO-DLS) to improve the performance of PSO. To promote information …

In this paper, a self-organizing radial basis function (SORBF) neural network is designed to
improve both accuracy and parsimony with the aid of adaptive particle swarm optimization …

This study introduces a novel strategy that can determine the optimal values of control
parameters of a PSO. These optimal control parameters will be very valuable to all the …