Particle swarm optimization (PSO) algorithm is generally improved by adaptively adjusting
the inertia weight or combining with other evolution algorithms. However, in most modified …

With the rapid development of the social economy, the rapid development of all social circles
places higher demands on the electricity industry. As a fundamental industry supporting the …

Particle swarm optimization (PSO) is a population-based algorithm designed to tackle
various optimization problems. However, its performance deteriorates significantly when …

We propose a new particle swarm optimization algorithm for problems where objective
functions are subject to zero-mean, independent, and identically distributed stochastic noise …

Although surrogate-assisted evolutionary algorithms (SAEAs) have been widely developed
to address computationally expensive multi-objective optimization problems (MOPs), they …

Particle Swarm Optimization (PSO) is a population-based algorithm designed to tackle
various optimization problems. However, its performance deteriorates significantly when …

In this paper, we introduce the concept of population density in PSO, and accordingly, we
discuss the relationship between the search capability of PSO and the population density …

Multiphase machines are complex multi-variable electro-mechanical systems that are
receiving special attention from industry due to their better fault tolerance and power-per …

Particle swarm optimization (PSO) is a metaheuristic designed to find good solutions to
optimization problems. However, when optimization problems are subject to noise, the …

Almost all optimization techniques are restricted by the problems' dimensions and large
search spaces. This research focuses on a special hybrid method combining two meta …