This article investigates the evolution of the velocity vector as the AdPSO (Adaptive PSO)
algorithm optimizes a set of parameters through a number of epochs. Experimental results …

Particle Swarm Optimization (PSO) is an evolutionary computing algorithm and is
successfully used to solve complex real world optimization problems. Due to the complex …

Due to the growing need for metaheuristics with features that allow their implementation for
highly complex and real-time problems, this paper describes a gradient-based mechanism …

A simple, effective, computationally cheap, and easily tuned method is presented for
improving PSO performance by automatically adapting acceleration coefficients. While this …

PSO, like many stochastic search methods, is very sensitive to efficient parameter setting. As
modifying a single parameter may result in a large effect. In this paper, we propose a new a …

The present article investigates the optimal setup of a particle swarm algorithm for a specific
natural language processing task. This task consists of identifying syntactic phrases in …

In particle swarm optimization (PSO) algorithms there is a delicate balance to maintain
between exploitation (local search) and exploration (global search). When facing multimodal …

This paper presents a modified particle swarm optimization (PSO) algorithm to improve the
performance of standard PSO. The proposed approach is called HPSO, which modifies the …

The performance of particle swarm optimization (PSO) algorithms have shown to be very
sensitive to the main control parameters. Due to this sensitivity, various approaches have …

The velocity updating formula of the standard particle swarm optimization (PSO) only
considers two particles: the local best particle and the global best particle. The global best …