This chapter discusses the workings of PSO in two research fields with special importance in real-world applications, namely noisy and dynamic environments. Noise simulation schemes are presented and experimental results on benchmark problems are reported. In addition, we present the application of PSO on a simulated real world problem, namely the particle identification by light scattering. Mo...

In this paper we describe improvements to the particle swarm optimizer (PSO) made by inclusion of an unscented Kalman filter to guide particle motion. We demonstrate the effectiveness of the unscented Kalman filter PSO by comparing it with the original PSO algorithm and its variants designed to improve performance. The PSOs were tested firstly on a number of common synthetic benchmarking functi...

Particle Swarm Optimization (PSO) algorithm is a new optimization approach, which has been widely used to solve various and complex optimization problems. However, there are still some imperfections, such as premature convergence and low accuracy. To address such defects, an improved PSO is proposed in this paper. The improved PSO algorithm introduces a uniform search strategy that makes partic...

The Vehicle Routing Problem can be expressed as the problem of designing optimal collection or delivery routes from one or multiple depots to a number of terrestrially scattered customers or cities, subject to side constraints such as time, capacity, mileage etc. The VRP plays a key role in the fields of logistics and transportation. There exist a number of variants of VRPs. Mostly VRPs with fi...

With an ever-growing attention Particle Swarm Optimization (PSO) has found many application areas for many challenging optimization problems. It is, however, a known fact that PSO has a severe drawback in the update of its global best (gbest) particle, which has a crucial role of guiding the rest of the swarm. In this paper, we propose two efficient solutions to remedy this problem using a stoc...

Particle swarm optimization (PSO) algorithm has shown promising performances on various benchmark functions and engineering optimization problems. However, it is still difficult to achieve a satisfying trade-off between exploration and exploitation for all the optimizationproblems and different evolving stages. Furthermore, control parameters of some related mechanisms need pre-experience by th...

Particle swarm optimisation (PSO) algorithm is known for its easy implementation and has been empirically shown to perform well in many optimisation problems. Thus, it is expected to mitigate the computational burden associated with the solutions of non-linear transcendental equations relevant to problems related to power converter systems. This paper starts with an overview of the general conc...

Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms in literature. Although original PSO has shown good performance, it still severely suffers from premature convergence. As a result, many researchers have been modifying resulting large number variants with either slightly or significantly better performance. Mainly, standard modified by four main strategie...

Particle swarm optimization (PSO) is an evolutionary algorithm known for its simplicity and effectiveness in solving various optimization problems. PSO should have strong yet balanced exploration and exploitation capabilities to enhance its performance. A superior solution guided PSO (SSG-PSO) framework integrated with an individual level based mutation operator and different local search techn...