James Kennedy

Particle swarm optimization

Abstract Particle swarm optimization (PSO) has undergone many changes since its introduction in 1995. As researchers have learned about the technique, they have derived new versions, developed new applications, and published theoretical studies of the effects of the various parameters and aspects of the algorithm. This paper comprises a snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems.

A new optimizer using particle swarm theory

The optimization of nonlinear functions using particle swarm methodology is described. Implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm. Benchmark testing of both paradigms is described, and applications, including neural network training and robot task learning, are proposed. Relationships between particle swarm optimization and both artificial life and evolutionary computation are reviewed.

The particle swarm - explosion, stability, and convergence in a multidimensional complex space

The particle swarm is an algorithm for finding optimal regions of complex search spaces through the interaction of individuals in a population of particles. This paper analyzes a particles trajectory as it moves in discrete time (the algebraic view), then progresses to the view of it in continuous time (the analytical view). A five-dimensional depiction is developed, which describes the system completely. These analyses lead to a generalized model of the algorithm, containing a set of coefficients to control the systems convergence tendencies. Some results of the particle swarm optimizer, implementing modifications derived from the analysis, suggest methods for altering the original algorithm in ways that eliminate problems and increase the ability of the particle swarm to find optima of some well-studied test functions.

PARTICLE SWARM OPTIMIZATION: AN OVERVIEW

Abstract Particle swarm optimization (PSO) has undergone many changes since its introduction in 1995. As researchers have learned about the technique, they have derived new versions, developed new applications, and published theoretical studies of the effects of the various parameters and aspects of the algorithm. This paper comprises a snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems.

Population structure and particle swarm performance

The effects of various population topologies on the particle swarm algorithm were systematically investigated. Random graphs were generated to specifications, and their performance on several criteria was compared. What makes a good population structure? We discovered that previous assumptions may not have been correct.

Defining a Standard for Particle Swarm Optimization

Particle swarm optimization has become a common heuristic technique in the optimization community, with many researchers exploring the concepts, issues, and applications of the algorithm. In spite of this attention, there has as yet been no standard definition representing exactly what is involved in modern implementations of the technique. A standard is defined here which is designed to be a straightforward extension of the original algorithm while taking into account more recent developments that can be expected to improve performance on standard measures. This standard algorithm is intended for use both as a baseline for performance testing of improvements to the technique, as well as to represent PSO to the wider optimization community

Bare bones particle swarms

The particle swarm algorithm has just enough moving parts to make it hard to understand. The formula is very simple, it is even easy to describe the working of the algorithm verbally, yet it is very difficult to grasp in ones mind how the particles oscillate around centers that are constantly changing; how they influence one another; how the various parameters affect the trajectory of the particle; how the topology of the swarm affects its performance; and so on. This paper strips away some traditional features of the particle swarm in the search for the properties that make it work. The particle swarm algorithm is modified by eliminating the velocity formula. Variations are compared. In the process some of the mysteries of the algorithm are revealed, we discover its similarity to other stochastic population-based problem solving methods, and new avenues of investigation are suggested or implied.

Matching algorithms to problems: an experimental test of the particle swarm and some genetic algorithms on the multimodal problem generator

A multimodal problem generator was used to test three versions of a genetic algorithm and the binary particle swarm algorithm in a factorial time-series experiment. Specific strengths and weaknesses of the various algorithms were identified.

Neighborhood topologies in fully informed and best-of-neighborhood particle swarms

In this study, we vary the way an individual in the particle swarm interacts with its neighbors. The performance of an individual depends on population topology as well as algorithm version. It appears that a fully informed particle swarm is more susceptible to alterations in the topology, but with a good topology, it can outperform the canonical version

The Behavior of Particles

The particle swarm algorithm has been shown to optimize a wide variety of complex functions. This paper examines a simplified version of the algorithm in an effort to understand the trajectories of particles as they search for solutions. Findings address optimal parameter values, point out issues for future research, and contribute to understanding this new optimization method.