F. van den Bergh

A Cooperative approach to particle swarm optimization

The particle swarm optimizer (PSO) is a stochastic, population-based optimization technique that can be applied to a wide range of problems, including neural network training. This paper presents a variation on the traditional PSO algorithm, called the cooperative particle swarm optimizer, or CPSO, employing cooperative behavior to significantly improve the performance of the original algorithm. This is achieved by using multiple swarms to optimize different components of the solution vector cooperatively. Application of the new PSO algorithm on several benchmark optimization problems shows a marked improvement in performance over the traditional PSO.

A study of particle swarm optimization particle trajectories

Particle swarm optimization (PSO) has shown to be an efficient, robust and simple optimization algorithm. Most of the PSO studies are empirical, with only a few theoretical analyses that concentrate on understanding particle trajectories. These theoretical studies concentrate mainly on simplified PSO systems. This paper overviews current theoretical studies, and extend these studies to investigate particle trajectories for general swarms to include the influence of the inertia term. The paper also provides a formal proof that each particle converges to a stable point. An empirical analysis of multi-dimensional stochastic particles is also presented. Experimental results are provided to support the conclusions drawn from the theoretical findings.

A new locally convergent particle swarm optimiser

This paper introduces a new Particle Swarm Optimisation (PSO) algorithm with strong local convergence properties. The new algorithm performs much better with a smaller number of particles, compared to the original PSO. This property is desirable when designing a niching PSO algorithm.

Training product unit networks using cooperative particle swarm optimisers

The cooperative particle swarm optimiser (CPSO) is a variant of the particle swarm optimiser (PSO) that splits the problem vector, for example a neural network weight vector, across several swarms. The paper investigates the influence that the number of swarms used (also called the split factor) has on the training performance of a product unit neural network. Results are presented, comparing the training performance of the two algorithms, PSO and CPSO, as applied to the task of training the weight vector of a product unit neural network.

Using neighbourhoods with the guaranteed convergence PSO

The standard particle swarm optimiser (PSO) may prematurely converge on suboptimal solutions that are not even guaranteed to be local extrema. The guaranteed convergence modifications to the PSO algorithm ensure that the PSO at least converges on a local extremum at the expense of even faster convergence. This faster convergence means that less of the search space is explored reducing the opportunity of the swarm to find better local extrema. Various neighbourhood topologies inhibit premature convergence by preserving swarm diversity during the search. This paper investigates the performance of the guaranteed convergence PSO (GCPSO) using different neighbourhood topologies and compares the results with their standard PSO counterparts.

Solving systems of unconstrained equations using particle swarm optimization

A new particle swarm optimization algorithm (PSO), nbest, is developed in this paper to solve systems of unconstrained equations. For this purpose, the standard gbest PSO is adapted by redefining the fitness function in order to locate multiple solutions in one run of the algorithm. The new algorithm also introduces the concept of shrinking particle neighborhoods. The resulting nbest algorithm is a first attempt to develop a niching PSO algorithm. The paper presents results that show the new PSO algorithm to be successful in locating multiple solutions.

Scalability of niche PSO

In contrast to optimization techniques intended to find a single, global solution in a problem domain, niching (speciation) techniques have the ability to locate multiple solutions in multimodal domains. Numerous niching techniques have been proposed, broadly classified as temporal (locating solutions sequentially) and parallel (multiple solutions are found concurrently) techniques. Most research efforts to date have considered niching solutions through the eyes of genetic algorithms (GA), studying simple multimodal problems. Little attention has been given to the possibilities associated with emergent swarm intelligence techniques. Particle swarm optimization (PSO) utilizes properties of swarm behaviour not present in evolutionary algorithms such as GA, to rapidly solve optimization problems. This paper investigates the ability of two genetic algorithm niching techniques, sequential niching and deterministic crowding, to scale to higher dimensional domains with large numbers of solutions, and compare their performance to a PSO-based niching technique, Niche PSO.

Potential fire detection based on Kalman-driven change detection

A new active fire event detection algorithm for data collected with the Spinning Enhanced Visible and Infrared Imager (SE-VIRI) sensor, based on the extended Kalman filter, is introduced. Instead of using the observed temperatures of the spatial neighbours of a pixel to detect anomalous temperatures, the new algorithm only considers previous observations at the current pixel. The algorithm harnesses the Kalman filter to obtain a prediction of the expected brightness temperature at a given location, which is then compared to the actual SE-VIRI observation. An adaptive threshold is used to determine whether the observed difference is indicative of a potential fire event. Initial tests show that the performance of this method is comparable to that of the EUMETSAT FIR product.

Enhancing ABR through an improved control infrastructure

The ATMs success in the high speed networking field depends to a great extent on the performance of its ABR (available bit rate) traffic class. ABR allows ATM networks to provide an adaptive service class that maximizes bandwidth usage, which is ideal for IP traffic. ABR is an attractive solution for use in an autonomous system in the differentiated services model, especially when edge routing is used in conjunction with an ATM core. By using an improved communications infrastructure it is possible to enhance the performance and robustness of the current ABR flow control mechanism. The enhanced mechanism proposed in this paper reduces the response time when a switch senses congestion, resulting in a lower cell loss and reduced buffer space requirements in the switches. The enhanced explicit rate mechanism makes use of out-of-band signaling which improves the robustness of the flow control mechanism in times of severe congestion.