Results 121 to 130 of about 2,009 (157)

Cooperative charged particle swarm optimiser

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), 2008
Most optimisation algorithms from the computational intelligence field assume that the search landscape is static. However, this assumption is not valid for many real-world problems. Therefore, there is a need for efficient optimisation algorithms that can track changing optima.
Anna S. Rakitianskaia, Andries P. Engelbrecht   +1 more
openaire   +1 more source

CriPS: Critical Particle Swarm Optimisation

07/20/2015-07/24/2015, 2015
Particle Swarm Optimisation (PSO) is a metaheuristic used to solve search tasks and is inspired by the flocking behaviour of birds. Traditionally careful tuning of parameters are required to avoid stagnation. Many animals forage using search strategies that show power law distributions in their motions in the form of Levy flight random walks.
Adam Erskine, J. Michael Herrmann
openaire   +1 more source

Perceptive particle swarm optimisation: an investigation

Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005., 2005
Conventional particle swarm optimisation relies on exchanging information through social interaction among individuals. However for real-world problems involving control of physical agents (i.e., robot control), such detailed social interaction is not always possible. Recently, the perceptive particle swarm optimisation (PPSO) algorithm was proposed to
Boonserm Kaewkamnerdpong, Peter J. Bentley   +1 more
openaire   +1 more source

Particle Swarm Optimisation with Enhanced Memory Particles

2014
Particle swarm optimisation (PSO) is a general purpose optimisation algorithm in which a population of particles are attracted to their past success and the success of other particles. This paper introduces a new variant of the PSO algorithm, PSO with Enhanced Memory Particles, where the cognitive influence is enhanced by having particles remember ...
Ian Broderick, Enda Howley
openaire   +1 more source

Particle Swarm Optimisation

2018
This chapter covers the the inspiration, mathematical model, and main mechanisms of the Particle Swarm Optimisation (PSO). The binary version of this algorithm (BPSO) is also presented. Several experiments are conducted to analyze the performance of both PSO and BPSO qualitatively and quantitatively.
openaire   +1 more source

Beyond Standard Particle Swarm Optimisation

International Journal of Swarm Intelligence Research, 2010
Currently, two very similar versions of PSO are available that could be called “standard”. While it is easy to merge them, their common drawbacks still remain. Therefore, in this paper, the author goes beyond simple merging by suggesting simple yet robust changes and solving a few well-known, common problems, while retaining the classical structure ...
openaire   +1 more source

Particle swarm optimiser with neighbourhood operator

Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), 2003
In recent years population based methods such as genetic algorithms, evolutionary programming, evolution strategies and genetic programming have been increasingly employed to solve a variety of optimisation problems. Recently, another novel population based optimisation algorithm - namely the particle swarm optimisation (PSO) algorithm, was introduced ...
openaire   +1 more source

Particle Swarm Optimisation for Protein Motif Discovery

Genetic Programming and Evolvable Machines, 2004
In this paper, a modified particle swarm optimisation algorithm is proposed for protein sequence motif discovery. Protein sequences are represented as a chain of symbols and a protein sequence motif is a short sequence that exists in most of the protein sequence families. Protein sequence symbols are converted into numbers using a one to one amino acid
Bill C. H. Chang, Asanga Ratnaweera, Saman K. Halgamuge, Harry C. Watson   +3 more
openaire   +1 more source

Avoidance Strategies in Particle Swarm Optimisation

2015
Particle swarm optimisation (PSO) is an optimisation algorithm in which particles traverse a problem space moving towards promising locations which either they or their neighbours have previously visited. This paper presents a new PSO variant with the Avoidance of Worst Locations (AWL). This variation was inspired by animal behaviour.
Karl Mason, Enda Howley
openaire   +1 more source

An improved diversity-guided particle swarm optimisation for numerical optimisation

International Journal of Computing Science and Mathematics, 2014
Particle swarm optimisation PSO is a global optimisation technique, which has shown a good performance on many problems. However, PSO easily falls into local minima because of quick losing of diversity. Some diversity-guided PSO algorithms have been proposed to maintain diversity, but they often slow down the convergence rate. In this paper, we propose
Wenjun Wang 0001, Hui Wang 0002
openaire   +1 more source