Results 181 to 190 of about 1,929 (230)

Particle swarm optimisation with spatial particle extension

Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600), 2003
In this paper, we introduce spatial extension to particles in the PSO model in order to overcome premature convergence in iterative optimisation. The standard PSO and the new model (SEPSO) are compared w.r.t. performance on well-studied benchmark problems.
T. Krink, J.S. Vesterstrom, J. Riget
openaire   +1 more source

Perceptive Particle Swarm Optimisation

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.
Boonserm Kaewkamnerdpong, Peter J. Bentley   +1 more
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Particle swarm optimisation applications in FACTS optimisation problem

2013 IEEE 7th International Power Engineering and Optimization Conference (PEOCO), 2013
FACTS optimisation is one of the most important and difficult problems in power systems. For solving this problem, so many different approaches have been proposed in the literature. Among them, particle swarm optimisation (PSO) has exposed so promising behavior. In this paper, applications of PSO in FACTS optimisation problem are explained and analysed
Ahmad Rezaee Jordehi, Jasronita Jasni, Noor Izzri Abdul Wahab, Mohd Zainal Abidin Abd Kadir   +3 more
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Multi-swarm particle swarm optimiser with Cauchy mutation for dynamic optimisation problems

International Journal of Innovative Computing and Applications, 2009
Many real-world problems are dynamic, requiring an optimisation algorithm which is able to continuously track a changing optimum over time. In this paper, we present a new variant of particle swarm optimisation (PSO) specifically designed to work well in dynamic environments.
Chengyu Hu, Bo Wang, Yongji Wang
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.
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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 ...
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Nonlinear Mapping using Particle Swarm Optimisation

2005 IEEE Congress on Evolutionary Computation, 2005
Large datasets consisting of high-dimensional vectors commonly describe complex objects. Having these vectors exist in a smaller dimension where the topological characteristics of the original space are preserved, allows clusters or patterns inherent in the data to be identified.
A.I. Edwards, A.P. Engelbrecht, N. Franken   +2 more
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Particle swarm optimisation: a triggered approach

International Journal of Industrial and Systems Engineering, 2014
This paper presents a modification to the particle swarm optimisation (PSO) to tackle two difficulties observed in many applications: premature convergence of the solution, and the degree of confidence of the decision maker. This approach, known as triggered particle swarm optimisation, treats the problem in a dynamic environment and making each ...
Mohamed H. Gadallah, Mohamed B. Ali, Ahmed M. Emam   +2 more
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Particle swarm optimisation with differential mutation

International Journal of Intelligent Systems Technologies and Applications, 2012
Particle swarm optimisation PSO is population-based optimisation algorithm having stochastic in nature. PSO has quick convergence speed but often gets stuck into local optima due to lacks of diversity. In this work, first mutation operator adopted from Differential Evolution DE algorithm is applied in PSO with decreasing inertia weight PSO-DMLB.
Tapas Si, Nanda Dulal Jana
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Baseline detection using Particle Swarm Optimisation

2010 10th International Conference on Intelligent Systems Design and Applications, 2010
Signature is a popular method of seeking approval and authentication between various parties in many transaction applications. Signature pattern recognition is done by processing a set of data that consists of (x, y) coordinates, representing online signature.
Siti-Hakimah Manshor, Sofianita Mutalib, Yap May Lin, Shuzlina Abdul-Rahman, Azlinah Mohamed   +4 more
openaire   +1 more source