Results 241 to 250 of about 27,349 (293)

Particle Swarms for Linearly Constrained Optimisation

Fundamenta Informaticae, 2007
Particle Swarm Optimisation (PSO) has proved to be a very useful algorithm to optimise unconstrained functions. This paper extends PSO to a Linear PSO (LPSO) to optimise functions constrained by a set of equality constraints of the form Ax=b. By initialising particles within a constrained hyperplane, the LPSO is guaranteed to 'fly' only through this ...
Paquet, Ulrich, Engelbrecht, Andries P.
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Mean particle swarm optimisation for function optimisation

International Journal of Computational Intelligence Studies, 2009
In this paper, a new particle swarm optimisation algorithm, called MeanPSO, is presented, based on a novel philosophy by modifying the velocity update equation. This is done by replacing two terms of original velocity update equation by two new terms based on the linear combination of pbest and gbest.
Kusum Deep, Jagdish Chand Bansal
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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
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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 Rakitianskaia, Andries P. Engelbrecht   +1 more
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Geometric Particle Swarm Optimisation

2007
Using a geometric framework for the interpretation of crossover of recent introduction, we show an intimate connection between particle swarm optimization (PSO) and evolutionary algorithms. This connection enables us to generalize PSO to virtually any solution representation in a natural and straightforward way.
Alberto Moraglio, Cecilia Di Chio, Riccardo Poli   +2 more
openaire   +1 more source

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
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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
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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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