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Particle swarm optimisation for dynamic optimisation problems: a review
Neural Computing and Applications, 2014Some real-world optimisation problems are dynamic; that is, their objective function and/or constraints vary over time. Solving such problems is very challenging. Particle swarm optimisation (PSO) is a well-known and efficient optimisation algorithm. In this paper, the PSO variants, devised for dynamic optimisation problems, are reviewed.
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Nonlinear Mapping using Particle Swarm Optimisation
2005 IEEE Congress on Evolutionary Computation, 2005Large 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.
Auralia I. Edwards +2 more
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A novel particle swarm optimisation with hybrid strategies
International Journal of Computing Science and Mathematics, 2015Particle swarm optimisation PSO is an efficient optimisation technique, which has shown good search performance on many optimisation problems. However, the standard PSO easily falls into local minima because particles are attracted by their previous best particles and the global best particle. Though the attraction can accelerate the search process, it
Rongfang Chen, Jun Tang
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Preserving Diversity in Particle Swarm Optimisation
2007Particle Swarm Optimisation (PSO) is an optimisation algorithm that shows promise. However its performance on complex problems with multiple minima falls short of that of the Ant Colony Optimisation (ACO) algorithm when both algorithms are applied to travelling salesperson type problems (TSP).
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Perceptive Particle Swarm Optimisation
2005Conventional 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 +1 more
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On the moments of the sampling distribution of particle swarm optimisers
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation, 2007A method is presented that allows one to exactly determine all the characteristics of a PSO's sampling distribution and explain how it changes over time, in the presence stochasticity. The only assumption made is stagnation (particles are in search for a better personal best).
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Particle Swarm Optimisation in Feedforward Neural Network
2000The paper describes a new evolutionary system for evolving artificial neural networks (ANN’s) called PSONN, which is based on the particle swarm optimisation (PSO) algorithm. The PSO algorithm is used to evolve both the architecture and weights of ANN’s, this means that an ANN’s architecture is adaptively adjusted by PSO algorithm, then the nodes of ...
Chunkai Zhang, Huihe Shao
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Fitness estimation and the particle swarm optimisation algorithm
2007 IEEE Congress on Evolutionary Computation, 2007The time taken performing fitness calculations can dominate the total computational time when applying Particle Swarm Optimisation (PSO) to complex real life problems. This paper describes a method of estimating fitness, and the reliability of that estimation, that can be used as an alternative to performing some true fitness calculations.
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Connectivity-Aware Particle Swarm Optimisation for Swarm Shepherding
IEEE Transactions on Emerging Topics in Computational Intelligence, 2023Reem E. Mohamed +3 more
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Parameter settings in particle swarm optimisation algorithms: a survey
International Journal of Automation and Control, 2022Shi Cheng
exaly