Results 111 to 120 of about 2,009 (157)
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Boid particle swarm optimisation
International Journal of Innovative Computing and Applications, 2009Particle swarm optimisation (PSO) is a novel population-based stochastic optimisation algorithm inspired by the Reynolds' boid model. The original biological background of boid obeys three basic simple steering rules: separation, alignment and cohesion.
Zhihua Cui
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A Convergence Proof for the Particle Swarm Optimiser [PDF]
Fundamenta Informaticae, 2010 The Particle Swarm Optimiser (PSO) is a population based stochastic optimisation algorithm, empirically shown to be efficient and robust. This paper provides a proof to show that the original PSO does not have guaranteed convergence to a local optimum. A flaw in the original PSO is identified which causes stagnation of the swarm.
Frans van den Bergh +1 more
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Particle swarm optimisation with spatial particle extension
Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600), 2003In 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.
Krink, Thiemo +2 more
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Particle Swarms for Linearly Constrained Optimisation
Fundamenta Informaticae, 2007Particle 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 ...
Ulrich Paquet, Andries P. Engelbrecht
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Division of labor in particle swarm optimisation
Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600), 2003We introduce Division of Labor (DoL) from social insects to improve local optimisation of the Particle Swarm Optimiser (PSO). We compared the performance with the basic PSO, a GA and simulated annealing and found improvements around local optima. The PSO with DoL outperforms the basic PSO on most testcases and is comparable in local optimisation with ...
Vesterstrøm, J.S., Riget, J., Krink, T.
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SSRN Electronic Journal, 2007
Particle swarm optimization (PSO) has undergone many changes since its introduction in 1995. As researchers have learned about the technique, they have derived new versions, developed new applications, and published theoretical studies of the effects of the various parameters and aspects of the algorithm.
Riccardo Poli +2 more
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Particle swarm optimization (PSO) has undergone many changes since its introduction in 1995. As researchers have learned about the technique, they have derived new versions, developed new applications, and published theoretical studies of the effects of the various parameters and aspects of the algorithm.
Riccardo Poli +2 more
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2020
Particle Swarm Optimization (PSO) was built by mimicking the navigation pattern of entities, such as flock of birds or school of fishes. The algorithm uses established particles that wing over a search space for global optima location. Throughout the PSO iteration process, each particle updates its location based on the preceding knowledge or ...
Modestus O. Okwu, Lagouge K. Tartibu
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Particle Swarm Optimization (PSO) was built by mimicking the navigation pattern of entities, such as flock of birds or school of fishes. The algorithm uses established particles that wing over a search space for global optima location. Throughout the PSO iteration process, each particle updates its location based on the preceding knowledge or ...
Modestus O. Okwu, Lagouge K. Tartibu
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Geometric Particle Swarm Optimisation
2007Using 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 +2 more
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Novelty particle swarm optimisation for truss optimisation problems
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2021Bilevel optimisation has been successfully applied to truss optimisation to consider topology and sizing in upper and lower levels, respectively. This study proposes novelty particle swarm optimisation for the upper level to discover new designs by maximising novelty.
Hirad Assimi +3 more
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Particle swarm optimisation with differential mutation
International Journal of Intelligent Systems Technologies and Applications, 2012Particle 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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