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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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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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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, Zhongzhi Shi
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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 ...
Paquet, Ulrich, Engelbrecht, Andries P.
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Mean particle swarm optimisation for function optimisation
International Journal of Computational Intelligence Studies, 2009In 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, 2015Particle 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), 2008Most 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 +1 more
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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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