Proceedings ArticleDOI
Comparing inertia weights and constriction factors in particle swarm optimization
Russell C. Eberhart,Yuhui Shi +1 more
- Vol. 1, pp 84-88
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TLDR
It is concluded that the best approach is to use the constriction factor while limiting the maximum velocity Vmax to the dynamic range of the variable Xmax on each dimension.Abstract:
The performance of particle swarm optimization using an inertia weight is compared with performance using a constriction factor. Five benchmark functions are used for the comparison. It is concluded that the best approach is to use the constriction factor while limiting the maximum velocity Vmax to the dynamic range of the variable Xmax on each dimension. This approach provides performance on the benchmark functions superior to any other published results known by the authors.read more
Citations
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Journal ArticleDOI
The particle swarm - explosion, stability, and convergence in a multidimensional complex space
M. Clerc,James Kennedy +1 more
TL;DR: This paper analyzes a particle's trajectory as it moves in discrete time, then progresses to the view of it in continuous time, leading to a generalized model of the algorithm, containing a set of coefficients to control the system's convergence tendencies.
Proceedings ArticleDOI
Particle swarm optimization: developments, applications and resources
TL;DR: Developments in the particle swarm algorithm since its origin in 1995 are reviewed and brief discussions of constriction factors, inertia weights, and tracking dynamic systems are included.
Journal ArticleDOI
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
TL;DR: A novel parameter automation strategy for the particle swarm algorithm and two further extensions to improve its performance after a predefined number of generations to overcome the difficulties of selecting an appropriate mutation step size for different problems.
Journal ArticleDOI
The particle swarm optimization algorithm: convergence analysis and parameter selection
TL;DR: The particle swarm optimization algorithm is analyzed using standard results from the dynamic system theory and graphical parameter selection guidelines are derived, resulting in results superior to previously published results.
Journal ArticleDOI
Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems
TL;DR: This paper presents a detailed overview of the basic concepts of PSO and its variants, and provides a comprehensive survey on the power system applications that have benefited from the powerful nature ofPSO as an optimization technique.
References
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Book ChapterDOI
Parameter Selection in Particle Swarm Optimization
Yuhui Shi,Russell C. Eberhart +1 more
TL;DR: This paper first analyzes the impact that inertia weight and maximum velocity have on the performance of the particle swarm optimizer, and then provides guidelines for selecting these two parameters.
Proceedings ArticleDOI
The swarm and the queen: towards a deterministic and adaptive particle swarm optimization
TL;DR: A very simple particle swarm optimization iterative algorithm is presented, with just one equation and one social/confidence parameter, and the results are good enough so that it is certainly worthwhile trying the method on more complex problems.
Book
Computational intelligence PC tools
TL;DR: This book takes a hands-on, desktop-applications approach to the topic of computational intelligence, featuring examples of specific real-world implementations and detailed case studies, with all pertinent code and software included on a floppy disk packaged with the book.