Proceedings Article10.1109/CEC.2000.870279
Comparing inertia weights and constriction factors in particle swarm optimization
Russell C. Eberhart,Yuhui Shi +1 more
- 16 Jul 2000
- Vol. 1, pp 84-88
3.3K
TL;DR: 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.
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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.
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References
Particle swarm optimization
James Kennedy,Russell C. Eberhart +1 more
- 06 Aug 2002
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
44.1K
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.
3.9K
The swarm and the queen: towards a deterministic and adaptive particle swarm optimization
M. Clerc
- 06 Jul 1999
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.
1.6K
•Book
Computational intelligence PC tools
Russell C. Eberhart,Pat Simpson,Roy W. Dobbins +2 more
- 01 Jan 1996
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.
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