Open Access
The swarm and the queen : Towards a deterministic and adaptive particles swarm optimization
M. Clerc
- pp 1951-1957
About:
The article was published on 1999-01-01 and is currently open access. It has received 1542 citations till now. The article focuses on the topics: Swarm behaviour.read more
Citations
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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.
Proceedings ArticleDOI
Comparing inertia weights and constriction factors in particle swarm optimization
Russell C. Eberhart,Yuhui Shi +1 more
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.
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.
Book
Metaheuristics: From Design to Implementation
TL;DR: This book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking and bioinformatics to engineering design, routing, and scheduling.
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.
References
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Proceedings ArticleDOI
A new optimizer using particle swarm theory
TL;DR: The optimization of nonlinear functions using particle swarm methodology is described and implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm.
Proceedings ArticleDOI
A discrete binary version of the particle swarm algorithm
TL;DR: The paper reports a reworking of the particle swarm algorithm to operate on discrete binary variables, where trajectories are changes in the probability that a coordinate will take on a zero or one value.
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
Using selection to improve particle swarm optimization
TL;DR: A hybrid based on the particle swarm algorithm but with the addition of a standard selection mechanism from evolutionary computations is described that shows selection to provide an advantage for some (but not all) complex functions.
Proceedings ArticleDOI
Matching algorithms to problems: an experimental test of the particle swarm and some genetic algorithms on the multimodal problem generator
James Kennedy,William M. Spears +1 more
TL;DR: A multimodal problem generator was used to test three versions of a genetic algorithm and the binary particle swarm algorithm in a factorial time-series experiment.