Proceedings ArticleDOI
Particle swarm optimization with an oscillating inertia weight
Kyriakos Kentzoglanakis,Matthew Poole +1 more
- pp 1749-1750
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Results demonstrate that an oscillating inertia weight function is competitive and in some cases better than established inertia weight functions, in terms of consistency and speed of convergence.Abstract:
In this paper, we propose an alternative strategy of adapting the inertia weight parameter during the course of particle swarm optimization, by means of a non-monotonic inertia weight function of time. Results demonstrate that an oscillating inertia weight function is competitive and in some cases better than established inertia weight functions, in terms of consistency and speed of convergence.read more
Citations
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Proceedings ArticleDOI
Inertia Weight strategies in Particle Swarm Optimization
Jagdish Chand Bansal,Paramjeet Singh,Mukesh Saraswat,Abhishek Verma,Shimpi Singh Jadon,Ajith Abraham +5 more
TL;DR: 15 relatively recent and popular Inertia Weight strategies are studied and their performance on 05 optimization test problems is compared to show which are more efficient than others.
Journal ArticleDOI
Trajectory planning of free-floating space robot using Particle Swarm Optimization (PSO)
TL;DR: In this article, the authors investigated the application of Particle Swarm Optimization (PSO) strategy to trajectory planning of the kinematically redundant space robot in free-floating mode.
Journal ArticleDOI
Coordinated trajectory planning of dual-arm space robot using constrained particle swarm optimization
TL;DR: This paper investigates the application of particle swarm optimization (PSO) strategy to coordinated trajectory planning of the dual-arm space robot in free-floating mode and shows the effectiveness of the proposed method.
Journal ArticleDOI
On the Performance of Linear Decreasing Inertia Weight Particle Swarm Optimization for Global Optimization
TL;DR: An experiment was conducted to acquire a percentage value of the search space limits to compute the particle velocity limits in LDIW-PSO based on commonly used benchmark global optimization problems, and five well-known benchmark optimization problems were used to show the outstanding performance of LDIO over some of its competitors which have in the past claimed superiority over it.
Journal ArticleDOI
Inertia weight control strategies for particle swarm optimization: Too much momentum, not enough analysis
TL;DR: An overview of 18 inertia weight control strategies is provided, conditions required for the strategies to exhibit convergent behaviour are derived, and results of the empirical investigation show that none of the examined strategies even perform on par with a constant inertia weight.
References
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Journal ArticleDOI
Optimization by Simulated Annealing
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Journal ArticleDOI
Particle swarm optimization
TL;DR: A snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems, is included.
Proceedings ArticleDOI
A modified particle swarm optimizer
Yuhui Shi,Russell C. Eberhart +1 more
TL;DR: A new parameter, called inertia weight, is introduced into the original particle swarm optimizer, which resembles a school of flying birds since it adjusts its flying according to its own flying experience and its companions' flying experience.
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
Flocks, herds and schools: A distributed behavioral model
TL;DR: In this article, an approach based on simulation as an alternative to scripting the paths of each bird individually is explored, with the simulated birds being the particles and the aggregate motion of the simulated flock is created by a distributed behavioral model much like that at work in a natural flock; the birds choose their own course.