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.
read more
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
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
User-centered interior finishing material selection: An immersive virtual reality-based interactive approach
TL;DR: A novel immersive virtual reality (IVR)-based approach for user-centered interior finishing material selection which incorporates both visual aesthetics and conventional material performance is proposed.
41
Performance Metrics for Electric Warship Integrated Engineering Plant Battle Damage Response
TL;DR: Novel continuity-of-service metrics for IEPs are set forth herein, which provide a means of predicting the average and worst-case level of service the plant can provide as well as the worst- case scenario over a class of disruptions.
41
Enhancement of Speech Recognitions for Control Automation Using an Intelligent Particle Swarm Optimization
TL;DR: The proposed methodology aims to optimize speech recognition accuracy of a commercial speech recognizer in a noisy environment based on a beamformer, which is developed by an intelligent particle swarm optimization.
41
An N -State Markovian Jumping Particle Swarm Optimization Algorithm
TL;DR: A novel $N$ -state Markovian jumping PSO (NS-MJPSO) algorithm is presented where the velocity updating equation is adjusted based on the state evolution governed by a Markov chain and the performance of the proposed NS- MJPSO algorithm is evaluated via some widely used mathematical benchmark functions.
41
Placement Retargeting of Virtual Avatars to Dissimilar Indoor Environments.
TL;DR: This article develops an avatar placement method that preserves the semantics of the placement of the remote user in a different space as much as possible and shows the effectiveness of the methods by implementing a prototype AR-based telepresence system and user evaluations.
41
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.
Related Papers (5)
James Kennedy,Russell C. Eberhart +1 more
- 06 Aug 2002
Yuhui Shi,Russell C. Eberhart +1 more
- 04 May 1998
Russell C. Eberhart,James Kennedy +1 more
- 04 Oct 1995
Yuhui Shi,Russell C. Eberhart +1 more
- 06 Jul 1999