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Content Provider | IEEE Xplore Digital Library |
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Author | Ghosh, P. Zafar, H. Das, S. Abraham, A. |
Copyright Year | 2011 |
Description | Author affiliation: Faculty of Computer Science and Electrical Engineering, VSB - Technical University of Ostrava, Czech Republic (Abraham, A.) || Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata 700 032, India (Ghosh, P.; Zafar, H.; Das, S.) |
Abstract | Particle Swarm Optimization (PSO) is arguably one of the most popular nature-inspired algorithms for real parameter optimization at present. In this article, we introduce a new variant of PSO referred to as Hierarchical D-LPSO (Dynamic Local Neighborhood based Particle Swarm Optimization). In this new variant of PSO the particles are arranged following a dynamic hierarchy. Within each hierarchy the particles search for better solution using dynamically varying sub-swarms i.e. these sub-swarms are regrouped frequently and information is exchanged among them. Whether a particle will move up or down the hierarchy depends on the quality of its so-far best-found result. The swarm is largely influenced by the good particles that move up in the hierarchy. The performance of Hierarchical D-LPSO is tested on the set of 25 numerical benchmark functions taken from the competition and special session on real parameter optimization held under IEEE Congress on Evolutionary Computation (CEC) 2005. The results have been compared to those obtained with a few best-known variants of PSO as well as a few significant existing evolutionary algorithms. |
Starting Page | 757 |
Ending Page | 764 |
File Size | 1039780 |
Page Count | 8 |
File Format | |
ISBN | 9781424478347 |
e-ISBN | 9781424478354 |
DOI | 10.1109/CEC.2011.5949695 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2011-06-05 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Optimization Benchmark testing Heuristic algorithms Particle swarm optimization Topology Convergence Hierarchical D-LPSO PSO local PSO hierarchy D-LPSO |
Content Type | Text |
Resource Type | Article |
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