Browsing by Author "Jiang, Pei"

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Analysis of particle interaction in particle swarm optimization
(2010-05-06)
Chen, Ying-ping
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Jiang, Pei
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資訊工程學系
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Department of Computer Science
In this paper, we analyze the behavior of particle swarm optimization (PSO) on the facet of particle interaction. We firstly propose a statistical interpretation of particle swarm optimization in order to capture the stochastic behavior of the entire swarm. Based on the statistical interpretation, we investigate the effect of particle interaction by focusing on the social-only model and derive the upper and lower bounds of the expected particle norm. Accordingly, the lower and upper bounds of the expected progress rate on the sphere function are also obtained. Furthermore, the sufficient and necessary condition for the swarm to converge is derived to demonstrate the PSO convergence caused by the effect of particle interaction. (C) 2010 Elsevier B.V. All rights reserved.
Free lunches on the discrete Lipschitz class
(2011-04-08)
Jiang, Pei
;
Chen, Ying-ping
;
資訊工程學系
;
Department of Computer Science
The No-Free-Lunch theorem states that there does not exist a genuine general-purpose optimizer because all algorithms have the identical performance on average over all functions. However, such a result does not imply that search heuristics or optimization algorithms are futile if we are more cautious with the applicability of these methods and the search space. In this paper, within the No-Free-Lunch framework, we firstly introduce the discrete Lipschitz class by transferring the Lipschitz functions, i.e., functions with bounded slope, as a measure to fulfill the notion of continuity in discrete functions. We then investigate the properties of the discrete Lipschitz class, generalize an algorithm called subthreshold-seeker for optimization, and show that the generalized subthreshold-seeker outperforms random search on this class. Finally, we propose a tractable sampling-test scheme to empirically demonstrate the superiority of the generalized subthreshold-seeker under practical configurations. This study concludes that there exist algorithms outperforming random search on the discrete Lipschitz class in both theoretical and practical aspects and indicates that the effectiveness of search heuristics may not be universal but still general in some broad sense. (C) 2010 Elsevier B.V. All rights reserved.