Adaptive Bare Bones Particle Swarm Inspired by Cloud Model
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概要
- 論文の詳細を見る
Kennedy has proposed the bare bones particle swarm (BBPS) by the elimination of the velocity formula and its replacement by the Gaussian sampling strategy without parameter tuning. However, a delicate balance between exploitation and exploration is the key to the success of an optimizer. This paper firstly analyzes the sampling distribution in BBPS, based on which we propose an adaptive BBPS inspired by the cloud model (ACM-BBPS). The cloud model adaptively produces a different standard deviation of the Gaussian sampling for each particle according to the evolutionary state in the swarm, which provides an adaptive balance between exploitation and exploration on different objective functions. Meanwhile, the diversity of the swarms is further enhanced by the randomness of the cloud model itself. Experimental results show that the proposed ACM-BBPS achieves faster convergence speed and more accurate solutions than five other contenders on twenty-five unimodal, basic multimodal, extended multimodal and hybrid composition benchmark functions. The diversity enhancement by the randomness in the cloud model itself is also illustrated.
- 2011-08-01
著者
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Tang Zheng
Univ. Toyama Toyama‐shi Jpn
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TANG Zheng
Graduate School of Innovative Life Science, University of Toyama
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Tang Zheng
Department Of Computer Science And Technology The Key Laboratory Of Embedded System And Service Comp
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Ni Lina
College Of Info Sci & Engi Shandong University Of Science & Technology
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Zhang Junqi
Department Of Computer Science And Technology The Key Laboratory Of Embedded System And Service Comp
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ZHANG Junqi
the Department of Computer Science and Technology, the Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University
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NI Lina
the College of Info Sci & Engi, Shandong University of Science & Technology
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YAO Jing
the Department of Computer Science and Technology, the Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University
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WANG Wei
the Department of Computer Science and Technology, the Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University
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TANG Zheng
the Department of Computer Science and Technology, the Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University
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Yao Jing
The Department Of Computer Science And Technology The Key Laboratory Of Embedded System And Service Computing Ministry Of Education Tongji University
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Wang Wei
The Department Of Computer Science And Technology The Key Laboratory Of Embedded System And Service Computing Ministry Of Education Tongji University
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TANG Zheng
the Department of Computer Science and Technology, the Key Laboratory of Embedded System and Service Computing, Ministry of Education ; Tongji University
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ZHANG Junqi
the Department of Computer Science and Technology, the Key Laboratory of Embedded System and Service Computing, Ministry of Education ; Tongji University
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