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Title:
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DISTANCE-BASED PARTICLE SWARM OPTIMIZATION |
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Author(s):
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Chen-yi Liao , Wei-ping Lee , Xianghan Chen , Mei-ling Huang |
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ISBN:
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978-972-8924-39-3 |
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Editors:
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António Palma dos Reis, Katherine Blashki and Yingcai Xiao (series editors:Piet Kommers, Pedro Isaías and Nian-Shing Chen) |
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Year:
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2007 |
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Edition:
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Single |
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Keywords:
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Particle Swarm Optimization (PSO), Genetic Algorithm (GA), distance |
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Type:
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Short Paper |
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First Page:
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207 |
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Last Page:
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211 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Particle Swarm Optimization (PSO) is a stochastic, population-based evolutionary search technique. It has difficulties in
controlling the balance between exploration and exploitation. In order to improve the performance of PSO, we proposed a
novel algorithm called Distance-Based Particle Swarm Optimization (DBPSO). In DBPSO, the distance from gbest and
the particle is calculated in order to decide which algorithm should use. DBPSO is also a hybrid algorithm, it not only
uses Particle Swarm Optimization, but also uses Genetic Algorithm. Three benchmark functions are used for the
comparison of DBPSO with other algorithms. The experiments prove that DBPSO has better performance. |
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