Hierarchical Swarm Model: A New Approach to Optimization

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Particularly interesting group consists of algorithms that implement co-evolution or co-operation in natural environments, giving much more powerful implementations. The main aim is to obtain the algorithm which operation is not influenced by the environment. An unusual look at optimization algorithms made it possible to develop a new algorithm and its metaphors define for two groups of algorit...

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Particle swarm optimization (PSO) is a new evolutionary computation technique. Although PSO algorithm possesses many attractive properties, the methods of selecting inertia weight need to be further investigated. Under this consideration, the inertia weight employing random number uniformly distributed in [0,1] was introduced to improve the performance of PSO algorithm in this work. Three bench...

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The time consumption in solving computationally heavy problems has always been a concern for computer programmers. Due to simplicity of its implementation, the PSO (Particle Swarm Optimization) is a suitable meta-heuristic algorithm for solving computationally heavy problems. However, despite the simplicity, the algorithm is inefficient for solving real computationally heavy problems but the pr...

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Particle swarm optimization (PSO) has recently been modified to several versions. Heterogeneous PSO is a recent extension which includes behavioral heterogeneity of particles. Here we propose a further developed version that has hierarchical interaction patterns among heterogeneous particles, which we call hierarchical heterogeneous PSO (HHPSO). Two algorithm designs that have been developed an...

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ژورنال

عنوان ژورنال: Discrete Dynamics in Nature and Society

سال: 2010

ISSN: 1026-0226,1607-887X

DOI: 10.1155/2010/379649