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A new algorithm based on metaheuristics for data clustering

Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2010年第12期11卷 921-926页

作者： Tsutomu SHOHDOHJI fumihiko yano Yoshiaki TOYODA Department of Computer and Information Engineering Faculty of EngineeringNippon Institute of Technology Division of Integrated Sciences J.F.Oberlin University Aoyama Gakuin University

This paper presents a new algorithm for clustering a large amount of *** improved the ant colony clustering algorithm that uses an ant’s swarm intelligence,and tried to overcome the weakness of the classical cluster analysis *** our proposed algorithm,improvements in the efficiency of an agent operation were achieved,and a new function "cluster condensation" was *** proposed algorithm is a processing method by which a cluster size is reduced by uniting similar objects and incorporating them into the cluster *** with classical cluster analysis methods,the number of steps required to complete the clustering can be suppressed to 1% or less by performing this procedure,and the dispersion of the result can also be ***,our clustering algorithm has the advantage of being possible even in a small-field cluster *** addition,the number of objects that exist in the field decreases because the cluster condenses;therefore,it becomes possible to add an object to a space that has become *** other words,first,the majority of data is put on *** are then clustered,gradually adding parts of the standby data to the clustering *** method can be adopted for a large amount of *** experiments confirmed that our proposed algorithm can theoretically applied to an unrestricted volume of data.

关键词： Metaheuristics Ant colony clustering Data clustering Swarm intelligence

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An Algorithm for Global Optimization Using Formula Manupulation

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Applied Mathematics 2012年第11期3卷 1601-1606页

作者： Tsutomu Shohdohji fumihiko yano Department of Computer and Information Engineering Nippon Institute of Technology Saitama Japan Division of Integrated Sciences J. F. Oberlin University Tokyo Japan

Constrained nonlinear optimization problems are well known as very difficult problems. In this paper, we present a new algorithm for solving such problems. Our proposed algorithm combines the Branch-and-Bound algorithm and Lipschitz constant to limit the search area effectively;this is essential for solving constrained nonlinear optimization problems. We obtain a more appropriate Lipschitz constant by applying the formula manipulation system of each divided area. Therefore, we obtain a better approximate solution without using a lot of searching points. The efficiency of our proposed algorithm has been shown by the results of some numerical experiments.

关键词： Global Optimization Lipschitz Constant Lipschitz Condition Branch-and-Bound Algorithm Formula Manipulation

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