دانلود مقاله ISI انگلیسی شماره 79080
عنوان فارسی مقاله

الگوریتم خوشه بندی خودکار بر اساس PSO ایمن چند هدفه برای طبقه بندی اقدامات مدلهای انسانی 3D

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
79080 2013 13 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Automatic clustering algorithm based on multi-objective Immunized PSO to classify actions of 3D human models
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Engineering Applications of Artificial Intelligence, Volume 26, Issues 5–6, May–June 2013, Pages 1429–1441

کلمات کلیدی
خوشه بندی به صورت خودکار - PSO ایمن چند هدفه؛ MOPSO؛ MOCLONAL؛ MOCK؛ نمره Minkowski
پیش نمایش مقاله
پیش نمایش مقاله الگوریتم خوشه بندی خودکار بر اساس PSO ایمن چند هدفه برای طبقه بندی اقدامات مدلهای انسانی 3D

چکیده انگلیسی

Multi-objective clustering algorithms are preferred over its conventional single objective counterparts as they incorporate additional knowledge on properties of data in the from of objectives to extract the underlying clusters present in many datasets. Researchers have recently proposed some standardized multi-objective evolutionary clustering algorithms based on genetic operations, particle swarm optimization, clonal selection principles, differential evolution and simulated annealing, etc. In many cases it is observed that hybrid evolutionary algorithms provide improved performance compared to that of individual algorithm. In this paper an automatic clustering algorithm MOIMPSO (Multi-objective Immunized Particle Swarm Optimization) is proposed, which is based on a recently developed hybrid evolutionary algorithm Immunized PSO. The proposed algorithm provides suitable Pareto optimal archive for unsupervised problems by automatically evolving the cluster centers and simultaneously optimizing two objective functions. In addition the algorithm provides a single best solution from the Pareto optimal archive which mostly satisfy the users' requirement. Rigorous simulation studies on 11 benchmark datasets demonstrate the superior performance of the proposed algorithm compared to that of the standardized automatic clustering algorithms such as MOCK, MOPSO and MOCLONAL. An interesting application of the proposed algorithm has also been demonstrated to classify the normal and aggressive actions of 3D human models.

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