دانلود مقاله ISI انگلیسی شماره 46200
عنوان فارسی مقاله

الگوریتم کلنی زنبور عسل پویا چندکلونی مصنوعی برای بهینه سازی چندهدفه

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
46200 2015 20 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
A dynamic multi-colony artificial bee colony algorithm for multi-objective optimization ☆
منبع

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

Journal : Applied Soft Computing, Volume 35, October 2015, Pages 766–785

کلمات کلیدی
بهینه سازی چند هدفه - مدل چند مستعمره - الگوریتم کلونی زنبور عسل - استراتژی مهاجرت - آزمون فریدمن
پیش نمایش مقاله
پیش نمایش مقاله الگوریتم کلنی زنبور عسل پویا چندکلونی مصنوعی برای بهینه سازی چندهدفه

چکیده انگلیسی

This paper suggests a dynamic multi-colony multi-objective artificial bee colony algorithm (DMCMOABC) by using the multi-deme model and a dynamic information exchange strategy. In the proposed algorithm, K colonies search independently most of the time and share information occasionally. In each colony, there are S bees containing equal number of employed bees and onlooker bees. For each food source, the employed or onlooker bee will explore a temporary position generated by using neighboring information, and the better one determined by a greedy selection strategy is kept for the next iterations. The external archive is employed to store non-dominated solutions found during the search process, and the diversity over the archived individuals is maintained by using crowding-distance strategy. If a randomly generated number is smaller than the migration rate R, then an elite, defined as the intermediate individual with the maximum crowding-distance value, is identified and used to replace the worst food source in a randomly selected colony. The proposed DMCMOABC is evaluated on a set of unconstrained/constrained test functions taken from the CEC2009 special session and competition in terms of four commonly used metrics EPSILON, HV, IGD and SPREAD, and it is compared with other state-of-the-art algorithms by applying Friedman test on the mean of IGD. The test results show that DMCMOABC is significantly better than or at least comparable to its competitors for both unconstrained and constrained problems.

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