【摘要】：基于記憶的人工蜂群算法(ABCM)通過記住成功使用的鄰居和系數(shù)指導(dǎo)人工蜂群下一步的搜索,需消耗多次函數(shù)評價收斂到吸引子,且始終使用與上次相同的排斥系數(shù),造成收斂速度不快、多樣性不足,易陷入局部最優(yōu)解.提出一種改進ABCM(IABCM),當使用吸引系數(shù)時,候選解只消耗一次函數(shù)評價收斂到吸引子,如果候選解好于當前解,則替換當前解,否則直接刪除該記憶,這樣可以利用盡量小的代價得到盡量大的收益.當使用排斥系數(shù)時,該系數(shù)的數(shù)值部分重新隨機生成,以增加多樣性和隨機性,有利于算法跳出局部最優(yōu)解.在22個不同類型函數(shù)上的實驗表明,IABCM在收斂速度和精度方面明顯優(yōu)于ABCM.
[Abstract]:The memory-based artificial beehive algorithm (ABCM) guides the next search of the artificial bee colony by remembering the neighbors and coefficients successfully used. It needs to consume many times of function evaluation to converge to the attractor, and always use the same rejection coefficient as the last time, resulting in the convergence speed is not fast, the diversity is insufficient, and it is easy to fall into the local optimal solution. In this paper, an improved ABCM (IABCM), is proposed, when the attraction coefficient is used, the candidate solution only consumes one time to converge to the attractor. If the candidate solution is better than the current solution, the current solution is replaced, otherwise the memory is deleted directly, so that the maximum benefit can be obtained by using the smallest cost. When the rejection coefficient is used, the numerical part of the coefficient is regenerated randomly to increase the diversity and randomness, which is helpful for the algorithm to jump out of the local optimal solution. The experiments on 22 different types of functions show that IABCM is obviously superior to ABCM. in convergence speed and accuracy.
【作者單位】： 韓山師范學(xué)院計算機與信息工程學(xué)院;上海海事大學(xué)信息工程學(xué)院;
【基金】：國家自然科學(xué)基金項目(61672338,61373028)
【分類號】：TP18
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本文編號：2516183