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Modified Krill Herd (MKH) algorithm and its application in dimensional synthesis of a four-bar linkage

机译:改进的磷虾群算法(MKH)及其在四连杆机构尺寸综合中的应用

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摘要

The paper considers the application of the modified Krill Herd (KH) algorithm for obtaining optimal solutions in dimensional synthesis of a four-bar linkage as a path generator. Certain modifications have been made for the purpose of increasing the performance of the standard KH algorithm for the considered examples of synthesis. In the first modification, besides the initialization of fitness functions, there is also the initialization of the vectors which represent the food location. This modification reflects the actual behavior of krills in their natural environment, i.e. the tendency for optimum swarm density and the best position in relation to the food. The second modification relates to the replacement of the crossover operator with the combination of columns of fitness functions obtained in one iteration. The newly obtained sequence of krills is corrected by the value of physical diffusion, which results in repeated search of the solution space, in the same iteration, in order to improve the optimum found. This is how the Modified Krill Herd (MKH) algorithm tested on four benchmark examples from the synthesis of a four-bar linkage has been obtained. The results obtained by this algorithm confirm its efficiency, i.e. they considerably outperform the results obtained in the cited literature. (C) 2015 Elsevier Ltd. All rights reserved.
机译:本文考虑了改进的Krill Herd(KH)算法在获得作为路径生成器的四连杆机构的尺寸合成中的最优解的应用。为了提高所考虑的合成示例的标准KH算法的性能,已进行了某些修改。在第一修改中,除了适应度函数的初始化之外,还代表食物位置的向量的初始化。这种修改反映了磷虾在其自然环境中的实际行为,即最佳群体密度和相对于食物的最佳位置的趋势。第二修改涉及用一次迭代中获得的适应度函数的列的组合来代替交叉算子。新获得的磷虾序列通过物理扩散值进行校正,从而在同一迭代中重复搜索解空间,以提高找到的最优值。这就是从四杆连杆的综合中获得的在四个基准示例上测试的改良磷虾牧群(MKH)算法的方式。通过该算法获得的结果证实了其效率,即,其性能大大优于引用文献中获得的结果。 (C)2015 Elsevier Ltd.保留所有权利。

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