基于遗传蝙蝠算法的多目标选择性拆卸序列规划

Zhi Gang Xu
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引用次数: 0

摘要

遗传蝙蝠算法在复杂的多目标产品选择性拆卸序列规划问题中的应用正受到积极研究。为了扩大搜索空间范围并提高整体搜索效率,传统的蝙蝠算法进行了离散化处理,在构建适应度函数模型时加入了交叉突变机制。为了评估这种新方法的有效性,我们利用一个工业机械臂作为代表性案例进行了研究。与传统的蝙蝠算法相比,所提出的方法在不同种群规模下的收敛时间更短,从而验证了其有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Genetic Bat Algorithm-Based Multi-Objective Selective Disassembly Sequence Planning
The genetic bat algorithm is being actively investigated for its application in the complex multi-objective product selective disassembly sequence planning problem. To broaden the scope of the search space and enhance the overall search efficiency, the traditional bat algorithm has undergone discretization, incorporating a cross-mutation mechanism into the construction of the fitness function model. To assess the efficacy of this novel approach, an industrial mechanical arm is utilized as a representative case study. Upon comparison with the traditional bat algorithm, the proposed method exhibits shorter convergence times across a range of population sizes, thus validating its effectiveness.
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