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A hybrid honey-bees mating optimization algorithm for assembly sequence planning problem

机译:装配序列计划问题的混合蜜蜂交配优化算法

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Assembly sequence planning (ASP) refers to taking the related constraint factors such as assembly features, assembly tools and machines into consideration to generate a low-cost feasible sequence. In this paper, a mathematical model of assembly sequence planning problem based on connectors is constructed, and a hybrid honey-bees mating optimization (HBMO) algorithm is proposed for solving this ASP problem. The proposed algorithm has two main innovative features compared to the conventional HBMO algorithm. Firstly, a crossover operator, called Multipoint Precedence Crossover (MPX), is proposed, which can avoid the generation of infeasible solutions and preserve the meaningful characteristics of the queen and broods. Secondly, worker bees utilize the simulated annealing (SA) algorithm as a local search method to improve the broods, which makes the proposed algorithm achieve the right balance between intensification and diversification. The hybrid HBMO algorithm is tested on three practical instances and compared with other approaches, such as Guided-GAs, MAs (memetic algorithm) and AIS (artificial immune systems). The superior results on these practical instances validate the effectiveness of the proposed algorithm.
机译:装配顺序计划(ASP)指的是考虑相关的约束因素,例如装配特征,装配工具和机器,以生成低成本的可行序列。本文建立了一个基于连接器的装配序列计划问题的数学模型,并提出了一种混合蜜蜂交配优化(HBMO)算法来解决该ASP问题。与常规HBMO算法相比,该算法具有两个主要的创新功能。首先,提出了一种称为多点优先交叉(MPX)的交叉算子,它可以避免生成不可行的解决方案并保留皇后区和育雏区的有意义的特征。其次,工蜂利用模拟退火算法(SA)作为局部搜索方法来改善亲代,从而使算法在集约化和多样化之间达到了适当的平衡。混合型HBMO算法在三个实际情况下进行了测试,并与其他方法(如Guided-GA,MA(模因算法)和AIS(人工免疫系统))进行了比较。在这些实际实例上的优异结果验证了所提出算法的有效性。

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