基于Pareto改进猫群优化算法的多目标拆卸线平衡问题
发布时间：2019-06-04 00:52
【摘要】：为求解多目标拆卸线平衡问题,提出了一种改进的猫群优化算法.在该算法中,针对拆卸线平衡问题以拆卸序列为编码的特点,提出一种基于随机数和固定扰动的搜寻模式确保猫在当前位置附近有效的随机搜索.将遗传算法交叉操作和变异操作引入跟踪模式中指导种群向全局最优逼近,有效地克服了传统猫群优化算法容易早熟的缺点.建立外部档案集并采用精英保留策略加速算法的收敛.最后,通过将该算法用于求解经典的多目标拆卸线平衡问题算例并与其它算法对比,验证了算法的有效性.
[Abstract]:In order to solve the multi-objective disassembly line balance problem, an improved cat swarm optimization algorithm is proposed. In this algorithm, aiming at the problem of disassembly line balance, which is encoded by disassembly sequence, a search mode based on random number and fixed disturbance is proposed to ensure the effective random search of cat near the current position. The genetic algorithm cross operation and mutation operation are introduced into the tracking mode to guide the population to approximate to the global optimal, which effectively overcome the disadvantage that the traditional cat swarm optimization algorithm is easy to precocious. The external file set is established and the elite retention strategy is used to accelerate the convergence of the algorithm. Finally, the algorithm is applied to solve the classical multi-objective disassembly line balance problem and compared with other algorithms to verify the effectiveness of the algorithm.
【作者单位】： 西南交通大学机械工程学院;
【基金】：国家自然科学基金资助项目(51205328,51405403)
【分类号】：TH186;TP18
本文编号：2492354
[Abstract]:In order to solve the multi-objective disassembly line balance problem, an improved cat swarm optimization algorithm is proposed. In this algorithm, aiming at the problem of disassembly line balance, which is encoded by disassembly sequence, a search mode based on random number and fixed disturbance is proposed to ensure the effective random search of cat near the current position. The genetic algorithm cross operation and mutation operation are introduced into the tracking mode to guide the population to approximate to the global optimal, which effectively overcome the disadvantage that the traditional cat swarm optimization algorithm is easy to precocious. The external file set is established and the elite retention strategy is used to accelerate the convergence of the algorithm. Finally, the algorithm is applied to solve the classical multi-objective disassembly line balance problem and compared with other algorithms to verify the effectiveness of the algorithm.
【作者单位】： 西南交通大学机械工程学院;
【基金】：国家自然科学基金资助项目(51205328,51405403)
【分类号】：TH186;TP18
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