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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.
In order to solve the problem of multi-objective disassembly line balance, an improved cat swarm optimization algorithm is proposed.In order to solve the problem of disassembly line balance, disassembly sequence coding is a new algorithm based on random numbers and fixed disturbances To ensure the cat near the current location of effective random search.Cross-operation and mutation operation of genetic algorithm to guide the population to guide the population to the global optimum approximation, effectively overcome the shortcomings of the traditional cat group optimization algorithm is easy to premature.Establish external archives and The elitist retention strategy is used to accelerate the convergence of the algorithm.Finally, the algorithm is proved to be effective by solving the classical example of multi-objective disassembly line balancing problem and comparing with other algorithms.