混合文化优化算法及在车间调度中的应用

针对文化算法收敛速度慢、易陷入局部最优解以及种群多样性少的问题, 本文对文化算法进行优化设计, 提出一种将带有精英保留策略的遗传算法(GA)和模拟退火算法(SA)纳入文化算法(CA)框架的混合优化算法. 此算法基于协同进化的思想, 算法分为下层种群空间和上层信念空间, 两个空间采用了相同的进化机制, 但使用不同的参数. 在文化算法的基础上加入带有精英保留策略的遗传算法, 使种群中的优秀个体直接进入下一代, 以此提高收敛速度; 加入模拟退火算法, 利用其具有突变的特点, 概率性的跳出局部最优并接受劣质解, 以此增加种群多样性. 函数优化结果证明了算法的有效性, 将此算法用于求解最小化最大完工时间的流水车间调度问题, 仿真结果显示, 此算法在收敛速度和精度方面都优于其他几个具有代表性的算法.

Hybrid Cultural Optimization Algorithm and Its Application in Workshop Scheduling

Given the various problems of the cultural algorithm, such as slow convergence speed, high likeliness to fall into local optimum, and low population diversity, this study optimizes the design of the cultural algorithm and proposes a hybrid optimization algorithm that incorporates a genetic algorithm (GA) with an elite retention strategy and a simulated annealing (SA) algorithm into the framework of the cultural algorithm (CA). In light of the idea of co-evolution, this algorithm is divided into a lower population space and an upper belief space that share the same evolutionary mechanism but use different parameters. On the basis of the CA, a GA with an elite retention strategy is added so that the outstanding individuals in the population can directly enter the next generation to improve the convergence speed. An SA algorithm is added as its mutation characteristics can be leveraged to enable the algorithm to probabilistically jump out of the local optimum and accept inferior solutions and thereby increase population diversity. The function optimization results prove the effectiveness of the proposed algorithm. This algorithm is applied to solve the flow shop scheduling problem of minimizing the maximum completion time. The simulation results show that the proposed algorithm is superior to several other representative algorithms in convergence speed and accuracy.