摘要
针对蝙蝠算法求解Job-shop调度问题的局限性,采用字符串编码、NEH初始化种群粒子和增加随机扰动的方法,对现有蝙蝠算法进行改进。通过对Job-shop调度问题基准算例的求解,并和模拟退火算法、标准遗传算法和粒子群算法进行比较,验证了该算法操作简单,收敛速度快,结果精度高,能有效求解Job-shop调度问题。
In order to overcome the bat algorithm＇ s drawback in solving the Job-shop scheduling problem, the existing bat algorithm is improved by the use of string encoding, NEH initialization population and increasing particle random perturbation. Compared with simulated annealing algorithm, the standard genetic algorithm and particle swarm algorithm, the simulation results for benchmark instances verify that Bat Algorithm has shown merits of sim- ple operation, fast convergence and high accuracy of the results, which can effectively solve the Job-shop scheduling problems.
出处
《科技与管理》
2014年第1期37-40,61,共5页
Science-Technology and Management
基金
国家自然科学基金项目(71271138)
教育部人文社会科学规划基金项目(10YJA630187)
上海市教育委员会科研创新项目(12ZS133)
上海市一流学科项目(S1201YLXK)
关键词
Job—shop调度
蝙蝠算法
随机扰动
字符串编码
NEH初始化
job-shop scheduling
bat algorithm
random perturbation
string encoding
Nawaz-Enscore-Ham （NEH） initialization
