An enhanced Pareto-based artificial bee colony algorithm for the multi-objective flexible job-shop scheduling

被引:86
作者
Wang, Ling [1 ]
Zhou, Gang [1 ]
Xu, Ye [1 ]
Liu, Min [1 ]
机构
[1] Tsinghua Univ, Dept Automat, TNList, Beijing 100084, Peoples R China
基金
美国国家科学基金会;
关键词
Flexible job-shop scheduling problem Multi-objective optimization; Artificial bee colony algorithm; Machine assignment; Operation sequence; Critical path; PARTICLE SWARM OPTIMIZATION; GENETIC ALGORITHM; TABU SEARCH; ABC ALGORITHM;
D O I
10.1007/s00170-011-3665-z
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, an enhanced Pareto-based artificial bee colony (EPABC) algorithm is proposed to solve the multi-objective flexible job-shop scheduling problem with the criteria to minimize the maximum completion time, the total workload of machines, and the workload of the critical machine simultaneously. First, it uses multiple strategies in a combination way to generate the initial solutions as the food sources with certain quality and diversity. Second, exploitation search procedures for both the employed bees and the onlooker bees are designed to generate the new neighbor food sources. Third, crossover operators are designed for the onlooker bee to exchange useful information. Meanwhile, it uses a Pareto archive set to record the nondominated solutions that participate in crossover with a certain probability. To enhance the local intensification, a local search based on critical path is embedded in the onlooker bee phase, and a recombination and select strategy is employed to determine the survival of the individuals. In addition, population is suitably adjusted to maintain diversity in scout bee phase. By using Taguchi method of design of experiment, the influence of several key parameters is investigated. Simulation results based on the benchmarks and comparisons with some existing algorithms demonstrate the effectiveness of the proposed EPABC algorithm.
引用
收藏
页码:1111 / 1123
页数:13
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