求解置换流水车间调度问题的离散蝴蝶启发优化算法

IF 0.7 4区计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Network World Pub Date : 2020-01-01 DOI:10.14311/nnw.2020.30.015

X. Qi, Yuan Zhonghu, Xiaowei Han, Shixin Liu

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引用次数: 1

摘要

置换流水车间调度问题是一个经典的组合优化问题，存在于许多制造系统中。研究表明，包含3台以上机器的PFSP属于NP-hard问题，难以求解。本文基于一种新的仿生算法——人工蝴蝶优化(ABO)算法，提出了一种离散人工蝴蝶优化(DABO)算法，以寻找完成时间最小或总流时间最小的排列。在著名的Car、Reeves和Taillard基准测试套件上测试了该算法的性能。实验结果表明，该算法能够在大多数基准函数上提供非常有前景和竞争力的结果。然后将DABO算法应用于一个生产优化问题。

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A discrete butterfly-inspired optimization algorithm for solving Permutation Flow-Shop scheduling Problems

Permutation Flow-Shop Scheduling Problem (PFSP) which exists in many manufacturing systems is a classic combinatorial optimization problem. Studies have shown that the PFSP including more than three machines belongs to the NP-hard problems and is difficult to solve. Based on a new bio-inspired algorithm – Artificial Butterfly Optimization (ABO) algorithm, this paper presents a Discrete Artificial Butterfly Optimization (DABO) algorithm to find the permutation that gives the smallest completion time or the smallest total flow time. The performance of the proposed algorithm is tested on well-known benchmark suites of Car, Reeves and Taillard. The experimental results show that the proposed algorithm is able to provide very promising and competitive results on most benchmark functions. The DABO algorithm is then employed for one production optimization problem.

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来源期刊

Neural Network World 工程技术-计算机：人工智能

CiteScore

1.80

自引率

0.00%

发文量

审稿时长

12 months

期刊介绍： Neural Network World is a bimonthly journal providing the latest developments in the field of informatics with attention mainly devoted to the problems of: brain science, theory and applications of neural networks (both artificial and natural), fuzzy-neural systems, methods and applications of evolutionary algorithms, methods of parallel and mass-parallel computing, problems of soft-computing, methods of artificial intelligence.