A Hybrid Imperialist Competitive Algorithm for the Flexible Job Shop Problem

被引：6

作者：

Ghasemishabankareh, Behrooz ^{[1
]}

Shahsavari-Pour, Nasser ^{[2
]}

Basiri, Mohammad-Ali ^{[3
]}

Li, Xiaodong ^{[1
]}

机构：

[1] RMIT Univ, Sch Comp Sci & IT, Melbourne, Vic, Australia

[2] Vali E Asr Univ, Dept Ind Management, Rafsanjan, Iran

[3] Islamic Azad Univ, Dept Ind Engn, Sci & Res Branch, Kerman, Iran

来源：

ARTIFICIAL LIFE AND COMPUTATIONAL INTELLIGENCE, ACALCI 2016 | 2016年 / 9592卷

关键词：

Flexible job shop scheduling problem; Imperialist competitive algorithm; Genetic algorithm; Simulated annealing algorithm; Taguchi parameter design; DEPENDENT SETUP TIMES; SCHEDULING PROBLEMS; GENETIC ALGORITHM; OPTIMIZATION; HYBRIDIZATION; TARDINESS; MECHANISM;

D O I：

10.1007/978-3-319-28270-1_19

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Flexible job shop scheduling problem (FJSP) is one of the hardest combinatorial optimization problems known to be NP-hard. This paper proposes a novel hybrid imperialist competitive algorithm with simulated annealing (HICASA) for solving the FJSP. HICASA explores the search space by using imperial competitive algorithm (ICA) and use a simulated annealing (SA) algorithm for exploitation in the search space. In order to obtain reliable results from HICASA algorithm, a robust parameter design is applied. HICASA is compared with the widely-used genetic algorithm (GA) and the relatively new imperialist competitive algorithm (ICA). Experimental results suggest that HICASA algorithm is superior to GA and ICA on the FJSP.

引用

页码：221 / 233

页数：13

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