Paper Info
Title
Self-adaptive bat algorithm for large scale cloud manufacturing service composition.
Abstract
In order to cope with the current economic situation and the trend of global manufacturing, Cloud Manufacturing Mode (CMM) is proposed as a new manufacturing model recently. Massive manufacturing capabilities and resources are provided as manufacturing services in CMM. How to select the appropriate services optimally to complete the manufacturing task is the Manufacturing Service Composition (MSC) problem, which is a key factor in the CMM. Since MSC problem is NP hard, solving large scale MSC problems using traditional methods may be highly unsatisfactory. To overcome this shortcoming, this paper investigates the MSC problem firstly. Then, a Self-Adaptive Bat Algorithm (SABA) is proposed to tackle the MSC problem. In SABA, three different behaviors based on a self-adaptive learning framework, two novel resetting mechanisms including Local and Global resetting are designed respectively to improve the exploration and exploitation abilities of the algorithm for various MSC problems. Finally, the performance of the different flying behaviors and resetting mechanisms of SABA are investigated. The statistical analyses of the experimental results show that the proposed algorithm significantly outperforms PSO, DE and GL25.
Year
DOI
Venue
2018
10.1007/s12083-017-0588-y
Peer-to-Peer Networking and Applications
Keywords
Field
DocType
Manufacturing service composition, Self-adaptive learning, Bat algorithm, Dual resetting
Cloud manufacturing,Global manufacturing,Bat algorithm,Computer science,Service composition,Self adaptive,Distributed computing
Journal
Volume
Issue
ISSN
11
5
1936-6442
Citations 
PageRank 
References 
3
0.37
23
Authors
6
Name
Order
Citations
PageRank
Bin Xu1112.86
Qi, J.2163.34
Xiaoxuan Hu3454.54
Kwong-Sak Leung41887205.58
Yanfei Sun514118.08
Yu Xue619716.61