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A Forager Adjustment Strategy Used by the Bees Algorithm for Solving Optimization Problems in Cloud Manufacturing

[+] Author Affiliations
Yongquan Xie, Zude Zhou, Quan Liu, Wenjun Xu, Ping Lou, Sisi Tian

Wuhan University of Technology, Wuhan, China

Duc Truong Pham, Chunqian Ji

University of Birmingham, Birmingham, UK

Paper No. MSEC2015-9255, pp. V002T04A014; 7 pages
doi:10.1115/MSEC2015-9255
From:
  • ASME 2015 International Manufacturing Science and Engineering Conference
  • Volume 2: Materials; Biomanufacturing; Properties, Applications and Systems; Sustainable Manufacturing
  • Charlotte, North Carolina, USA, June 8–12, 2015
  • Conference Sponsors: Manufacturing Engineering Division
  • ISBN: 978-0-7918-5683-3
  • Copyright © 2015 by ASME

abstract

Intelligent technologies have become increasingly important in manufacturing nowadays. Optimal service management and allocation in current cloud manufacturing model are impossible without applications of appropriate intelligent tools. The Bees Algorithm (BA) is a swarm-based intelligent optimizer that provides support for smart decision-making process in manufacturing models. A novel forager adjustment strategy (FAS) is proposed in this paper to manage the forager division in the algorithm, so as to make the entire colony perform with higher efficiency. The proposed FAS based Bees Algorithm (FAS-BA) is able to realize flexible allocation of its forager resources between different roles in accordance with the solution fitness sampled by current scout population. The proposed algorithm is presented in detail. Experiments are conducted based on a set of well-known benchmark functions and a case study. Comparisons between FAS-BA and an improved Bees Algorithm are made to highlight the effectiveness of FAS. The results demonstrate that the proposed algorithm requires less function evaluation cost than the improved version but is capable of obtaining at least the same optimal solution to a problem.

Copyright © 2015 by ASME

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