By Topic

A Hybrid Self-Adaptive Pso Algorithm and its Applications for Partner Selection in Holonic Manufacturing System (HMS)

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Fu-qing Zhao ; School of Computer and Communication Engineering, Lanzhou University of Technolog, Lanzhou 730050, China. E-MAIL: ; Qiu-yu Zhang ; Ya-hong Yang

Partner selection is a very popular problem in the research of HMS, the key step in the formation of HMS is the decision making on partner selection. In this paper, collaboration process between holons is modeling with contract net protocol; and an activity network based multi-objective partner selection model is put forward. Then a new hybrid self-adaptive PSO (HAMPSO) algorithm based on particle swarm optimization (PSO) and genetic algorithm (GA) is proposed to solve the multi-objective problem. PSO employs a collaborative population-based search, which is inspired by the social behavior of bird flocking. GA provides the optimization parameter of PSO to get a good performance during the hybrid search process. HAMPSO implements easily and reserves the generality of PSO and GA. The hybrid algorithm combines the high speed of PSO with the powerful ability to avoid being trapped in local minimum by velocity mutation. We compare the hybrid algorithm to both the standard PSO and GA model. The simulation results show that the proposed model and algorithm are effective. Moreover, such HAMPSO can be applied to many combinatorial optimization problems by simple modification

Published in:

2006 International Conference on Machine Learning and Cybernetics

Date of Conference:

13-16 Aug. 2006