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

Content Provider	IEEE Xplore Digital Library
Author	Fu-Qing Zhao Qiu-Yu Zhang Ya-Hong Yang
Copyright Year	2006
Description	Author affiliation: Sch. of Comput. & Commun. Eng., Lanzhou Univ. of Technol. (Fu-Qing Zhao; Qiu-Yu Zhang)
Abstract	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
Sponsorship	IEEE Syst., Man and Cybernetics Hebei Univ.
Starting Page	2536
Ending Page	2541
File Size	255471
Page Count	6
File Format	PDF
ISBN	1424400619
DOI	10.1109/ICMLC.2006.258845
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Manufacturing systems Collaboration Contracts Particle swarm optimization Genetic algorithms Power system modeling Application software Computer aided manufacturing Civil engineering Decision making Holonic Manufacturing System Particle Swarm Optimization (PSO) Genetic Algorithm (GA) Partner Selection
Content Type	Text
Resource Type	Article

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