A Web-Based Platform for Modular Design of Intelligent Instrument Based on Improved Genetic Algorithm

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The design of new products is a creative work based on designer’s knowledge or experience. This paper develops a web-based design platform for intelligent instrument with the technology of Java and web database. It aims at offering near-optimal solutions of product design scheme that meets user requirement with the selection of module. An improved genetic algorithm with a binary encoding scheme is proposed to accomplish the selection of module more effectively.

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291-295

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August 2011

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© 2011 Trans Tech Publications Ltd. All Rights Reserved

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[1] Ericsson, A., & Erixon, G. Controlling design variants: modular product platforms. New York: ASME. (1999).

Google Scholar

[2] Information on http: / http: /www. oracle. com/technetwork/java/index. html.

Google Scholar

[3] Andersson, J. Multi-objective optimization in engineering design, applications to fluid power systems, PhD thesis. Institute of Technology, Linko¨ pings Universitet. (2001).

Google Scholar

[4] Goldberg, D.E. Genetic Algorithms in search, optimization & machine learning. Addison-Wesley Publishing Company. (1989).

Google Scholar

[5] Sette, S., Boullart, L. Genetic programming: principles and applications. Engineering applications of artificial intelligence 14, 727–736. (2001).

DOI: 10.1016/s0952-1976(02)00013-1

Google Scholar

[6] Augusto O.B., Rabeau S. Multi-objective genetic algorithms: a way to improve the convergence rate, Engineering Application of Artificial Intelligence, Elsevier Science. (2005).

DOI: 10.1016/j.engappai.2006.01.010

Google Scholar

[7] Burcin Cakir, Fulya Altiparmak, Berna Dengiz. Multi-objective optimization of a stochastic assembly line balancing: A hybrid simulated annealing algorithm. Computers & Industrial Engineering, 60, Issue 3, 376-384. (2011).

DOI: 10.1016/j.cie.2010.08.013

Google Scholar

[8] Falkenauer, E. Genetic algorithms and grouping problems. Chichester: Wiley. (1998).

Google Scholar

[9] Lit, P. D., Falkenauer, E., & Delchambre, A. Grouping genetic algorithms: an efficient method to solve the cell formation problem. Mathematics and Computers in simulation, 51, 257–271. (2000).

DOI: 10.1016/s0378-4754(99)00122-6

Google Scholar