| 摘要(英) |
In human activities a lot of decision-making issues exist, among them, the decision-making process, tool, and approach, all these need to acquire the best solution to meet their requirements. Inevitably, the solution-seeking process focuses on the experience-accumulation and tool-application. Following the activity scope of human-from narrow to wide; and their mutual link-from loose to tight, the facing problems have become more and more complex. Earlier experiences or tools seem not possible to deal with existing big problem efficiently.
Scholars explored the potential intelligence from the system of biological colony behavior, fueled by the fast development of calculator technology, to present a series of solution derived from the biological intelligence concept. However, the particle swarm optimization algorithm (PSO) is a solution formula through the calculation on self experience and colony experiences of creatures. Its merits include few parameters setting, prompt sourcing speed, and high feasibility. Consequently, many scholars had massively announced the related applications in practice.
This thesis adopts the merits of PSO to deal with the optimization problem of enterprise’s network procurement, in order to provide references of more simple, effective, and optimal standards. Thereby it offers a more efficient tool for the project-performer in the course of decision making.
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| 參考文獻 |
[1] Jan A. Snyman (2005). Practical Mathematical Optimization: An Introduction to Basic Optimization Theory and Classical and New Gradient-Based Algorithms. Springer Publishing.
[2] S.S. Rao (1984). Optimization Theory and Applications, Second Edition, Wiley Eastern Limited, New Delhi.
[3] Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein. Introduction to Algorithms, Second Edition. MIT Press and McGraw-Hill, 2001. Section 29.3: The simplex algorithm, pp.790–804.
[4] Hopfield, J. and Tank, D. (1985). Neural computation of decisions in optimization problems. Biological Cybernetics, 52:141–152.
[5] Hopfield J.J. and Tank D.W., "Computing with neural circuits: a model", Science, vol.233, 1986, pp. 625-633.
[6] Goldberg, D.E. (1989) Genetic algorithms in search optimization and machine learning. Addison-Wesley, MA, USA.
[7] Rudolph, G., (1994): "Convergence analysis of canonical genetic algorithms", IEEE Transactions on Neural Networks, 5, pp. 96–101.
[8] Kirkpatrick, S.; Gelatt, C. D.; Vecchi, M. P. (1983). "Optimization by Simulated Annealing". Science, Vol. 220, pp. 671–680.
[9] Kirkpatrick, S. (1984), "Optimization by Simulated Annealing: Quantitative Studies", Journal of Statistical Physics, Vol. 34, pp. 975–986.
[10] G. Beni and J. Wang (1989), "Swarm intelligence in cellular robotics systems", Proceedings of NATO Advanced Workshop on Robots and Biological System, pp. 703–712.
[11] E. Bonabeau, M. Dorigo, G. Theraulaz (1999), Swarm intelligence: from natural to artificial systems.
[12] Kennedy, J.; Eberhart, R. (1995). "Particle Swarm Optimization". Proceedings of IEEE International Conference on Neural Networks. IV. pp. 1942–1948.
[13] Shi, Y.; Eberhart, R.C. (1998). "A modified particle swarm optimizer". Proceedings of IEEE International Conference on Evolutionary Computation. pp. 69–73.
[14] Shi, Y.; Eberhart, R.C. (1999). "Empirical study of particle swarm optimization". Proceedings of the Congress on Evolutionary Computation, pp. 1945–1950.
[15] Shi, Y.; Eberhart, R.C. (1998). "Parameter selection in particle swarm optimization". Proceedings of Evolutionary Programming VII (EP98). pp. 591–600.
[16] Kennedy, J. (1997). "The particle swarm: social adaptation of knowledge". Proceedings of IEEE International Conference on Evolutionary Computation. pp. 303–308.
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