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Erschienen in:
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2015 | OriginalPaper | Buchkapitel

Adaptive Tunning of All Parameters in a Multi-Swarm Particle Swarm Optimization Algorithm: An Application to the Probabilistic Traveling Salesman Problem

verfasst von : Yannis Marinakis, Magdalene Marinaki, Athanasios Migdalas

Erschienen in: Optimization, Control, and Applications in the Information Age

Verlag: Springer International Publishing

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Abstract

One of the main issues in the application of a particle swarm optimization (PSO) algorithm and of every evolutionary optimization algorithm is the finding of the suitable parameters of the algorithm. Usually, a trial and error procedure is used but, also, a number of different procedures have been applied in the past. In this chapter, we use a new adaptive version of a PSO algorithm where random values are assigned in the initialization of the algorithm and, then, during the iterations the parameters are optimized together and simultaneously with the optimization of the objective function of the problem. This idea is used for the solution of the probabilistic traveling salesman problem (PTSP). The algorithm is tested on a number of benchmark instances and it is compared with a number of algorithms from the literature.

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Vorheriges Kapitel On Nash Equilibria in Stochastic Positional Games with Average Payoffs
Nächstes Kapitel Eigendecomposition of the Mean-Variance Portfolio Optimization Model
Literatur
1.
Bahmani-Firouzi, B., Farjah, E., Azizipanah-Abarghooee, R.: An efficient scenario-based and fuzzy self-adaptive learning particle swarm optimization approach for dynamic economic emission dispatch considering load and wind power uncertainties. Energy 50, 232–244 (2013)CrossRef
2.
Balaprakash, P., Birattari, M., Stutzle, T., Yuan, Z., Dorigo, M.: Estimation-based ant colony optimization and local search for the probabilistic traveling salesman problem. Swarm Intell. 3, 223–242 (2009)CrossRef
3.
Bertsimas, D.J.: Probabilistic combinatorial optimization problems. Ph.D. thesis, MIT, Cambridge (1988)
4.
Bianchi, L.: Ant colony optimization and local search for the probabilistic traveling salesman problem: a case study in stochastic combinatorial optimization. Ph.D. thesis, Universite Libre de Bruxelles, Belgium (2006)
5.
Branke, J., Guntsch, M.: Solving the probabilistic TSP with ant colony optimization. J. Math. Model. Algorithms 3(4), 403–425 (2004)MathSciNetCrossRefMATH
6.
Chen, D.B., Zhao, C.X.: Particle swarm optimization with adaptive population size and its application. Appl. Soft Comput. 9, 39–48 (2009)CrossRef
7.
8.
Clerc, M., Kennedy, J.: The particle swarm: explosion, stability and convergence in a multi-dimensional complex space. IEEE Trans. Evol. Comput. 6, 58–73 (2002)CrossRef
9.
Cooren, Y., Clerc, M., Siarry, P.: Initialization and displacement of the particles in TRIBES, a parameter-free particle swarm optimization algorithm. In: Cotta, C., et al. (eds.) Adaptive and Multilevel Metaheuristics, SCI, vol. 136, pp. 199–219. Springer, Berlin/Heidelberg (2008)CrossRef
10.
Cooren, Y., Clerc, M., Siarry, P.: Performance evaluation of TRIBES, an adaptive particle swarm optimization algorithm. Swarm Intell. 3, 149–178 (2009)CrossRef
11.
Crawford, B., Soto, R., Monfroy, E., Palma, W., Castro, C., Paredesm, F.: Parameter tuning of a choice-function based hyperheuristic using particle swarm optimization. Expert Syst. Appl. 40, 1690–1695
12.
Eslami, M., Shareef, H., Taha, M.R., Khajehzadeh, M.: Adaptive particle swarm optimization for simultaneous design of UPFC damping controllers. Electr. Power Energy Syst. 57, 116–128 (2014)CrossRef
13.
14.
15.
16.
Glover, F., Laguna, M., Marti, R.: Scatter search and path relinking: advances and applications. In: Glover, F., Kochenberger, G.A. (eds.) Handbook of Metaheuristics, pp. 1–36. Kluwer Academic Publishers, Boston (2003)
17.
Han, F., Ling, Q.H.: A new approach for function approximation incorporating adaptive particle swarm optimization and a priori information. Appl. Math. Comput. 205, 792–798 (2008)MathSciNetCrossRefMATH
18.
Hansen, P., Mladenovic, N.: Variable neighborhood search: principles and applications. Eur. J. Oper. Res. 130, 449–467 (2001)MathSciNetCrossRefMATH
19.
Hasanzadeh, M., Meybodi, M.R., Ebadzadeh, M.M.: Adaptive cooperative particle swarm optimizer. Appl. Intell. 39, 397–420 (2013)CrossRef
20.
Hashemi, A.B., Meybodi, M.R.: Adaptive parameter selection scheme for PSO: a learning automata approach. In: Proceedings of the 14th International CSI Computer Conference (CSICC’09), IEEE, pp. 403–411 (2009)
21.
Hashemi, A.B., Meybodi, M.R.: A note on the learning automata based algorithms for adaptive parameter selection in PSO. Appl. Soft Comput. 11, 689–705 (2011)CrossRef
22.
Ismail, A., Engelbrecht, A.P.: Self-adaptive particle swarm optimization. In: Bui, L.T., et al. (eds.) SEAL 2012. Lecture Notes in Computer Science, vol. 7673, pp. 228–237 (2012)CrossRef
23.
Ismail, A., Engelbrecht, A.P.: The self-adaptive comprehensive learning particle swarm optimizer. In: Dorigo, M., et al. (eds.) ANTS 2012. Lecture Notes in Computer Science, vol. 7461, pp. 156–167 (2012)CrossRef
24.
Jaillet, P.: Probabilistic traveling salesman problems. Ph.D. thesis, MIT, Cambridge (1985)
25.
Jaillet, P.: A priori solution of a traveling salesman problem in which a random subset of the customers are visited. Oper. Res. 36(6), 929–936 (1988)MathSciNetCrossRefMATH
26.
Jiang, Y., Li, X., Huang, C.: Automatic calibration a hydrological model using a masterslave swarms shuffling evolution algorithm based on self-adaptive particle swarm optimization. Expert Syst. Appl. 40, 752–757 (2013)CrossRef
27.
Jiang, B., Wang, N., Wang, L.: Particle swarm optimization with age-group topology for multimodal functions and data clustering. Commun. Nonlinear Sci. Numer. Simul. 18, 3134–3145 (2013)MathSciNetCrossRef
28.
Juang, Y.T., Tung, S.L., Chiu, H.C.: Adaptive fuzzy particle swarm optimization for global optimization of multimodal functions. Inform. Sci. 181, 4539–4549 (2011)MathSciNetCrossRefMATH
29.
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of 1995 IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)
30.
Leu, M.S., Yeh, M.F.: Grey particle swarm optimization. Appl. Soft Comput. 12, 2985–2996 (2012)CrossRef
31.
Leu, M.S., Yeh, M.F., Wang, S.C.: Particle swarm optimization with grey evolutionary analysis. Appl. Soft Comput. 12, 4047–4062 (2012)CrossRef
32.
Li, X.L., Li, L.H., Zhang, B.L., Guo, Q.J.: Hybrid self-adaptive learning based particle swarm optimization and support vector regression model for grade estimation. Neurocomputing 118, 179–190 (2013)CrossRef
33.
Lichtblau, D.: Discrete optimization using Mathematica. In: Callaos, N., Ebisuzaki, T., Starr, B., Abe, J.M., Lichtblau, D. (eds.) World Multi-Conference on Systemics, Cybernetics and Informatics (SCI 2002). International Institute of Informatics and Systemics, vol. 16, pp. 169–174 (2002)
34.
Liu, Y.-H.: A hybrid scatter search for the probabilistic traveling salesman problem. Comput. Oper. Res. 34(10), 2949–2963 (2007)CrossRefMATH
35.
Liu, Y.-H.: Different initial solution generators in genetic algorithms for solving the probabilistic traveling salesman problem. Appl. Math. Comput. 216, 125–137 (2010)MathSciNetCrossRefMATH
36.
Lu, H. Chen, W.: Self-adaptive velocity particle swarm optimization for solving constrained optimization problems. J. Glob. Optim. 41, 427–445 (2008)CrossRefMATH
37.
Marinakis, Y., Marinaki, M.: A hybrid honey bees mating optimization algorithm for the probabilistic traveling salesman problem. In: IEEE Congress on Evolutionary Computation (CEC 2009), 18–21 May, Trondheim (2009)
38.
Marinakis, Y., Marinaki, M.: A hybrid multi-swarm particle swarm optimization algorithm for the probabilistic traveling salesman problem. Comput. Oper. Res. 37, 432–442 (2010)MathSciNetCrossRefMATH
39.
Marinakis, Y., Marinaki, M.: Particle swarm optimization with expanding neighborhood topology for the permutation flowshop scheduling problem. Soft Comput. 17(7), 1159–1173 (2013)CrossRef
40.
Marinakis, Y., Migdalas, A., Pardalos, P.M.: Expanding neighborhood search GRASP for the probabilistic traveling salesman problem. Optim. Lett. 2(3), 351–361 (2008)MathSciNetCrossRefMATH
41.
Marinakis, Y., Marinaki, M., Dounias, G.: A hybrid particle swarm optimization algorithm for the vehicle routing problem. Eng. Appl. Artif. Intell. 23, 463–472 (2010)CrossRef
42.
Modares, H., Alfi, A., Naghibi Sistani, M.B.: Parameter estimation of bilinear systems based on an adaptive particle swarm optimization. Eng. Appl. Artif. Intell. 23, 1105–1111 (2010)CrossRef
43.
Neshat, M.: FAIPSO: fuzzy adaptive informed particle swarm optimization. Neural Comput. Appl. 23(1 Suppl.), 95–116 (2013)CrossRef
44.
Nickabadi, A., Ebadzadeh, M.M., Safabakhsh, R.: A novel particle swarm optimization algorithm with adaptive inertia weight. Appl. Soft Comput. 11, 3658–3670 (2011)CrossRef
45.
Niknam, T.: A new fuzzy adaptive hybrid particle swarm optimization algorithm for non-linear, non-smooth and non-convex economic dispatch problem. Appl. Energy 87, 327–339 (2010)CrossRef
46.
Niknam, T., Farsani, E.A.: A hybrid self-adaptive particle swarm optimization and modified shuffled frog leaping algorithm for distribution feeder reconfiguration. Eng. Appl. Artif. Intell. 23, 1340–1349 (2010)CrossRef
47.
Niknam, T., Doagou Mojarrad, H., Nayeripour, M.: A new fuzzy adaptive particle swarm optimization for non-smooth economic dispatch. Energy 35, 1764–1778 (2010)CrossRef
48.
Onwubolu, G.C.: TRIBES application to the flow shop scheduling problem. In: Onwubolu, G.C., et al. (eds.) New Optimization Techniques in Engineering, pp. 517–536. Springer, Berlin/Heidelberg (2004)CrossRef
49.
Powell, W.B., Jaillet, P., Odoni, A.: Stochastic and dynamic networks and routing. In: Ball M.O., Magnanti T.L., Momma C.L., Nemhauser G.L. (eds.) Network Routing, Handbooks in Operations Research and Management Science, vol. 8, pp. 141–295. Elsevier Science B. V., Amsterdam (1995)
50.
Ratnaweera, A., Halgamuge, S.K., Watson, H.C.: Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans. Evol. Comput. 8(3), 240–255 (2004)CrossRef
51.
Senthil Arumugam, M., Rao, M.V.C.: On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (RMS) variants for computing optimal control of a class of hybrid systems. Appl. Soft Comput. 8, 324–336 (2008)CrossRef
52.
Song, Y.D., Zhang, L., Han, P.: An adaptive tribe-particle swarm optimization. In: Tan, Y., et al. (eds.) ICSI 2011, Part I. Lecture Notes in Computer Science, vol. 6728, pp. 86–92 (2011)CrossRef
53.
Tripathi, P.K., Bandyopadhyay, S., Pal, S.K.: Multi-objective particle swarm optimization with time variant inertia and acceleration coefficients. Inf. Sci. 177, 5033–5049 (2007)MathSciNetCrossRefMATH
54.
Wang, J.: Particle swarm optimization with adaptive parameter control and opposition. J. Comput. Inf. Syst. 7(12), 4463–4470 (2011)
55.
Wang, Y., Li, B., Weise, T., Wang, J., Yuan, B., Tian, Q.: Self-adaptive learning based particle swarm optimization. Inf. Sci. 181, 4515–4538 (2011)CrossRefMATH
56.
Wang, J., Lu, H., Dong, Y., Chi, D.: The model of chaotic sequences based on adaptive particle swarm optimization arithmetic combined with seasonal term. Appl. Math. Model. 36, 1184–1196 (2012)MathSciNetCrossRefMATH
57.
Wang, Y., Zhou, J., Zhou, C., Wang, Y., Qin, H., Lu, Y.: An improved self-adaptive PSO technique for short-term hydrothermal scheduling. Expert Syst. Appl. 39, 2288–2295 (2012)CrossRef
58.
Xu, G.: An adaptive parameter tuning of particle swarm optimization algorithm. Appl. Math. Comput. 219, 4560–4569 (2013)MathSciNetCrossRef
59.
Zhan, Z.H., Zhang, J., Li, Y., Chung, H.S.H.: Adaptive particle swarm optimization. IEEE Trans. Syst. Man Cybern. B Cybern. 39(6), 1362–1381 (2009)CrossRef
60.
Zhang, J., Ding, X.: A multi-swarm self-adaptive and cooperative particle swarm optimization. Eng. Appl. Artif. Intell. 24, 958–967 (2011)MathSciNetCrossRef
61.
Zhang, W., Liu, Y.: Adaptive particle swarm optimization for reactive power and voltage control in power systems. In: Wang, L., Chen, K., Ong, Y.S. (eds.) ICNC 2005. Lecture Notes in Computer Science, vol. 3612, pp. 449–452 (2005)CrossRef
62.
Zhang, L., Mei, T., Liu, Y., Tao, D., Zhou, H.Q.: Visual search reranking via adaptive particle swarm optimization. Pattern Recogn. 44, 1811–1820 (2011)CrossRef
Metadaten
Titel
Adaptive Tunning of All Parameters in a Multi-Swarm Particle Swarm Optimization Algorithm: An Application to the Probabilistic Traveling Salesman Problem
verfasst von
Yannis Marinakis
Magdalene Marinaki
Athanasios Migdalas
Copyright-Jahr
2015
Buch
Optimization, Control, and Applications in the Information Age

Print ISBN: 978-3-319-18566-8

Electronic ISBN: 978-3-319-18567-5

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-319-18567-5_10