Skip to main content
SpringerLink
Log in

The Optimization of Computational Stock Market Model Based Complex Adaptive Cyber Physical Logistics System: A Computational Intelligence Perspective

  • Chapter
  • First Online:
Computational Intelligence for Decision Support in Cyber-Physical Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 540))

Abstract

This chapter makes an attempt to address three critical issues that, from a computational intelligence perspective, will arise when computational stock market model (CSMM) based complex adaptive cyber physical logistics system (CACPLS) is implemented in the future supply network. The chapter starts with an introduction and background description about the necessity of introducing the CSMM-based CACPLS; then the focal problems (i.e., developing investment strategy, predicting stock price, and controlling extreme events) of this chapter is stated in the problem statement section; a detailed description about our approaches, i.e., training artificial neural network via particle swarm optimization, genetic algorithm for stock price forecasting, and agent-based modeling and simulation for preventing extreme events, together with three example studies can be found in the subsequent proposed methodology sections; right after this, the potential research directions regarding the key problems considered in this chapter are highlighted in the future trends section; finally, the conclusions drawn at the last section closes this chapter.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. M. Abdechiri, M.R. Meybodi, H. Bahrami, Gases Brownian motion optimization: an algorithm for optimization (GBMO). Appl. Soft Comput. 13(5), 2932–2946 (2013). http://dx.doi.org/10.1016/j.asoc.2012.03.068

  2. A. Abuhamdah, M. Ayob, Hybridization multi-neighbourhood particle collision algorithm and great deluge for solving course timetabling problems. Paper presented at the 2nd Conference On Data Mining and Optimization, (Selangor, 27–28 Oct 2009a), pp. 108–114

    Google Scholar 

  3. A. Abuhamdah, M. Ayob, Multi-neighbourhood particle collision algorithm for solving course timetabling problems. Paper presented at the 2nd Conference On Data Mining and Optimization (Selangor, 27–28 Oct 2009b), pp. 21–27

    Google Scholar 

  4. S. Afshari, B. Aminshahidy, M.R. Pishvaie, Application of an improved harmony search algorithm in well placement optimization using streamline simulation. J. Petrol. Sci. Eng. 78, 664–678 (2011)

    Article  Google Scholar 

  5. M.A. Al-Betar, I.A. Doush, A.T. Khader, M.A. Awadallah, Novel selection schemes for harmony search. Appl. Math. Comput. 218, 6095–6117 (2012)

    Article  MATH  Google Scholar 

  6. M.A. Al-Betar, A.T. Khader, A harmony search algorithm for university course timetabling. Ann. Oper. Res. 194(1), 3–31 (2012)

    Article  MATH  MathSciNet  Google Scholar 

  7. M.A. Al-Betar, A.T. Khader, F. Nadi, Selection mechanisms in memory consideration for examination timetabling with harmony search. Paper presented at the Annual Conference on Genetic and Evolutionary Computation (GECCO) (Portland, 7–11 July 2010), pp. 1203–1210

    Google Scholar 

  8. B. Alatas, Chaotic harmony search algorithms. Appl. Math. Comput. 216, 2687–2699 (2010)

    Article  MATH  Google Scholar 

  9. B. Alatas, ACROA: artificial chemical reaction optimization algorithm for global optimization. Expert Syst. Appl. 38, 13170–13180 (2011)

    Article  Google Scholar 

  10. O.M. Alia, R. Mandava, The variants of the harmony search algorithm: an overview. Artif. Intell. Rev. 36, 49–68 (2011)

    Google Scholar 

  11. A.R.A. Alsewari, K.Z. Zamli, Design and implementation of a harmony-search-based variable-strength t-way testing strategy with constraints support. Inf. Softw. Technol. 54, 553–568 (2012)

    Article  Google Scholar 

  12. A.R.A. Alsewari, K.Z. Zamli, A harmony search based pairwise sampling strategy for combinatorial testing. Int. J. Phys. Sci. 7(7), 1062–1072 (2012)

    Google Scholar 

  13. M.T. Ameli, M. Shivaie, S. Moslehpour, Transmission network expansion planning based on hybridization model of neural networks and harmony search algorithm. Int. J. Ind. Eng. Comput. 3, 71–80 (2012)

    Google Scholar 

  14. C. Anandaraman, A.V.M. Sankar, R. Natarajan, A new evolutionary algorithm based on bacterial evolution and its applications for scheduling a flexible manufacturing system. Jurnal Teknik Industri 14(1), 1–12 (2012)

    Article  Google Scholar 

  15. A. Askarzadeh, A. Rezazadeh, A grouping-based global harmony search algorithm for modeling of proton exchange membrane fuel cell. Int. J. Hydrogen Energy 36, 5047–5053 (2011)

    Article  Google Scholar 

  16. P. Aungkulanon, P. Luangpaiboon, Hybridisations of variable neighbourhood search and modified simplex elements to harmony search and shuffled frog leaping algorithms for process optimisations. Paper presented at the LAENG Transactions on Engineering Technologies, Special Edition of the International MultiConference of Engineers and Computer Scientists (2010)

    Google Scholar 

  17. M.T. Ayvaz, Simultaneous determination of aquifer parameters and zone structures with fuzzy C-means clustering and meta-heuristic harmony search algorithm. Adv. Water Resour. 30, 2326–2338 (2007)

    Article  Google Scholar 

  18. A. Bahrololoum, H. Nezamabadi-pour, H. Bahrololoum, M. Saeed, A prototype classifier based on gravitational search algorithm. Appl. Soft Comput. 12, 819–825 (2012)

    Article  Google Scholar 

  19. M. Batty, B. Jiang, Multi-agent simulation: new approaches to exploring space-time dynamics within GIS Working Paper Series, Paper 10. University College London: Centre for Advanced Spatial Analysis (1999)

    Google Scholar 

  20. M.A. Behrang, E. Assareh, M. Ghalambaz, M.R. Assari, A.R. Noghrehabadi, Forecasting future oil demand in Iran using GSA (gravitational search algorithm). Energy 36, 5649–5654 (2011)

    Article  Google Scholar 

  21. K.E. Boulding, General systems theory:the skeleton of science. Manag. Sci. 2(3), 197–208 (1956)

    Article  Google Scholar 

  22. A. Chatterjee, G.K. Mahanti, N. Pathak, Comparative performance of gravitational search algorithm and modified particle swarm optimization algorithm for synthesis of thinned scanned concentric ring array antenna. Prog. Electromagn Res. B 25, 331–348 (2010)

    Article  Google Scholar 

  23. H. Chen, S. Li, Z. Tang, Hybrid gravitational search algorithm with random-key encoding scheme combined with simulated annealing. Int. J. Comput. Sci. Netw. Secur. 11(6), 208–217 (2011)

    MATH  Google Scholar 

  24. T.Y. Choi, K.J. Dooley, M. Rungtusanatham, Supply networks and complex adaptive systems: control versus emergence. J. Oper. Manag. 19, 351–366 (2001)

    Article  Google Scholar 

  25. R. Damodaram, M.L. Valarmathi, Phishing website detection and optimization using modified bat algorithm. Int. J. Eng. Res. Appl. 2(1), 870–876 (2012)

    Google Scholar 

  26. S. Das, Intelligent market-making in artificial financial markets. Unpublished Master Thesis, Massachusetts Institute of Technology 2003

    Google Scholar 

  27. S. Duman, U. Güvenç, Y. Sönmez, N. Yörükeren, Optimal power flow using gravitational search algorithm. Energy Convers. Manag. 59, 86–95 (2012)

    Article  Google Scholar 

  28. M. Dworkis, D. Huang, Genetic algorithms and investment strategy development: Report: 12 May 2008, The Wharton School, University of Pennsylvania 2008

    Google Scholar 

  29. M. Eslami, H. Shareef, A. Mohamed, M. Khajehzadeh, Gravitational search algorithm for coordinated design of PSS and TCSC as damping controller. J. Central South Univ. Technol. 19(4), 923–932 (2012)

    Article  Google Scholar 

  30. G.I. Evers, An automatic regrouping mechanism to deal with stagnation in particle swarm optimization. Unpublished Master Thesis, University of Texas-Pan American 2009

    Google Scholar 

  31. G.I. Evers, Particle swarm optimization research toolbox documentation: version: 20110515i (2011) www.georgeevers.org/pso_research_toolbox.htm. Accessed 06 June 2013

  32. M. Gauci, T.J. Dodd, R. Groß, Why ‘GSA: a gravitational search algorithm’ is not genuinely based on the law of gravity. Nat. Comput. 11(4), 719–720 (2012)

    Google Scholar 

  33. M. Ghalambaz, A.R. Noghrehabadi, M.A. Behrang, E. Assareh, A. Ghanbarzadeh, N. Hedayat, A hybrid neural network and gravitational search algorithm (HNNGSA) method to solve well known Wessinger’s equation. World Acad. Sci. Eng. Technol. 73, 803–807 (2011)

    Google Scholar 

  34. R.L. Goldstone, U. Wilensky, Promoting transfer by grounding complex systems principles. J. Learn. Sci. 17(4), 465–516 (2008)

    Google Scholar 

  35. A. Gorbenko, V. Popov, The force law design of artificial physics optimization for robot anticipation of motion. Adv. Stud. Theor. Phys. 6(13), 625–628 (2012)

    Google Scholar 

  36. T.E. Gorochowski, M.D. Bernardo, C.S. Grierson, Evolving dynamical networks: a formalism for describing complex systems. Complexity 17, 18–25 (2012)

    Article  Google Scholar 

  37. X. Han, X. Chang, A chaotic digital secure communication based on a modified gravitational search algorithm filter. Inf. Sci. 208, 14–27 (2012)

    Article  Google Scholar 

  38. X. Han, X. Chang, Chaotic secure communication based on a gravitational search algorithm filter. Eng. Appl. Artif. Intell. 25, 766–774 (2012)

    Article  Google Scholar 

  39. G. Hartvigsen, A. Kinzing, G. Peterson, Use and analysis of complex adaptive systems in ecosystem science: overview of special section. Ecosystems 1, 427–430 (1998)

    Article  Google Scholar 

  40. A. Hatamlou, S. Abdullah, H. Nezamabadi-pour, A combined approach for clustering based on K-means and gravitational search algorithms. Swarm and Evolutionary Computation 6, 47–52 (2012)

    Google Scholar 

  41. J.H. Holland, Adaptation in Neural and Artificial Systems (University of Michigan Press, MI, 1975)

    Google Scholar 

  42. J.H. Holland, Adaptation in Natural and Artificial Systems : An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, 2nd edn. (MIT Press, Cambridge, 1992)

    Google Scholar 

  43. J.H. Holland, Hidden order: how adaptation builds complexity (Helix Books, Addison-Wesley, New York, 1995)

    Google Scholar 

  44. J.H. Holland, Exploring the evolution of complexity in signaling networks. Complexity 7, 34–45 (2001)

    Article  MathSciNet  Google Scholar 

  45. J.H. Holland, Complex adaptive systems and spontaneous emergence, in Complexity and Industrial Clusters, ed. by A.Q. Curzio, M. Fortis (Physica, Heidelberg, 2002), pp. 25–34

    Chapter  Google Scholar 

  46. J.H. Holland, Studying complex adaptive systems. J. Syst. Sci. Complexity 19(1), 1–8 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  47. K. Ioannidis, G.C. Sirakoulis, I. Andreadis, Cellular ants: a method to create collision free trajectories for a cooperative robot team. Robot. Auton. Syst. 59, 113–127 (2011)

    Article  Google Scholar 

  48. D. Ivanov, B. Sokolov, The inter-disciplinary modelling of supply chains in the context of collaborative multi-structural cyber-physical networks. J. Manuf. Technol. Manag. 23(8), 976–997 (2012)

    Article  Google Scholar 

  49. M. Kampouridis, Computational intelligence in financial forecasting and agent-based modeling: applications of genetic programming and self-organizing maps. Unpublished Doctoral Thesis, University of Essex (2011)

    Google Scholar 

  50. N. Keshavarz, D. Nutbeam, L. Rowling, F. Khavarpour, Schools as social complex adaptive systems: a new way to understand the challenges of introducing the health promoting schools concept. Soc. Sci. Med. 70, 1467–1474 (2010)

    Article  Google Scholar 

  51. M. Khajehzadeh, M. Eslami, Gravitational search algorithm for optimization of retaining structures. Indian J. Sci. Technol. 5(1), 1821–1827 (2012)

    Google Scholar 

  52. M. Khajehzadeh, M.R. Taha, A. El-Shafie, M. Eslami, A modified gravitational search algorithm for slope stability analysis. Eng. Appl. Artif. Intell. 25(8), 1589–1597 (2012)

    Google Scholar 

  53. B. LeBaron, Agent-based computational finance: suggested readings and early research. J. Econ. Dyn. Control 24, 679–702 (2000)

    Article  MATH  Google Scholar 

  54. B. LeBaron, Empirical regularities from interacting long- and short-memory investors in an agent-based stock market. IEEE Trans. Evol. Comput. 5(5), 442–455 (2001)

    Article  Google Scholar 

  55. B. LeBaron, W.B. Arthur, R. Palmer, Time series properties of an artificial stock market. J. Econ. Dyn. Control 23, 1487–1516 (1999)

    Article  MATH  Google Scholar 

  56. T.A. Lemma,F.B.M. Hashim, Use of fuzzy systems and bat algorithm for exergy modeling in a gas turbine generator. Paper presented at the IEEE Colloquium on Humanities, Science and Engineering Research (CHUSER), 5–6 December, Penang, pp. 305–310 (2011)

    Google Scholar 

  57. S.A. Levin, Ecosystems and the biosphere as complex adaptive systems. Ecosystems 1, 431–436 (1998)

    Article  Google Scholar 

  58. C. Li, J. Zhou, Parameters identification of hydraulic turbine governing system using improved gravitational search algorithm. Energy Convers. Manag. 52, 374–381 (2011)

    Article  Google Scholar 

  59. C. Li, J. Zhou, J. Xiao, H. Xiao, Parameters identification of chaotic system by chaotic gravitational search algorithm. Chaos, Solitons Fractals 45, 539–547 (2012)

    Article  Google Scholar 

  60. H. Li, Financial prediction and trading via reinforcement learning and soft computing. Unpublished Doctoral Thesis, University of Missouri-Rolla (2005)

    Google Scholar 

  61. P. Li, H. Duan, Path planning of unmanned aerial vehicle based on improved gravitational search algorithm. Sci. China Technol. Sci. 55(10), 2712–2719 (2012)

    Google Scholar 

  62. F.M. Longin, The asymptotic distribution of extreme stock market returns. J. Bus. 69, 383–408 (1996)

    Article  Google Scholar 

  63. E.F.P. da Luz, J.C. Becceneri, H.F. de campos Velho, A new multi-particle collision algorithm for optimization in a high performance environment. J. Comput. Interdisc. Sci. 1(1), 3–10 (2008)

    Google Scholar 

  64. E.F.P. da Luz, J.C. Becceneri, H.F. de campos Velho, Multiple particle collision algorithm applied to radiative transference and pollutant localization inverse problems. Paper presented at the IEEE international symposium on parallel and distributed processing workshops and Ph.D. forum (IPDPSW), pp. 347–351 (2011)

    Google Scholar 

  65. M.J. Mauboussin, Revisiting market efficiency: the stock market as a complex adaptive system. J. Appl. Corp. Finan. 14, 47–55 (2002)

    Article  Google Scholar 

  66. B. McKelvey, C. Wycisk, M. Hülsmann, Designing an electronic auction market for complex ‘smart parts’ logistics: options based on LeBaron’s computational stock market. Int. J. Prod. Econ. 120, 476–494 (2009)

    Article  Google Scholar 

  67. M.D. Mills-Harris, A. Soylemezoglu, C. Saygin, Adaptive inventory management using RFID data. Int. J. Adv. Manuf. Technol. 32, 1045–1051 (2007)

    Article  Google Scholar 

  68. S. Mirjalili, S.Z.M. Hashim, A new hybrid PSOGSA algorithm for function optimization. Paper presented at the proceedings of the international conference on computer and information application (ICCIA), pp. 374–377 (2010)

    Google Scholar 

  69. F.S. Mishkin, The Economics of Money, Banking, and Financial Markets (The Addison-Wesley, Reading, 2004)

    Google Scholar 

  70. L. Monostori, K. Ueda, Design of complex adaptive systems: introduction. Adv. Eng. Inform. 20, 223–225 (2006)

    Article  Google Scholar 

  71. P. Musikapun, P. Pongcharoen, Solving multi-stage multi-machine multi-product scheduling problem using bat algorithm. Paper presented at the 2nd international conference on management and artificial intelligence, vol. 35, pp. 98–102 (2012)

    Google Scholar 

  72. J.V. Neumann, Theory of Self-Reproducing Automata (University of Illinois Press, Urbana, 1966)

    Google Scholar 

  73. E.W.T. Ngai, D.C.K. Chau, J.K.L. Poon, A.Y.M. Chan, B.C.M. Chan, W.W.S. Wu, Implementing an RFID-based manufacturing process management system: lessons learned and success factors. J. Eng. Tech. Manage. 29, 112–130 (2012)

    Article  Google Scholar 

  74. T. Niknam, F. Golestaneh, A. Malekpour, Probabilistic energy and operation management of a microgrid containing wind/photovoltai/fuel cell generation and energy storage devices based on point estimate method and self-adaptive gravitational search algorithm. Energy. 43(1), 427–437 (2012)

    Google Scholar 

  75. R.G. Palmer, W.B. Arthur, J.H. Holland, B. LeBaron, An artificial stock market. Artif. Life Robot. 3, 27–31 (1999)

    Article  Google Scholar 

  76. J.P. Papa, A. Pagnin, S.A. Schellini, A. Spadotto, R.C. Guido, M., Ponti, G. Chiachia, A.X. Falcão, Feature selection through gravitational search algorithm. Paper presented at the IEEE international conference on acoustics speech (ICASSP), pp. 2052–2055 (2011)

    Google Scholar 

  77. S.D. Pathak, J.M. Day, A. Nair, W.J. Sawaya, M.M. Kristal, Complexity and adaptivity in supply networks: building supply network theory using a complex adaptive systems perspective. Decis. Sci. 38(4), 547–580 (2007)

    Article  Google Scholar 

  78. S.D. Pathak, D.M. Dilts, G. Biswas, Simulating growth dynamics in complex adaptive supply networks. Paper presented at the 2004 winter simulation conference, pp. 774–782 (2004)

    Google Scholar 

  79. P. Rabanal, I. Rodríguez, F. Rubio, Using river formation dynamics to design heuristic algorithms. ed. by C.S. Calude, S.G. Akl, M.J. Dinneen, G. Rozenber, H.T. Wareham , UC 2007, LNCS, vol. 4618 (Springer, Heidelberg, 2007) pp. 163–177

    Google Scholar 

  80. P. Rabanal, I. Rodríguez, F. Rubio, Finding Minimum Spanning/Distances Trees by Using River Formation Dynamics, vol. 5217, ed. by M. Dorigo, ANTS 2008, LNCS 5217 (Springer, Berlin, 2008a) pp. 60–71

    Google Scholar 

  81. P. Rabanal, I. Rodríguez, F. Rubio, Solving dynamic TSP by using river formation dynamics. Paper presented at the 4th international conference on natural computation (ICNC), pp. 246–250 (2008b)

    Google Scholar 

  82. P. Rabanal, I. Rodríguez, F. Rubio, Applying river formation dynamics to the Steiner tree problem. Paper presented at the 9th IEEE international conference on cognitive informatics (ICCI), pp. 704–711 (2010)

    Google Scholar 

  83. T. Rambharose, Artificial neural network training add-in for PSO research toolbox. Department of Computing & Information Technology, The University of the West Indies, St. Augustine (2010), http://www.tricia-rambharose.com. Accessed 06 June 2013

  84. E. Rashedi, H. Nezamabadi-pour, S. Saryazdi, GSA: a gravitational search algorithm. Inf. Sci. 179, 2232–2248 (2009)

    Article  MATH  Google Scholar 

  85. E. Rashedi, H. Nezamabadi-pour, S. Saryazdi, BGSA: binary gravitational search algorithm. Nat. Comput. 9(3), 727–745 (2010)

    Article  MATH  MathSciNet  Google Scholar 

  86. E. Rashedi, H. Nezamabadi-pour, S. Saryazdi, Filter modeling using gravitational search algorithm. Eng. Appl. Artif. Intell. 24, 117–122 (2011)

    Article  Google Scholar 

  87. P.K. Roy, B. Mandal, K. Bhattacharya, Gravitational search algorithm based optimal reactive power dispatch for voltage stability enhancement. Electr. Power Compon. Syst. 40, 956–976 (2012)

    Article  Google Scholar 

  88. B. Rundh, Radio frequency identification (RFID): invaluable technology or a new obstacle in the marketing process? Mark. Intell. Planning 26(1), 97–114 (2008)

    Article  Google Scholar 

  89. W.F. Sacco, C.R.E. de Oliveira, A new stochastic optimization algorithm based on a particle collision metaheuristic. Paper presented at the 6th World Congresses of Structural and Multidisciplinary Optimization (Rio de Janeiro, 30 May–03 June 2005) pp. 1–6

    Google Scholar 

  90. S. Sarafrazi, H. Nezamabadi-pour, S. Saryazdi, Disruption: a new operator in gravitational search algorithm. Scientia Iranica D 18(3), 539–548 (2011)

    Article  Google Scholar 

  91. B. Shaw, V. Mukherjee, S.P. Ghoshal, A novel opposition-based gravitational search algorithm for combined economic and emission dispatch problems of power systems. Electr. Power Energ. Syst. 35, 21–33 (2012)

    Article  Google Scholar 

  92. S. Soni, Applications of ANNs in the stock market prediction: a survey. Int. J. Comput. Sci. Eng. Technol. 2(3), 71–83 (2010)

    MathSciNet  Google Scholar 

  93. H.S. Sudhira, Integration of agent-based and cellular automata models for simulating urban sprawl. Unpublished Master Thesis, International Institute for Geo-Information Science and Earth Observation & Department of Space, Indian Institute of Remote Sensing, National Remote Sensing Agency (NRSA) (Enschede, Dehradun, 2004)

    Google Scholar 

  94. N. Suhadolnik, J. Galimberti, S.D. Silva, Robot traders can prevent extreme events in complex stock markets. Physica A 389, 5182–5192 (2010)

    Article  Google Scholar 

  95. A. Surana, S. Kumara, M. Greaves, U.N. Raghavan, Supply-chain networks: a complex adaptive systems perspective. Int. J. Prod. Res. 43(20), 4235–4365 (2005)

    Article  Google Scholar 

  96. J.M. Swaminathan, S.F. Smith, N.M. Sadeh, Modeling supply chain dynamics: a multiagent approach. Decis. Sci. 29(3), 607–632 (1998)

    Article  Google Scholar 

  97. M. Taherdangkoo, M.H. Shirzadi, M.H. Bagheri, A novel meta-heuristic algorithm for numerical function optimization: blind, naked mole-rats (BNMR) algorithm. Sci. Res. Essays 7(41), 3566–3583 (2012)

    Google Scholar 

  98. J. Tan, H.J. Wen, N. Awad, Health care and services delivery systems as complex adaptive systems. Commun. ACM 48(5), 36–44 (2005)

    Article  Google Scholar 

  99. L.D. Thurston, Jacksonville to construct first refrigerated crossdock. Caribbean Bus. 36(40), 41 (2008)

    Google Scholar 

  100. P. Wang, Y. Cheng, Relief supplies scheduling based on bean optimization algorithm. Econ. Res. Guide 8, 252–253 (2010)

    Google Scholar 

  101. R.A. Watson, C.L. Buckley, R. Mills, Optimization in self-modeling complex adaptive systems. Complexity 16, 17–26 (2011)

    Article  Google Scholar 

  102. Y.-M. Wei, S.-J. Ying, Y. Fan, B.-H. Wang, The cellular automaton model of investment behavior in the stock market. Phys. A 325, 507–516 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  103. U. Wilensky, NetLogo (Version 4.1) center for connected Learning and Computer-Based Modeling http://ccl.northwestern.edu/netlogo/ (Northwestern University, Evanston, 1999)

  104. Y. Wu, A dual-response strategy for global logistics under uncertainty: a case study of a third-party logistics company. Int. Trans. Oper. Res. 19(3), 397–419 (2012)

    Article  MATH  MathSciNet  Google Scholar 

  105. C. Wycisk, B. McKelvey, M. Hülsmann, “Smart parts” supply networks as complex adaptive systems: analysis and implications. Int. J. Phys. Distrib. Logist. Manag. 38(2), 108–125 (2008)

    Article  Google Scholar 

  106. L. Xie, J. Zeng, R.A. Formato, Convergence analysis and performance of the extended artificial physics optimization algorithm. Appl. Math. Comput. 218, 4000–4011 (2011)

    Article  MATH  MathSciNet  Google Scholar 

  107. B. Xing, W.-J. Gao, Computational Intelligence in Remanufacturing (IGI Global, Hershey, 2014) ISBN 978-1-4666-4908-8

    Google Scholar 

  108. B. Xing, and W.-J. Gao, Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms (Springer, Cham, 2014) ISBN 978-3-319-03403-4

    Google Scholar 

  109. S.-D. Yang, Y.-L. Yi, Z.-Y. Shan, Gbest-guided artificial chemical reaction algorithm for global numerical optimization. Procedia Eng. 24, 197–201 (2011)

    Article  Google Scholar 

  110. X.-S. Yang, A.H. Gandomi, Bat algorithm: a novel approach for global engineering optimization. Eng. Comput. 29(5), 464–483 (2012)

    Article  Google Scholar 

  111. C.Y. Yi, E.W.T. Ngai, K.-L. Moon, Supply chain flexibility in an uncertain environment: exploratory findings from five case studies. Supply Chain Manag. Int. J. 16(4), 271–283 (2011)

    Article  Google Scholar 

  112. M. Yin, Y. Hu, F. Yang, X. Li, W. Gu, A novel hybrid K-harmonic means and gravitational search algorithm approach for clustering. Expert Syst. Appl. 38, 9319–9324 (2011)

    Article  Google Scholar 

  113. X. Zhang, K. Jiang, H. Wang, W. Li, B. Sun, An Improved Bean Optimization Algorithm for Solving TSP, vol. 7331, ed. by Y. Tan, Y. Shi, Z. Ji, ICSI 2012, Part I, LNCS 7331 (Springer, Berlin, 2012), pp. 261–267

    Google Scholar 

  114. X. Zhang, B. Sun, T. Mei, R. Wang, Post-disaster restoration based on fuzzy preference relation and bean optimization algorithm. Paper presented at the IEEE Youth Conference onInformation Computing and Telecommunications (YC-ICT), (28–30 Nov 2010), pp. 271–274

    Google Scholar 

  115. Z.-N. Zhang, Z.-L. Liu, Y. Chen, Y.-B. Xie, Knowledge flow in engineering design: an ontological framework. Proc. Inst. Mech. Eng. [C] J. Mech. Eng. Sci. 227(4), 760–770 (2013)

    Article  Google Scholar 

  116. W. Zhou, S. Piramuthu, Remanufacturing with RFID item-level information: optimization, waste reduction and quality improvement. Int. J. Prod. Econ. 145(2), 647–657 (2013)

    Google Scholar 

  117. B. Zibanezhad, K. Yamanifar, R.S. Sadjady, Y. Rastegari, Applying gravitational search algorithm in the QoS-based Web service selection problem. J. Zhejiang Univ. Sci. C (Comput. Electron.), 12(9), 730–742 (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical and Aeronautical Engineering, Faculty of Engineering, Built Environment and Information Technology, University of Pretoria, Pretoria, South Africa

    Bo Xing

Authors
  1. Bo Xing
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bo Xing .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, National University of Science and Technology (NUST), Islamabad, Pakistan

    Zeashan H Khan

  2. School of Engineering and Technology, CQ University, North Rockhampton, Queensland, Australia

    A. B. M. Shawkat Ali

  3. Fraunhofer Institute, Bonn, Germany

    Zahid Riaz

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media Singapore

About this chapter

Cite this chapter

Xing, B. (2014). The Optimization of Computational Stock Market Model Based Complex Adaptive Cyber Physical Logistics System: A Computational Intelligence Perspective. In: Khan, Z., Ali, A., Riaz, Z. (eds) Computational Intelligence for Decision Support in Cyber-Physical Systems. Studies in Computational Intelligence, vol 540. Springer, Singapore. https://doi.org/10.1007/978-981-4585-36-1_12

Download citation

  • DOI: https://doi.org/10.1007/978-981-4585-36-1_12

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-4585-35-4

  • Online ISBN: 978-981-4585-36-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation