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

## 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.

### Keywords

Complex adaptive system Artificial stock market model Cyber physical logistics system Artificial neural network Genetic algorithm Multi-agent system### 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. 108–114Google 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. 21–27Google 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)CrossRefGoogle 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)CrossRefMATHGoogle 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)CrossRefMATHMathSciNetGoogle 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. 1203–1210Google Scholar - 8.B. Alatas, Chaotic harmony search algorithms. Appl. Math. Comput.
**216**, 2687–2699 (2010)CrossRefMATHGoogle Scholar - 9.B. Alatas, ACROA: artificial chemical reaction optimization algorithm for global optimization. Expert Syst. Appl.
**38**, 13170–13180 (2011)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle Scholar - 21.K.E. Boulding, General systems theory:the skeleton of science. Manag. Sci.
**2**(3), 197–208 (1956)CrossRefGoogle 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)CrossRefGoogle 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)MATHGoogle 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)CrossRefGoogle 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 2003Google Scholar
- 27.S. Duman, U. Güvenç, Y. Sönmez, N. Yörükeren, Optimal power flow using gravitational search algorithm. Energy Convers. Manag.
**59**, 86–95 (2012)CrossRefGoogle Scholar - 28.M. Dworkis, D. Huang, Genetic algorithms and investment strategy development: Report: 12 May 2008, The Wharton School, University of Pennsylvania 2008Google 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)CrossRefGoogle 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 2009Google 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)CrossRefGoogle 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)CrossRefGoogle Scholar - 38.X. Han, X. Chang, Chaotic secure communication based on a gravitational search algorithm filter. Eng. Appl. Artif. Intell.
**25**, 766–774 (2012)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefMathSciNetGoogle 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. 25–34CrossRefGoogle Scholar - 46.J.H. Holland, Studying complex adaptive systems. J. Syst. Sci. Complexity
**19**(1), 1–8 (2006)CrossRefMATHMathSciNetGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefMATHGoogle 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)CrossRefGoogle 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)CrossRefMATHGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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. 163–177Google 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. 60–71Google 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)CrossRefMATHGoogle Scholar - 85.E. Rashedi, H. Nezamabadi-pour, S. Saryazdi, BGSA: binary gravitational search algorithm. Nat. Comput.
**9**(3), 727–745 (2010)CrossRefMATHMathSciNetGoogle Scholar - 86.E. Rashedi, H. Nezamabadi-pour, S. Saryazdi, Filter modeling using gravitational search algorithm. Eng. Appl. Artif. Intell.
**24**, 117–122 (2011)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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. 1–6Google 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)CrossRefGoogle 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)CrossRefGoogle 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)MathSciNetGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefMATHMathSciNetGoogle 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)CrossRefMATHMathSciNetGoogle 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)CrossRefGoogle 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)CrossRefMATHMathSciNetGoogle Scholar - 107.B. Xing, W.-J. Gao,
*Computational Intelligence in Remanufacturing*(IGI Global, Hershey, 2014) ISBN 978-1-4666-4908-8Google 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)CrossRefGoogle Scholar - 110.X.-S. Yang, A.H. Gandomi, Bat algorithm: a novel approach for global engineering optimization. Eng. Comput.
**29**(5), 464–483 (2012)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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. 271–274Google 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)CrossRefGoogle 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