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

Chapter
Part of the Studies in Computational Intelligence book series (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.

Keywords

Complex adaptive system Artificial stock market model Cyber physical logistics system Artificial neural network Genetic algorithm Multi-agent system 

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Copyright information

© Springer Science+Business Media Singapore 2014

Authors and Affiliations

  1. 1.Department of Mechanical and Aeronautical Engineering, Faculty of Engineering, Built Environment and Information TechnologyUniversity of PretoriaPretoriaSouth Africa

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