Accelerated Particle Swarm Optimization and Support Vector Machine for Business Optimization and Applications

  • Xin-She Yang
  • Suash Deb
  • Simon Fong
Conference paper

DOI: 10.1007/978-3-642-22185-9_6

Part of the Communications in Computer and Information Science book series (CCIS, volume 136)
Cite this paper as:
Yang XS., Deb S., Fong S. (2011) Accelerated Particle Swarm Optimization and Support Vector Machine for Business Optimization and Applications. In: Fong S. (eds) Networked Digital Technologies. NDT 2011. Communications in Computer and Information Science, vol 136. Springer, Berlin, Heidelberg

Abstract

Business optimization is becoming increasingly important because all business activities aim to maximize the profit and performance of products and services, under limited resources and appropriate constraints. Recent developments in support vector machine and metaheuristics show many advantages of these techniques. In particular, particle swarm optimization is now widely used in solving tough optimization problems. In this paper, we use a combination of a recently developed Accelerated PSO and a nonlinear support vector machine to form a framework for solving business optimization problems. We first apply the proposed APSO-SVM to production optimization, and then use it for income prediction and project scheduling. We also carry out some parametric studies and discuss the advantages of the proposed metaheuristic SVM.

Keywords

Accelerated PSO business optimization metaheuristics PSO support vector machine project scheduling 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Xin-She Yang
    • 1
  • Suash Deb
    • 2
  • Simon Fong
    • 3
  1. 1.Department of EngineeringUniversity of CambridgeCambridgeUK
  2. 2.Department of Computer Science & EngineeringC.V. Raman College of EngineeringBhubaneswarIndia
  3. 3.Department of Computer and Information Science, Faculty of Science and TechnologyUniversity of MacauTaipaMacau

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