Networked Digital Technologies

Volume 136 of the series Communications in Computer and Information Science pp 53-66

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

  • Xin-She YangAffiliated withDepartment of Engineering, University of Cambridge
  • , Suash DebAffiliated withDepartment of Computer Science & Engineering, C.V. Raman College of Engineering
  • , Simon FongAffiliated withDepartment of Computer and Information Science, Faculty of Science and Technology, University of Macau

* Final gross prices may vary according to local VAT.

Get Access


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.


Accelerated PSO business optimization metaheuristics PSO support vector machine project scheduling