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.
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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. https://doi.org/10.1007/978-3-642-22185-9_6
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DOI: https://doi.org/10.1007/978-3-642-22185-9_6
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