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

TL;DR

This paper introduces an integrated framework combining Accelerated Particle Swarm Optimization with Support Vector Machine to enhance business optimization tasks like production, income prediction, and project scheduling.

Contribution

It presents a novel APSO-SVM framework that improves business optimization solutions by combining advanced metaheuristics with machine learning techniques.

Findings

Effective in production optimization

Accurate income prediction results

Enhanced project scheduling performance

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.