Intelligent Business Process Execution using Particle Swarm Optimization

Intelligent Business Process Execution using Particle Swarm Optimization

Markus Kress (University of Karlsruhe, Germany), Sanaz Mostaghim (University of Karlsruhe, Germany) and Detlef Seese (University of Karlsruhe, Germany)
DOI: 10.4018/978-1-61520-965-1.ch319


In this chapter, the authors study a new variant of Particle Swarm Optimization (PSO) to efficiently execute business processes. The main challenge of this application for the PSO is that the function evaluations typically take a high computation time. They propose the Gap Search (GS) method in combination with the PSO to perform a better exploration in the search space and study its influence on the results of our application. They replace the random initialization of the solutions for the initial population as well as for the diversity preservation method with the GS method. The experimental results show that the GS method significantly improves the quality of the solutions and obtains better results for the application as compared to the results of a standard PSO and Genetic Algorithms. Moreover, the combination of the methods the authors used show promising results as tools to be applied for improvement of Business Process Optimization.

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