Evolutionary Multi-objective Optimization of Business Process Designs with MA-NSGAII

Abstract

Optimization is known as the process of finding the best possible solution to a problem given a set of constraints. The problem becomes challenging when dealing with conflicting objectives, which leads to a multiplicity of solutions. Evolutionary algorithms, which use a population approach in their search procedures, are advised to suitably solve the problem. In this article, we present an approach for an evolutionary combinatorial multi-objective optimization of business process designs using a variation of NSGAII, baptized MA-NSGAII. The variants of NSGAII are numerous. In fact, the vast majority deals either with the crossover operator or with the crowding distance. We discuss an optimization Framework that uses (i) a proposal of effective Fitness function, (ii) 02 contradictory criteria to optimize and (iii) an original selection technique. We test the proposed Framework with a real life case of multi-objective optimization of business process designs. The obtained results clearly indicate that an effectual Fitness function combined with the appropriate selection operator affects undeniably quality and quantity of solutions.

An Elaborate Preprocessing Phase (p3) in Composition and Optimization of Business Process Models

“…The bpo F framework is well established and has been utilized both by the authors [5] and [6,35] and other researchers [41,42] with significant findings both for business process optimization and the performance of the EMOAs. The initial addition of preprocessing presented in [6] increased the capability of the framework in generating diverse designs and selecting those with optimal objective values for business processes in reduced time.…”