Academic Editor: Youssef EL FOUTAYENI
Received |
Accepted |
Published |
30 December 2019 |
14 January 2020 |
10 March 2020 |
Abstract: This article deals with an optimization up to two objectives of business process designs using evolutionary computing as defined by [1]. The authors present an approach for an evolutionary combinatorial multi objective optimization of business process designs with a specified genetic algorithm. The latter differs from the canonical genetic algorithm [2] by its use of not one but several populations [3] simultaneously during its iterations. So, the proposed approach uses (i) an effective mathematical proposition for solution representation, (ii) an original evolutionary algorithm and (iii) two contradictory criteria to optimize. In order to show the efficiency of the whole, five different test scenarios proposed by [4] and [5] have been used. The obtained results show that the optimization approach is capable of producing a satisfactory number of optimized designs alternatives and adding more populations has shown an increase of non-dominated solutions in reduced iterations amount.
