@inproceedings{scholars16732,
             doi = {10.1145/3545801.3545810},
            note = {cited By 1; Conference of 7th International Conference on Big Data and Computing, ICBDC 2022 ; Conference Date: 27 May 2022 Through 29 May 2022; Conference Code:182616},
       publisher = {Association for Computing Machinery},
           pages = {58--66},
            year = {2022},
           title = {Different Approaches of Evolutionary Algorithms to Multiple Objective RCPSP},
         journal = {ACM International Conference Proceeding Series},
        keywords = {Ant colony optimization; Artificial intelligence; Combinatorial optimization; Decision making; Multiobjective optimization; Scheduling; Software design, Compromise programming; Multi-objective optimization problem; Multiple-objectives; NGSA-2; Planning process; Project planning; Project quality; RCPSP; Scheduling; Tasks assignments, Genetic algorithms},
            isbn = {9781450396097},
          author = {Ngo, S. T. and Jaafar, J. B. and Aziz, I. A. and Tong, G. T. and Nguyen, G. H. and Bui, A. N.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138368169&doi=10.1145\%2f3545801.3545810&partnerID=40&md5=7278c8bbc529bf72d9d60d5f6d2b9c09},
        abstract = {Task assignments to the project members play a vital role in the project planning process. The project's quality, development time, and cost are criteria to indicate a success or failure project. These are affected by every single arrangement generated by the optimizer. Unfortunately, the task assignment problem is a multi-objective optimization problem (MOP) and complex scheduling problem in software development projects (MOP-PSP) and other domains. There are several approaches to MOP introduced in the literature. Our study aims to evaluate different evolutionary algorithms (EAs), including the compromise programming-based and Pareto frontier-based for the MOP-PSP. To make this work, we calibrate the parameters for the genetic algorithm (GA) developed in the previous research and design a new version of the ant colony algorithm (ACO) to solve the compromised problem. They are then compared with the NGSA-2, a Multi objective EA. Experiments show that compromise programming is an effective method for the decision-making process in practice. {\^A}{\copyright} 2022 ACM.}
}