TY - CONF EP - 66 UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138368169&doi=10.1145%2f3545801.3545810&partnerID=40&md5=7278c8bbc529bf72d9d60d5f6d2b9c09 A1 - Ngo, S.T. A1 - Jaafar, J.B. A1 - Aziz, I.A. A1 - Tong, G.T. A1 - Nguyen, G.H. A1 - Bui, A.N. N1 - 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 ID - scholars16732 Y1 - 2022/// KW - Ant colony optimization; Artificial intelligence; Combinatorial optimization; Decision making; Multiobjective optimization; Scheduling; Software design KW - Compromise programming; Multi-objective optimization problem; Multiple-objectives; NGSA-2; Planning process; Project planning; Project quality; RCPSP; Scheduling; Tasks assignments KW - Genetic algorithms TI - Different Approaches of Evolutionary Algorithms to Multiple Objective RCPSP N2 - 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. © 2022 ACM. SN - 9781450396097 AV - none SP - 58 PB - Association for Computing Machinery ER -