Improved Multi-objective Particle Swarm Optimization in Software Engineering Supervision




Software engineering supervision, IDMPSO, Multi-objective network planning optimization, Pareto-optimal set


In the 21st century, the software industry has achieved great development. The development complexity and volume of software projects are also continuously increasing. The design of software engineering supervision network plans is becoming increasingly important. In response to the poor optimization performance and poor convergence and distribution of optimal solutions in existing network planning algorithms, the Pareto optimal solution set construction method, global extremum selection method, and fitness value determination method of multi-objective particle swarm optimization algorithm are improved to enhance the convergence and distribution of the algorithm. Traditional methods only optimize one or two objectives of network planning, resulting in inconsistency with actual engineering. A multi-objective model based on resources, duration, cost, and quality is established for comprehensive optimization. Based on the results, the Pareto optimal solution curves obtained by the proposed algorithm on three classic test functions are consistent with the actual theoretical Pareto frontier curves. The proposed method is applied to engineering project examples. 10 solutions that meet the schedule requirements are obtained. Most engineering projects have a quality of over 80%, which verifies the practicality of the algorithm. The algorithm has achieved good results in optimizing engineering quality. Therefore, this model has the ability to consider various indicators such as resources and costs to obtain software engineering quality improvement plans. It has certain application potential.


Download data is not yet available.


Rabbani, M., Oladzad-Abbasabady, N., & Akbarian-Saravi, N. (2022). Ambulance routing in disaster response considering variable patient condition: NSGA-II and MOPSO algorithms. Journal of Industrial & Management Optimization, 18(2), 1035-1062.

Habib, H., Menhas, R., & McDermott, O. (2022). Managing engineering change within the paradigm of product lifecycle management. Processes, 10(9), 1770.

Eito-Brun, R., Gómez-Berbís, J. M., & de Amescua Seco, A. (2022). Knowledge tools to organise software engineering data: Development and validation of an ontology based on ECSS standard. Advances in Space Research, 70(2), 485-495.

Hasani, A., Mokhtari, H., & Fattahi, M. (2021). A multi-objective optimization approach for green and resilient supply chain network design: a real-life case study. Journal of Cleaner Production, 278, 123199.

Ye, X., Chen, B., Jing, L., Zhang, B., & Liu, Y. (2019). Multi-agent hybrid particle swarm optimization (MAHPSO) for wastewater treatment network planning. Journal of environmental management, 234, 525-536.

Tun, H. M. (2021). Radio network planning and optimization for 5G telecommunication system based on physical constraints. Journal of Computer Science Research, 3(1), 1-15.

Zeidan, M., Li, P., & Ostfeld, A. (2021). DMA segmentation and multiobjective optimization for trading off water age, excess pressure, and pump operational cost in water distribution systems. Journal of Water Resources Planning and Management, 147(4), 04021006.

Devaraj, A. F. S., Elhoseny, M., Dhanasekaran, S., Lydia, E. L., & Shankar, K. (2020). Hybridization of firefly and improved multi-objective particle swarm optimization algorithm for energy efficient load balancing in cloud computing environments. Journal of Parallel and Distributed Computing, 142, 36-45.

Xu, G., Luo, K., Jing, G., Yu, X., Ruan, X., & Song, J. (2020). On convergence analysis of multi-objective particle swarm optimization algorithm. European Journal of operational research, 286(1), 32-38.

Yuen, M. C., Ng, S. C., & Leung, M. F. (2020). A competitive mechanism multi-objective particle swarm optimization algorithm and its application to signalized traffic problem. Cybernetics and Systems, 52(1), 73-104.

Rasoulzadeh, M., Edalatpanah, S. A., Fallah, M., & Najafi, S. E. (2022). A multi-objective approach based on Markowitz and DEA cross-efficiency models for the intuitionistic fuzzy portfolio selection problem. Decision Making: Applications in Management and Engineering, 5(2), 241-259.

Nafei, A., Huang, C. Y., Chen, S. C., Huo, K. Z., Lin, Y. C., & Nasseri, H. (2023). Neutrosophic Autocratic Multi-Attribute Decision-Making Strategies for Building Material Supplier Selection. Buildings, 13(6), 1373.

Nafei, A., Huang, C. Y., Azimi, S. M., & Javadpour, A. (2023). An optimized model for neutrosophic multi-choice goal programming. Miskolc Mathematical Notes, 24(2), 915-931.‏

Akram, M., Shah, S. M. U., Al-Shamiri, M. M. A., & Edalatpanah, S. A. (2023). Extended DEA method for solving multi-objective transportation problem with Fermatean fuzzy sets. Aims Math, 8, 924-961.

Mekawy, I. M. (2022). A novel method for solving multi- objective linear fractional programming problem under uncertainty. Journal of Fuzzy Extension and Applications, 3(2), 169-176.

Farnam, M., & Darehmiraki, M. (2021). Solution procedure for multi-objective fractional programming problem under hesitant fuzzy decision environment. Journal of Fuzzy Extension and Applications, 2(4), 364-376.

Ghasemi, P., Hemmaty, H., Pourghader Chobar, A., Heidari, M. R., & Keramati, M. (2023). A multi-objective and multi-level model for location-routing problem in the supply chain based on the customer's time window. Journal of Applied Research on Industrial Engineering, 10(3), 412-426.

Liu, Z., Xiang, B., Song, Y., Lu, H., & Liu, Q. (2019). An improved unsupervised image segmentation method based on multi-objective particle swarm optimization clustering algorithm. Computers, Materials & Continua, 58(2), 451-461.

Shuxiao, M., Yun, T., Binhe, C., & Yibo, Z. (2021). Optimization of Development Intensity Index of Regulatory Land under the Constraint of Bearing Capacity of Road Network: A Case Study of Xingtang County. Journal of Landscape Research, 13(1), 73-80.

Hajgató, G., Paál, G., & Gyires-Tóth, B. (2020). Deep reinforcement learning for real-time optimization of pumps in water distribution systems. Journal of Water Resources Planning and Management, 146(11), 04020079.

Aikhuele, D. (2023). Development of a statistical reliability-based model for the estimation and optimization of a spur gear system. Journal of Computational and Cognitive Engineering, 2(2), 168-174.

Choudhuri, S., Adeniye, S., & Sen, A. (2023). Distribution alignment using complement entropy objective and adaptive consensus-based label refinement for partial domain adaptation. Artificial Intelligence and Applications, 1(1), 43-51.

Devi Priya, R., Sivaraj, R., Abraham, A., Pravin, T., Sivasankar, P., & Anitha, N. (2022). Multi-Objective Particle Swarm Optimization Based Preprocessing of Multi-Class Extremely Imbalanced Datasets. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 30(05), 735-755.

Salehi, N., & Askarzadeh, H. R. (2018). Optimum solar and wind model with particle optimization (PSO). International Journal of Research in Industrial Engineering, 7(4), 460-467.

Dirik, M. (2022). Type-2 fuzzy logic controller design optimization using the PSO approach for ECG prediction. Journal of fuzzy extension and applications, 3(2), 158-168.

Rajeshkumar, G., Kumar, M. V., Kumar, K. S., Bhatia, S., Mashat, A., & Dadheech, P. (2023). An Improved Multi-Objective Particle Swarm Optimization Routing on MANET. Computer Systems Science & Engineering, 44(2), 1187-1200.



How to Cite

Yue, P., & Wang, Z. (2024). Improved Multi-objective Particle Swarm Optimization in Software Engineering Supervision. Decision Making: Applications in Management and Engineering, 7(2), 257–274.