A Hybrid Artificial Bee Colony and Artificial Fish Swarm Algorithms for Software Cost Estimation


  • Hawar Othman Sharif Department of Computer Science, College of Science, University of Sulaimani, Iraq
  • Mazen Ismaeel Ghareb Department of Computer Science, College of Science and Technology, University of Human Development, Kurdistan Region, Iraq https://orcid.org/0000-0002-3937-2835
  • Hoshmen Murad Mohamedyusf Department of Plastic Arts, Halabja Fine Arts Institute, Halabja, Iraq




Artificial Fish Swarm Algorithm, Software Cost Estimation, Artificial Fish Swarm Algorithm, Software Cost Estimation, Artificial Bee Colony Algorithm, COCOMO Model., Constructive Cost Model


Software cost estimation (SCE), estimating the cost and time required for software development, plays a highly significant role in managing software projects. A somewhat accurate SCE is necessary for a software project to be successful. It allows effective control of construction time and cost. In the past few decades, various models have been presented to evaluate software projects, including mathematical models and machine learning algorithms. In this paper, a new model based on the hybrid of the artificial fish swarm algorithm (AFSA) and the artificial bee colony (ABC) algorithm is presented for SCE. The initial population of AFSA, which includes the values of the effort factors, is generated using the ABC algorithm. ABC algorithm is used to solve the problems of the AFSA algorithm such as population diversity and getting stuck in a local optimum. ABC algorithm achieves the best solutions using observer and scout bees. The evaluation of the combined method has been implemented on eight different data sets and evaluated based on eight different criteria such as mean magnitude of relative error and PRED (0.25). The proposed method is more error-free than current SCE methods, according to the results. The error value of the proposed method is lower on NASA60, NASA63, and NASA93 datasets.


S. Hameed, Y. Elsheikh and M. Azzeh. “An optimized case-based software project effort estimation using genetic algorithm.” Information and Software Technology, vol. 153, p. 107088, 2023.

N. Govil and A. Sharma. “Estimation of cost and development effort in Scrum-based software projects considering dimensional success factors.” Advances in Engineering Software, vol. 172, p. 103209, 2022.

H. M. Sneed and C. Verhoef. “Cost-driven software migration: An experience report.” Journal of Software: Evolution and Process, vol. 32, no. 7, p. e2236, 2020.

S. Tariq, M. Usman and A. C. Fong. “Selecting best predictors from large software repositories for highly accurate software effort estimation.” Journal of Software: Evolution and Process, vol. 32, no. 10, p. e2271, 2020.

K. Rak, Ž. Car and I. Lovrek. “Effort estimation model for software development projects based on use case reuse.” Journal of Software: Evolution and Process, vol. 31, no. 2, p. e2119, 2019.

T. Hacaloglu and O. Demirors. “An exploratory case study using events as a software size measure.” Information Technology and Management, vol. 24, pp. 1-20, 2023.

E. Feizpour, H. Tahayori and A. Sami. “CoBRA without experts: New paradigm for software development effort estimation using COCOMO metrics.” Journal of Software: Evolution and Process, vol. 35, p. e2569, 2023.

A. K. Bardsiri and S. M. Hashemi. “A differential evolution-based model to estimate the software services development effort.” Journal of Software: Evolution and Process, vol. 28, no. 1, pp. 57- 77, 2016.

X. L. Li, Z. J. Shao and J. X. Qian. “An optimizing method based on autonomous animats: Fish-swarm algorithm.” Systems Engineering-Theory and Practice, vol. 22, no. 11, pp. 32-38, 2002.

D. Karaboga. “An Idea Based on Honey Bee Swarm for Numerical Optimization. Technical Report-tr06, Erciyes University, Engineering Faculty, Computer.” Erciyes University, Engineering Faculty, Computer Engineering Department, Kayseri/Türkiye, 2005.

P. K. Sethy and S. Rani. “Improvement in COCOMO Modal Using Optimization Algorithms to Reduce MMRE Values for Effort Estimation. In: 2019 4th International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU).” pp. 1-4, 2019.

P. Rijwani and S. Jain. “Enhanced software effort estimation using multi layered feed forward artificial neural network technique.” Procedia Computer Science, vol. 89, no. 1, pp. 307-312, 2016.

N. Rankovic, D. Rankovic, M. Ivanovic and L. Lazic. “Improved effort and cost estimation model using artificial neural networks and taguchi method with different activation functions.” Entropy, vol. 23, p. 854, 2021.

Z. Shahpar, V. K. Bardsiri and A. K. Bardsiri. “Polynomial analogy-based software development effort estimation using combined particle swarm optimization and simulated annealing.” Concurrency and Computation: Practice and Experience, vol. 33, no. 20, p. e6358, 2021.

S. W. Ahmad and G. R. Bamnote. “Whale-crow optimization (WCO)-based Optimal Regression model for Software Cost Estimation.” Sādhanā, vol. 44, no. 4, p. 94, 2019.

A. Puspaningrum and R. Sarno. “A hybrid cuckoo optimization and harmony search algorithm for software cost estimation.” Procedia Computer Science, vol. 124, no. 1, pp. 461-469, 2017.

S. Chhabra and H. Singh. “Optimizing design of fuzzy model for software cost estimation using particle swarm optimization algorithm.” International Journal of Computational Intelligence and Applications, vol. 19, no. 1, p. 2050005, 2020.

S. P. Singh, G. Dhiman, P. Tiwari and R. H. Jhaveri. “A soft computing based multi-objective optimization approach for automatic prediction of software cost models.” Applied Soft Computing, vol. 113, no. 1, p. 107981, 2021.

U. Aman, W. Bin, S. Jinfang, L. Jun, A. Muhammad and S. Zejun. “Optimization of software cost estimation model based on biogeography-based optimization algorithm.” Intelligent Decision Technologies, vol. 14, no. 1, pp. 441-448, 2020.

S. K. Gouda and A. K. Mehta. “A new evolutionism based self-adaptive multi-objective optimization method to predict software cost estimation.” Software: Practice and Experience, vol. 52, no. 8, pp. 1826-1848, 2022.

B. W. Boehm, C. Abts, A. W. Brown, S. Chulani, B. K. Clark, E. Horowitz, R. Madachy, D. J. Reifer and B. Steece. “Software Cost Estimation with COCOMO II.” Prentice Hall Press, Upper Saddle River, NJ, USA, 2009.

R. H. Martin and D. Raffo. “A Comparison of Software Process Modeling Techniques. In: Innovation in Technology Management: The Key to Global Leadership, PICMET’97.” pp. 577-580, 1997.

S. Mirjalili and A. Lewis. “The whale optimization algorithm.” Advances in Engineering Software, vol. 95, pp. 51-67, 2016.

S. Mirjalili. “SCA: A Sine Cosine algorithm for solving optimization problems.” Knowledge-Based Systems, vol. 96, pp. 120-133, 2016.

S. Mirjalili. “Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm.” Knowledge-Based Systems, vol. 89, pp. 228-249, 2015.

Z. Abdelali, H. Mustapha and N. Abdelwahed. “Investigating the use of random forest in software effort estimation.” Procedia Computer Science, vol. 148, no. pp. 343-352, 2019.



How to Cite

Sharif, H. O., Ghareb, M. I., & Mohamedyusf, H. M. (2024). A Hybrid Artificial Bee Colony and Artificial Fish Swarm Algorithms for Software Cost Estimation. UHD Journal of Science and Technology, 8(1), 129–141. https://doi.org/10.21928/uhdjst.v8n1y2024.pp129-141