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Using Multi-objective Artificial Fish Swarm Algorithm to Solve the Software Project Scheduling Problem

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
Sarah E. Almshhadany, Laheeb M. Ibrahim

Sarah E Almshhadany and Laheeb M Ibrahim. Using Multi-objective Artificial Fish Swarm Algorithm to Solve the Software Project Scheduling Problem. International Journal of Computer Applications 181(16):6-13, September 2018. BibTeX

	author = {Sarah E. Almshhadany and Laheeb M. Ibrahim},
	title = {Using Multi-objective Artificial Fish Swarm Algorithm to Solve the Software Project Scheduling Problem},
	journal = {International Journal of Computer Applications},
	issue_date = {September 2018},
	volume = {181},
	number = {16},
	month = {Sep},
	year = {2018},
	issn = {0975-8887},
	pages = {6-13},
	numpages = {8},
	url = {},
	doi = {10.5120/ijca2018917753},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


In this paper, a new multi-objective artificial fish swarm algorithm was proposed based on the principles of PAES algorithm and it is used to solve SPSP. The aim of this proposal is to solve the software project scheduling problem with artificial fish swarm algorithm and to overcome some disadvantages that AFSA suffer from. The performance of the proposed algorithm was compared with another multi-objective AFSA based on the use of global information (GAFSA), in terms of speed, quality of produced solutions and complexity of algorithm operations. The results show that the proposed algorithm is faster, easier to implement, require less computations, and had obtained better nondominated solutions than the other algorithm.


  1. Leondro L M., Dirk S., Xin Y. 2014. Improved evolutionary algorithm design for the project scheduling problem based on runtime analysis. IEEE. Vol.40-No.1, pp. 83-102.
  2. Broderick C., Ricardo S., Franklin J., Eric M., Fernando P. 2014. A max-min ant system algorithm to solve the software project scheduling problem. Expert System with Applications. Vol.41-No.15, pp. 6634-6645.
  3. Francisco L., David L G., Francisco C., Miguel A V. 2014. The software project scheduling problem: A scalability analysis of multi-objective metaheuristics. Applied Soft Computing. Vol.15, pp. 136-148.
  4. Maghsoud A., Javad Pashaei B. 2015. New approach for solving software project scheduling problem using differential evolutionary algorithm. International Journal in Foundation of Computer Science and Technology (IJFCST). Vol.5-No.1.
  5. Broderick C., Ricardo S., Franklin J., Sanjay M., Fernando P. 2014. The use of metaheuristics to software project scheduling problem. In International Conference on Computational Science and It's Application. Springer. pp. 215-226.
  6. Ana Maria AC R., M. Fernanda P C., Edite M.G.P. F. 2016. A shifted hyperbolic augmented lagrangian-based artificial fish two-swarm algorithm with guarantee convergence for constrained global optimization. Engineering Optimization. Vol.48-No.12. pp. 2114-2140.
  7. Reza A. 2014. Empirecal study of artificial fish swarm algorithm. arXiv preprint aeXiv:1405.4138.
  8. Mingyan J., Kongcun Z. 2011. Multi-objective optimization by artificial fish swarm algorithm. In Computer Science and Automation Engineering (CSAE) 2011 IEEE International Conference. IEEE. Vol.3. pp. 506-511.
  9. Valdi R., M Anwar M. 2017. Comparative analysis of ant colony extended and mix-min ant system in software project scheduling problem. In Big Data and Information Security (IWBIS), 2017 International Workshop on. IEEE. pp. 85-91.
  10. Natash N. 2017. Model-based dynamic software project scheduling. In Proceedings of 2017 11th Joint Meeting on Foundations of Software Engineering. ACM. pp. 1042-1045.
  11. Broderick C., Ricardo S., Franklin J., Carlos V., Fernando P. 2016. Firefly Algorithm to Solve a Project Scheduling Problem. In Artificial Intelligence Perspective in Intelligent Systems. Springer. pp. 449-458.
  12. Xiuli W., Pietro C., Leandro M., Gabriela O., Xin Y. 2016. An evolutionary hyper-heuristic for the software project scheduling Problem. In International Conference on Parallel Problem Solving from Nature. Springer. pp.
  13. Broderick C., Ricardo S., Gino A., Eduardo O. 2016. Intelligent water drop algorithm (IWD) to solve software project scheduling problem. In Information Systems and Technologies (CISTI), 2016 11th Iberian Conference on. IEEE. pp. 1-4.
  14. Jing X., Mei-Ling G., Huang M. 2015. Empirical Study of Multi-objective Ant Colony Optimization to Software Project Scheduling Problem. In Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation. ACM. pp. 759-766.
  15. Huabo X. 2017. Application of combinatorial Heuristic Artificial Fish Swarm Algorithm in Non-linear Optimization Problems. Boletin Tecnico. Vol.55-No.5. pp. 174-180.
  16. Zeqiang Z., Kaipu W., Lixia Z., and Yi W. 2017. A Pareto improved artificial fish swarm algorithm for solving a multi-objective fuzzy disassembly line balancing problem. Expert Systems with Applications. Vol.86. pp. 165-176.
  17. Wei Y., Gan X., Lei L. 2017. Stock Price Trend Prediction Based on RBF Neural Network and Artificial Fish Swarm Algorithm.
  18. Yuhong Z., Jiguang D., Limin S. 2016. Application of Artificial Fish Swarm Algorithm in Radial Basis Function Neural Network. TELKOMNIKA (Telecommunication Computing Electronics and Control). Vol.14-No.2. pp. 699-706.
  19. Y Y., Y L., J L. 2014. A new hysteretic model for magnetorheological elastomer base isolator and parameter identification based on modified artificial fish swarm algorithm. In ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction. Vilnius Gediminas Technical University, Department of Construction Economics and Property. Vol.31. pp. 1.
  20. Guohua F., Wei G., Xianfeng H., Xinyi S., Fei Y., Qian L., Ke Y. 2012. A new multi-objective optimization algorithm: MOAFSA and its application. Przegląd Elektrotechniczny. Vol.88-No.9b. pp. 172-176.
  21. Mehdi N., Ghodrat S., Mehdi S., Adel Najaran T. 2014. Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications. Artif Intell Rev. Vol.42. pp. 965-997.


Software project scheduling problem, multi-objective optimization, artificial fish swarm algorithm.