A comparative study of particle swarm optimization and genetic algorithm

  • Authors

    • Saman M. Almufti computer science
    • Amar Yahya Zebari Statistic
    • Herman Khalid Omer computer science
    2019-10-19
    https://doi.org/10.14419/jacst.v8i2.29401
  • Particle Swarm Optimization (PSO), Genetic Algorithms (GAS), Swarm Intelligence, PSO and GA Comparison.
  • This paper provides an introduction and a comparison of two widely used evolutionary computation algorithms: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) based on the previous studies and researches. It describes Genetic Algorithm basic functionalities including various steps such as selection, crossover, and mutation.

     

     

  • References

    1. [1] Alba, E. and Troya, J. (1999). A survey of parallel distributed genetic algorithms. Complexity, 4(4), pp.31-52. https://doi.org/10.1002/(SICI)1099-0526(199903/04)4:4<31::AID-CPLX5>3.0.CO;2-4.

      [2] A.J., U. and P.D., S. (2015). CROSSOVER OPERATORS IN GENETIC ALGORITHMS: A REVIEW. ICTACT Journal on Soft Computing, 06(01), pp.1083-1092. https://doi.org/10.21917/ijsc.2015.0150.

      [3] Almufti, S. (2017). Using Swarm Intelligence for solving NPHard Problems. Academic Journal of Nawroz University, 6(3), pp. 46-50. https://doi.org/10.25007/ajnu.v6n3a78.

      [4] Almufti, S. (2015). U-Turning Ant Colony Algorithm powered by Great Deluge Algorithm for the solution of TSP Problem. [online] Hdl.handle.net. Available at: http://hdl.handle.net/11129/1734 [Accessed 5 Aug. 2018].

      [5] Almufti S., & Shaban A., (2018), U-Turning Ant Colony Algorithm for Solving Symmetric Traveling Salesman Problem, Academic Journal of Nawroz University, vol. 7, no. 4, pp. 45-49, Available: 10.25007/ajnu. v6n4a270. https://doi.org/10.25007/ajnu.v6n4a270.

      [6] Almufti, S., R. Asaad, R., & B. Salim, (2019). Review on Elephant Herding Optimization Algorithm Performance in Solving Optimization Problems. International Journal of Engineering & Technology, 7(4), 6109-6114.

      [7] Almufti, S., Marqas, R., & Ashqi V., (2019). Taxonomy of bio-inspired optimization algorithms. Journal Of Advanced Computer Science & Technology, 8(2), 23. https://doi.org/10.14419/jacst.v8i2.29402.

      [8] Almufti, S., Marqas, R., & Asaad, R. (2019). Comparative study between elephant herding optimization (EHO) and U-turning ant colony optimization (U-TACO) in solving symmetric traveling salesman problem (STSP). Journal Of Advanced Computer Science & Technology, 8(2), 32. https://doi.org/10.14419/jacst.v8i2.29403.

      [9] Asaad, R., Abdulnabi, N. (2018). Using Local Searches Algorithms with Ant Colony Optimization for the Solution of TSP Problems. Academic Journal of Nawroz University, 7(3), 1-6. https://doi.org/10.25007/ajnu.v7n3a193.

      [10] Coello, Carlos. "An updated survey of GA-based multiobjective optimization techniques." ACM Computing Surveys, vol.32, no.2, p.109-143 (June 2000). https://doi.org/10.1145/358923.358929.

      [11] D. E. Goldberg. Genetic Algorithms in Search, Optimization & Machine Learning, Addison-Wesley, 1989.

      [12] D.E. Goldberg. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, New York,

      [13] DeJong, K. 1975. An Analysis of the Behavior of a Class of Genetic Adaptive Systems, PhD Dissertation, Department of Computer and Communication Sciences, University of Michigan, Ann Arbor.

      [14] G. Sywerda, Uniform crossover in genetic algorithms, in Proceedings of the 3rd International Conference on Genetic Algorithms, 1989, pp. 2-9.

      [15] Hochbaum, S. (1997), Approximation Algorithms for NP-Hard Problems. PWS Publishing Company, Boston. https://doi.org/10.1145/261342.571216.

      [16] Holland, J. H. 1975. Adaptation in Natural and Artificial Systems, University of Michigan Press. Ann Arber.

      [17] Kennedy J., Eberhart R. (1995), Particle swarm optimization, in: IEEE Inter-national Conference on Neural Networks Proceedings, vols. 1– 6, pp.1942–1948.

      [18] Kennedy, J., and Mendes, R. (2002). Population Structure and Particle Swarm Performance. Proceedings of the 2002 World Congress on Computational Intelligence. https://doi.org/10.1109/CEC.2002.1004493.

      [19] Kora, P. and Yadlapalli, P. (2017). Crossover Operators in Genetic Algorithms: A Review. International Journal of Computer Applications, 162(10), pp.34-36. https://doi.org/10.5120/ijca2017913370.

      [20] Li, Z., Liu, X., Duan, X. and Huang, F. (2010). Comparative Research on Particle Swarm Optimization and Genetic Algorithm. Computer and Information Science, 3(1). https://doi.org/10.5539/cis.v3n1p120.

      [21] Lim, S., Sultan, A., Sulaiman, M., Mustapha, A. and Leong, K. (2017). Crossover and Mutation Operators of Genetic Algorithms. International Journal of Machine Learning and Computing, 7(1), pp.9-12. https://doi.org/10.18178/ijmlc.2017.7.1.611.

      [22] Mazza, C. and Piau, D. (2001). On the effect of selection in genetic algorithms. Random Structures and Algorithms, 18(2), pp.185-200. https://doi.org/10.1002/1098-2418(200103)18:2<185::AID-RSA1005>3.0.CO;2-7.

      [23] Poli, R., Kennedy, J. and Blackwell, T. (2007). Particle swarm optimization. Swarm Intelligence, 1(1), pp.33-57. https://doi.org/10.1007/s11721-007-0002-0.

      [24] Sivanandam S.N. and Deepa S. N.2007, Introduction to Genetic Algorithms, Springer, ISBN 9783540731894.

      [25] Thengade, A. and Dondal, R. (2012). Genetic Algorithm – Survey Paper. International Journal of Computer Applications (IJCA), (0975 - 8887), pp.25-29.

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  • How to Cite

    M. Almufti, S., Yahya Zebari, A., & Khalid Omer, H. (2019). A comparative study of particle swarm optimization and genetic algorithm. Journal of Advanced Computer Science & Technology, 8(2), 40-45. https://doi.org/10.14419/jacst.v8i2.29401