CFP last date
01 August 2024
Reseach Article

Validation of Hybridized Particle Swarm Optimization (PSO) Algorithm with the Pheromone Mechanism of Ant Colony Optimization (ACO) using Standard Benchmark Function

by Mogaji Stephen Alaba, Alese Boniface Kayode, Adetunmbi Adebayo O
Communications on Applied Electronics
Foundation of Computer Science (FCS), NY, USA
Volume 7 - Number 23
Year of Publication: 2018
Authors: Mogaji Stephen Alaba, Alese Boniface Kayode, Adetunmbi Adebayo O

Mogaji Stephen Alaba, Alese Boniface Kayode, Adetunmbi Adebayo O . Validation of Hybridized Particle Swarm Optimization (PSO) Algorithm with the Pheromone Mechanism of Ant Colony Optimization (ACO) using Standard Benchmark Function. Communications on Applied Electronics. 7, 23 ( Dec 2018), 13-20. DOI=10.5120/cae2018652799

@article{ 10.5120/cae2018652799,
author = { Mogaji Stephen Alaba, Alese Boniface Kayode, Adetunmbi Adebayo O },
title = { Validation of Hybridized Particle Swarm Optimization (PSO) Algorithm with the Pheromone Mechanism of Ant Colony Optimization (ACO) using Standard Benchmark Function },
journal = { Communications on Applied Electronics },
issue_date = { Dec 2018 },
volume = { 7 },
number = { 23 },
month = { Dec },
year = { 2018 },
issn = { 2394-4714 },
pages = { 13-20 },
numpages = {9},
url = { },
doi = { 10.5120/cae2018652799 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2023-09-04T20:02:21.902758+05:30
%A Mogaji Stephen Alaba
%A Alese Boniface Kayode
%A Adetunmbi Adebayo O
%T Validation of Hybridized Particle Swarm Optimization (PSO) Algorithm with the Pheromone Mechanism of Ant Colony Optimization (ACO) using Standard Benchmark Function
%J Communications on Applied Electronics
%@ 2394-4714
%V 7
%N 23
%P 13-20
%D 2018
%I Foundation of Computer Science (FCS), NY, USA

Swarm intelligence (SI) is the communal behavior of devolved, self-organized structures, natural or artificial. SI systems consist typically of a population of simple agents interacting locally with one another and with their environment. The inspiration often comes from nature, especially biological systems. The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of "intelligent" global behavior, unknown to the individual agents This research work aims at hybridizing the conventional Particle Swarm Optimization (PSO) algorithm with the pheromone mechanism of Ant Colony Optimization (ACO) to attain faster convergence on a feasible standard PSO solution space then benchmarked against standard optimization test functions using Python Programming language to prove the correctness and convergence of the Hybridized PSO optimization mode for minimization. The result shows that hybridizing swarm intelligence performs better in solving difficult continuous optimization problems.

  1. Ackley, D. H. (1987) "A connectionist machine for genetic hill-climbing", Kluwer Academic Publishers, Boston MA.
  2. Angeline, P.J. (1998). Evolutionary optimization versus particle swarm optimization: philosophy and performance difference, in: V.W. Porto et al. (Eds.), Proceedings of 7th Annual Conference on Evolutionary Programming, Lecture Notes in Computer Science, vol. 1447, Springer, Berlin, 1998, pp. 601–610.
  3. Amudha, P., and Abdul Rauf, H. (2012). A Study on Swarm Intelligence Techniques in Intrusion Detection, IJCA Special Issue on Computational Intelligence & Information Security
  4. Bao, F., Chen I.R., Chang M., and Cho, J.H. (2011). Hierarchical trust management for wireless sensor networks and its application to trust-based routing. Proceedings of the 2011 ACM Symposium on Applied Computing, pp. 1732- 1738.
  5. Bao, F., Chen, R., Chang, M., and Cho, J. H. (2012). Hierarchical trust management for wireless sensor networks and its applications to trust-based routing and intrusion detection, Network and service Management, IEEE Transactions on, vol. 9, pp. 169-183.
  6. Beni, G., W.J. (1989). Swarm intelligence in cellular robotics systems: NATO Advanced Workshop on Robots and Biological System.
  7. Bin, Y., Zhong-Zhen, Y., and Baozhen, Y. (2009). An improved ant colony optimization for vehicle routing sensor networks, IEEE Communications Surveys & Tutorials, pp: 2-28.
  8. Dong, Y., Tang, J., Xu, B., and Wang D., (2005). An application of swarm optimization to nonlinear programming, Computers & Mathematics with Applications 49 (11–12) pp1655–1668.
  9. Dong, P., Wang H., and Zhang H. (2009). Probability-based trust management model for distributed e-commerce, Network Infrastructure and Digital Content. IC-NIDC. IEEE International Conference, pp. 419-423.
  10. Dorigo, M. (1992). Optimization, Learning and Natural Algorithms (in Italian). Ph.D. thesis, Dipartimento di Elettronica, Politecnico di Milano, Italy.
  11. Dorigo, M., Blum C. (2005). Ant colony optimization theory: a survey, Theoretical Computer Science 344 (2–3) (2005) 243–278
  12. Dorigo, M., Maniezzo, V., and Colorni, A. (1996). The ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics - Part B 26(1):29–41
  13. Dorigo, M., Gambardella, L.M. (1997). Ant colony system: A cooperative learning approach to the travelling salesman problem. IEEE Transactions on Evolutionary Computation
  14. Dorigo, M., Di Caro, G. (1999). The ant colony optimization meta-heuristic, McGraw-Hill Ltd.,UK, Maidenhead, UK, England, pp 11–32 1:53–66
  15. Dorigo, M., Caro, G.D., Gambardella, L.M. (1999). Ant algorithms for discrete optimization. Artificial Life 5(2):137–172
  16. Dorigo, M,. and Stützle, T. (2004). Ant Colony Optimization. MIT Press, Cambridge, ISBN: 978-0-262-04219-2.
  17. Goss, S., Aron, S., Deneubourg, J., and Pasteels, J. (1989). Self-Organized Shortcuts in the Argentine Ant, Naturwissenchaften, Vol. 76, pp. 579-581,
  18. Hao, Y., et al. (2004). Security in mobile ad hoc networks: challenges and solutions. Wireless Communications, IEEE, 11(1): p. 38-47.
  19. Hazem, A., and Janice, G. (2012). Swarm Intelligence: Concepts, Models and Applications. Technical Report School of Computing Queen’s University Ontario Canada.
  20. He, Q., Wu, D., and Khosla, (2004) P. “SORI: A Secure and Objective Reputation-based Incentive Scheme for Ad-Hoc Networks,” Proc. IEEE Wireless Communications and Networking Conf., vol. 2, pp. 825- 830.
  21. Iftikhar, M.S., and Fraz, M.R. (2013). A Survey on Application of Swarm Intelligence in Network Security in Transactions on Machine Learning and Artificial Intelligence, Volume 1, No 1, PP 01-15.
  22. Karlson, P., and Lüscher, M. (1959). Pheromones: a new term for a class of biologically active substances. Nature, Vol. 183, pp. 55–56.
  23. Kennedy, J., and Eberhart, R.C. (1995). Particle swarm optimization. In Neural Networks, 1995. Proceedings. IEEE International Conference on. IEEE.
  24. Kennedy, J., and Eberhart, R.C (1995). Particle Swarm Optimization. In Proceedings of IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1948.
  25. Kennedy, J., and Eberhart, R.C (1995). A new optimizer using particle swarm theory. In Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, pp. 39–43.
  26. Li, W., Parker, J., and Joshi A. (2012). Security through collaboration and trust in MANETs, Mobile Networks and Applications, vol. 17, pp. 342-352.
  27. Li, R., and Li, J. (2013). Requirements and design for neutral trust management framework in unstructured
  28. Lloyd, C. (2003). The alarm pheromones of social insects: A review, Technical report, Colorado State University.
  29. Luo, J., Liu, X., and Fan, M. (2009). A trust model based on fuzzy recommendation for mobile ad-hoc networks. Computer Networks, vol. 53, pp. 2396-2407.
  30. Nagalakshmi, S., and Rakesh, P. (2016). Performance Comparison and Evaluation of Efficient Routing Protocols for MANETs: Ant Inspired Adaptive Routing. International Journal of Advanced Research in Computer and Communication Engineering ISO 3297:2007 Certified Vol. 5, Issue 9.
  31. Nekkantim R.K., and Lee, C. (2004) “Trust-based Adaptive On Demand Ad Hoc Routing Protocol,” Proc. 42th Annual ACM Southeast Regional Conf., Huntsville, Alabama, 2004, pp. 88-93.
  32. Ourique, C.O., Biscaia, E.C., and Pinto, J.C.(2002). The use of particle swarm optimization for dynamical analysis in chemical processes, Computers & Chemical Engineering 26 (12) 1783–1793.
  33. Paterlini, S., and Krink, T. (2006). Differential evolution and particle swarm optimisation in partitional clustering, Computational Statistics & Data Analysis” 50 (5) 1220–1247.
  34. Rastrigin, L. A. (1974) "Systems of extremal control." Mir, Moscow.
  35. Reynolds, C.W. (1987). Flocks, herds, and schools: A distributed behavioural model, Computer Graphics (ACM SIGGRAPH ‘87 Conference Proceedings), Vol. 21, No. 4, pp. 25–34.
  36. Rosenbrock, H.H. (1960). "An automatic method for finding the greatest or least value of a function". The Computer Journal. 3 (3): 175–184. doi:10.1093/comjnl/3.3.175. ISSN 0010-4620
  37. Shabut, A.R.M. (2015). Trust Computational Models for Mobile Ad Hoc Networks, a Ph.D. thesis submitted to School of Computing Faculty of Engineering and Informatics University of Bradford
  38. Shabut, A., Dahal, K.P., and Awan, I. (2013). A Recommendation-Based Trust Model for MANETs to Enhance Dynamic Recommender Selection Using Multiple Rules, Seventh International Open Conference HET-NETs 2013, UK, Ilkely.
  39. Shabut, A., Dahal, K.P., Awan, I. (2013). A Trust-Based Monitoring Model to Secure Routing Protocol in MANETs Using Enhanced Trust Metric”, Seventh International Open Conference HET-NETs 2013, UK, Ilkely, 2013.
  40. Shabut, A., and Dahal, K.P. (2011). Trust and Security Management in Distributed Systems, University of Bradford school of computing, School Research seminars.
  41. Sharvani G.S. (2012) “Development of Swarm Intelligent Systems for MANET” A Ph.D Thesis submitted to the Department of Computer Science and Engineering, Faculty of Engineering, Avinashilingam University for Women, Coimbatore
  42. Shelokar, P.S., Jayaraman, V.K., and Kulkarni, B.D. (2004). An ant colony classifier system: application to some process engineering problems. Computers & Chemical Engineering 28 (9) (2004) 1577–1584
  43. Shelokar, P., Siarry, P., Jayaraman, V. K., & Kulkarni, B. D. (2007). Particle swarm and ant colony algorithms hybridized for improved continuous optimization. Applied Mathematics and Computation, 188(1), 129–142.
  44. Shi, Y (2004) “Feature article on particle swarm optimization”, IEEE Neural Network Society, Feature Article, pp. 8–13.
  45. Shyu, S.J., Yin, P.Y., Lin, B.M.T. (2004). An ant colony optimization algorithm for the minimum weight vertex cover problem. Annals of Operations Research 131:283–304
  46. Shyu, S.J., Lin, B.M.T., and Hsiao, T.S. (2006). Ant colony optimization for the cell assignment problem in PCS networks, Computers & Operations Research 33 (6) (2006) 1713–1740.
  47. Wang Y.D., and Emurian, H .H. (2005). An overview of online trust: Concepts, elements, and implications, Computers in human behavior, vol. 21, pp. 105-125, 2005.
  48. Wang, Y.F., Hori, Y., and Sakurai, K. (2008). Characterizing economic and social properties of trust and reputation systems in P2P environment, Journal of Computer Science and Technology, vol. 23, pp. 129-140, 2008.
  49. Wang, Y. and Vassileva, J. (2003). Trust and reputation model in peer-to-peer networks. Peer-to-Peer Computing, 2003. (P2P 2003). Proceedings. Third International Conference on, 2003, pp. 150-157
  50. Yin, P.Y., and Wang, J.Y., (2006). Ant colony optimization for the nonlinear resource allocation problem. Applied Mathematics & Computation 174 (2)
  51. Yunfang, F (2007) “Adaptive Trust Management in MANETs,” Proc. 2007 Int’l Conf. on Computational Intelligence and Security, Harbin, China, pp 804-808.
Index Terms

Computer Science
Information Sciences


Hybridization Pheromone mechanism Benchmark functions pbest gbest PSO ACO