Huawen Hu
ABSTRACT
Network topology construction is critical for designing efficient and resilient networks. Although ant colony optimization (ACO) has been widely applied in these tasks owing to its advantage of the pheromone update mechanism, it still exhibits limitations in solving such multi-objective optimization problems and converges slowly. This paper proposes a Diffusion-Based Multi-Objective Ant Colony Optimization (DMAC) algorithm for effective and reliable network topology construction. DMAC integrates pheromone diffusion mechanisms with multi-objective ACO to optimize multiple critical objectives concurrently and accelerate convergence. Experiments on simulation and real-world implementation demonstrate DMAC's ability to construct high-quality topologies balancing key tradeoffs within a small iteration budget. The proposed combination of diffusion mechanisms and multi-objective ACO addresses the limitations of prior ACO methods and provides new effective approach to automated network topology construction under multiple design constraints. DMAC demonstrates promising performance improvements on this complex multi-objective optimization problem and has great application potential in other fields and algorithms.
- M. M. Alam, M. Y. Arafat, S. Moh, and J. Shen, “Topology control algorithms in multi-unmanned aerial vehicle networks: An extensive survey,” Journal of Network and Computer Applications, vol. 207, p. 103495, Nov. 2022, doi: 10.1016/j.jnca.2022.103495.Google Scholar
Digital Library
- Omar Sami Oubbati, Abderrahmane Lakas, Fen Zhou, Mesut Güneş, and Mohamed Bachir Yagoubi. 2017. A survey on position-based routing protocols for Flying Ad hoc Networks (FANETs). Vehicular Communications 10, (October 2017), 29–56. DOI: https://doi.org/10.1016/j.vehcom.2017.10.003Google ScholarCross Ref
- Muhammad Yeasir Arafat and Sangman Moh. 2019. Routing Protocols for Unmanned Aerial Vehicle Networks: A Survey. IEEE Access 7, (2019), 99694–99720. DOI:https://doi.org/10.1109/ACCESS.2019.2930813Google ScholarCross Ref
- Yong Zeng and Rui Zhang. 2017. Energy-Efficient UAV Communication With Trajectory Optimization. IEEE Transactions on Wireless Communications 16, 6 (June 2017), 3747–3760. DOI:https://doi.org/10.1109/TWC.2017.2688328Google ScholarDigital Library
- J. Crank, The Mathematics of Diffusion. Clarendon Press, 1979.Google Scholar
- L. Young, C. W. Chen, C. M. Fan, and C. C. Tsai. 2006. The method of fundamental solutions with eigenfunction expansion method for nonhomogeneous diffusion equation. Numerical Methods for Partial Differential Equations 22, 5 (2006), 1173–1196. DOI:https://doi.org/10.1002/num.20148Google ScholarCross Ref
- Fajie Wang, Wen Chen, António Tadeu, and Carla G. Correia. 2017. Singular boundary method for transient convection–diffusion problems with time-dependent fundamental solution. International Journal of Heat and Mass Transfer 114, (November 2017), 1126–1134. DOI:https://doi.org/10.1016/j.ijheatmasstransfer.2017.07.007Google ScholarCross Ref
- Ji Lin, S.Y. Reutskiy, and Jun Lu. 2018. A novel meshless method for fully nonlinear advection–diffusion-reaction problems to model transfer in anisotropic media. Applied Mathematics and Computation 339, (December 2018), 459–476. DOI:https://doi.org/10.1016/j.amc.2018.07.045Google ScholarCross Ref
- Zhuo-Jia Fu, Qiang Xi, Wen Chen, and Alexander H. -D. Cheng. 2018. A boundary-type meshless solver for transient heat conduction analysis of slender functionally graded materials with exponential variations. Computers & Mathematics with Applications 76, 4 (August 2018), 760–773. DOI:https://doi.org/10.1016/j.camwa.2018.05.017Google ScholarCross Ref
- Xingxing Yue, Fajie Wang, Qingsong Hua, and Xiang-Yun Qiu. 2019. A novel space–time meshless method for nonhomogeneous convection–diffusion equations with variable coefficients. Applied Mathematics Letters 92, (June 2019), 144–150. DOI:https://doi.org/10.1016/j.aml.2019.01.018Google ScholarCross Ref
- Ivo Babuška. 1970. The finite element method for elliptic equations with discontinuous coefficients. Computing 5, 3 (September 1970), 207–213. DOI:https://doi.org/10.1007/BF02248021Google ScholarCross Ref
- Marco Dorigo and Thomas Stützle. 2010. Ant Colony Optimization: Overview and Recent Advances. In Handbook of Metaheuristics, Michel Gendreau and Jean-Yves Potvin (eds.). Springer US, Boston, MA, 227–263. DOI:https://doi.org/10.1007/978-1-4419-1665-5_8Google ScholarCross Ref
- Gianni Di Caro. 2004. Ant Colony Optimization and its Application to Adaptive Routing in Telecommunication Networks. (January 2004).Google Scholar
- Kwang Mong Sim and Weng Hong Sun. 2003. Ant colony optimization for routing and load-balancing: survey and new directions. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 33, 5 (September 2003), 560–572. DOI: https://doi.org/10.1109/TSMCA.2003.817391Google ScholarDigital Library
- Carlos A. Coello. 2000. An updated survey of GA-based multiobjective optimization techniques. ACM Comput. Surv. 32, 2 (June 2000), 109–143. DOI:https://doi.org/10.1145/358923.358929Google ScholarDigital Library
- Yannis Marinakis and Magdalene Marinaki. 2010. A Hybrid Multi-Swarm Particle Swarm Optimization algorithm for the Probabilistic Traveling Salesman Problem. Computers & Operations Research 37, 3 (March 2010), 432–442. DOI:https://doi.org/10.1016/j.cor.2009.03.004Google ScholarDigital Library
- Serap Ercan Comert and Harun Resit Yazgan. 2023. A new approach based on hybrid ant colony optimization-artificial bee colony algorithm for multi-objective electric vehicle routing problems. Engineering Applications of Artificial Intelligence 123, (August 2023), 106375. DOI:https://doi.org/10.1016/j.engappai.2023.106375Google ScholarDigital Library
- Zana Azeez Kakarash, Sarkhel H. Taher Karim, Nawroz Fadhil Ahmed, and Govar Abubakr Omar. 2021. New Topology Control base on Ant Colony Algorithm in Optimization of Wireless Sensor Network. Passer Journal of Basic and Applied Sciences 3, 2 (September 2021), 123–129. DOI:https://doi.org/10.24271/psr.22Google ScholarCross Ref
- W. F. Ames, Numerical Methods for Partial Differential Equations. Academic Press, 2014.Google Scholar
- Lun Li, David Alderson, Walter Willinger, and John Doyle. 2004. A first-principles approach to understanding the internet's router-level topology. SIGCOMM Comput. Commun. Rev. 34, 4 (August 2004), 3–14. DOI:https://doi.org/10.1145/1030194.1015470Google ScholarDigital Library
- V. Latora and M. Marchiori. 2001. Efficient behavior of small-world networks. Phys Rev Lett 87, 19 (November 2001), 198701. DOI:https://doi.org/10.1103/PhysRevLett.87.198701Google ScholarCross Ref
- V. Rosato, L. Issacharoff, F. Tiriticco, S. Meloni, S. D. Porcellinis, and R. Setola. 2008. Modelling interdependent infrastructures using interacting dynamical models. Int. J. Crit. Infrastructures (2008). Retrieved July 19, 2023 from https://www.semanticscholar.org/paper/Modelling-interdependent-infrastructures-using-Rosato-Issacharoff/a69594ba3a5d98473a1c3edfe5c4535064d49aabGoogle Scholar
- Y. Shang, “Effect of link oriented self-healing on resilience of networks,” J. Stat. Mech., vol. 2016, no. 8, p. 083403, Aug. 2016, doi: 10.1088/1742-5468/2016/08/083403.Google ScholarCross Ref
- James P. G. Sterbenz, David Hutchison, Egemen K. Çetinkaya, Abdul Jabbar, Justin P. Rohrer, Marcus Schöller, and Paul Smith. 2010. Resilience and survivability in communication networks: Strategies, principles, and survey of disciplines. Computer Networks 54, 8 (June 2010), 1245–1265. DOI:https://doi.org/10.1016/j.comnet.2010.03.005Google ScholarDigital Library
- Q. Wu, C. Yang, W. Zhao, Y. He, D. Wipf, and J. Yan, “DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion.” arXiv, May 28, 2023. Accessed: Jul. 19, 2023. [Online]. Available: http://arxiv.org/abs/2301.09474Google Scholar
- E. L. Cussler, Diffusion: Mass Transfer in Fluid Systems. Cambridge University Press, 1997.Google Scholar
- Jean Philibert. 2004. One and a Half Century of Diffusion: Fick, Einstein, Before and Beyond. Diffusion Fundamentals 2, (November 2004).Google Scholar
- C. García-Martínez, O. Cordón, and F. Herrera. 2007. A taxonomy and an empirical analysis of multiple objective ant colony optimization algorithms for the bi-criteria TSP. European Journal of Operational Research 180, 1 (July 2007), 116–148. DOI:https://doi.org/10.1016/j.ejor.2006.03.041.Google ScholarCross Ref
- Jingfa Liu and Jun Liu. 2019. Applying multi-objective ant colony optimization algorithm for solving the unequal area facility layout problems. Applied Soft Computing 74, (January 2019), 167–189. DOI:https://doi.org/10.1016/j.asoc.2018.10.012Google ScholarCross Ref
- Aleem Akhtar. 2019. Evolution of Ant Colony Optimization Algorithm – A Brief Literature Review. Retrieved July 19, 2023 from http://arxiv.org/abs/1908.08007Google Scholar
- Ines Alaya, Christine Solnon, and Khaled Ghedira. 2007. Ant Colony Optimization for Multi-Objective Optimization Problems. In 19th IEEE International Conference on Tools with Artificial Intelligence(ICTAI 2007), IEEE, Patras, Greece, 450–457. DOI: https://doi.org/ 10.1109/ ICTAI.2007.108.sGoogle Scholar
Index Terms
- A Diffusion-Based Multi-Objective Ant Colony Algorithm for Optimizing Network Topology Design
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