[1] Gambardella, Luca M and Dorigo, Marco. Ant-Q: A reinforcement learning approach to the traveling salesman problem, pp. 252–260. Elsevier, 1995.
[2] Dorigo, Marco and Gambardella, Luca Maria. Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Transactions on evolutionary computation, 1(1):53–66, 1997.
[3] Maniezzo, Vittorio and Carbonaro, Antonella. An ANTS heuristic for the frequency assignment problem. Future Generation Computer Systems, 16(8):927–935, 2000.
[4] Blum, Christian and Dorigo, Marco. The hyper-cube framework for ant colony optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 34(2):1161–1172, 2004.
[5] Dorigo, Marco and Stützle, Thomas. Ant colony optimization: overview and recent advances, pp. 311–351. Springer, 2019.
[6] Seeley, Thomas D. The wisdom of the hive: the social physiology of honey bee colonies. Harvard University Press, 2009.
[7] Wu, Xin Jie, Hao, Duo, and Xu, Chao. An improved method of artificial bee colony algorithm. in Applied mechanics and materials, vol. 101, pp. 315–319. Trans Tech Publ.
[8] Jabbarpour, Mohammad Reza, Jalooli, Ali, Shaghaghi, Erfan, Noor, Rafidah Md, Rothkrantz, Leon, Khokhar, Rashid Hafeez, and Anuar, Nor Badrul. Ant-based vehicle congestion avoidance system using vehicular networks. Engineering Applications of Artificial Intelligence, 36:303– 319, 2014.
[9] Cong, Zhe, De Schutter, Bart, and Babuška, Robert. Ant colony routing algorithm for freeway networks. Transportation Research Part C: Emerging Technologies, 37:1–19, 2013.
[10] Kponyo, Jerry, Kung, Yujun, and Zhang, Enzhan. Dynamic travel path optimization system using ant colony optimization. in 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, pp. 142–147. IEEE.
[11] Jerry, Kponyo. An ant colony optimization solution to the optimum travel path determination problem in vanets. in The Fifth International Conference on Mobile Services, Resources, and Users (MOBILITY 2015).
[12] Wang, Zhen, Li, Jianqing, Fang, Manlin, and Li, Yang. A multimetric ant colony optimization algorithm for dynamic path planning in vehicular networks. International Journal of Distributed Sensor Networks, 11(10):271067, 2015.
[13] Li, Deng-Feng. Topsis-based nonlinear-programming methodology for multiattribute decision making with interval-valued intuitionistic fuzzy sets. IEEE Transactions on Fuzzy Systems, 18(2):299–311, 2010.
[14] Karaboga, Dervis and Basturk, Bahriye. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (abc) algorithm. Journal of global optimization, 39(3):459–471, 2007.
[15] Karaboga, Dervis and Basturk, Bahriye. On the performance of artificial bee colony (abc) algorithm. Applied soft computing, 8(1):687–697, 2008.
[16] Krishnanand, KR, Nayak, Santanu Kumar, Panigrahi, Bijaya K, and Rout, Pravat K. Comparative study of five bio-inspired evolutionary optimization techniques. in 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), pp. 1231–1236. IEEE.
[17] Teodorović, Dušan and Dell’Orco, Mauro. Mitigating traffic congestion: solving the ride-matching problem by bee colony optimization. Transportation Planning and Technology, 31(2):135–152, 2008.
[18] Wedde, Horst F, Lehnhoff, Sebastian, van Bonn, Bernhard, Bay, Zeynep, Becker, Sven, Bottcher, S, Brunner, Christian, Buscher, A, Furst, T, and Lazarescu, Anca M. A novel class of multi-agent algorithms for highly dynamic transport planning inspired by honey bee behavior. in 2007 IEEE Conference on Emerging Technologies and Factory Automation (EFTA 2007), pp. 1157–1164. IEEE.
[19] Ghosal, Prasun, Chakraborty, Arijit, and Banerjee, Sabyasachee. Honey bee based vehicular traffic optimization and management. in Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012), pp. 455–463. Springer.
[20] Gen, Mitsuo, Cheng, Runwei, and Wang, Dingwei. Genetic algorithms for solving shortest path problems. in Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC’97), pp. 401–406. IEEE.
[21] Delavar, MR, Samadzadegan, F, and Pahlavani, P. A gisassisted optimal urban route finding approach based on genetic algorithms. International archives of photogrammetry remote sensing and spatial information sciences, 35(Part 2):305–308, 2004.
[22] Alhalabi, Sanaa M, Al-Qatawneh, Sokyna M, and Samawi, Venus W. Developing a route navigation system using genetic algorithm. in 2008 3rd International Conference on Information and Communication Technologies: From Theory to Applications, pp. 1–6. IEEE.
[23] Nanayakkara, Suranga Chandima, Srinivasan, Dipti, Lup, Lai Wei, German, Xavier, Taylor, Elizabeth, and Ong, Sim Heng. Genetic algorithm based route planner for large urban street networks. in 2007 IEEE Congress on Evolutionary Computation, pp. 4469–4474. IEEE.
[24] Yu, Haicong and Lu, Feng. A multi-modal route planning approach with an improved genetic algorithm. Advances in geo-spatial information science, 193, 2012.
[25] Dezani, Henrique, Bassi, Regiane DS, Marranghello, Norian, Gomes, Luís, Damiani, Furio, and Da Silva, Ivan Nunes. Optimizing urban traffic flow using genetic algorithm with petri net analysis as fitness function. Neurocomputing, 124:162–167, 2014.
[26] Mohemmed, Ammar W, Sahoo, Nirod Chandra, and Geok, Tan Kim. Solving shortest path problem using particle swarm optimization. Applied Soft Computing, 8(4):1643– 1653, 2008.
[27] Doolan, Ronan and Muntean, Gabriel-Miro. Time-ants: an innovative temporal and spatial ant-based vehicular routing mechanism. in 2014 IEEE Intelligent Vehicles Symposium Proceedings, pp. 951–956. IEEE.
[28] Kammoun, Habib M, Kallel, Ilhem, Casillas, Jorge, Abraham, Ajith, and Alimi, Adel M. Adapt-traf: An adaptive multiagent road traffic management system based on hybrid ant-hierarchical fuzzy model. Transportation Research Part C: Emerging Technologies, 42:147–167, 2014.
[29] Zhang, Yudong, Jun, Yan, Wei, Geng, and Wu, Lenan. Find multi-objective paths in stochastic networks via chaotic immune pso. Expert Systems with Applications, 37(3):1911–1919, 2010.
[30] Yang, Licai, Lin, Jie, Wang, Dewei, and Jia, Lei. Dynamic route guidance algorithm based on artificial immune system. Journal of control theory and applications, 5(4):385– 390, 2007.
)
