Agarwal, P., & Mehta, S. (2014). Nature-inspired algorithms: State-of-art, problems and prospects. International Journal of Computer Applications, 100(14), 14–21.

Bäck, T. (2018). Evolutionary computation 1: Basic algorithms and operators. CRC Press.

Bonyadi, M. R., & Michalewicz, Z. (2017). Particle swarm optimization for single objective continuous space problems: A review. Evolutionary Computation, 25(1), 1–54. https://doi.org/10.1162/EVCO_r_00180

Carlisle, A. G., & Dozier, G. B. (2001). An off-the-shelf PSO. In Proceedings of the Workshop on Particle Swarm Optimization (pp. 1–6).

Colorni, A., Dorigo, M., & Maniezzo, V. (1991). Distributed optimization by ant colonies. In Proceedings of the First European Conference on Artificial Life (pp. 134–142).

Deneubourg, J.-L., Aron, S., Goss, S., & Pasteels, J. M. (1990). The self-organizing exploratory pattern of the Argentine ant. Journal of Insect Behavior, 3, 159–168. https://doi.org/10.1007/BF01417909

Dorigo, M., & Di Caro, G. (1999). Ant colony optimization: A new meta-heuristic. In Proceedings of the 1999 Congress on Evolutionary Computation (CEC99) (Vol. 2, pp. 1470–1477). IEEE. https://doi.org/10.1109/CEC.1999.782657

Dorigo, M., Di Caro, G., & Gambardella, L. M. (1999). Ant algorithms for discrete optimization. Artificial Life, 5(2), 137–172. https://doi.org/10.1162/106454699568728

Dorigo, M., & Gambardella, L. M. (1997). Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation, 1(1), 53–66. https://doi.org/10.1109/4235.585892

Forrest, S. (1996). Genetic algorithms. ACM Computing Surveys, 28(1), 77–80. https://doi.org/10.1145/234313.234350

Hassanat, A., Almohammadi, K., Alkafaween, E., Abunawas, E., Hammouri, A., & Prasath, V. S. (2019). Choosing mutation and crossover ratios for genetic algorithms—A review with a new dynamic approach. Information, 10(12), 390. https://doi.org/10.3390/info10120390

He, J., Sun, X., Li, W., & Chen, J. (2017). A new pheromone update strategy for ant colony optimization. Journal of Intelligent & Fuzzy Systems, 32(5), 3355–3364.

Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN’95 – International Conference on Neural Networks (Vol. 4, pp. 1942–1948). IEEE. https://doi.org/10.1109/ICNN.1995.488968

Maniezzo, V., & Colorni, A. (1999). The ant system applied to the quadratic assignment problem. IEEE Transactions on Knowledge and Data Engineering, 11(5), 769–778. https://doi.org/10.1109/69.806935

Mavrovouniotis, M., Ellinas, G., & Polycarpou, M. (2019). Electric vehicle charging scheduling using ant colony system. In 2019 IEEE Congress on Evolutionary Computation (CEC). IEEE.

Mavrovouniotis, M., Li, C., Ellinas, G., & Polycarpou, M. (2019). Parallel ant colony optimization for the electric vehicle routing problem. In 2019 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE.

Mavrovouniotis, M., & Yang, S. (2013). Dynamic vehicle routing: A memetic ant colony optimization approach. In Automated Scheduling and Planning: From Theory to Practice (pp. 283–301). Springer.

Nannen, V., & Eiben, A. E. (2006). A method for parameter calibration and relevance estimation in evolutionary algorithms. In Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation (pp. 183–190). ACM.

Ostfeld, A. (2011). Ant colony optimization for water resources systems analysis—Review and challenges. In Ant Colony Optimization: Methods and Applications.

Parsons, S. (2005). Ant Colony Optimization by Marco Dorigo and Thomas Stützle, MIT Press, 305 pp., $40.00, ISBN 0-262-04219-3. The Knowledge Engineering Review, 20(1), 92–93.

Rezvanian, A., Vahidipour, S. M., & Sadollah, A. (2023). An overview of ant colony optimization algorithms for dynamic optimization problems.

Schoonderwoerd, R., Holland, O. E., Bruten, J. L., & Rothkrantz, L. J. (1997). Ant-based load balancing in telecommunications networks. Adaptive Behavior, 5(2), 169–207. https://doi.org/10.1177/105971239700500203

Shami, T. M., El-Saleh, A. A., Alswaitti, M., Al-Tashi, Q., Summakieh, M. A., & Mirjalili, S. (2022). Particle swarm optimization: A comprehensive survey. IEEE Access, 10, 10031–10061. https://doi.org/10.1109/ACCESS.2022.3142859

Teodorović, D., & Dell’Orco, M. (2005). Bee colony optimization—A cooperative learning approach to complex transportation problems. Advanced OR and AI Methods in Transportation, 51–60.

Trelea, I. C. (2003). The particle swarm optimization algorithm: Convergence analysis and parameter selection. Information Processing Letters, 85(6), 317–325. https://doi.org/10.1016/S0020-0190(02)00447-7

Van den Bergh, F., & Engelbrecht, A. P. (2006). A study of particle swarm optimization particle trajectories. Information Sciences, 176(8), 937–971. https://doi.org/10.1016/j.ins.2005.02.003

Vassiliadis, V., & Dounias, G. (2009). Nature-inspired intelligence: A review of selected methods and applications. International Journal on Artificial Intelligence Tools, 18(4), 487–516. https://doi.org/10.1142/S0218213009000241

Wagner, I. A., Lindenbaum, M., & Bruckstein, A. M. (1998). Efficiently searching a graph by a smell-oriented vertex process. Annals of Mathematics and Artificial Intelligence, 24, 211–223.

Yang, L., Wang, Y., & Zhang, J. (2020). Parameter analysis and simulation experiment of ant colony optimization on small-scale TSP problem. IOP Conference Series: Materials Science and Engineering.