Abstract
In the recent years, extensive discussion takes place in literature, on the effectiveness of meta-heuristics, and especially Nature Inspired Algorithms. Usually, authors state that such an approach should embody a well-balanced exploration and exploitation strategy. Sonar Inspired Optimization (SIO) is a recently presented algorithm, which counts already a number of successful real-world applications. Its novel mechanisms provide this equilibrium between exploration and exploitation, as it has been stated in previous studies. In this work, authors prove that this equilibrium exists and also, it is one of the main reasons behind the high quality performance of SIO.
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References
Tzanetos, A., Dounias, G.: Sonar inspired optimization (SIO) in engineering applications. Evolving Syst. 1–9 (2018). https://doi.org/10.1007/s12530-018-9250-z
Tzanetos, A., Kyriklidis, C., Papamichail, A., Dimoulakis, A., Dounias, G.: A Nature Inspired metaheuristic for Optimal Leveling of Resources in Project Management. In: Proceedings of the 10th Hellenic Conference on Artificial Intelligence. p. 17. ACM (2018)
Ntardas, D., Tzanetos, A., Dounias, G.: Resource leveling optimization in construction projects of high voltage substations using nature-inspired intelligent evolutionary algorithms. Int. J. Electr. Comput. Eng. 14, 6–13 (2020). https://doi.org/10.5281/zenodo.3607880
Tzanetos, A., Vassiliadis, V., Dounias, G.: Boosting the performance of hybrid nature-inspired algorithms: application from the financial optimization domain. Logic J. IGP. 28, 239–247 (2018). https://doi.org/10.1093/jigpal/jzy048
Boulas, K., Tzanetos, A., Dounias, G.: Acquisition of approximate throughput formulas for serial production lines with parallel machines using intelligent techniques. In: Proceedings of the 10th Hellenic Conference on Artificial Intelligence. p. 18. ACM (2018)
Tzanetos, A., Dounias, G.: Sonar inspired optimization in energy problems related to load and emission dispatch. In: Matsatsinis, N.F., Marinakis, Y., Pardalos, P. (eds.) Learning and Intelligent Optimization, pp. 268–283. Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-38629-0_22
Hussain, K., Salleh, M.N.M., Cheng, S., Shi, Y.: On the exploration and exploitation in popular swarm-based metaheuristic algorithms. Neural Comput. & Applic. 31, 7665–7683 (2019). https://doi.org/10.1007/s00521-018-3592-0
Salleh, M.N.M., Hussain, K., Cheng, S., Shi, Y., Muhammad, A., Ullah, G., Naseem, R.: Exploration and exploitation measurement in swarm-based metaheuristic algorithms: an empirical analysis. In: Ghazali, R., Deris, M.M., Nawi, N.M., Abawajy, J.H. (eds.) Recent Advances on Soft Computing and Data Mining, pp. 24–32. Springer International Publishing, Cham (2018)
Yang, X.-S., Deb, S., Hanne, T., He, X.: Attraction and diffusion in nature-inspired optimization algorithms. Neural Comput. & Applic. 31, 1987–1994 (2019). https://doi.org/10.1007/s00521-015-1925-9
Morales-Castañeda, B., Zaldívar, D., Cuevas, E., Fausto, F., Rodríguez, A.: A better balance in metaheuristic algorithms: does it exist? Swarm Evol. Comput. 54, 100671 (2020). https://doi.org/10.1016/j.swevo.2020.100671
Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput. Surv. 35, 268–308 (2003). https://doi.org/10.1145/937503.937505
Lurton, X.: An introduction to underwater acoustics: principles and applications. Springer Science & Business Media (2002)
Das, S., Suganthan, P.N.: Problem Definitions and Evaluation Criteria for CEC 2011 Competition on Testing Evolutionary Algorithms on Real World Optimization Problems. (2010)
Rahman, I., Mohamad-Saleh, J.: Hybrid bio-inspired computational intelligence techniques for solving power system optimization problems: a comprehensive survey. Appl. Soft Comput. 69, 72–130 (2018). https://doi.org/10.1016/j.asoc.2018.04.051
Chakraborty, S., Senjyu, T., Yona, A., Saber, A.Y., Funabashi, T.: Solving economic load dispatch problem with valve-point effects using a hybrid quantum mechanics inspired particle swarm optimisation. IET Gener. Transm. Distrib. 5(10), 1042–1052 (2011)
Coelho, L.S., Mariani, V.C.: Combining of chaotic differential evolution and quadratic programming for economic dispatch optimization with valve-point effect. IEEE Trans. Power Syst. 21, 989–996 (2006). https://doi.org/10.1109/TPWRS.2006.873410
Bhattacharya, A., Chattopadhyay, P.K.: Biogeography-based optimization for different economic load dispatch problems. IEEE Trans. Power Syst. 25, 1064–1077 (2010). https://doi.org/10.1109/TPWRS.2009.2034525
Li, Q., Liu, S.-Y., Yang, X.-S.: Influence of initialization on the performance of metaheuristic optimizers. Appl. Soft Comput. 91, 106193 (2020). https://doi.org/10.1016/j.asoc.2020.106193
Yang, X.-S.: Chapter 10 - bat algorithms. In: Yang, X.-S. (ed.) Nature-Inspired Optimization Algorithms, pp. 141–154. Elsevier, Oxford (2014). https://doi.org/10.1016/B978-0-12-416743-8.00010-5
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Tzanetos, A., Dounias, G. Exploration and exploitation analysis for the sonar inspired optimization algorithm. Ann Math Artif Intell 89, 857–874 (2021). https://doi.org/10.1007/s10472-021-09755-1
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DOI: https://doi.org/10.1007/s10472-021-09755-1