Published August 8, 2022 | Version v1
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Lion algorithm: Overview, modifications and applications

Authors/Creators

  • 1. Computer Science, Nawroz University, Kurdistan-Region, Iraq

Description

The increasing complexity of real-world problems motivated computer scientists and researchers to seek more efficient problem-solving strategies. Because of their ability to adapt to a wide range of conditions, Natural Inspired, Bio Inspired, Metaheuristics based on evolutionary computation, and Swarm Intelligence algorithms have been widely used for solving complex, real-world optimization problems. This paper presents a swarm based algorithm that is based on the cooperative behaviors between Loin’s, it is called lion algorithm (LA) algorithm. This paper provides a review of these algorithms, with a particular emphasis on the Lion algorithm. Lion Algorithm is based on lions' unique social behavior, which makes them the world's strongest animal. Loin Algorithm, like Genetic Algorithm, includes generation, mutation, crossover, and so on. The lion's territorial defense and territorial takeover behavior distinguishes this algorithm from others.

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References

  • Almufti, S.M. (2015). U-Turning Ant Colony Algorithm powered by Great Deluge Algorithm for the solution of TSP Problem. Retrieved from Hdl.handle.net
  • Almufti, S.M. (2017). Historical survey on metaheuristics algorithms. International Journal of Scientific World, 7(1), 1-12. doi:https://doi.org/10.14419/IJSW.V7I1.29497
  • Almufti, S.M. (2017). Using Swarm Intelligence for solving NP-Hard Problems. Academic Journal of Nawroz University, 6(3), 46-50. doi:https://doi.org/10.25007/ajnu.v6n3a78
  • Almufti, S.M. (2021). The novel social spider optimization algorithm: overview, modifications, and applications. ICONTECH international journal of surveys, engineering, technology, 5(2), 35-51.
  • Almufti, S.M. (2022). Hybridizing Ant Colony Optimization Algorithm for Optimizing Edge-Detector Techniques. Academic Journal of Nawroz University, 11(2), 135-145. doi:https://doi.org/10.25007/ajnu.v11n2a1320
  • Bauer, H., de, I.H., & Silvestre, I. (2003). Lion (Panthera leo) social behaviour in the West and Central African savannah belt. Mammalian Biology, 68(4), 239-243. doi:https://doi.org/10.1078/1616-5047-00090
  • BR, R. (2014). Lion algorithm for standard and large scale bilinear system identification: a global optimization based on lion's social behavior. IEEE congress on evolutionary computation (CEC), 2116–2123.
  • Chander, S., Vijaya, P., & Dhyani, P. (2018). Multi kernel and dynamic fractional lion optimization algorithm for data clustering. Alexandria Engineering Journal, 57(1), 267-276. doi:https://doi.org/10.1016/j.aej.2016.12.013
  • Chintalapalli, R.M. & Ananthula, V.R. (2018). M-LionWhale: multi-objective optimisation model for secure routing in mobile ad-hoc network. IET Communications, 12(12), 1406-1415. doi:10.1049/iet-com.2017.1279
  • G, J. & brunda, S.S. (2018). An Improved K-Lion Optimization Algorithm With Feature Selection Methods for Text Document Cluster. International Journal of Computer Sciences and Engineering, 6(7), 245-251.
  • Ihsan, R.R., Almufti, S.M., Ormani, B.M., Asaad, R.R., & Marqas, R.B. (2021). A Survey on Cat Swarm Optimization Algorithm. Asian Journal of Research in Computer Science, 10(2), 22-32. doi:10.9734/AJRCOS/2021/v10i230237
  • KC, L., JC, H., & JT, W. (2018). Feature selection with modified lion's algorithms and support vector machine for high-dimensional data. Appl Soft Comput 68.
  • Marqas, R.B., Almufti, S.M., Ahmed, H.B., & Asaad, R.R. (2021). Grey wolf optimizer: Overview, modifications and applications. International Research Journal of Science, Technology, Education, and Management, 1(1), 44-56. doi: https://doi.org/10.5281/zenodo.5195644
  • MB, W. & N,G. (2018). Route discovery for vehicular Ad hoc networks using modified lion algorithm. Alexandria Eng J 57.
  • Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software.
  • Rajakumar, B.R. (2012). The Lion's Algorithm: A New Nature-Inspired Search Algorithm. Procedia Technology, 6, 126-135. doi:https://doi.org/10.1016/j.protcy.2012.10.016
  • Rajakumar, B.R. (2020). Lion Algorithm and Its Applications. In M. Khosravy, N. Gupta, N. Patel, & T. Senjyu, Frontier Applications of Nature Inspired Computation (pp. 100-119). Springer Nature Singapore. doi:https://doi.org/10.1007/978-981-15-2133-1_5
  • Ranjan, N. M. & Prasadb, R. S. (n.d.). lion fuzzy neural network-based evolutionary model for text classification using context and sense based features. Applied Soft Computing. doi:https://doi.org/10.1016/j.asoc.2018.07.016
  • RK, A. & UD, K. (2017). AFL-TOHIP Adaptive fractional lion optimization to topology-hiding multipath routing in mobile Ad hoc network. International conference on ISMAC. doi:10.1109/I-SMAC.2017.8058274
  • RK, A. & UD, K. (2017). AFL-TOHIP: Adaptive fractional lion optimization to topology-hiding multi-path routing in mobile Ad hoc network. 727–732.
  • Salim, B.W., Almufti, S.M., & Asaad, R.R. (2019). Review on elephant herding optimization algorithm performance in solving optimization problems. International Journal of Engineering & Technology, 7(4), 6109-6114. doi:10.14419/ijet.v7i4.23127
  • Satish, C., P., V., & Praveen, D. (2017). Multi-objective-based adaptive dynamic directive operative fractional lion algorithm for data clustering. International Conference on Infocom Technologies and Unmanned Systems (Trends and Future Directions). doi:10.1109/ICTUS.2017.8286066
  • Y, L., Y, H., & M, Z. (2018). Short-term load forecasting for electric vehicle chargingstation based on niche immunity lion algorithm and convolutional neural network. Energies.
  • Yazdani, M. & Jolai, F. (2016). Lion Optimization Algorithm (LOA): A nature-inspired. Journal of Computational Design and Engineering, 3(1). doi:https://doi.org/10.1016/j.jcde.2015.06.003