Three Steps to Improve Jellyfish Search Optimiser
Abstract
This paper describes three different mechanisms used in Jellyfish Search (JS) optimiser. At first, an archive of good old solutions is used to prevent getting stuck in the local-optima area. Further, a distribution coefficient beta is adapted during the search process to control population diversity. Finally, an Eigen transformation of individuals in the reproduction process is used occasionally to cope with rotated functions. Three proposed variants of the JS optimiser are compared with the original JS algorithm and nine various well-known Nature-inspired optimisation methods when solving real-world problems of CEC 2011. Provided results achieved by statistical comparison show efficiency of the individual newly employed mechanisms.
References
Abdel-Basset, M., Mohamed, R., Chakrabortty, R. K., Ryan, M. J., and El-Fergany, A. An improved artificial jellysh search optimizer for parameter identification of photovoltaic models. Energies 14, 7 (2021).
Aydilek, I. B. A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems. Applied Soft Computing 66 (2018), 232-249.
Bujok, P. Enhanced tree-seed algorithm solving real-world problems. In 2020 7th International Conference on Soft Computing Machine Intelligence (ISCMI) (2020), pp. 12-16.
Bujok, P., Tvrdik, J., and Polakova, R. Comparison of nature-inspired population-based algorithms on continuous optimisation problems. Swarm and Evolutionary Computation 50 (2019), 100490.
Chou, J.-S., and Truong, D.-N. A novel meta-heuristic optimizer inspired by behavior of jellyfish in ocean. Applied Mathematics and Computation 389 (2021), 125535.
Das, S., and Suganthan, P. N. Problem definitions and evaluation criteria for CEC 2011 competition on testing evolutionary algorithms on real world optimization problems. Tech. rep., Jadavpur University, India and Nanyang Technological University, Singapore, 2010.
Fujisawa, K., Shinano, Y., and Waki, H., Eds. Optimization in the Real World. Springer Japan, 2016.
Gouda, E. A., Kotb, M. F., and El-Fergany, A. A. Jellyfish search algorithm for extracting unknown parameters of PEM fuel cell models: Steady-state performance and analysis. Energy 221 (2021), 119836.
Karaboga, D., and Akay, B. A comparative study of artificial bee colony algorithm. Applied Mathematics and Computation 214, 1 (2009), 108-132.
Kiran, M. S. Tsa: Tree-seed algorithm for continuous optimization. Expert Systems with Applications 42, 19 (2015), 6686-6698.
Mirjalili, S., Mirjalili, S. M., and Lewis, A. Grey wolf optimizer. Advances in Engineering Software 69 (2014), 46-61.
Polakova, R., and Bujok, P. Popular optimisation algorithms with diversity-based adaptive mechanism for population size. Recent Advances in Soft Computing and Cybernetics: Studies in Fuzziness and Soft Computing 403 (2021), 171-182.
Selvakumar, S., and Manivannan, S. S. A spectrum defragmentation algorithm using jellyfish optimization technique in elastic optical network (EON). Wireless Pers Commun (2021).
Shi, Y., and Eberhart, R. A modied particle swarm optimizer. In 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (1998), pp. 69-73.
Surantha, N., Lesmana, T., and Isa, S. M. Sleep stage classification using extreme learning machine and particle swarm optimization for healthcare big data. J Big Data 8, 14 (2021).
Wang, Y., Li, H.-X., Huang, T., and Li, L. Differential evolution based on covariance matrix learning and bimodal distribution parameter setting. Applied Soft Computing 18 (2014), 232-247.
Yang, X.-S. A new metaheuristic bat-inspired algorithm. In Nicso 2010: Nature Inspired Cooperative Strategies for Optimization (2010), J. Gonzalez, D. Pelta, C. Cruz, G. Terrazas, and N. Krasnogor, Eds., vol. 284 of Studies in Computational Intelligence, Univ Laguna; Carnary Govt; Spanish Govt, pp. 65-74. International Workshop on Nature Inspired Cooperative Strategies for Optimization NICSO 2008, Tenerife, Spain, 2008.
Yang, X.-S. Nature-Inspired Optimization Algorithms. Elsevier, 2014.
Yang, X.-S., and Deb, S. Cuckoo search via Levy flights. In 2009 World Congress on Nature Biologically Inspired Computing NaBIC (2009), pp. 210-214.
Zelinka, I., and Lampinen, J. SOMA - self organizing migrating algorithm. In MENDEL, 6th International Conference On Soft Computing, Brno, Czech Republic (2000), R. Matousek, Ed., pp. 177-187.
Copyright (c) 2021 MENDEL
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
MENDEL open access articles are normally published under a Creative Commons Attribution-NonCommercial-ShareAlike (CC BY-NC-SA 4.0) https://creativecommons.org/licenses/by-nc-sa/4.0/ . Under the CC BY-NC-SA 4.0 license permitted 3rd party reuse is only applicable for non-commercial purposes. Articles posted under the CC BY-NC-SA 4.0 license allow users to share, copy, and redistribute the material in any medium of format, and adapt, remix, transform, and build upon the material for any purpose. Reusing under the CC BY-NC-SA 4.0 license requires that appropriate attribution to the source of the material must be included along with a link to the license, with any changes made to the original material indicated.
