Abstract
Jaya is a metaheuristic algorithm that uses a pair of random internal parameters to adjust its exploration and exploitation search behaviors. Such a random setting can negatively affect the search performance of the algorithm by causing inappropriate search behavior in some iterations. To tackle this issue, the present study deals with developing a new fuzzy decision-making mechanism for dynamic adjusting the trade-off between the exploration and exploitation search behaviors of the Jaya method. The new algorithm is named Fuzzy Reinforced Jaya (FRJ) method. The search capability of the FRJ is evaluated in solving a suite of unconstrained mathematical benchmarks and constrained mechanical and structural optimization problems with buckling and natural frequency constraints. Also, the relevant decision variables are selected from both continuous and discrete domains. To provide a deeper insight into the effect of the defined auxiliary fuzzy module, the performance of the algorithm is evaluated and discussed using normalized diversity concept and behavioral diagrams. Also, employing different statistical analyses (e.g., Q–Q diagrams, Wilcoxson and Friedman tests), the significance of the outcomes is evaluated. Also, the numeric achievements are compared with six other well-stablished techniques. Attained outcomes indicate that the proposed FRJ, as a self-adaptive and parameter-free method, provides superior and promising results in the terms of stability, accuracy, and computational cost in solving mathematical and structural optimization problems.
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References
Chen C, Zou D, Li C (2020) Improved Jaya algorithm for economic dispatch considering valve-point effect and multi-fuel options IEEE. Access 8:84981–84995. https://doi.org/10.1109/ACCESS.2020.2992616
Chibing G (2017) An enhanced Jaya algorithm with a two group adaption. Int J Comput Intell Syst 10:1102–1115. https://doi.org/10.2991/ijcis.2017.10.1.73
Degertekin SO, Lamberti L, Ugur IB (2018) Sizing, layout and topology design optimization of truss structures using the Jaya algorithm. Appl Soft Comput 70:903–928. https://doi.org/10.1016/j.asoc.2017.10.001
El-Ashmawi WH, Ali AF, Slowik A (2020) An improved Jaya algorithm with a modified swap operator for solving team formation problem. Soft Comput 24:16627–16641. https://doi.org/10.1007/s00500-020-04965-x
Ghavidel S, Azizivahed A, Li L (2018) A hybrid Jaya algorithm for reliability–redundancy allocation problems. Eng Optimiz 50:698–715. https://doi.org/10.1080/0305215X.2017.1337755
Holland JH (1984) Genetic algorithms and adaptation. In: Selfridge OG, Rissland EL, Arbib MA (eds) Adaptive control of ill-defined systems. Springer US, Boston, pp 317–333. https://doi.org/10.1007/978-1-4684-8941-5_21
Iacca G, dos Santos Junior VC, Veloso de Melo V (2021) An improved Jaya optimization algorithm with Lévy flight. Expert Syst Appl 165:113902. https://doi.org/10.1016/j.eswa.2020.113902
Kennedy J, Eberhart R Particle swarm optimization. In: Neural Networks, 1995. In: Proceedings., IEEE international conference on, Nov/Dec 1995, vol. 1944, 1995. pp 1942–1948. https://doi.org/10.1109/ICNN.1995.488968
Kirkpatrick S, Gelatt CD, Vecchi MP (1987) Optimization by simulated annealing. In: Fischler MA, Firschein O (eds) Readings in computer vision. Morgan Kaufmann, San Francisco, pp 606–615. https://doi.org/10.1016/B978-0-08-051581-6.50059-3
Kumar N, Hussain I, Singh B, Panigrahi BK (2017) Rapid MPPT for uniformly and partial shaded PV system by using JayaDE algorithm in highly fluctuating atmospheric conditions. IEEE Trans Industr Inf 13:2406–2416. https://doi.org/10.1109/TII.2017.2700327
Mane S, Narsingrao M (2021) A chaotic-based improved many-objective Jaya algorithm for many-objective optimization problems. Int J Ind Eng Comput 12:49–62
Migallón H, Jimeno-Morenilla A, Sánchez-Romero JL, Rico H, Rao RV (2019) Multipopulation-based multi-level parallel enhanced Jaya algorithms. J Supercomput 75:1697–1716. https://doi.org/10.1007/s11227-019-02759-z
Moloodpoor M, Mortazavi A (2021) Simultaneous optimization of fuel type and exterior walls insulation attributes for residential buildings using a swarm intelligence. Int J Environ Sci Technol. https://doi.org/10.1007/s13762-021-03323-0
Mortazavi A (2019a) Comparative assessment of five metaheuristic methods on distinct problems. Dicle Univ J Eng 10:879. https://doi.org/10.24012/dumf.585790
Mortazavi A (2019b) The performance comparison of three metaheuristic algorithms on the size, layout and topology optimization of truss structures Mugla. J Sci Technol 5:28–41. https://doi.org/10.22531/muglajsci.593482
Mortazavi A (2020a) Large-scale structural optimization using a fuzzy reinforced swarm intelligence algorithm. Adv Eng Softw 142:102790. https://doi.org/10.1016/j.advengsoft.2020.102790
Mortazavi A (2020b) A new fuzzy strategy for size and topology optimization of truss structures. Appl Soft Comput 93:106412. https://doi.org/10.1016/j.asoc.2020.106412
Moscato P, Norman MG (1992) A memetic approach for the traveling salesman problem implementation of a computational ecology for combinatorial optimization on message-passing systems. Parallel Comput Transputer Appl 1:177–186
Awad NH, Ail MZ, Suganthan PN, Liang JJ, Qu BY (2017) Problem definitions and evaluation criteria for the CEC 2017 special session on real-parameter optimization Technical Report, Nanyang Technological University, Singapore, School of Computer Information Systems, Jordan University of Science and Technology, Jordan, School of Electrical Engineering, Zhengzhou University, Zhengzhou. https://doi.org/10.1007/s10732-008-9080-4
Rao R, Singh Keesari H, Taler J, Oclon P, Taler D (2023) Elitist Rao Algorithms and R-method for optimization of energy systems. Heat Transfer Eng 44:926–950. https://doi.org/10.1080/01457632.2022.2113448
Rao RV (2016) Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 7:19–34
Rao RV (2019) Jaya: an advanced optimization algorithm and its engineering applications. Springer, Cham. https://doi.org/10.1007/978-3-319-78922-4
Rao RV, Pawar RB (2023) Improved Rao algorithm: a simple and effective algorithm for constrained mechanical design optimization problems. Soft Comput 27:3847–3868. https://doi.org/10.1007/s00500-022-07589-5
Rao RV, Saroj A (2017) Constrained economic optimization of shell-and-tube heat exchangers using elitist-Jaya algorithm. Energy 128:785–800. https://doi.org/10.1016/j.energy.2017.04.059
Rao RV, Savsani VJ, Vakharia DP (2011) Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems. Comput Aided Design 43:303–315. https://doi.org/10.1016/j.cad.2010.12.015
Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179:2232–2248. https://doi.org/10.1016/j.ins.2009.03.004
Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Global Optimiz 11:341–359. https://doi.org/10.1023/a:1008202821328
Venkata Rao R, Saroj A (2017) A self-adaptive multi-population based Jaya algorithm for engineering optimization. Swarm Evol Comput 37:1–26. https://doi.org/10.1016/j.swevo.2017.04.008
Wang L, Huang C (2018) A novel Elite Opposition-based Jaya algorithm for parameter estimation of photovoltaic cell models. Optik 155:351–356. https://doi.org/10.1016/j.ijleo.2017.10.081
Wang L, Zhang Z, Huang C, Tsui KL (2018) A GPU-accelerated parallel Jaya algorithm for efficiently estimating Li-ion battery model parameters. Appl Soft Comput 65:12–20. https://doi.org/10.1016/j.asoc.2017.12.041
Xu Y, Cui Z, Zeng J Social emotional optimization algorithm for nonlinear constrained optimization problems. In: Swarm, Evolutionary, and Memetic Computing: First International Conference on Swarm, Evolutionary, and Memetic Computing, SEMCCO 2010, Chennai, India, December 16–18, 2010. Proceedings 1, 2010. Springer, pp 583–590
Yang X-S (2009) Firefly algorithms for multimodal optimization. In: Watanabe O, Zeugmann T (eds) Stochastic algorithms: foundations and applications. Springer, Berlin, pp 169–178
Yu X, Luo W, Rao RV (2022) Multi-strategy Jaya algorithm for industrial optimization tasks. J Intell Fuzzy Syst 43:4379–4393. https://doi.org/10.3233/JIFS-213471
Zhang Z, Tang Q, Han D, Qian X (2020) An enhanced multi-objective JAYA algorithm for U-shaped assembly line balancing considering preventive maintenance scenarios. Int J Product Res. https://doi.org/10.1080/00207543.2020.1804639
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Mortazavi, A. A fuzzy reinforced Jaya algorithm for solving mathematical and structural optimization problems. Soft Comput 28, 2181–2206 (2024). https://doi.org/10.1007/s00500-023-09206-5
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DOI: https://doi.org/10.1007/s00500-023-09206-5