Hybrid evolutionary JAYA algorithm for global and engineering optimization problems

LIU Jing-sen; YANG Jie; LI Yu

doi:10.13374/j.issn2095-9389.2021.10.27.002

Volume 45 Issue 3

Mar. 2023

Turn off MathJax

Article Contents

Article Navigation > Chinese Journal of Engineering > 2023 > 45(3): 431-445

LIU Jing-sen, YANG Jie, LI Yu. Hybrid evolutionary JAYA algorithm for global and engineering optimization problems[J]. Chinese Journal of Engineering, 2023, 45(3): 431-445. doi: 10.13374/j.issn2095-9389.2021.10.27.002

Citation:

LIU Jing-sen, YANG Jie, LI Yu. Hybrid evolutionary JAYA algorithm for global and engineering optimization problems[J]. Chinese Journal of Engineering, 2023, 45(3): 431-445. doi: 10.13374/j.issn2095-9389.2021.10.27.002

Citation:

PDF( 1129 KB)

Hybrid evolutionary JAYA algorithm for global and engineering optimization problems

doi: 10.13374/j.issn2095-9389.2021.10.27.002

LIU Jing-sen^{1, 2
,},
YANG Jie²,
LI Yu^{3
,
,}

1.
Institute of Intelligent Networks System, Henan University, Kaifeng 475004, China
2.
College of Software, Henan University, Kaifeng 475004, China
3.
Institute of Management Science and Engineering, Henan University, Kaifeng 475004, China

More Information

Corresponding author: E-mail: leey@henu.edu.cn
Received Date: 2021-10-27
Available Online: 2022-01-01
Publish Date: 2023-03-01

Abstract

Abstract

A swarm intelligence optimization algorithm is an effective method to rapidly solve large-scale complex optimization problems. The JAYA algorithm is a new swarm intelligence evolutionary optimization algorithm, which was proposed in 2016. Compared with other active evolutionary algorithms, the JAYA algorithm has several advantages, such as a clear mechanism, concise structure, and ease of implementation. Further, it has guiding characteristics, obtains the best solution, and avoids the worst solution. The JAYA algorithm has an excellent optimization effect on many problems, and it is one of the most influential algorithms in the field of swarm intelligence. However, when dealing with the CEC test suite, which contains and combines shifted, rotation, hybrid, combination, and other composite characteristics, and the complex engineering constrained optimization problems with considerable difficulty and challenges, the JAYA algorithm has some flaws, that is, it easily falls into the local extremum, its optimization accuracy is sometimes low, and its solution is unstable. To better solve complex function optimization and engineering constrained optimization problems and further enhance the optimization capability of the JAYA algorithm, a global optimization-oriented hybrid evolutionary JAYA algorithm is proposed. First, opposition-based learning is introduced to calculate the current best and worst individuals, which improves the possibility of the best and worst individuals jumping out of the local extremum region. Second, the sine–cosine operator and differential disturbance mechanism are introduced and integrated into individual position updating, which not only improves the diversity of the population but also better balances and meets the different requirements of the algorithm for exploration and mining in different iteration periods. Finally, in the algorithm structure, the hybrid evolution strategy with different parity states is adopted and the advantages of different evolution mechanisms are effectively used, which further improves the convergence and accuracy of the algorithm. Then, the pseudocode of the improved algorithm is given, and the theoretical analysis proves that the time complexity of the improved algorithm is consistent with the basic JAYA algorithm. Through the simulation experiment of function extremum optimization of six representative algorithms on multiple dimensions of the CEC2017 test suite, which contains and combines 30 benchmark functions and the optimal solution of six challenging engineering design problems, such as tension/compression spring, corrugated bulkhead, tubular column, reinforced concrete beam, welded beam, and car side impact. The optimal solution of the test results shows that the improved algorithm has significantly improved the optimization accuracy, convergence performance, and solution stability, and it has obvious advantages in solving CEC complex functions and engineering constrained optimization problems.
- JAYA algorithm,
- CEC2017,
- sine–cosine operator,
- parity evolution strategy,
- engineering design optimization problems

FullText(HTML)

References(32)

References

[1]	Kennedy J, Eberhart R. Particle swarm optimization // Proceedings of ICNN'95 - International Conference on Neural Networks. Perth, 1995: 1942
[2]	Coello C A C, Pulido G T, Lechuga M S. Handling multiple objectives with particle swarm optimization. IEEE Trans Evol Comput, 2004, 8(3): 256 doi: 10.1109/TEVC.2004.826067
[3]	Mirjalili S. SCA: A sine cosine algorithm for solving optimization problems. Knowl Based Syst, 2016, 96: 120 doi: 10.1016/j.knosys.2015.12.022
[4]	劉小娟, 王聯國. 一種基于差分進化的正弦余弦算法. 工程科學學報, 2020, 42(12):1674 Liu X J, Wang L G. A sine cosine algorithm based on differential evolution. Chin J Eng, 2020, 42(12): 1674
[5]	Mirjalili S, Lewis A. The whale optimization algorithm. Adv Eng Softw, 2016, 95: 51 doi: 10.1016/j.advengsoft.2016.01.008
[6]	Azizi M. Atomic orbital search: A novel metaheuristic algorithm. Appl Math Model, 2021, 93: 657 doi: 10.1016/j.apm.2020.12.021
[7]	Kar D, Ghosh M, Guha R, et al. Fuzzy mutation embedded hybrids of gravitational search and Particle Swarm Optimization methods for engineering design problems. Eng Appl Artif Intell, 2020, 95: 103847 doi: 10.1016/j.engappai.2020.103847
[8]	劉三陽, 靳安釗. 求解約束優化問題的協同進化教與學優化算法. 自動化學報, 2018, 44(9):1690 Liu S Y, Jin A Z. A co-evolutionary teaching-learning-based optimization algorithm for constrained optimization problems. Acta Autom Sin, 2018, 44(9): 1690
[9]	Banaie-Dezfouli M, Nadimi-Shahraki M H, Beheshti Z. R-GWO: Representative-based grey wolf optimizer for solving engineering problems. Appl Soft Comput, 2021, 106: 107328 doi: 10.1016/j.asoc.2021.107328
[10]	肖子雅, 劉升. 精英反向黃金正弦鯨魚算法及其工程優化研究. 電子學報, 2019, 47(10):2177 doi: 10.3969/j.issn.0372-2112.2019.10.020 Xiao Z Y, Liu S. Study on elite opposition-based golden-sine whale optimization algorithm and its application of project optimization. Acta Electron Sin, 2019, 47(10): 2177 doi: 10.3969/j.issn.0372-2112.2019.10.020
[11]	Nadimi-Shahraki M H, Taghian S, Mirjalili S. An improved grey wolf optimizer for solving engineering problems. Expert Syst Appl, 2021, 166: 113917 doi: 10.1016/j.eswa.2020.113917
[12]	汪逸暉, 高亮. 烏鴉搜索算法的改進及其在工程約束優化問題中的應用. 計算機集成制造系統, 2021, 27(7):1871 Wang Y H, Gao L. Improvement of crow search algorithm and its application in engineering constrained optimization problems. Comput Integr Manuf Syst, 2021, 27(7): 1871
[13]	Rao R. Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Industrial Eng Comput, 2016, 7(1): 19
[14]	Yu K J, Liang J J, Qu B Y, et al. Parameters identification of photovoltaic models using an improved JAYA optimization algorithm. Energy Convers Manag, 2017, 150: 742 doi: 10.1016/j.enconman.2017.08.063
[15]	Kang F, Li J J, Dai J H. Prediction of long-term temperature effect in structural health monitoring of concrete dams using support vector machines with Jaya optimizer and salp swarm algorithms. Adv Eng Softw, 2019, 131: 60 doi: 10.1016/j.advengsoft.2019.03.003
[16]	Ingle K K, Jatoth D R K. An efficient JAYA algorithm with lévy flight for non-linear channel equalization. Expert Syst Appl, 2020, 145: 112970 doi: 10.1016/j.eswa.2019.112970
[17]	Iacca G, Junior V C S, Melo V V. An improved Jaya optimization algorithm with Lévy flight. Expert Syst Appl, 2021, 165: 113902 doi: 10.1016/j.eswa.2020.113902
[18]	Zhao F Q, Ma R, Wang L. A self-learning discrete jaya algorithm for multiobjective energy-efficient distributed no-idle flow-shop scheduling problem in heterogeneous factory system. IEEE Trans Cybern, 6181, PP(99): 1
[19]	Kang F, Wu Y R, Li J J, et al. Dynamic parameter inverse analysis of concrete dams based on Jaya algorithm with Gaussian processes surrogate model. Adv Eng Inform, 2021, 49: 101348 doi: 10.1016/j.aei.2021.101348
[20]	Zhang Y Y, Chi A N, Mirjalili S. Enhanced Jaya algorithm: A simple but efficient optimization method for constrained engineering design problems. Knowl Based Syst, 2021, 233: 107555 doi: 10.1016/j.knosys.2021.107555
[21]	Nayak D R, Zhang Y D, Das D S, et al. MJaya-ELM: A Jaya algorithm with mutation and extreme learning machine based approach for sensorineural hearing loss detection. Appl Soft Comput, 2019, 83: 105626 doi: 10.1016/j.asoc.2019.105626
[22]	Zhang Y Y, Ma M D, Jin Z G. Comprehensive learning Jaya algorithm for parameter extraction of photovoltaic models. Energy, 2020, 211: 118644 doi: 10.1016/j.energy.2020.118644
[23]	Zhang Y Y, Jin Z G. Comprehensive learning Jaya algorithm for engineering design optimization problems. J Intell Manuf, 2021: 1
[24]	劉景森, 馬義想, 李煜. 改進蝴蝶算法求解多維復雜函數優化問題. 電子學報, 2021, 49(6):1068 doi: 10.12263/DZXB.20200148 Liu J S, Ma Y X, Li Y. Improved butterfly algorithm for multi-dimensional complex function optimization problem. Acta Electron Sin, 2021, 49(6): 1068 doi: 10.12263/DZXB.20200148
[25]	Liu J S, Mao Y N, Liu X Z, et al. A dynamic adaptive firefly algorithm with globally orientation. Math Comput Simul, 2020, 174: 76 doi: 10.1016/j.matcom.2020.02.020
[26]	Wu G, Mallipeddi R, Suganthan P N. Problem definitions and evaluation criteria for the CEC 2017 competition on constrained real-parameter optimization [EB/OL]. Technical Repore Online (2017-09) [2021-10-27]. https://www.researchgate.net/profile/Guohua-Wu-5/publication/317228117_Problem_Definitions_and_Evaluation_Criteria_for_the_CEC_2017_Competition_and_Special_Session_on_Constrained_Single_Objective_Real-Parameter_Optimization/links/5982cdbaa6fdcc8b56f59104/Problem-Definitions-and-Evaluation-Criteria-for-the-CEC-2017-Competition-and-Special-Session-on-Constrained-Single-Objective-Real-Parameter-Optimization.pdf
[27]	Aydilek ? B. A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems. Appl Soft Comput, 2018, 66: 232 doi: 10.1016/j.asoc.2018.02.025
[28]	Derrac J, García S, Molina D, et al. A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput, 2011, 1(1): 3 doi: 10.1016/j.swevo.2011.02.002
[29]	Kumar A, Wu G H, Ali M Z, et al. A test-suite of non-convex constrained optimization problems from the real-world and some baseline results. Swarm Evol Comput, 2020, 56: 100693 doi: 10.1016/j.swevo.2020.100693
[30]	Li Y, Zhao Y R, Liu J S. Dimension by dimension dynamic sine cosine algorithm for global optimization problems. Appl Soft Comput, 2021, 98: 106933 doi: 10.1016/j.asoc.2020.106933
[31]	Salgotra R, Singh U, Singh S, et al. Self-adaptive salp swarm algorithm for engineering optimization problems. Appl Math Model, 2021, 89: 188 doi: 10.1016/j.apm.2020.08.014
[32]	劉景森, 馬義想, 李煜. 改進鯨魚算法求解工程設計優化問題. 計算機集成制造系統, 2021, 27(7):1884 Liu J S, Ma Y X, Li Y. Improved whale algorithm for solving engineering design optimization problems. Comput Integr Manuf Syst, 2021, 27(7): 1884