Skip to main content
SpringerLink
Log in

An improved arithmetic optimization algorithm with hybrid elite pool strategies

  • Optimization
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

This paper presents an improved arithmetic optimization algorithm that incorporates hybrid elite pool strategies to address the limitations of the arithmetic optimization algorithm (AOA). In AOA, the linear mathematical optimization acceleration (MOA) function cannot balance global exploitation and local exploration well. Therefore, the accuracy and convergence speed of the algorithm cannot be guaranteed. To improve the performance of AOA, this paper reconstructed a nonlinear MOA function, which is expected to balance the exploitation and the exploration of AOA. Furthermore, four hybrid elite pool strategies are integrated to enhance the ability to escape local optima. The proposed algorithm inherits the fast convergence of AOA and develops the performance of escaping local optima. Numerical experiment results on benchmark functions and engineering problems show that the proposed algorithm outperforms other compared meta-heuristic algorithms in terms of convergence speed and accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data availability

Data will be made available on reasonable request.

References

  • Abualigah L (2019) Feature selection and enhanced krill herd algorithm for text document clustering. In: Studies in computational intelligence, vol 816. Springer, Cham, Switzerland

    Google Scholar 

  • Abualigah L, Diabat A, Mirjalili S, Abd Elaziz M, Gandomi AH (2021a) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng 376:113609

    Article  MathSciNet  Google Scholar 

  • Abualigah L, Yousri D, Abd Elaziz M, Ewees AA, Al-Qaness MA, Gandomi AH (2021b) Aquila optimizer: a novel meta-heuristic optimization algorithm. Comput Indus Eng 157:107250

    Article  Google Scholar 

  • Abualigah L, Abd Elaziz M, Sumari P, Geem ZW, Gandomi AH (2022a) Reptile Search Algorithm (RSA): a nature-inspired meta-heuristic optimizer. Expert Syst Appl 191:116158

    Article  Google Scholar 

  • Abualigah L, Almotairi KH, Abd Elaziz M, Shehab M, Altalhi M (2022b) Enhanced flow direction arithmetic optimization algorithm for mathematical optimization problems with applications of data clustering. Eng Anal Bound Elem 138:13–29

    Article  MathSciNet  Google Scholar 

  • Askari Q, Saeed M, Younas I (2020) Heap-based optimizer inspired by corporate rank hierarchy for global optimization. Expert Syst Appl 161:113702

    Article  Google Scholar 

  • Bansal P, Gehlot K, Singhal A, Gupta A (2022) Automatic detection of osteosarcoma based on integrated features and feature selection using binary arithmetic optimization algorithm. Multimed Tools Appl 81(6):8807–8834

    Article  Google Scholar 

  • Bhattacharyya T, Chatterjee B, Singh PK, Yoon JH, Geem ZW, Sarkar R (2020) Mayfly in harmony: a new hybrid meta-heuristic feature selection algorithm. IEEE Access 8:195929–195945

    Article  Google Scholar 

  • Dahou A, Al-qaness MA, Abd Elaziz M, Helmi A (2022) Human activity recognition in IoHT applications using arithmetic optimization algorithm and deep learning. Meas Sci Technol 199:111445

    Google Scholar 

  • Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30

    MathSciNet  Google Scholar 

  • Devan P, Hussin FA, Ibrahim RB, Bingi K, Nagarajapandian M, Assaad M (2022) An arithmetic-trigonometric optimization algorithm with application for control of real-time pressure process plant. Sensors 22(2):617

    Article  Google Scholar 

  • Dhiman G, Kaur A (2019) A hybrid algorithm based on particle swarm and spotted hyena optimizer for global optimization. In: Soft computing for problem solving: SocProS 2017, vol 1. Springer Singapore, pp 599–615

  • Djemame S, Batouche M, Oulhadj H, Siarry P (2019) Solving reverse emergence with quantum PSO application to image processing. Soft Comput 23(16):6921–6935

    Article  Google Scholar 

  • Gao D, Wang GG, Pedrycz W (2020) Solving fuzzy job-shop scheduling problem using DE algorithm improved by a selection mechanism. IEEE Trans Fuzzy Syst 28(12):3265–3275

    Article  Google Scholar 

  • Ghorbani N, Babaei E (2014) Exchange market algorithm. Appl Soft Comput 19:177–187

    Article  Google Scholar 

  • Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Future Gener Comput Syst 97:849–872

    Article  Google Scholar 

  • Izci D, Ekinci S, Kayri M, Eker E (2022) A novel improved arithmetic optimization algorithm for optimal design of PID controlled and Bode’s ideal transfer function based automobile cruise control system. Evol Syst 13(3):453–468

    Article  Google Scholar 

  • Kaveh A, Hamedani KB (2022) Improved arithmetic optimization algorithm and its application to discrete structural optimization. Structures, vol 35. Elsevier, Amsterdam, pp 748–764

    Google Scholar 

  • Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN'95-international conference on neural networks, vol 4. IEEE, pp 1942–1948

  • Khishe M, Mosavi MR (2020) Chimp optimization algorithm. Expert Syst Appl 149:113338

    Article  Google Scholar 

  • Kirkpatrick S, Gelatt CD Jr, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680

    Article  MathSciNet  Google Scholar 

  • Komathi C, Umamaheswari MG (2019) Design of gray wolf optimizer algorithm-based fractional order PI controller for power factor correction in SMPS applications. IEEE Trans Power Electron 35(2):2100–2118

    Google Scholar 

  • Li XD, Wang JS, Hao WK, Zhang M, Wang M (2022) Chaotic arithmetic optimization algorithm. Appl Intell 52(14):16718–16757

    Article  Google Scholar 

  • Ma C, Huang H, Fan Q, Wei J, Du Y, Gao W (2022) Grey wolf optimizer based on Aquila exploration method. Expert Syst Appl 205:117629

    Article  Google Scholar 

  • Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl Based Syst 89:228–249

    Article  Google Scholar 

  • Mirjalili S (2016) SCA: a Sine Cosine Algorithm for solving optimization problems. Knowl Based Syst 96:120–133

    Article  Google Scholar 

  • Mirjalili S, Lewis A (2016) The Whale Optimization Algorithm. Adv Eng Softw 95:51–67

    Article  Google Scholar 

  • Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  • Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27:495–513

    Article  Google Scholar 

  • Nadimi-Shahraki MH, Taghian S, Mirjalili S (2021) An improved grey wolf optimizer for solving engineering problems. Expert Syst Appl 166:113917

    Article  Google Scholar 

  • Örnek BN, Aydemir SB, Düzenli T, Özak B (2022) A novel version of slime mould algorithm for global optimization and real world engineering problems: enhanced slime mould algorithm. Math Comput Simul 198:253–288

    Article  MathSciNet  Google Scholar 

  • Rao RV, Savsani VJ, Vakharia DP (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des Appl 43(3):303–315

    Article  Google Scholar 

  • Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248

    Article  Google Scholar 

  • Shi Y (2011) Brain storm optimization algorithm. In: Advances in swarm intelligence: second international conference, ICSI 2011, Chongqing, China, June 12–15, 2011, Proceedings, Part I 2. Springer Berlin Heidelberg, pp 303–309

  • Storn R, Price K (1997) Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

    Article  MathSciNet  Google Scholar 

  • Tabak A, İlhan İ (2022) An effective method based on simulated annealing for automatic generation control of power systems. Appl Soft Comput 126:109277

    Article  Google Scholar 

  • Tian D, Shi Z (2018) MPSO: modified particle swarm optimization and its applications. Swarm Evol Comput 41:49–68

    Article  Google Scholar 

  • Whitley D (1994) A genetic algorithm tutorial. Comput Stat Data Anal 4:65–85

    Google Scholar 

  • Wu G, Mallipeddi R, Suganthan PN (2019) Ensemble strategies for population-based optimization algorithms—a survey. Evol Comput 44:695–711

    Article  Google Scholar 

  • Yan M, Yuan H, Xu J, Yu Y, Jin L (2021) Task allocation and route planning of multiple UAVs in a marine environment based on an improved particle swarm optimization algorithm. EURASIP J Adv Signal Process 2021:1–23

    Article  Google Scholar 

  • Yang W, Xia K, Fan S, Wang L, Li T, Zhang J, Feng Y (2022) A multi-strategy Whale Optimization Algorithm and its application. Eng Appl Artif Intell 108:104558

    Article  Google Scholar 

  • Yuan G, Yang W (2019) Study on optimization of economic dispatching of electric power system based on Hybrid Intelligent Algorithms (PSO and AFSA). Energy Rep 183:926–935

    Google Scholar 

Download references

Funding

This work was supported in part by Natural Science Foundation of China [11991022, 11971443], National Key R and D Program of China [2021YFB2012300], Chongqing Science and Technology Commission [cstc2020jcyj-msxmX0071, cstc2021jcyj-msxmX0229], Chongqing Municipal Education Commission [KJQN201901506, KJQN202001507, KJZD-K202000501, KJZD-K202001503, YJG182019], Basic and Applied Basic Research Funding of Guangdong Province [2022A1515011558], and Guangzhou Science and Technology Funding [20210200433].

Author information

Authors and Affiliations

  1. School of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing, 401331, China

    Haiyang Liu & Hanxiao Zhang

  2. School of Mathematics Science, Chongqing Normal University, Chongqing, 401331, China

    Xingong Zhang

  3. School of Electronics and Communication Engineering, Guangzhou University, Guangzhou, 510006, China

    Zhong Cao

  4. School of Mathematics, Physics and Data Science, Chongqing University of Science and Technology, Chongqing, 401331, China

    Zhaohui Chen

Authors
  1. Haiyang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xingong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hanxiao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhong Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhaohui Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhaohui Chen.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, H., Zhang, X., Zhang, H. et al. An improved arithmetic optimization algorithm with hybrid elite pool strategies. Soft Comput 28, 1127–1155 (2024). https://doi.org/10.1007/s00500-023-09153-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-023-09153-1

Keywords

Search

Navigation