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A modified adaptive sparrow search algorithm based on chaotic reverse learning and spiral search for global optimization

  • S.I.: Applications and Techniques in Cyber Intelligence (ATCI2022)
  • Published:
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Abstract

A population-based metaheuristic algorithm that takes its cues from the foraging strategy of sparrows is called the sparrow search algorithm (SSA). While SSA is competitive when compared to other algorithms, it nevertheless has a propensity to carry out imbalanced exploitation and exploration and find the local optimum. Therefore, the modified adaptive sparrow search algorithm (MASSA), an SSA modification, is created to address these problems. To increase population variety, the MASSA uses a chaotic reverse learning technique. Second, to balance the exploitation and exploration capacities, a dynamic adaptive weight is added. In the end, an adaptive spiral search technique improves algorithm performance. Among 23 classical test functions, of which 13 are multidimensional and the other 10 are fixed dimensional, the best chaotic operator is found. It is proven that MASSA is superior. Simulation studies demonstrate that the MASSA described in this study is superior to previous algorithms in terms of stability, convergence speed, and convergence accuracy. Finally, a sample robot path planning problem is resolved using MASSA, and the experimental outcomes confirmed the viability and usefulness of MASSA.

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Data availability statement

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Faris H, Al-Zoubi AM, Heidari AA, Aljarah I, Mafarja M, Hassonah MA, Fujita H (2019) An intelligent system for spam detection and identification of the most relevant features based on evolutionary Random Weight Networks. Inf Fusion. https://doi.org/10.1016/j.inffus.2018.08.002

    Article  Google Scholar 

  2. Abbassi R, Abbassi A, Heidari AA, Mirjalili S (2019) An efficient salp swarm-inspired algorithm for parameters identification of photovoltaic cell models. Energy Convers Manag. https://doi.org/10.1016/j.enconman.2018.10.069

    Article  Google Scholar 

  3. Wu G, Pedrycz W, Suganthan PN, Mallipeddi R (2015) A variable reduction strategy for evolutionary algorithms handling equality constraints. Appl Soft Comput J. https://doi.org/10.1016/j.asoc.2015.09.007

    Article  Google Scholar 

  4. Droste S, Jansen T, Wegener I (2006) Upper and lower bounds for randomized search heuristics in black-box optimization. Theory Comput Syst. https://doi.org/10.1007/s00224-004-1177-z

    Article  MathSciNet  MATH  Google Scholar 

  5. Wang GG, Tan Y (2019) Improving metaheuristic algorithms with information feedback models. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2017.2780274Y

    Article  Google Scholar 

  6. Abd Elaziz M, Yousri D, Al-qaness MAA, AbdelAty AM, Radwan AG, Ewees AAA (2021) Grunwald–Letnikov based Manta ray foraging optimizer for global optimization and image segmentation. Eng Appl Artif Intell. https://doi.org/10.1016/j.engappai.2020.104105.

  7. Alweshah M (2021) Solving feature selection problems by combining mutation and crossover operations with the monarch butterfly optimization algorithm. Appl Intell. https://doi.org/10.1007/s10489-020-01981-0

    Article  Google Scholar 

  8. Alweshah M, Khalaileh SAl, Gupta BB, Almomani A, Hammouri AI, Al-Betar MA (2020) The monarch butterfly optimization algorithm for solving feature selection problems. Neural Comput Appl. https://doi.org/10.1007/s00521-020-05210-0.

  9. Zheng Xu MM, Kamruzzaman, Jinyao Shi (2022) Method of generating face image based on text description of generating adversarial network. J Electronic Imag 31(5):051411.

  10. Mizuno S, Ohba H (2022) Optimizing intra-facility crowding in Wi-Fi environments using continuous-time Markov chains. Discov Internet Things 2:5

    Article  Google Scholar 

  11. Tang A, Zhou H, Han T, Xie L (2021) A modified manta ray foraging optimization for global optimization problems. IEEE Access. https://doi.org/10.1109/ACCESS.2021.3113323

    Article  Google Scholar 

  12. John H (1992) Holland. Adaptation in natural and artificial systems. Michigan Press, Ann Arbor

    Google Scholar 

  13. Sarker RA, Elsayed SM, Ray T (2014) Differential evolution with dynamic parameters selection for optimization problems. IEEE Trans Evol Comput. https://doi.org/10.1109/TEVC.2013.2281528

    Article  Google Scholar 

  14. Fogel DB (1993) Applying evolutionary programming to selected traveling salesman problems. Cybern Syst. https://doi.org/10.1080/01969729308961697

    Article  MathSciNet  Google Scholar 

  15. Beyer H-G, Schwefel H-P (2002) Evolution strategies—a comprehensive introduction. Nat Comput. https://doi.org/10.1023/A:1015059928466

    Article  MathSciNet  MATH  Google Scholar 

  16. Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci (Ny). https://doi.org/10.1016/j.ins.2009.03.004

    Article  MATH  Google Scholar 

  17. Yang X (2010) Nature-inspired metaheuristic algorithms. ISBN 9781905986286.

  18. Wei Z, Huang C, Wang X, Han T, Li Y (2019) Nuclear reaction optimization: a novel and powerful physics-based algorithm for global optimization. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2918406

    Article  Google Scholar 

  19. Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowledge-Based Syst. https://doi.org/10.1016/j.knosys.2015.12.022

    Article  Google Scholar 

  20. Eskandar H, Sadollah A, Bahreininejad A, Hamdi M (2012) Water cycle algorithm—a novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput Struct. https://doi.org/10.1016/j.compstruc.2012.07.010

    Article  Google Scholar 

  21. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks—conference proceedings; 1995.

  22. Dorigo M, Di Caro G (1999) Ant colony optimization: a new meta-heuristic. In: Proceedings of the proceedings of the 1999 congress on evolutionary computation, CEC 1999.

  23. Wang GG, Deb S, Cui Z (2019) Monarch butterfly optimization. Neural Comput Appl. https://doi.org/10.1007/s00521-015-1923-y

    Article  Google Scholar 

  24. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw. https://doi.org/10.1016/j.advengsoft.2013.12.007

    Article  Google Scholar 

  25. Arora S, Singh S (2019) Butterfly optimization algorithm: a novel approach for global optimization. Soft Comput. https://doi.org/10.1007/s00500-018-3102-4

    Article  Google Scholar 

  26. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm. J Glob Optim. https://doi.org/10.1007/s10898-007-9149-x

    Article  MathSciNet  MATH  Google Scholar 

  27. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw. https://doi.org/10.1016/j.advengsoft.2016.01.008

    Article  Google Scholar 

  28. Wang GG (2018) Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Memetic Comput. https://doi.org/10.1007/s12293-016-0212-3

    Article  Google Scholar 

  29. Wang GG, Deb S, Dos Santos Coelho L (2018) Earthworm optimisation algorithm: a bio-inspired metaheuristic algorithm for global optimisation problems. Int J Bio-Inspired Comput. https://doi.org/10.1504/ijbic.2018.093328

    Article  Google Scholar 

  30. Wang GG, Deb S, Coelho LDS (2016) Elephant herding optimization. In: Proceedings of the Proceedings—2015 3rd international symposium on computational and business intelligence, ISCBI 2015.

  31. Xie L, Han T, Zhou H, Zhang Z-R, Han B, Tang A (2021) Tuna swarm optimization: a novel swarm-based metaheuristic algorithm for global optimization. Comput Intell Neurosci. https://doi.org/10.1155/2021/9210050

    Article  Google Scholar 

  32. Xue J, Shen B (2020) A novel swarm intelligence optimization approach: sparrow search algorithm. Syst Sci Control Eng. https://doi.org/10.1080/21642583.2019.1708830

    Article  Google Scholar 

  33. Wang GG, Guo L, Gandomi AH, Hao GS, Wang H (2014) Chaotic Krill Herd algorithm. Inf Sci (Ny). https://doi.org/10.1016/j.ins.2014.02.123

    Article  Google Scholar 

  34. Wang GG, Gandomi AH, Alavi AH (2013) A chaotic particle-swarm krill herd algorithm for global numerical optimization. Kybernetes. https://doi.org/10.1108/K-11-2012-0108

    Article  MathSciNet  MATH  Google Scholar 

  35. Tang A, Zhou H, Han T, Xie L (2021) A Chaos Sparrow search algorithm with logarithmic spiral and adaptive step for engineering problems. Comput Model Eng Sci. https://doi.org/10.32604/cmes.2021.017310.

  36. Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Futur Gener Comput Syst. https://doi.org/10.1016/j.future.2019.02.028

    Article  Google Scholar 

  37. Faramarzi A, Heidarinejad M, Stephens B, Mirjalili S (2020) Equilibrium optimizer: a novel optimization algorithm. Knowledge-Based Syst. https://doi.org/10.1016/j.knosys.2019.105190

    Article  Google Scholar 

  38. Kaur S, Awasthi LK, Sangal AL, Dhiman G (2020) Tunicate Swarm Algorithm: a new bio-inspired based metaheuristic paradigm for global optimization. Eng Appl Artif Intell. https://doi.org/10.1016/j.engappai.2020.103541

    Article  Google Scholar 

Download references

Funding

The author acknowledges funding received from the following science foundations: National Natural Science Foundation of China (Grant Number 51907109).

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Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo, 255000, Shandong Province, China

    Junqi Geng, Xianming Sun & Haihua Wang

  2. State Grid Shandong Electric Power Company Zibo Power Supply Company, Zibo, 255000, Shandong Province, China

    Junqi Geng, Daohuan Liu & Fei Li

  3. State Grid Shandong Electric Power Company Zouping Power Supply Company, Binzhou, 256600, Shandong Province, China

    Xianghai Bu

  4. Beijing Institute of Precision Mechatronics and Controls, Beijing, 100081, Beijing, China

    Zengwu Zhao

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  1. Junqi Geng
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Correspondence to Xianming Sun.

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Geng, J., Sun, X., Wang, H. et al. A modified adaptive sparrow search algorithm based on chaotic reverse learning and spiral search for global optimization. Neural Comput & Applic 35, 24603–24620 (2023). https://doi.org/10.1007/s00521-023-08207-7

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