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Adaptive chimp optimization algorithm with chaotic map for global numerical optimization problems

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Abstract

Chimp optimization algorithm (ChOA) is a meta-heuristic algorithm inspired by individual intelligence and sexual motivation during group hunting. It is designed to speed up the convergence of the optimal solution. Because of its simplicity and low computational cost, the algorithm has been widely used to solve complex global optimization problem. But in the process of searching, it is easy to fall into the local optima, and the balance between exploitation and exploration cannot be realized well. In this paper, an adaptive chimp optimization algorithm called AChOA is proposed. Firstly, the Tent chaotic map is firstly used to initialize the chimp population to obtain a better initial solutions and improve convergence precision. Secondly, adaptive non linear convergence factor and adaptive weight are introduced in the global search stage, and the parameters vary adaptively according to the number of iterations and population diversity, so as to improve the population diversity. Thirdly, the Lévy flight strategy is introduced into the position update formula to mitigate the shortcomings of ChOA algorithm, such as finding the local optima rather than the global optima, and lack of balance between the exploitation and exploration process. Finally, a comparison with 10 famous algorithms on 19 benchmark functions of the solving accuracy and convergence speed of AChOA is presented. The results show that AChOA has the advantages of fast convergence speed, high solution accuracy.

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References

  1. Zang H, Zhang S, Hapeshi K (2010) A review of nature-inspired algorithms. J Bionic Eng 7(4):S232–S237

    Google Scholar 

  2. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82

    Google Scholar 

  3. Peng W, Mu J, Chen L, Lin J (2021) A novel non-dominated sorting genetic algorithm for solving the triple objective project scheduling problem. Memet Comput 13(2):271–284

    Google Scholar 

  4. Wang X-B, Yang Z-X, Wong PK, Deng C (2019) Novel paralleled extreme learning machine networks for fault diagnosis of wind turbine drivetrain. Memet Comput 11(2):127–142

    Google Scholar 

  5. Yuan J, Li Y (2021) Solving binary multi-objective knapsack problems with novel greedy strategy. Memet Comput 13(4):447–458

    Google Scholar 

  6. Pant M, Rani D et al (2021) Large scale reservoir operation through integrated meta-heuristic approach. Memet Comput 13(3):359–382

    Google Scholar 

  7. Xiang S, Wang L, Xing L, Du Y (2021) An effective memetic algorithm for uav routing and orientation under uncertain navigation environments. Memet Comput 13(2):169–183

    Google Scholar 

  8. Holland JH (1992) Genetic algorithms. Sci Am 267(1):66–73

    Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  10. Li H, He F, Chen Y, Pan Y (2021) Mlfs-ccde: multi-objective large-scale feature selection by cooperative coevolutionary differential evolution. Memet Comput 13(1):1–18

    Google Scholar 

  11. Wang L, Pan J, Jiao L-C (2000) The immune algorithm. Acta Electron Sin 28(7):74–78

    Google Scholar 

  12. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95-International Conference on Neural Networks, vol 4, pp 1942–1948

  13. Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization. 1

  14. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (abc) algorithm. J Global Optim 39(3):459–471

    MathSciNet  MATH  Google Scholar 

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

    Google Scholar 

  16. Mirjalili S (2015) How effective is the grey wolf optimizer in training multi-layer perceptrons. Appl Intell 43(1):150–161

    Google Scholar 

  17. Shan L, Qiang H, Li J, Wang Z-q (2005) Chaotic optimization algorithm based on tent map. Control Decis 20(2):179–182

    MATH  Google Scholar 

  18. Wei ZL, Zhao H, Li MD, Wang Y (2016) A grey wolf optimization algorithm based on nonlinear adjustment strategy of control parameter. J Air Force Eng Univ (Natural Science Edition) 17(3):68–72

    Google Scholar 

  19. Gai W, Qu C, Liu J, Zhang J (2018) An improved grey wolf algorithm for global optimization. In: 2018 Chinese Control and Decision Conference (CCDC), pp 2494–2498

  20. Shi Q, Xu Q, Zhang J (2019) Improvement for dv-hop based on distance correcting and grey wolf optimization algorithm. J Transduct Technol 32(10):1549–1555

    Google Scholar 

  21. Kirkpatrick S, Gelatt CD Jr, Vecchi MP (1987) Optimization by simulated annealing

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

    MATH  Google Scholar 

  23. Glover F (1989) Tabu search-part i. ORSA J Comput 1(3):190–206

    MATH  Google Scholar 

  24. Glover F (1990) Tabu search-part ii. ORSA J Comput 2(1):4–32

    MATH  Google Scholar 

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

    Google Scholar 

  26. Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In 2007 IEEE Congress on Evolutionary Computation, pp 4661–4667

  27. Khishe M, Mosavi Mohammad Reza (2020) Chimp optimization algorithm. Expert Syst Appl 149:113338

    Google Scholar 

  28. Kaur M, Kaur R, Singh N, Dhiman G (2021) Schoa: a newly fusion of sine and cosine with chimp optimization algorithm for hls of datapaths in digital filters and engineering applications. Eng Comput 2–4:1–29

    Google Scholar 

  29. Jia H, Sun K, Zhang W, Leng X (2021) An enhanced chimp optimization algorithm for continuous optimization domains. Complex Intell Syst 8(1):65–82

    Google Scholar 

  30. Kharrich M, Mohammed OH, Kamel S, Aljohani M, Mossad MI (2021) Optimal design of microgrid using chimp optimization algorithm. In: IEEE ICA/ACCA2021: 2021 IEEE International Conference on Automation/XXIV Congress of the Chilean Association of Automatic Control (ICA-ACCA), pp. 1–5

  31. Fathy A, Yousri D, Abdelaziz AY, Ramadan HS (2021) Robust approach based chimp optimization algorithm for minimizing power loss of electrical distribution networks via allocating distributed generators. Sustain Energy Technol Assess 47:101359

    Google Scholar 

  32. Hu T, Khishe M, Mohammadi M, Parvizi GR, Rashid TA (2021) Realtime covid-19 diagnosis from x-ray images using deep cnn and extreme learning machines stabilized by chimp optimization algorithm. Biomed Signal Process Control 68(15):102764

    Google Scholar 

  33. Khishe M, Mosavi MR (2020) Classification of underwater acoustical dataset using neural network trained by chimp optimization algorithm. Appl Acoust 157:107005

    Google Scholar 

  34. Fultz A, Brent L, Breaux SD, Grand AP (2013) An evaluation of nest-building behavior by sanctuary chimpanzees with access to forested habitats. Folia primatol 84(6):405–420

    Google Scholar 

  35. Mitani JC, Watts DP, Muller MN (2002) Recent developments in the study of wild chimpanzee behavior. Evolut Anthropol Issues News Rev 11(1):9–25

    Google Scholar 

  36. Denton TA, Diamond GA, Helfant RH, Khan S, Karagueuzian H (1990) Fascinating rhythm: a primer on chaos theory and its application to cardiology. Am Heart J 120(6):1419–1440

    Google Scholar 

  37. Shan L, Qiang H, Li J, Wang Z-q (2005) Chaotic optimization algorithm based on tent map. Control Decis 20(2):179–182

    MATH  Google Scholar 

  38. Saremi S, Mirjalili S, Lewis A (2014) Biogeography-based optimisation with chaos. Neural Comput Appl 25(5):1077–1097

    Google Scholar 

  39. Hu X, Jing C (2018) Application of improved gray wolf optimization algorithm in wsn node deployment. J Sens Technol 31(05):101–106

    Google Scholar 

  40. Shi Y, Eberhart RC (1999) Empirical study of particle swarm optimization. In: Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), vol 3, pp 1945–1950

  41. Shi Y, Eberhart RC. (2001) Particle swarm optimization with fuzzy adaptive inertia weight. 2001

  42. Zhan Z-H, Zhang J, Li Y, Chung HS (2009) Adaptive particle swarm optimization. 39: 1362–1381

  43. Ratnaweera A, Halgamuge SK, Watson HC (2004) Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans Evol Comput 8(3):240–255

    Google Scholar 

  44. Yang and Ye (2013) Applying the new firefly algorithm to solve the job-shop scheduling problem. Comput Eng Appl 49(11):213–215

  45. Yang XS, Deb S (2010) Engineering optimisation by cuckoo search. Int J Math Model Numer Optim 1(4):330–343

    MATH  Google Scholar 

  46. Viswanathan GM, Afanasyev V, Buldyrev SV, Murphy EJ, Prince PA, Stanley HE (1996) Levy flight search patterns of wandering albatrosses. Nature 381(6581):413–415

    Google Scholar 

  47. Zitouni F, Harous S, Belkeram A, Hammou Leb. (2021) The archerfish hunting optimizer: a novel metaheuristic algorithm for global optimization. Preprint arXiv:2102.02134

  48. Laarhoven Van PJM, Aarts EHL (1987) Simulated annealing

  49. Civicioglu and Pinar (2013) Backtracking search optimization algorithm for numerical optimization problems. Appli Math Comput 219(15):8121–8144

  50. Deb K (1998) Genetic algorithm in search and optimization: the technique and applications. pp 58–87

  51. Ghafil HN, Jármai K (2020) Dynamic differential annealed optimization: New metaheuristic optimization algorithm for engineering applications. Appl Soft Comput 93:106392

    Google Scholar 

  52. Doğan B, Ölmez T (2015) A new metaheuristic for numerical function optimization: vortex search algorithm. Inf Sci 293:125–145

    Google Scholar 

  53. Yilmaz S, Sen S (2020) Electric fish optimization: a new heuristic algorithm inspired by electrolocation. Neural Comput Appl 32(15):11543–11578

    Google Scholar 

  54. 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

    MathSciNet  MATH  Google Scholar 

  55. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans Evol Comput 6(2):182–197

    Google Scholar 

Download references

Acknowledgements

The authors wish to acknowledge the National Natural Science Foundation of China (Grant No. U1731128); the Natural Science Foundation of Liaoning Province (Grant No. 2019-MS-174); the Foundation of Liaoning Province Education Administration (Grant No. LJKZ0279, 2019LNJC12) for the financial support.

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  1. School of Science, University of Science and Technology Liaoning, Anshan, 114051, China

    Yiwen Wang, Hao Liu, Guiyan Ding & Liangping Tu

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  1. Yiwen Wang
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Wang, Y., Liu, H., Ding, G. et al. Adaptive chimp optimization algorithm with chaotic map for global numerical optimization problems. J Supercomput 79, 6507–6537 (2023). https://doi.org/10.1007/s11227-022-04886-6

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