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A novel arithmetic optimization algorithm based on chaotic maps for global optimization

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Abstract

Chaotic maps are effective in developing evolutionary algorithms (EAs) to avoid local optima and speed convergence. Because of this capability of chaotic maps, these maps have been hybridized with various optimization algorithms. In this study, a new optimization method based on the combination of chaotic maps and arithmetic optimization algorithm (CAOA) is proposed. AOA represents the behavior of four basic arithmetic operators. Therefore, AOA performs the optimization process in a wide search space with its mathematical model. Therefore AOA has an effective convergence capability. In addition, ten different chaotic maps are applied on the AOA. In this study, 7 scenarios were created with chaotic maps, taking into account different phases of AOA. The proposed CAOA is tested on eighteen benchmark problems. CAOA produces successful and promising results in solving optimization problems compared to original AOA algorithm. The proposed CAOA is also compared with the original AOA. The superior aspects of CAOA over AOA are discussed. CAOA convergence performance is also discussed. The statistical significance of the proposed hybrid algorithm is tested with the Wilcoxon sign-rank method.

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References

  1. Abualigah L, Diabat A, Mirjalili S, Abd Elaziz M, Gandomi AH (2021) The arithmetic optimization algorithm. Computer methods in applied mechanics and engineering 376:113609

    MathSciNet  MATH  Google Scholar 

  2. Abualigah L, Yousri D, Abd Elaziz M, Ewees AA, Al-qaness MA, Gandomi AH (2021) Aquila optimizer: A novel meta-heuristic optimization algorithm. Computers Industrial Engineering 157:107250

    Google Scholar 

  3. Alcalá-Fdez J, Sánchez L, Garcia S, del Jesus MJ, Ventura S, Garrell JM, Otero J, Romero C, Bacardit J, Rivas VM et al (2009) Keel: a software tool to assess evolutionary algorithms for data mining problems. Soft Comput 13:307–318

    Google Scholar 

  4. Anthony M, Bartlett PL (2009) Neural network learning: theoretical foundations. Cambridge University Press

    MATH  Google Scholar 

  5. Askari Q, Younas I, Saeed M (2020) Political optimizer: A novel socio-inspired meta-heuristic for global optimization. Knowledge-Based Systems 195:105709

    Google Scholar 

  6. Azizi M (2021) Atomic orbital search: a novel metaheuristic algorithm. Appl Math Modell 93:657–683

    MathSciNet  MATH  Google Scholar 

  7. Beni G, Wang J (1993) Swarm intelligence in cellular robotic systems. Robots and biological systems: towards a new bionics? Springer, pp 703–712

    Google Scholar 

  8. Bingol H, Alatas B (2020) Chaos based optics inspired optimization algorithms as global solution search approach. Chaos Solitons Fractals 141:110434

    MathSciNet  MATH  Google Scholar 

  9. Booker LB, Goldberg DE, Holland JH (1989) Classifier systems and genetic algorithms. Artif Intell 40:235–282

    Google Scholar 

  10. Črepinšek M, Liu SH, Mernik M (2013) Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput Surv (CSUR) 45:1–33

    MATH  Google Scholar 

  11. Dorigo M, Blum C (2005) Ant colony optimization theory: a survey. Theor Comput Sci 344:243–278

    MathSciNet  MATH  Google Scholar 

  12. El-Kenawy ES, Eid M (2020) Hybrid gray wolf and particle swarm optimization for feature selection. International Journal of Innovative Computing Information and Control 16:831–844

    Google Scholar 

  13. El Sehiemy RA, Selim F, Bentouati B, Abido M (2020) A novel multi-objective hybrid particle swarm and salp optimization algorithm for technical-economical-environmental operation in power systems. Energy 193:116817

    Google Scholar 

  14. Guesmi T, Farah A, Marouani I, Alshammari B, Abdallah HH (2020) Chaotic sine-cosine algorithm for chance-constrained economic emission dispatch problem including wind energy. IET Renew Power Gener 14:1808–1821

    Google Scholar 

  15. Hansen N, Auger A, Ros R, Finck S, Pošík P (2010) Comparing results of 31 algorithms from the black-box optimization benchmarking bbob-2009. In: Proceedings of the 12th annual conference companion on Genetic and evolutionary computation, pp. 1689–1696

  16. Hansen N, Müller SD, Koumoutsakos P (2003) Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (cma-es). Evol Comput 11:1–18

    Google Scholar 

  17. Hashim FA, Houssein EH, Mabrouk MS, Al-Atabany W, Mirjalili S (2019) Henry gas solubility optimization: a novel physics-based algorithm. Fut Gener Comput Syst 101:646–667

    Google Scholar 

  18. Hatamlou A (2013) Black hole: a new heuristic optimization approach for data clustering. Inform Sci 222:175–184

    MathSciNet  Google Scholar 

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

    Google Scholar 

  20. Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501

    Google Scholar 

  21. Jang JS, Sun CT (1995) Neuro-fuzzy modeling and control. Proc IEEE 83:378–406

    Google Scholar 

  22. Jenkinson O (2019) Ergodic optimization in dynamical systems. Ergod Theory Dyn Syst 39:2593–2618

    MathSciNet  MATH  Google Scholar 

  23. Jiao S, Chong G, Huang C, Hu H, Wang M, Heidari AA, Chen H, Zhao X (2020) Orthogonally adapted harris hawks optimization for parameter estimation of photovoltaic models. Energy 203:117804

    Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  25. Kashan AH (2014) League championship algorithm (lca): an algorithm for global optimization inspired by sport championships. Appl Soft Comput 16:171–200

    Google Scholar 

  26. Kaur G, Arora S (2018) Chaotic whale optimization algorithm. J Comput Design Eng 5:275–284

    Google Scholar 

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

  28. Khamis N, Selamat H, Ismail FS, Lutfy OF, Haniff MF, Nordin INAM (2020) Optimized exit door locations for a safer emergency evacuation using crowd evacuation model and artificial bee colony optimization. Chaos Solitons Fractals 131:109505

    MathSciNet  Google Scholar 

  29. Kohli M, Arora S (2018) Chaotic grey wolf optimization algorithm for constrained optimization problems. J Comput Design Eng 5:458–472

    Google Scholar 

  30. Koupaei JA, Hosseini SMM, Ghaini FM (2016) A new optimization algorithm based on chaotic maps and golden section search method. Eng Appl Artif Intell 50:201–214

    Google Scholar 

  31. Kurtuluş E, Yıldız AR, Sait SM, Bureerat S (2020) A novel hybrid harris hawks-simulated annealing algorithm and rbf-based metamodel for design optimization of highway guardrails. Mater Test 62:251–260

    Google Scholar 

  32. Li S, Chen H, Wang M, Heidari AA, Mirjalili S (2020) Slime mould algorithm: a new method for stochastic optimization. Fut Gener Comput Syst 111:300–323

    Google Scholar 

  33. Lin L, Gen M (2009) Auto-tuning strategy for evolutionary algorithms: balancing between exploration and exploitation. Soft Comput 13:157–168

    MATH  Google Scholar 

  34. Lin WY (2010) A ga-de hybrid evolutionary algorithm for path synthesis of four-bar linkage. Mech Mach Theory 45:1096–1107

    MATH  Google Scholar 

  35. Liu B, Wang L, Jin YH, Tang F, Huang DX (2005) Improved particle swarm optimization combined with chaos. Chaos Solitons Fractals 25:1261–1271

    MATH  Google Scholar 

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

    Google Scholar 

  37. Mirjalili S (2016) Sca: a sine cosine algorithm for solving optimization problems. Knowledge-Based Syst 96:120–133

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  40. Mukherjee A, Mukherjee V (2015) Solution of optimal power flow using chaotic krill herd algorithm. Chaos Solitons Fractals 78:10–21

    MathSciNet  Google Scholar 

  41. Niknamfar AH, Niaki STA, Niaki SAA (2017) Opposition-based learning for competitive hub location: a bi-objective biogeography-based optimization algorithm. Knowledge-Based Syst 128:1–19

    Google Scholar 

  42. Ollagnier JM (2007) Ergodic theory and statistical mechanics, vol 1115. Springer

    MATH  Google Scholar 

  43. Osher S, Wang B, Yin P, Luo X, Barekat F, Pham M, Lin A (2018) Laplacian smoothing gradient descent. arXiv preprint, http://arxiv.org/abs/1806.06317,arXiv:1806.06317

  44. Pan WT (2012) A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowledge-Based Syst 26:69–74

    Google Scholar 

  45. Pecora LM, Carroll TL (1990) Synchronization in chaotic systems. Phys Rev Lett 64:821

    MathSciNet  MATH  Google Scholar 

  46. Pereira JLJ, Francisco MB, Diniz CA, Oliver GA, Cunha SS Jr, Gomes GF (2021) Lichtenberg algorithm: a novel hybrid physics-based meta-heuristic for global optimization. Exp Syst Appl 170:114522

    Google Scholar 

  47. Rao RV, Savsani VJ, Vakharia D (2012) Teaching-learning-based optimization: an optimization method for continuous non-linear large scale problems. Inform Sci 183:1–15

    MathSciNet  Google Scholar 

  48. Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) Gsa: a gravitational search algorithm. Information sciences 179:2232–2248

    MATH  Google Scholar 

  49. Regis RG (2013) Evolutionary programming for high-dimensional constrained expensive black-box optimization using radial basis functions. IEEE Trans Evol Comput 18:326–347

    Google Scholar 

  50. Sadollah A, Bahreininejad A, Eskandar H, Hamdi M (2013) Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems. Appl Soft Comput 13:2592–2612

    Google Scholar 

  51. dos Santos Coelho L, Mariani VC (2008) Use of chaotic sequences in a biologically inspired algorithm for engineering design optimization. Exp Syst Appl 34:1905–1913

    Google Scholar 

  52. Shamir O, Zhang T (2013) Stochastic gradient descent for non-smooth optimization: Convergence results and optimal averaging schemes. In: International conference on machine learning, PMLR. pp. 71–79

  53. Sharma S, Rangaiah GP (2013) Multi-objective optimization applications in chemical engineering. Multi-Object Optim Chem Eng Dev Appl 3:35–102

    Google Scholar 

  54. Shi X, Liang Y, Lee H, Lu C, Wang L (2005) An improved ga and a novel pso-ga-based hybrid algorithm. Inform Process Lett 93:255–261

    MathSciNet  MATH  Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  56. Wang M, Chen H, Yang B, Zhao X, Hu L, Cai Z, Huang H, Tong C (2017) Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses. Neurocomputing 267:69–84

    Google Scholar 

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

    Google Scholar 

  58. Yang D, Li G, Cheng G (2007) On the efficiency of chaos optimization algorithms for global optimization. Chaos Solitons Fractals 34:1366–1375

    Google Scholar 

  59. Yang XS (2010) A new metaheuristic bat-inspired algorithm. Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, pp 65–74

    Google Scholar 

  60. Yang Y, Chen H, Heidari AA, Gandomi AH (2021) Hunger games search: visions, conception, implementation, deep analysis, perspectives, and towards performance shifts. Exp Syst Appl 177:114864

    Google Scholar 

  61. Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3:82–102

    Google Scholar 

  62. Yıldız BS, Pholdee N, Panagant N, Bureerat S, Yildiz AR, Sait SM (2021) A novel chaotic henry gas solubility optimization algorithm for solving real-world engineering problems. Eng Comput 10:1–13

    Google Scholar 

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Correspondence to Salih Berkan Aydemir.

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Aydemir, S.B. A novel arithmetic optimization algorithm based on chaotic maps for global optimization. Evol. Intel. 16, 981–996 (2023). https://doi.org/10.1007/s12065-022-00711-4

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