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A hybrid grasshopper optimization algorithm with bat algorithm for global optimization

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Abstract

This paper introduces a hybrid grasshopper optimization algorithm with bat algorithm (BGOA) for global optimization. In the BGOA, the Levy flight with variable coefficient is employed to enhance the exploration capability of the GOA. Then, the local search operation of bat algorithm (BA) is combined to balance the exploration and exploitation. Additionally, the random strategy is introduced and applied to high quality population to improve the exploitation capability in the searching process. The performance of BGOA is evaluated on 23 benchmark test functions, and compares with genetic algorithm (GA), bat algorithm (BA), moth-flame optimization algorithm (MFO), dragonfly algorithm (DA) and basic GOA. The results establish that the BGOA is able to provide better outcomes than the other algorithms.

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References

  1. Arora S, Anand P (2019) Chaotic grasshopper optimization algorithm for global optimization. Neural Comput & Applic 31(8):4385–4405

    Article  Google Scholar 

  2. Chu X, Gao D, Chen J, Cui J, Cui C, Xu SX, Qin Q (2019) Adaptive differential search algorithm with multi-strategies for global optimization problems. Neural Comput & Applic 31(12):8423–8440

    Article  Google Scholar 

  3. Digalakis JG, Margaritis KG (2001) On benchmarking functions for genetic algorithms. Int J Comput Math 77(4):481–506

    Article  MathSciNet  Google Scholar 

  4. Eberhart, Russell, and James Kennedy (1995). “A new optimizer using particle swarm theory." MHS’95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science. Ieee

  5. Ewees AA, Elaziz MA, Houssein EH (2018) Improved grasshopper optimization algorithm using opposition-based learning. Expert Syst Appl 112:156–172

    Article  Google Scholar 

  6. Hazra S, Pal T, Roy PK (2019) Renewable energy based economic emission load dispatch using grasshopper optimization algorithm. International Journal of Swarm Intelligence Research (IJSIR) 10(1):38–57

    Article  Google Scholar 

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

    Article  Google Scholar 

  8. Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8(1):687–697

    Article  Google Scholar 

  9. Liang H, Jia H, Xing Z, Ma J, Peng X (2019) Modified grasshopper algorithm-based multilevel thresholding for color image segmentation. IEEE Access 7:11258–11295

    Article  Google Scholar 

  10. Liao, Ling, and Yongquan Zhou (2019). “A Neighborhood Centroid Opposition-Based Grasshopper Optimization Algorithm.” J Phys Conf Ser. Vol. 1176. No. 3. IOP Publishing

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

    Article  Google Scholar 

  12. Mirjalili S (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput & Applic 27(4):1053–1073

    Article  MathSciNet  Google Scholar 

  13. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Article  Google Scholar 

  14. Molga, Marcin, and Czesław Smutnicki (2005). “Test functions for optimization needs”101 : 48.

  15. Nouiri M, Bekrar A, Jemai A, Niar S, Ammari AC (2018) An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem. J Intell Manuf 29(3):603–615

    Article  Google Scholar 

  16. Ohri, Jyoti, Naveen Kumar, and Minakshi Chinda (2014). “An improved genetic algorithm for PID parameter tuning.” Proceedings of the 2014 International Conference on Circuits, Systems, Signal Processing

  17. Santillan JH, Tapucar S, Manliguez C, Calag V (2018) Cuckoo search via Lévy flights for the capacitated vehicle routing problem. Journal of Industrial Engineering International 14(2):293–304

    Article  Google Scholar 

  18. Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47

    Article  Google Scholar 

  19. Satapathy SC, Sri Madhava Raja N, Rajinikanth V, Ashour AS, Dey N (2018) Multi-level image thresholding using Otsu and chaotic bat algorithm. Neural Comput & Applic 29(12):1285–1307

    Article  Google Scholar 

  20. Schaffer, J. David, et al (1989). “A study of control parameters affecting online performance of genetic algorithms for function optimization.” Proceedings of the 3rd international conference on genetic algorithms

  21. Shareef H, Ibrahim AA, Mutlag AH (2015) Lightning search algorithm. Appl Soft Comput 36:315–333

    Article  Google Scholar 

  22. Tharwat A, Elhoseny M, Hassanien AE, Gabel T, Kumar A (2019) Intelligent Bézier curve-based path planning model using chaotic particle swarm optimization algorithm. Clust Comput 22(2):4745–4766

    Article  Google Scholar 

  23. Topaz CM, Bernoff AJ, Logan S, Toolson W (2008) A model for rolling swarms of locusts. The European Physical Journal Special Topics 157(1):93–109

    Article  Google Scholar 

  24. Wu J, Wang H, Li N, Yao P, Huang Y, Su Z, Yu Y (2017) Distributed trajectory optimization for multiple solar-powered UAVs target tracking in urban environment by adaptive grasshopper optimization algorithm. Aerosp Sci Technol 70:497–510

    Article  Google Scholar 

  25. Yang, Xin-She (2010). “A new metaheuristic bat-inspired algorithm.” Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, Berlin, Heidelberg. 65–74

  26. Yang X-S (2010) Firefly algorithm, stochastic test functions and design optimisation. International journal of bio-inspired computation 2(2):78–84

    Article  Google Scholar 

  27. Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3(2):82–102

    Article  Google Scholar 

  28. Yoshida H, Fukuyama Y (2018) Parallel multipopulation differential evolutionary particle swarm optimization for voltage and reactive power control. Electrical Engineering in Japan 204(3):31–40

    Article  Google Scholar 

  29. Yue X, Zhang H, Haiyue Y (2020) A hybrid grasshopper optimization algorithm with invasive weed for global optimization. IEEE Access 8:5928–5960

    Article  Google Scholar 

  30. Zhang X, Miao Q, Zhang H, Wang L (2018) A parameter-adaptive VMD method based on grasshopper optimization algorithm to analyze vibration signals from rotating machinery. Mech Syst Signal Process 108:58–72

    Article  Google Scholar 

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  1. School of Mechatronic Engineering, Changchun University of Technology, Changchun, 130012, People’s Republic of China

    Shenghan Yue & Hongbo Zhang

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  1. Shenghan Yue
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  2. Hongbo Zhang
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Correspondence to Shenghan Yue.

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Yue, S., Zhang, H. A hybrid grasshopper optimization algorithm with bat algorithm for global optimization. Multimed Tools Appl 80, 3863–3884 (2021). https://doi.org/10.1007/s11042-020-09876-5

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  • DOI: https://doi.org/10.1007/s11042-020-09876-5

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