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An improved grasshopper optimization algorithm based on dynamic dual elite learning and sinusoidal mutation


Grasshopper optimization algorithm (GOA) is a meta-heuristic algorithm for solving optimization problems by modeling the biological habit and social behavior of grasshopper swarms in nature. Compared with other optimization algorithms, GOA still has room to improve its performance on solving complex problems. Therefore, this paper proposes an improved grasshopper optimization algorithm (EMGOA) based on dynamic dual elite learning and sinusoidal mutation. First of all, dynamic elite learning strategy is adopted to improve the influence of elites on the update process, enabling the algorithm to have a faster convergence speed. Then, sinusoidal function is utilized to guide the mutation of the current global optimal individual during each iteration to avoid the algorithm falling into the local optimum and improve the convergence accuracy of the algorithm. In order to investigate the performance of the proposed EMGOA algorithm, experiments are conducted on 26 benchmark functions and CEC2019 in this paper. The experimental results show that the optimization performance of EMGOA is obviously better than GOA, and EMGOA is competitive with six state-of-the-art meta-heuristic optimization algorithms.

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This work is supported by the National Natural Science Foundation of China (Grant No. 61535008) and the National Natural Science Foundation of Tianjin (Grant No. 20JCQNJC00430) and Science and Technology Research Team in Higher Education Institutions of Hebei Province (Grant No. ZD2018045).

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Correspondence to Lei Chen.

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Chen, L., Tian, Y. & Ma, Y. An improved grasshopper optimization algorithm based on dynamic dual elite learning and sinusoidal mutation. Computing (2021).

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  • Grasshopper optimization algorithm
  • Dynamic elite learning
  • Sinusoidal mutation
  • Optimization algorithm
  • Meta-heuristic algorithm

Mathematics Subject Classification

  • 65K10