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A multi-strategy improved snake optimizer for three-dimensional UAV path planning and engineering problems

Created by
  • Haebom

Author

Genliang Li, Yaxin Cui, Jinyu Su

Outline

This paper proposes a novel Multi-Strategy Improved Snake Optimizer (MISO) to improve the slow convergence speed and easy local optimum of the existing Snake Optimizer (SO) algorithm. MISO introduces an adaptive random perturbation strategy based on a sine function, an adaptive Levy flight strategy based on a size coefficient and a leader, and a position update strategy combining elite leadership and Brownian motion to escape from local optimum and improve the convergence speed. Experimental results on the CEC2017 and CEC2022 test functions, six engineering design problems, and the unmanned aerial vehicle (UAV) 3D path planning problem demonstrate that MISO outperforms existing algorithms.

Takeaways, Limitations

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Takeaways:
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The MISO algorithm effectively improves the shortcomings of the existing SO algorithm, enhancing the convergence speed and optimal solution search ability.
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Its practical value is demonstrated by verifying its applicability to various problems (CEC2017/2022 test function, engineering design problem, UAV path planning problem).
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The proposed adaptive random perturbation strategy, adaptive Levy flight strategy, and position update strategy can be utilized as general improvement strategies applicable to other metaheuristic algorithms.
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Limitations:
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The experimental results presented in this paper are limited to specific test functions and problems, and additional experiments on more diverse and complex problems are needed.
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There is a lack of detailed analysis and guidance on parameter tuning of the MISO algorithm. Since the optimal parameter settings may vary depending on the characteristics of the problem, further research on parameter tuning is needed.
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Analysis of the algorithm's complexity and computational cost is lacking. In particular, the efficiency of the algorithm should be analyzed in more detail to evaluate its applicability to large-scale problems.
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