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

TL;DR

This paper introduces a multi-strategy improved snake optimizer (MISO) that enhances convergence speed and avoids local optima, demonstrating superior performance in benchmark functions and practical UAV 3D path planning problems.

Contribution

The paper proposes novel adaptive strategies for the snake optimizer, improving its ability to escape local optima and accelerate convergence, with extensive testing on benchmark functions and UAV path planning.

Findings

MISO outperforms 11 popular algorithms on CEC2017 and CEC2022 test suites.

MISO achieves higher solution quality and stability in UAV 3D path planning.

The proposed strategies effectively enhance the optimizer's global search capability.

Abstract

Metaheuristic algorithms have gained widespread application across various fields owing to their ability to generate diverse solutions. One such algorithm is the Snake Optimizer (SO), a progressive optimization approach. However, SO suffers from the issues of slow convergence speed and susceptibility to local optima. In light of these shortcomings, we propose a novel Multi-strategy Improved Snake Optimizer (MISO). Firstly, we propose a new adaptive random disturbance strategy based on sine function to alleviate the risk of getting trapped in a local optimum. Secondly, we introduce adaptive Levy flight strategy based on scale factor and leader and endow the male snake leader with flight capability, which makes it easier for the algorithm to leap out of the local optimum and find the global optimum. More importantly, we put forward a position update strategy combining elite leadership and Brownian motion, effectively accelerating the convergence speed while ensuring precision. Finally, to demonstrate the performance of MISO, we utilize 30 CEC2017 test functions and the CEC2022 test suite, comparing it with 11 popular algorithms across different dimensions to validate its effectiveness. Moreover, Unmanned Aerial Vehicle (UAV) has been widely used in various fields due to its advantages of low cost, high mobility and easy operation. However, the UAV path planning problem is crucial for flight safety and efficiency, and there are still challenges in establishing and optimizing the path model. Therefore, we apply MISO to the UAV 3D path planning problem as well as 6 engineering design problems to assess its feasibility in practical applications. The experimental results demonstrate that MISO exceeds other competitive algorithms in terms of solution quality and stability, establishing its strong potential for application.