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Improved chimp optimization algorithm for three-dimensional path planning problem
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DOI：10.1007/s11042-022-12882-4.png)
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Abstract
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The three-dimensional (3D) path planning of unmanned aerial vehicle (UAV) focuses on avoiding collision with obstacles and finding the optimal path to reach the target position in the complex environment. An improved Chimp optimization algorithm (IChOA) based on somersault foraging strategy with adaptive weight was proposed to solve the three-dimensional path planning problem. Firstly, the position vector updating equation was dynamically adjusted by introducing the weighting factor derived from coefficient vector of the ChOA. Secondly, the somersault foraging strategy was introduced to prevent the group from falling into a local optimum in the later stage, and at the same time, the population diversity in the early stage was slightly improved. The algorithm was tested on CEC2019 functions and three-dimensional path planning. Compared with other methods, the results show that this algorithm can provide more competitive results.
Keywords:
Three-dimensional path planning
Chimp optimization algorithm
Somersault foraging strategy
Dynamic weighting factor
Metaheuristic
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3
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1.9W
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3.2W
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APPLIED ACOUSTICS
APPLIED ACOUSTICSIF3.6
PREAI
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