Improved Grey Wolf Optimization Algorithm
Description
Aiming at the problems of slow convergence speed and easy to fall into local optimum of grey wolf optimization (GWO) in unmanned aerial vehicle path planning, an improved grey wolf optimization (I-GWO) with multi-strategy fusion is proposed. The improved algorithm adds a population confrontation strategy in the initialization stage to accelerate the speed of convergence in the first period. Secondly, in order to balance the developmental and exploratory capabilities of the algorithm, a cosine strategy is introduced to improve the calculation of the control factor. Meanwhile, the Cauchy distribution inverse cumulative distribution function and tangent flight operator are introduced in the position updating phase to prevent the algorithm from stagnating at the local optimum.
Files
Institutions
- Zhengzhou University of Light Industry