Beetle Antennae Search Strategy Improved Grey Wolf Optimization Algorithm

In view of the problem of being easy to fall into local optimization when the gray wolf optimization algorithm handles complex optimization problems, this thesis starts from the two angles of perspectives of gray wolf search strategy and nonlinear control strategy to propose an improved gray wolf optimization algorithm based on beetle antennae search strategy (BIGWO). Firstly, it gives the leading wolves the ability of free exploration and communication by combining with the beetle antennae search strategy, which helps the improvement of the global search performance of the algorithm. Besides, it carries out the design of a nonlinear control strategy to balance the exploration and development of the algorithm. Then, the optimization test is carried out for 10 benchmark functions to verify the optimization ability of the algorithm. Finally, it makes the application of the algorithm into the actual optimization problem of vehicle planning task allocation with capacity constraints, so as to verify the ability of the algorithm to solve practical problems. According to the experimental results, it indicates that BIGWO algorithm has better optimization accuracy, stability and convergence speed, which is both suitable for the optimization of characteristic function and the practical optimization problems.