关键词: 鲸群优化算法, 对数惯性权重, 高斯差分变异, 群智能优化算法, 收敛性能

Abstract:

Aiming at the problem that whale optimization algorithm has slow convergence speed, easy to fall into local optimum and low convergence precision when dealing with high-dimensional problems, a whale optimization algorithm based on logarithmic inertia weight and Gaussian difference mutation is proposed. Firstly, Gaussian difference mutation is used. The whale position update equation is mutated, which increases the population diversity and improves the global search ability of the whale algorithm to prevent premature phenomenon. Then, the logarithmic inertia weight is introduced into the prey hunting stage, which improves the algorithm’s optimization accuracy while balancing the global search and local development capabilities. Finally, the algorithm is tested from experiments. The experimental results show that the improved algorithm has higher optimization precision and faster convergence speed.

Key words: whale optimization algorithm, logarithmic inertia weight, Gaussian difference mutation, group intelligent optimization algorithm, convergence performance