关键词: 灰狼优化算法, Cubic映射, 反向学习, 拉伸弹簧设计, 非线性

Abstract: Aiming at the problem that the grey wolf optimization algorithm (GWO) is easy to fall into the local optimal solution to the complex optimization problems, from the perspective of chaos initia- lization and nonlinear control strategy, a grey wolf optimization algorithm based on cubic mapping and opposition-based learning is proposed (COGWO). Firstly, the cubic mapping and opposition-based learning strategies are used to initialize the population, and the parameters are adjusted by a nonlinear parameter control strategy in the optimization process. Then, the optimization experiment on six benchmark test functions show that the COGWO algorithm has better convergence accuracy, convergence speed and stability. Finally, the COGWO algorithm is applied to a practical engineering optimization problem.

Key words: grey wolf optimization algorithm, Cubic mapping, opposition-based learning, tensile spring design, nonlinear