基于非线性云化的自适应帝王蝶优化算法

针对帝王蝶算法多样性退化、易陷入局部最优导致寻优精度不高的问题,提出一种基于非线性云化的自适应帝王蝶算法(NCSMBO).深入探究帝王蝶算法的进化机制,指出其本质为网格式搜索算法;在迁移和调整算子中,采用正向正态云发生器对父代帝王蝶个体执行非线性云化操作,增加候选解的数量,提高局部开发能力;对云化后的后代个体引入贪婪策略,增强算法的可行性;为从发生概率上对突变进行控制,进一步给出双圆正切形式的自适应调整率.在12个不同特征基准测试函数上对包含NCSMBO在内的7种优化算法进行综合评估,以及对两类数学规划问题求解验证,实验验证结果均表明所提算法具有更高的收敛精度和稳定性.

Self-adaptive monarch butterfly optimization based on nonlinear cloud-transfer

In order to solve the problem of low precision of monarch butterfly optimization(MBO)causing by diversity degradation and being easy to fall into local optimum, a self-adaptive monarch butterfly optimization based on nonlinear cloud-transfer(NCSMBO) is proposed. The evolution mechanism of the MBO is deeply explored which indicates the nature of the MBO is grid search. Nonlinear cloud-transfer operation to parent monarchs is executed utilizing a forward normal-cloud generator in both migration operators and adjusting operators, which can increase candidate solutions and improve the ability of local exploitation. Meanwhile, a greedy strategy is introduced in offspring from a cloud-transfer to enhance the feasibility. A self-adaptive adjustment rate in the form of double-circle-tangent is given to control mutation based on occurrence probability. Seven optimization algorithms including the NCSMBO are overall evaluated on the 12 benchmark functions with different features, and two types of mathematical planning problems are solved and verified. All of the simulation results show that the proposed algorithm has better convergence accuracy and stability.