一种基于鲸鱼优化算法的 飞行动作识别规则提取方法

A Rule Extraction Method for Flight Action Recognition Based on Whale Optimization Algorithm

摘要:

针对飞行动作识别规则提取,传统智能优化算法存在调节参数多、易陷入局部极值等问题,介绍了一种新型仿生智能算法:鲸鱼优化算法。利用该算法参数简单,快速收敛性和全局优化的优点对符号化的飞行参数规则属性进行组合寻优,计算出相应的飞行动作识别规则。经过测试函数及仿真实例表明,鲸鱼优化算法寻优精度高于传统智能优化算法 PSO,提取的规则相比 BPSO方法更简洁、有效。

Abstract:

For the extraction of flight motion recognition rules, the traditional intelligent optimization algorithm has manyproblems such as many adjustment parameters and easy to fall into local extremum. In this paper, a new type of bionic in.telligence algorithm: whale optimization algorithm, was introduced. Using the algorithm's simple parameters, rapid conver.gence and its advantages of global optimization, the symbolic flight-parameter rule attributes were combined to calculatethe corresponding flight action recognition rules. The test function and simulation examples show that the optimization ac.curacy of the whale optimization algorithm is higher than that of the traditional intelligent optimization algorithm PSO, andthe extracted rules are more concise and effective than the BPSO method.