基于反馈机制的鲸鱼优化算法

Whale Optimization Algorithm Based on Feedback Mechanism

针对基本鲸鱼优化算法容易陷入局部最优和收敛速度慢的问题,提出了一种基于反馈机制的鲸鱼优化算法。为了提高算法的全局搜索能力以及局部开发能力,在鲸鱼寻找食物的阶段,通过鲸鱼的自我反馈以及鲸鱼间的反馈(基于均值个体)来提高鲸鱼算法的全局搜索能力,避免算法陷入局部最优。且通过最优位置的鲸鱼自我反馈、其它鲸鱼于最优位置的鲸鱼反馈(基于正态分布)来解决收敛速度慢的问题。实验表明,与基本的鲸鱼算法、粒子群算法、遗传算法相比,改进的算法在收敛的精度、速度以及算法的稳定性方面都有一定的提高。

In order to solve the problem that the basic whale optimization algorithm（ WOA） is easy to fall into a local optimal and the convergence speed is slow,a whale optimization algorithm（ FWOA） based on feedback mechanism is proposed. During a whale＇s search for prey,the self-feedback of a whale and the feedback between whales（ based on a mean individual）,are used to improve the global exploration ability,avoiding the algorithm falling into a local optimum effectively. In addition,slow convergence rate is solved by the feedback from the whale in its optimal position and the feedbacks from other whales in their optimal positions（ based on a normal distribution）. The experimental results show that compared with the basic WOA,particle swarm algorithm（ PSO） and genetic algorithm（ GA）,the improved algorithm makes some improvement in the convergence accuracy,speed and the stability.