动态搜索和协同进化的鲸鱼优化算法.

Aiming at the shortages of the basic whale optimization algorithm with low optimization precision, slow convergence speed and easy to fall into local optimum, this paper proposed a new algorithm based on dynamic search and cooperative learning. Firstly, it used equivalent replacement and Faure sequences to enhance the quality of initial solution. Secondly, it improved the population diversity and enhanced the ability to jump out of local optimal solutions through the division of population. Finally, in order to improve the convergence rate and precision, it dynamically adjusted the search strategy of the algorithms according to the population evolutionary information. The experimental results show that the proposed algorithm is much better than basic whale optimization algorithm and its several improved algorithms in optimization performance. [ABSTRACT FROM AUTHOR]