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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]

In order to improve the performance of whale optimization algorithm for solving complex function optimization problems, this paper proposed an improved whale optimization algorithm based on adaptive parameters and niche technology. Firstly, the algorithm introduced an adaptive probability threshold to coordinate the global exploration and local development abilities. Then, the algorithm used adaptive position weights to adjust the whale position update formula to improve the convergence speed and search precision. Finally, the algorithm used preselection niche technology to avoid premature convergence. The results on 12 typical benchmark functions show that the improved algorithm has faster convergence speed and higher search precision than other comparison algorithms. It proves that the improvement strategy can effectively improve the performance of the whale optimization algorithm for solving complex function optimization problems. [ABSTRACT FROM AUTHOR]