基于曲线自适应和模拟退火的蝗虫优化算法.

This paper proposed curve adaptive and simulation annealing grasshopper optimization algorithm to avoid the disadvantages that the grasshopper optimization algorithm was easy to fall into local optimum, slow convergence speed and poor search accuracy. First of all, this paper introduced the adaptive curve to replace the linear adaptation of the grasshopper optimization algorithm key parameter, which improved the global search ability of the algorithm. Then, on this basis, this paper introduced the simulated annealing algorithm, which had a certain probability of receiving the inferior solution of the grasshopper algorithm to jump out of local optimization and achieve global optimization. Adaptive reduction the range of random solutions of grasshopper position in simulated annealing was beneficial to further improve the ability of grasshopper algorithm's exploitation. Through test function, the experimental results show that the improved new algorithm has higher quality and better convergence speed. [ABSTRACT FROM AUTHOR]