TENT CHAOS AND NONLINEAR CONVERGENCE FACTOR WHALE OPTIMIZATION ALGORITHM

摘要

To solve the problems of slow convergence speed and difficulty in balancing exploration and development in whale optimization algorithm (WOA), a TWOA algorithm integrating Tent chaos and nonlinear convergence factors is proposed. First, the whale population in WOA algorithm uses random walk, which causes the uneven distribution of whale individuals in the exploration stage, and by adding the Tent chaotic mapping strategy and taking advantage of the ergodicity of Tent chaotic mapping, the whale population is distributed more evenly in the random walk stage, which enhances the global search ability of the algorithm. Second, because WOA algorithm adopts linear convergence factor, it cannot effectively solve the balance problem of balancing exploration and development, and by changing linear convergence factor into nonlinear convergence factor, the optimization accuracy of the algorithm can be improved. Finally, by using 10 standard test functions, the problems of unimodal functions and multimodal functions were tested. The results show that the whale optimization algorithm with Tent chaotic map and nonlinear convergence factor is superior to the original algorithm in both mean and standard deviation. From the convergence curve, it can be observed that the convergence speed and convergence accuracy are obviously improved.