An Improved Dolphin Swarm Algorithm Based on Kernel Fuzzy C-Means in the Application of Solving the Optimal Problems of Large-Scale Function
Weibiao Qiao, Zhe Yang
Abstract
The solution of high dimensional function has always been a hot topic. In this paper, a novel algorithm based on Kernel Fuzzy C-means and dolphin swarm algorithm are proposed to solve high-dimensional functions more accurately. First, to improve the global convergence ability of dolphin swarm algorithm, Kernel Fuzzy C-means is introduced into the algorithm, named as Kernel Fuzzy C-means dolphin swarm algorithm (KFCDSA); Second, the five typical high-dimensional functions are applied to test the performance of the combination of KFCDSA. Finally, some indicators are used to evaluate the performance of different meta-heuristic algorithms. The results show that: the performance of the proposed algorithm exceeds that of the dolphin swarm algorithm and some advanced metaheuristic algorithms considered for comparison based on five different evaluating indicators. Through the test results, it can be concluded that introducing Kernel Fuzzy C-means into dolphin swarm algorithm is an effective improvement and provides a possibility for obtaining global optimal solutions for high-dimensional functions.