自适应沙丘猫鲸鱼优化算法

An Adaptive Sand Cat and Whale Optimization Algorithm

摘要: 鲸鱼优化算法原理简单、参数较少、全局搜索能力强，但在迭代后期易陷入局部最优且求解精度较低。本文将自适应收敛因子策略和沙丘猫群优化算法中随机搜索策略引入到鲸鱼优化算法中，提出了一种自适应沙丘猫鲸鱼优化算法。首先，采用自适应收敛因子策略，动态地调整算法参数a，使搜索更具连续性、稳定性与多样性，提高优化精度；其次，将沙丘猫群优化算法中引入到鲸鱼优化算法中，防止算法陷入局部最优，提高全局搜索能力；然后，通过基准测试函数进行仿真实验，并与其他几种智能群体算法进行比较，仿真实验结果表明，改进算法具有较高的优化性能；最后，利用改进算法求解机械工程优化问题，验证了改进算法的有效性和实用性。

Abstract: The whale optimization algorithm boasts a simple principle, fewer parameters, and strong global search ability; however, it tends to fall into local optimum and yields low solution accuracy in the later stages of iteration. In this paper, we introduce the self-adaptive convergence factor strategy and random search strategy from the sand cat swarm optimization algorithm into the whale optimization algorithm, proposing an adaptive sand cat whale optimization algorithm. Firstly, the self-adaptive convergence factor strategy is employed to dynamically adjust the algorithm parameter a, enhancing the continuity, stability, and diversity of the search, and improving optimization accuracy. Secondly, the sand cat swarm optimization algorithm is incorporated into the whale optimization algorithm to prevent it from getting trapped in local optimum and enhance its global search ability. Subsequently, simulation experiments are conducted using benchmark test functions and compared with several other intelligent swarm algorithms. The simulation experiment results demonstrate that the improved algorithm exhibits superior optimization performance. Finally, the improved algorithm is applied to solve optimization problems in mechanical engineering, validating its effectiveness and practicality.