Unimodal benchmark functions.

The standard whale algorithm is prone to suboptimal results and inefficiencies in high-dimensional search spaces. Therefore, examining the whale optimization algorithm components is critical. The computer-generated initial populations often exhibit an uneven distribution in the solution space, leading to low diversity. We propose a fusion of this algorithm with a discrete recombinant evolutionary strategy to enhance initialization diversity. We conduct simulation experiments and compare the proposed algorithm with the original WOA on thirteen benchmark test functions. Simulation experiments on unimodal or multimodal benchmarks verified the better performance of the proposed RESHWOA, such as accuracy, minimum mean, and low standard deviation rate. Furthermore, we performed two data reduction techniques, Bhattacharya distance and signal-to-noise ratio. Support Vector Machine (SVM) excels in dealing with high-dimensional datasets and numerical features. When users optimize the parameters, they can significantly improve the SVM’s performance, even though it already works well with its default settings. We applied RESHWOA and WOA methods on six microarray cancer datasets to optimize the SVM parameters. The exhaustive examination and detailed results demonstrate that the new structure has addressed WOA’s main shortcomings. We conclude that the proposed RESHWOA performed significantly better than the WOA.

标准鲸鱼优化算法（Whale Optimization Algorithm, WOA）在高维搜索空间中易陷入次优解且运行效率低下，因此对该算法的各组成模块开展分析至关重要。计算机生成的初始种群在解空间中往往分布不均，导致种群多样性不足。为此，本文提出将该算法与离散重组进化策略相融合，以提升初始种群的多样性。本文在13个基准测试函数上开展仿真实验，将所提算法与原始WOA进行对比。针对单峰或多峰基准函数的仿真实验验证了所提RESHWOA的优异性能，其在求解精度、最小平均目标值及低标准差等指标上均表现更优。此外，本文采用了两种数据降维技术：巴氏距离（Bhattacharya Distance）与信噪比（Signal-to-Noise Ratio, SNR）。支持向量机（Support Vector Machine, SVM）在处理高维数据集与数值型特征方面性能优异，即便在默认参数设置下已具备不错的表现，对其参数进行优化仍可显著提升模型性能。本文将RESHWOA与原始WOA应用于6个癌症微阵列数据集，以优化SVM的参数。详尽的验证与详细的实验结果表明，该改进算法的新结构有效解决了原始WOA的主要缺陷。综上，本文所提的RESHWOA性能显著优于原始鲸鱼优化算法。