自适应常春藤 ADIVY 优化算法设计及其工程应用
Journal: 空天科技 DOI: 10.12238/ast.v1i1.13708
Abstract
针对常春藤（IVY）优化算法在复杂优化问题中的全局搜索能力不足和收敛速度较慢的问题，提出了一种自适应常春藤ADIVY算法，提升优化效率和精度。首先，采用拉丁超立方体采样(Latin Hypercube Sampling, LHS)与黄金分割比的非均匀初始化(Golden Ratio-based Nonuniform Initialization, GRN)融合，扩展初始种群的搜索范围，并增强了种群分布的均匀性和多样性，并设计动态压缩归一化映射(Dynamic Compression Normalization Mapping, DCNM)增强边界区域探索能力，其次，融合自适应创新点和多样性维持机制(Adaptive Innovation and Diversity Maintenance Mechanism, AIDM)，在早熟收敛或多样性不足时对部分个体进行重置或变异，增强了跳出局部最优的能力。此外，设计了一种动态位置翻筋斗扰动选择(The dynamic position tumbling disturbance selection can be abbreviated as, DPTDS)机制，改进全新的GV长向量，结合全局最优解和局部梯度信息，平衡了大范围跳跃与精细微调，提升搜索速度与精度。最后，提出动态平滑飞行-HHO（Dynamic Smoothing Flight in Harris Hawks Optimization, DSF-HHO)策略，通过引入哈里斯鹰算法的“渐变逃逸能量”概念，减少了解空间的抖动，增强全局搜索能力。基准函数和实际工程应用的对比实验及Wilcoxon秩和检验结果表明，改进的ADIVY算法在全局搜索、鲁棒性和收敛速度上均优于原始算法与其他主流算法，验证了其在工程应用中的有效性和优越性。
Keywords
IVY优化算法；多策略融合；多样性维持机制；动态平滑飞行；工程应用；翻筋斗云；渐变逃逸能量；全局搜索能力
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