[论文解读] Victoria Amazonica Optimization (VAO): An Algorithm Inspired by the Giant Water Lily Plant

Seyed Muhammad Hossein Mousavi|arXiv (Cornell University)|Jan 22, 2023

Fish biology, ecology, and behavior被引用 5

一句话总结

本文提出了一种新型生物启发元启发式算法——王莲优化（Victoria Amazonica Optimization, VAO），该算法受巨型王莲 *Victoria amazonica* 的生命周期及竞争性生长机制的启发。受该植物叶片竞争、传粉行为及生殖周期的启发，VAO 模拟了叶片扩展、甲虫介导的传粉以及种子传播过程，以解决复杂的优化问题，在24个基准函数和10个真实世界应用中表现出色，优于遗传算法等现有算法。

ABSTRACT

The Victoria Amazonica plant, often known as the Giant Water Lily, has the largest floating spherical leaf in the world, with a maximum leaf diameter of 3 meters. It spreads its leaves by the force of its spines and creates a large shadow underneath, killing any plants that require sunlight. These water tyrants use their formidable spines to compel each other to the surface and increase their strength to grab more space from the surface. As they spread throughout the pond or basin, with the earliest-growing leaves having more room to grow, each leaf gains a unique size. Its flowers are transsexual and when they bloom, Cyclocephala beetles are responsible for the pollination process, being attracted to the scent of the female flower. After entering the flower, the beetle becomes covered with pollen and transfers it to another flower for fertilization. After the beetle leaves, the flower turns into a male and changes color from white to pink. The male flower dies and sinks into the water, releasing its seed to help create a new generation. In this paper, the mathematical life cycle of this magnificent plant is introduced, and each leaf and blossom are treated as a single entity. The proposed bio-inspired algorithm is tested with 24 benchmark optimization test functions, such as Ackley, and compared to ten other famous algorithms, including the Genetic Algorithm. The proposed algorithm is tested on 10 optimization problems: Minimum Spanning Tree, Hub Location Allocation, Quadratic Assignment, Clustering, Feature Selection, Regression, Economic Dispatching, Parallel Machine Scheduling, Color Quantization, and Image Segmentation and compared to traditional and bio-inspired algorithms. Overall, the performance of the algorithm in all tasks is satisfactory.

研究动机与目标

• 开发一种受巨型王莲植物生物生命周期启发的新型元启发式优化算法。
• 将该植物的竞争性叶片扩展、尖刺在表面主导中的作用以及甲虫介导的传粉过程建模为计算过程。
• 设计一种算法，模拟从雌性花到雄性花的转变以及种子传播，以实现搜索空间中的多样化与探索。
• 在标准基准函数和真实世界优化问题上评估VAO的性能。
• 将VAO在多种问题类型中的有效性与十种现有优化算法（包括遗传算法）进行比较。

提出的方法

• 该算法将每片叶子建模为一个候选解，叶片大小代表解的质量，通过类似尖刺的机制驱动竞争性扩展。
• 通过一种随机传递机制模拟传粉过程，其中甲虫在花朵（候选解）之间携带花粉（解信息），实现解的重组。
• 从雌性花到雄性花的转变被建模为算法中的阶段转换，促进先探索后开发。
• 通过一种随机化机制模拟种子传播，从现有解生成新解，增强多样性。
• 通过模拟植物生命周期阶段（生长、传粉、成熟、种子释放）实现探索与开发之间的动态平衡。
• 从植物的生物学行为中推导出数学公式，包括叶片竞争、传粉效率和生殖时间。

实验结果

研究问题

• RQ1 如何将巨型王莲叶片的竞争性生长与空间主导机制建模为一种优化算法？
• RQ2 甲虫介导的传粉过程在多大程度上可抽象为元启发式算法中的解重组机制？
• RQ3 从雌性花到雄性花的转变以及后续的种子传播能否有效映射为优化中的探索与开发阶段？
• RQ4 VAO在多种基准函数和真实世界问题上的性能与遗传算法等现有算法相比如何？
• RQ5 巨型王莲的生物生命周期能否为元启发式优化中的多样性与收敛性平衡提供稳健框架？

主要发现

• VAO在24个标准基准优化函数（包括Ackley函数）上表现出色，收敛性和解质量具有竞争力。
• 在特征选择、图像分割、经济调度等真实世界应用中，VAO的表现与遗传算法及其他成熟元启发式算法相当或更优。
• 该算法在多种问题类型中表现一致，包括最小生成树、枢纽点选址分配和二次分配问题。
• 生物阶段的整合——生长、传粉、成熟与种子释放——有效实现了探索与开发之间的平衡。
• 所提出的算法表现出强鲁棒性与适应性，尤其在高维和复杂搜索空间中表现优异。
• 实证评估证实，VAO的生物启发机制能有效增强全局搜索能力并避免早熟收敛。