In this article, we propose a new metaheuristic inspired by the morphogenetic cellular movements of endothelial cells (ECs) that occur during the tumor angiogenesis process. This algorithm starts with a random initial population. In each iteration, the best candidate selected as the tumor, while the other individuals in the population are treated as ECs migrating toward the tumor's direction following a coordinated dynamics through a spatial relationship between tip and follower ECs. EC movements mathematical model in angiogenic morphogenesis are detailed in the article. This algorithm has an advantage compared to other similar optimization metaheuristics: the model parameters are already configured according to the tumor angiogenesis phenomenon modeling, preventing researchers from initializing them with arbitrary values. Subsequently, the algorithm is compared against well-known benchmark functions, and the results are validated through a comparative study with Particle Swarm Optimization (PSO). The results demonstrate that the algorithm is capable of providing highly competitive outcomes. Also the proposed algorithm is applied to a real-world problem. The results showed that the proposed algorithm performed effective in solving constrained optimization problems, surpassing other known algorithms.

翻译：本文提出了一种受肿瘤血管生成过程中内皮细胞（ECs）形态发生运动启发的新型元启发式算法。该算法从随机初始种群开始。在每次迭代中，选择最优候选个体作为肿瘤，而种群中的其他个体则被视为内皮细胞，通过尖端细胞与跟随细胞之间的空间关系遵循协调动力学向肿瘤方向迁移。文中详细阐述了血管生成形态发生中内皮细胞运动的数学模型。与其他类似优化元启发式算法相比，该算法具有一个优势：模型参数已根据肿瘤血管生成现象建模进行预配置，避免了研究者以任意值对其进行初始化。随后，该算法与已知基准函数进行了比较，并通过与粒子群优化算法（PSO）的对比研究验证了结果。结果表明，该算法能够提供极具竞争力的结果。此外，所提算法还被应用于一个实际问题。结果显示，该算法在解决约束优化问题方面表现有效，超越了其他已知算法。