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. 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. Furthermore, the proposed algorithm is applied to real-world problems (cantilever beam design, pressure vessel design, tension/compression spring and sustainable explotation renewable resource). The results showed that the proposed algorithm worked effectively in solving constrained optimization problems. The results obtained were compared with several 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形态发生运动启发的新型元启发算法。该算法从随机初始种群开始，每次迭代将最优候选解视为肿瘤，而种群中其他个体则作为朝向肿瘤方向迁移的内皮细胞，通过尖端细胞与跟随细胞之间的空间关系遵循协同动态。相较于其他类似优化元启发算法，本算法的优势在于：模型参数已根据肿瘤血管生成现象建模进行预配置，避免研究者使用任意值进行初始化。随后，算法与经典基准函数进行对比，并通过与粒子群优化算法的比较研究验证结果，表明该算法能够提供极具竞争力的性能。此外，将所提算法应用于实际工程问题（悬臂梁设计、压力容器设计、拉伸/压缩弹簧设计及可持续开发的可再生资源），结果表明该算法在求解约束优化问题时表现优异，所得结果与多种已知算法进行了对比验证。