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：本文提出了一种新的元启发式算法，该算法灵感来源于肿瘤血管生成过程中内皮细胞（ECs）发生的形态发生细胞运动。该算法从随机初始种群开始。在每次迭代中，最佳候选解被选作肿瘤，而种群中的其他个体被视为内皮细胞，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。与其他类似的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了配置，从而避免了研究者使用任意值进行初始化。随后，将该算法与众所周知的基准函数进行比较，并通过与粒子群优化（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，该算法被应用于实际问题（悬臂梁设计、压力容器设计、拉伸/压缩弹簧设计以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方面效果显著。所得结果与若干已知算法进行了比较。