  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.
