Tumoral Angiogenic Optimizer: A new bio-inspired based metaheuristic

• URL: http://arxiv.org/abs/2309.05947v3
• Date: Wed, 20 Sep 2023 22:32:16 GMT
• Title: Tumoral Angiogenic Optimizer: A new bio-inspired based metaheuristic
• Authors: Hern\'andez Rodr\'iguez, Mat\'ias Ezequiel
• Abstract summary: We propose a new metaheuristic inspired by the morphogenetic cellular movements of endothelial cells (ECs) that occur during the tumor angiogenesis process. The proposed algorithm is applied to real-world problems (cantilever beam design, pressure vessel design, tension/compression spring and sustainable explotation renewable resource)
• Score: 5.013833066304755
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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