Tumoral Angiogenic Optimizer: A new bio-inspired based metaheuristic

TL;DR

This paper introduces a novel bio-inspired metaheuristic based on tumor angiogenesis, demonstrating its effectiveness on benchmark and real-world constrained optimization problems with competitive results.

Contribution

A new metaheuristic algorithm inspired by tumor angiogenesis, with parameters set by biological modeling, avoiding arbitrary initialization.

Findings

Achieved competitive results on benchmark functions.

Successfully applied to real-world constrained optimization problems.

Outperformed several known algorithms in tests.

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.