Bombardier Beetle Optimizer: A Novel Bio-Inspired Algorithm for Global Optimization
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866918163954794496 |
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| author | Shehadeh, Hisham A. Idris, Mohd Yamani Idna Jebril, Iqbal H. |
| author_facet | Shehadeh, Hisham A. Idris, Mohd Yamani Idna Jebril, Iqbal H. |
| contents | In this paper, a novel bio-inspired optimization algorithm is proposed, called Bombardier Beetle Optimizer (BBO). This type of species is very intelligent, which has an ability to defense and escape from predators. The principles of the former one is inspired by the defense mechanism of Bombardier Beetle against the predators, which the Bombardier Beetle triggers a toxic chemical spray when it feels threatened. This reaction occurs in a specialized reaction chamber inside its abdomen and includes a well regulated enzymatic mechanism, which comprises hot water vapor, oxygen, and irritating substances like p-benzoquinones. In addition, the proposed BBO simulates also the escape mechanism of Bombardier Beetle from predator, which it has the ability to calculate its distance from predator and it can fly away. The BBO is tested with optimizing Congress on Evolutionary Computation (CEC 2017) test bed suites. Moreover, it is compared against well-known metaheuristic optimization algorithms includes Chernobyl Disaster Optimizer (CDO), Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Bermuda Triangle Optimizer (BTO), Sperm Swarm Optimization (SSO) and Gravitational Search Algorithm (GSA). The outcomes of this paper prove the BBO's efficiency in which outperforms the other algorithms in terms of convergence rate and quality of results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_17005 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bombardier Beetle Optimizer: A Novel Bio-Inspired Algorithm for Global Optimization Shehadeh, Hisham A. Idris, Mohd Yamani Idna Jebril, Iqbal H. Neural and Evolutionary Computing In this paper, a novel bio-inspired optimization algorithm is proposed, called Bombardier Beetle Optimizer (BBO). This type of species is very intelligent, which has an ability to defense and escape from predators. The principles of the former one is inspired by the defense mechanism of Bombardier Beetle against the predators, which the Bombardier Beetle triggers a toxic chemical spray when it feels threatened. This reaction occurs in a specialized reaction chamber inside its abdomen and includes a well regulated enzymatic mechanism, which comprises hot water vapor, oxygen, and irritating substances like p-benzoquinones. In addition, the proposed BBO simulates also the escape mechanism of Bombardier Beetle from predator, which it has the ability to calculate its distance from predator and it can fly away. The BBO is tested with optimizing Congress on Evolutionary Computation (CEC 2017) test bed suites. Moreover, it is compared against well-known metaheuristic optimization algorithms includes Chernobyl Disaster Optimizer (CDO), Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Bermuda Triangle Optimizer (BTO), Sperm Swarm Optimization (SSO) and Gravitational Search Algorithm (GSA). The outcomes of this paper prove the BBO's efficiency in which outperforms the other algorithms in terms of convergence rate and quality of results. |
| title | Bombardier Beetle Optimizer: A Novel Bio-Inspired Algorithm for Global Optimization |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2510.17005 |