ABCO: Adaptive Bacterial Colony Optimisation
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912358048202752 |
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| author | Kogam, Barisi Kovalchuk, Yevgeniya Gaber, Mohamed Medhat |
| author_facet | Kogam, Barisi Kovalchuk, Yevgeniya Gaber, Mohamed Medhat |
| contents | This paper introduces a new optimisation algorithm, called Adaptive Bacterial Colony Optimisation (ABCO), modelled after the foraging behaviour of E. coli bacteria. The algorithm follows three stages--explore, exploit and reproduce--and is adaptable to meet the requirements of its applications. The performance of the proposed ABCO algorithm is compared to that of established optimisation algorithms--particle swarm optimisation (PSO) and ant colony optimisation (ACO)--on a set of benchmark functions. Experimental results demonstrate the benefits of the adaptive nature of the proposed algorithm: ABCO runs much faster than PSO and ACO while producing competitive results and outperforms PSO and ACO in a scenario where the running time is not crucial. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_01320 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ABCO: Adaptive Bacterial Colony Optimisation Kogam, Barisi Kovalchuk, Yevgeniya Gaber, Mohamed Medhat Neural and Evolutionary Computing This paper introduces a new optimisation algorithm, called Adaptive Bacterial Colony Optimisation (ABCO), modelled after the foraging behaviour of E. coli bacteria. The algorithm follows three stages--explore, exploit and reproduce--and is adaptable to meet the requirements of its applications. The performance of the proposed ABCO algorithm is compared to that of established optimisation algorithms--particle swarm optimisation (PSO) and ant colony optimisation (ACO)--on a set of benchmark functions. Experimental results demonstrate the benefits of the adaptive nature of the proposed algorithm: ABCO runs much faster than PSO and ACO while producing competitive results and outperforms PSO and ACO in a scenario where the running time is not crucial. |
| title | ABCO: Adaptive Bacterial Colony Optimisation |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2505.01320 |