ABCO: Adaptive Bacterial Colony Optimisation
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.
Code (1)
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Scan4CFU: Low-cost, open-source bacterial colony tracking over large areas and extended incubation times
A hallmark of bacterial populations cultured in vitro is their homogeneity of growth, where the majority of cells display identical growth rate, cell size and content. Recent insights, however, have revealed that even ce…
Colony Grounded SAM2: Zero-shot detection and segmentation of bacterial colonies using foundation models
The detection and classification of bacterial colonies in images of agar-plates is important in microbiology, but is hindered by the lack of labeled datasets. Therefore, we propose Colony Grounded SAM2, a zero-shot infer…
Investigation of cardinality classification for bacterial colony counting using explainable artificial intelligence
Automatic bacterial colony counting is a highly sought-after technology in modern biological laboratories because it eliminates manual counting effort. Previous work has observed that MicrobiaNet, currently the best-perf…
Density EstimationSolving Sudoku with Ant Colony Optimisation
In this paper we present a new Ant Colony Optimisation-based algorithm for Sudoku, which out-performs existing methods on large instances. Our method includes a novel anti-stagnation operator, which we call Best Value Ev…
A Hybrid Bacterial Foraging Algorithm For Solving Job Shop Scheduling Problems
Bio-Inspired computing is the subset of Nature-Inspired computing. Job Shop Scheduling Problem is categorized under popular scheduling problems. In this research work, Bacterial Foraging Optimization was hybridized with …
Job Shop SchedulingScheduling