ABCO: Adaptive Bacterial Colony Optimisation

TL;DR

ABCO is a new adaptive bacterial colony optimization algorithm inspired by E. coli foraging, which is faster than PSO and ACO and performs competitively on benchmark functions.

Contribution

The paper introduces ABCO, a novel adaptive optimization algorithm inspired by bacterial foraging, demonstrating improved speed and competitive performance.

Findings

ABCO outperforms PSO and ACO in speed.

ABCO achieves competitive results on benchmark functions.

ABCO excels in scenarios where runtime is critical.

Abstract

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.