ABCO: Adaptive Bacterial Colony Optimisation

2025-05-02

AI Summary

Existing swarm intelligence algorithms—such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO)—struggle to balance convergence speed and solution quality. To address this, this paper proposes an Adaptive Bacterial Optimization (ABO) algorithm inspired by the foraging behavior of *Escherichia coli*. ABO innovatively integrates a dynamic bacterial life-cycle mechanism—comprising exploration, exploitation, and reproduction phases—with an adaptive parameter control strategy, enabling real-time adjustment of search behavior. This work is the first to deeply couple biologically inspired life-cycle modeling with adaptive control for synergistic optimization of both convergence efficiency and global solution accuracy. Experimental evaluations on standard benchmark functions demonstrate that ABO achieves significantly faster convergence than PSO and ACO; moreover, under sufficient iterations, it attains superior solution precision, thereby exhibiting both high efficiency and strong competitive performance.

Technology Category

• Search and Optimization: Evolutionary Computation
• Machine Learning: Bio-inspired Learning
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

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.

Problem

• Proposes Adaptive Bacterial Colony Optimisation (ABCO) algorithm
• Compares ABCO with PSO and ACO on benchmark functions
• Demonstrates ABCO's faster runtime and competitive performance

Innovation

• Adaptive Bacterial Colony Optimisation algorithm
• Three stages: explore, exploit, reproduce
• Outperforms PSO and ACO in speed