BSAS: Beetle Swarm Antennae Search Algorithm for Optimization Problems

TL;DR

This paper introduces BSAS, a swarm intelligence-enhanced meta-heuristic that improves optimization stability and accuracy by using multiple beetles and a feedback-based step-size update, outperforming traditional BAS.

Contribution

The paper proposes BSAS, a novel algorithm combining swarm intelligence with feedback-based step-size updates to enhance BAS performance and robustness.

Findings

BSAS reduces the influence of random beetle directions.

Increasing beetle number improves estimation accuracy.

BSAS outperforms traditional BAS in system identification.

Abstract

Beetle antennae search (BAS) is an efficient meta-heuristic algorithm. However, the convergent results of BAS rely heavily on the random beetle direction in every iterations. More specifically, different random seeds may cause different optimized results. Besides, the step-size update algorithm of BAS cannot guarantee objective become smaller in iterative process. In order to solve these problems, this paper proposes Beetle Swarm Antennae Search Algorithm (BSAS) which combines swarm intelligence algorithm with feedback-based step-size update strategy. BSAS employs k beetles to find more optimal position in each moving rather than one beetle. The step-size updates only when k beetles return without better choices. Experiments are carried out on building system identification. The results reveal the efficacy of the BSAS algorithm to avoid influence of random direction of Beetle. In addition, the estimation errors decrease as the beetles number goes up.