Title: Artificial bee colony algorithm with hyperbolic spiral-based local search

Authors: Shiv Kumar Agarwal; Surendra Yadav

Addresses: Department of Computer Science and Engineering, Career Point University, Kota, India ' Department of Computer Science and Engineering, Career Point University, Kota, India

Abstract: Swarm intelligence-based algorithms successfully solved various complex optimisation problems. In this class, artificial bee colony (ABC) algorithm added in year 2005 by Karaboga that simulates the intelligent behaviour of honey bees. In order to search a quality food source honey bee update their position using some steps. In ABC step size is the combination of the random component ϕ_ij and a difference vector of existing and randomly chosen solution. Sometimes this step size is very large due to huge difference between the vectors and large value of random component ϕ_ij. This large step size leads to poor exploitation. Therefore, to improve exploitation potential of ABC algorithm, a local search step added in basic ABC. The new local search inspired by hyperbolic spiral and named as hyperbolic spiral local search (HSLS). The proposed variant of ABC is named as hyperbolic spiral-based ABC (HSABC). The performance of HSABC tested on 11 renowned benchmarks problems and experimental results compared with basic ABC, fitness-based position update in ABC (FPABC) and best-so-far ABC (BSFABC). Results prove that the newly anticipated HSABC is a good alternate of ABC algorithm.

Keywords: swarm intelligence; meta-heuristics; nature inspired algorithm; optimisation; hyperbolic spiral.

DOI: 10.1504/IJAISC.2025.148058

International Journal of Artificial Intelligence and Soft Computing, 2025 Vol.9 No.1, pp.50 - 61

Received: 27 Jul 2019
Accepted: 14 Nov 2019
Published online: 25 Aug 2025 *