Title: A hybrid firefly algorithm based on modified neighbourhood attraction

Authors: Rongfang Chen; Jun Tang

Addresses: Department of Management Engineering, Hunan Urban Construction College, Xiangtan 411101, China ' Department of Construction Equipment Engineering, Hunan Urban Construction College, Xiangtan 411101, China

Abstract: Some studies reported that firefly algorithm (FA) had high computational time complexity. To tackle this problem, different attraction models were designed including random attraction, probabilistic attraction, and neighbourhood attraction. This paper concentrates on improving the efficiency of neighbourhood attraction. Then, a hybrid FA based on modified neighbourhood attraction (called HMNaFA) is proposed. In our new approach, the best solution selected from the current neighbourhood is used for competition. If the best solution wins the competition, the current solution flies towards the best one; otherwise a new neighbourhood search is employed to produce high quality solutions. Experiments are validated on several classical problems. Simulation results show HMNaFA surpasses FA with neighbourhood attraction and several other FA algorithms.

Keywords: firefly algorithm; modified neighbourhood attraction; generalised opposition-based learning; neighbourhood search.

DOI: 10.1504/IJICA.2022.128436

International Journal of Innovative Computing and Applications, 2022 Vol.13 No.5/6, pp.290 - 295

Received: 13 Jul 2020
Accepted: 23 Nov 2020
Published online: 23 Jan 2023 *

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