A hybrid firefly algorithm based on modified neighbourhood attraction. (19th January 2023)

• Record Type: Journal Article
• Authors: Chen, Rongfang; Tang, Jun
• Is Part Of: International journal of innovative computing and applications. Volume 13:Number 5/6(2022)
• Page Start: 290
• Page End: 295
• Publication Date: 2023-01-19
• Subjects: firefly algorithm -- modified neighbourhood attraction -- generalised opposition-based learning -- neighbourhood search; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

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.