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QL-ADIFA : Hybrid optimization using Q-learning and an adaptive logarithmic spiral-levy firefly algorithm
Tan, Shuang Zhao, Shangrui Wu, Jinran
Tan, Shuang
Zhao, Shangrui
Wu, Jinran
Author
Abstract
Optimization problems are ubiquitous in engineering and scientific research, with a large number of such problems requiring resolution. Meta-heuristics offer a promising approach to solving optimization problems. The firefly algorithm (FA) is a swarm intelligence meta-heuristic that emulates the flickering patterns and behaviour of fireflies. Although FA has been significantly enhanced to improve its performance, it still exhibits certain deficiencies. To overcome these limitations, this study presents the Q-learning based on the adaptive logarithmic spiral-Levy flight firefly algorithm (QL-ADIFA). The Q-learning technique empowers the improved firefly algorithm to leverage the firefly's environmental awareness and memory while in flight, allowing further refinement of the enhanced firefly. Numerical experiments demonstrate that QL-ADIFA outperforms existing methods on 15 benchmark optimization functions and twelve engineering problems: cantilever arm design, pressure vessel design, three-bar truss design problem, and 9 constrained optimization problems in CEC2020.
Keywords
Q-learning algorithm, firefly algorithm, meta-heuristics, optimization problems
Date
2023
Type
Journal article
Journal
Mathematical Biosciences and Engineering
Book
Volume
20
Issue
8
Page Range
13542-13561
Article Number
ACU Department
Institute for Positive Psychology and Education
Faculty of Education and Arts
Faculty of Education and Arts
Relation URI
Source URL
Event URL
Open Access Status
Published as ‘gold’ (paid) open access
License
CC BY 4.0
File Access
Open