A Systematic Review on Firefly Algorithm:  Past, Present, and Future

<abstract><p>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.</p></abstract>

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A Systematic Review on Firefly Algorithm: Past, Present, and Future

Abstract: A comprehensive review of firefly algorithm is presented and various characteristics are discussed with various variant of FA such as binary, multi-objective and hybrid.

Cited by 2 publications

References 85 publications

Study on Optimization of Unreliable Server Queueing Systems: A PSO Based Survey

Study on Optimization of Unreliable Server Queueing Systems: A PSO Based Survey

QL-ADIFA: Hybrid optimization using Q-learning and an adaptive logarithmic spiral-levy firefly algorithm

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