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

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 im-prove 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 fire-fly. Numerical experiments demonstrate that QL-ADIFA outperforms existing methods on 15 bench-mark optimization functions and twelve engineering problems: cantilever arm design, pressure vessel design, three-bar truss design problem, and 9 constrained optimization problems in CEC2020.

Automated summary

Optimization problems are common in engineering and scientific research, and meta-heuristics provide a promising solution. The firefly algorithm (FA) imitates the behavior of fireflies and has been improved, but still has limitations. To overcome these limitations, this study proposes Q-learning based on the adaptive logarithmic spiral-Levy flight firefly algorithm (QL-ADIFA), which enhances the algorithm by leveraging the firefly's awareness and memory. Numerical experiments demonstrate that QL-ADIFA outperforms existing methods on benchmark functions and engineering problems.