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Fourier transform optimizer: A novel physics-inspired metaheuristic algorithm for optimization problems
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DOI：10.1016/j.knosys.2026.115651.png)
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Abstract
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The rapid advancement of modern intelligent systems has resulted in increasingly complex optimization problems across diverse domains. While metaheuristic algorithms have demonstrated considerable effectiveness, persistent challenges, including premature convergence, inadequate exploration, and performance degradation in high-dimensional or nonconvex search spaces, continue to limit their robustness. To mitigate these issues, this paper introduces the Fourier Transform Optimizer (FTO), a metaheuristic framework that incorporates frequency-domain analysis through the Discrete Fourier Transform (DFT). The proposed method enables solution updates driven by frequency-based transformations, intending to achieve a more effective balance between exploration and exploitation. FTO integrates several enhancement mechanisms, including differential frequency mixing, Lévy flight perturbations, orthogonal learning (OL), and Runge-Kutta (RK)-inspired update strategies, to improve search dynamics. The performance of FTO is evaluated using the CEC2017 and CEC2022 benchmark suites. Experimental results demonstrate that FTO attains competitive performance compared with established optimization algorithms across a broad range of test functions. Furthermore, the optimizer is validated on multiple engineering design problems and medical data-driven feature selection tasks, where it exhibits stable and reliable behavior. These results suggest that frequency-domain-guided search represents a promising strategy for addressing complex optimization problems. The MATLAB source code of the FTO algorithm is available at: https://www.mathworks.com/matlabcentral/fileexchange/181955-fourier-transform-optimizer-fto
Keywords:
Fourier Transform Optimizer
metaheuristic algorithm
frequency-domain analysis
optimization problems
differential frequency mixing
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7.6
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1.2W
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