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Chaos theory, which addresses these limitations utilizing its special features of deterministic nonlinearity, sensitiveness to initial conditions, and pseudo-randomness, has emerged as a transformative paradigm in FD. Chaotic systems enhance FD methods by enabling complex system modeling, sensitivity to initial conditions improves SB methods by enhancing signal-to-noise ratios, and pseudo-random trajectories boost global optimization capabilities in DD techniques. 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