Candle Flame Optimization: A Physics-Based Metaheuristic for Global Optimization

In this paper, the Candle Flame Optimization (CFO) algorithm is developed as a novel physics-based metaheuristic inspired by the dynamic behavior of a candle flame.CFO models the oscillatory and propagative nature of flames to balance exploration and exploitation in the search space.A rigorous mathematical formulation is constructed to accurately map the abstract concept of flame propagation into an optimization framework, ensuring an efficient search mechanism.The algorithm's performance is evaluated using 29 benchmark functions from the CEC 2017 test suite, providing a comprehensive assessment of its effectiveness.Experimental results show that CFO delivers superior optimization capabilities in terms of convergence speed, solution accuracy, and robustness.The algorithm is compared against nine state-of-the-art metaheuristic algorithms, with statistical analyses confirming CFO's significant superiority in most cases.The Wilcoxon Rank Sum test further verifies its statistical dominance.These findings indicate that CFO is an effective and efficient optimization algorithm suitable for various optimization applications across different scientific disciplines.