用动力系统理论分析蝙蝠算法参数范围，揭示探索与利用的动态机制。
Analysis of Parameter Settings for the Bat Algorithm Using Variance Evolution
- 基于种群方差演化理论推导蝙蝠算法参数合理区间。
- 理论预测与数值实验结果一致，验证了参数设置的有效性。
- 适合对算法机理和参数设计感兴趣的优化研究者。
进化算法和元启发式算法中的参数设置至关重要，因为参数值会影响算法性能。尽管许多数值实验表明某算法在实践中表现良好，但通常缺乏对参数设置的理论分析。本文通过动力系统理论与种群方差演化分析，为蝙蝠算法提供了参数范围的理论依据，并证明数值实验结果与理论边界一致。该分析从方差演化、探索与利用的转换以及收敛行为等角度，揭示了算法特性，为理解算法内在机制提供了新视角。
原文摘要 · Abstract (English)
Parameter settings in evolutionary algorithms and metaheuristics are important because such parameter values can influence the performance of algorithms under evaluation. For a given algorithm, there are many different numerical experiments to show that the algorithm can work well in practice; however, in most cases there is no theoretical analysis of parameter settings. In this work, we show that theoretical analysis using the theory of dynamical systems and evolution of population variance can give some good results in terms of parameter ranges for the bat algorithm. We also show that results from numerical experiments are consistent with theoretical bounds. Such analyses can provide good insights from different perspectives about the algorithmic characteristics such as variance evolution, transition between exploration and exploitation as well as convergence behaviour.
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