Mergos Panagiotis E, Yang Xin-She
Department of Civil Engineering, City, University of London, London, EC1V 0HB UK.
Structural Engineering, Research Centre for Civil Engineering Structures, City, University of London, London, EC1V 0HB UK.
Soft comput. 2021;25(22):14429-14447. doi: 10.1007/s00500-021-06230-1. Epub 2021 Sep 12.
The flower pollination algorithm (FPA) is a highly efficient metaheuristic optimization algorithm that is inspired by the pollination process of flowering species. FPA is characterised by simplicity in its formulation and high computational performance. Previous studies on FPA assume fixed parameter values based on empirical observations or experimental comparisons of limited scale and scope. In this study, a comprehensive effort is made to identify appropriate values of the FPA parameters that maximize its computational performance. To serve this goal, a simple non-iterative, single-stage sampling tuning method is employed, oriented towards practical applications of FPA. The tuning method is applied to the set of 28 functions specified in IEEE-CEC'13 for real-parameter single-objective optimization problems. It is found that the optimal FPA parameters depend significantly on the objective functions, the problem dimensions and affordable computational cost. Furthermore, it is found that the FPA parameters that minimize mean prediction errors do not always offer the most robust predictions. At the end of this study, recommendations are made for setting the optimal FPA parameters as a function of problem dimensions and affordable computational cost.
花朵授粉算法（FPA）是一种高效的元启发式优化算法，其灵感来源于开花植物的授粉过程。FPA的特点是公式简单且计算性能高。以往对FPA的研究基于经验观察或有限规模和范围的实验比较来假设固定的参数值。在本研究中，我们进行了全面的努力来确定FPA参数的合适值，以最大化其计算性能。为实现这一目标，我们采用了一种简单的非迭代、单阶段采样调优方法，该方法面向FPA的实际应用。该调优方法应用于IEEE-CEC'13中针对实参数单目标优化问题指定的28个函数集。研究发现，FPA的最优参数显著依赖于目标函数、问题维度和可承受的计算成本。此外，研究还发现，使平均预测误差最小的FPA参数并不总是能提供最稳健的预测。在本研究结束时，我们给出了根据问题维度和可承受的计算成本设置FPA最优参数的建议。
