In this study, population‐based meta‐heuristic algorithms—artificial bee colony, differential evolution, particle swarm optimization, and real‐coded genetic algorithm—are applied to affine template matching for performance comparison. It is necessary to optimize six parameters for affine template matching. This is a combinatorial optimization problem, and the number of candidate solutions is very large. For such a problem, population‐based meta‐heuristic algorithms can efficiently search a global optimum. There is research that applies the algorithms to affine template matching. However, they select a specific algorithm without understanding the characteristics of affine template matching and comparing different algorithms. This means the selected algorithm may not be suitable for affine template matching. Hence, this research first analyzes the characteristics of affine template matching and compares the performance of the four algorithms. In addition, we propose a new method to measure population diversity for performance comparison. Finally, we confirmed that artificial bee colony achieves the best performance of the four methods. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

中文翻译：

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仿射模板匹配中基于种群的元启发式算法的性能比较

在这项研究中，基于群体的元启发式算法（人工蜂群，差分进化，粒子群优化和实编码遗传算法）被应用于仿射模板匹配以进行性能比较。有必要为仿射模板匹配优化六个参数。这是一个组合优化问题，候选解决方案的数量非常多。对于这样的问题，基于总体的元启发式算法可以有效地搜索全局最优值。有研究将该算法应用于仿射模板匹配。但是，他们选择特定的算法而不了解仿射模板匹配的特征并比较不同的算法。这意味着所选算法可能不适用于仿射模板匹配。因此，本研究首先分析了仿射模板匹配的特点，并比较了四种算法的性能。此外，我们提出了一种新的方法来衡量人口多样性以进行绩效比较。最后，我们确认了人工蜂群在四种方法中均表现最佳。©2020日本电气工程师学会。由Wiley Periodicals LLC发布。