Performance Comparison of Population-Based Meta-Heuristic Algorithms in Affine Template Matching

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. (c) 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.