Beetle Swarm With Constrained Lévy Flight for Image Matching

Image matching is an essential part of many processes in practical industrial applications. Using optimization algorithms to optimize the actual problems in industrial production can lead to more efficient use of resources. This paper presents a new algorithm called Beetle Swarm with Constrained L e vy Flight (BSL) algorithm for solving problems in industrial production where image matching cannot be done quickly and accurately. This algorithm is based on the beetle antennae search algorithm. It combines the swarm intelligence algorithm with the constrained L e vy flight and quickly finds the optimal solution through the long-horned beetle’s judgment of the left and right odour concentration, which reduces the blindness of L e vy flight and dramatically improves the convergence speed of the algorithm. In the performance test, compared with other commonly used meta-heuristic algorithms, BSL shows stable performance with low time cost of convergence and better fitness results, which means BSL avoids the effect of random direction caused by a single beetle. In addition, this algorithm significantly improves the speed and accuracy of image matching in the application of Printed Circuit Board (PCB) defect detection. In practical application tests, the BSL algorithm was faster than the BAS algorithm, with a reduction in the fitness value. The subsequent robustness experiments further prove that the BSL algorithm has better noise immunity and is more suitable for application in actual production than Cuckoo Search (CS) and beetle antennae search (BAS). Note to Practitioners —This paper was motivated by the problem of matching images in PCB defect detection. Existing meta-heuristic approaches cannot achieve quick and accurate image matching due to the low convergence speed. This paper suggests a method that combines the beetle antennae search algorithm with the constrained L e vy flight. In this paper, we improve the L e vy flight mechanism to update the next position of each beetle to eliminate invalid solutions. Then, new directions are generated in combination with the odour concentration around the aspen whiskers, which reduces the blindness of L e vy flight to a certain extent and, indeed, leads to the optimal solution. We also incorporate the Normalized cross-correlation (NCC) algorithm to improve the matching speed and noise immunity. The application in automated optical inspection (AOI) shows that the algorithm significantly improved the speed and accuracy when applied to production.