Grey wolf optimizer-based learning automata for solving block matching problem

Block matching problem is of great importance, and it is the basic element of many computer vision systems such as video compression, object tracking, motion analysis, and traffic control. This paper proposes a novel grey wolf optimizer (GWO) algorithm based on learning automata (LA) to solve block matching problem for motion estimation. Two main contributions are presented in this paper. Firstly, for improving the exploration and exploitation abilities of the GWO technique, an enhanced GWO method based on LA algorithm is proposed. LA is integrated in the GWO to learn the objective function and decide whether it is an unimodal or multimodal function. Unimodal function needs a good exploitation of promising area in the search space. However, multimodal function requires high exploration ability. The classification obtained using LA is then used to create new solutions in the appropriate areas. In the creation phase, two equations are used. The first one is based on a Gaussian distribution, to enrich the exploitation for the unimodal function, and the second is based on a random distribution to support the exploration in multimodal function. The second contribution of this paper consists of applying our enhanced GWO algorithm in block matching problem. The proposed algorithm is validated on two phases. Firstly, we evaluate our enhanced GWO algorithm on eight well-known benchmark functions. The reported results show that the enhanced GWO algorithm has the potential to improve the optimization abilities of the conventional GWOs. Then, the proposed enhanced GWO algorithm-based block matching is tested on six video sequences and compared with several state-of-the-art methods. Simulation results show the effectiveness of the proposed BM algorithm and prove the applicability of our enhanced GWO to real-world optimization problem.