Earticle

Cat swarm optimization (CSO) is a novel meta-heuristic for evolutionary optimization algorithms based on swarm intelligence. CSO imitates the behavior of cats through two sub-modes: seeking and tracing. Previous studies have indicated that CSO algorithms outperform other well-known meta-heuristics, such as genetic algorithms and particle swarm optimization, because of complexity, sometimes the pure CSO takes a long time to converge to reach to optimal solution. For improving the convergence of CSO with better accuracy and less computational time, this study presents an improvement structure of cat swarm optimization (ICSO), capable of improving search efficiency within the problem space under the conditions of a small population size and a few iteration numbers. In this paper, an improved algorithm is presented by mixing two concepts, first concept found in parallel cat swarm optimization (PCSO) method for solving numerical optimization problems. The parallel cat swarm optimization (PCSO) method is an optimization algorithm designed to solve optimization problems Based on cats’ cooperation and competition for improving the convergence of Cat Swarm Optimization,, the second concept found in Average-Inertia Weighted CSO (AICSO) by adding a new parameter to the velocity update equation as an inertia weight and used a new form of the position update equation in the tracing mode of algorithm. The performance of ICSO is sensitive to the control parameters selection. The experimental results show that the proposed algorithm gets higher accuracy than the existing methods and requires less computational time and has much better convergence than pure CSO, and the proposed effective algorithm can provide the optimum block matching in a very short time, finding the best solution in less iteration and suitable for video tracking applications.

Block matching (BM) motion estimation plays a very important role in video coding. In a BM approach, image frames in a video sequence are divided into blocks. For each block in the current frame, the best matching block is identified inside a region of the previous frame, aiming to minimize the mean square error (MSE). Unfortunately, the MSE evaluation is computationally expensive and represents the most consuming operation in the BM process. Therefore, BM motion estimation can be approached as an optimization problem, where the goal is to find the best matching block within a search space. Recently, several fast BM algorithms have been proposed to reduce the number of MSE operations by calculating only a fixed subset of search locations at the price of poor accuracy. The parallel cat swarm optimization (PCSO) & enhanced parallel cat swarm optimization (EPCSO) methods are an optimization algorithms designed to solve numerical optimization problems under the conditions of a small population size and a few iteration numbers. In this paper, a new algorithm based on Hybrid Cat Swarm Optimization (HCSO) is proposed to reduce the number of search locations in the BM process. In proposed algorithm, the computation of search locations is drastically reduced by adopting a fitness calculation strategy which indicates when it is feasible to calculate or only estimate new search locations. Conducted simulations show that the proposed method achieves the best balance over other fast BM algorithms, in terms of both estimation accuracy and computational time and find the optimal solutions in a very short time.