Aiming for efficient coordination mechanisms for the distributed weighted set cover problem, we study from ordinal potential game theoretic learning and propose a Nash equilibrium selection algorithm (NESA). An ordinal potential game model is established, where the local utility function is designed by incorporating a greedy heuristic. To distinguish Nash equilibria of different global fitness, we further classify them into the inferior Nash equilibrium (INE) and the superior Nash equilibrium (SNE), and show that the optimal solution must be an SNE. High-quality SNE solutions are obtained by assigning each player a local stochastic rule based on its category and a finite memory. By demonstrating the existence of a finite improvement path from each INE to an SNE, we prove finite-time convergence of the NESA. Numerical experiments are carried out and comparisons against representative methods are presented, which demonstrate the effectiveness as well as the superiority of our methodology to the state-of-the-art.

Due to multiplicative speckle noise in Synthetic Aperture Radar(SAR) image and significant intensity difference between different data, it is difficult to match SAR and optical image accurately. In this paper, we propose a novel template matching method for SAR and optical image named multi-Dimensional Matching Histogram of Oriented Gradient (mDM-HOG). Firstly, in order to reduce the negative effect of speckle noise on gradient calculation, the ratio of exponentially weighted averages(ROEWA) operator is introduced to calculate the gradient magnitude and orientation in SAR image. Then, using the obtained gradient information, we extract the 3-D pixelwise HOG feature for both images. Finally, we separate the 3-D feature map to nine sub-maps and measure the similarity of the sub-maps to obtain the template matching result. The experimental result shows that in comparison with the existing methods, the proposed template matching method has higher accuracy when locating the position of SAR image in optical image.

Ant Colony Optimization (ACO) algorithm is a novel meta-heuristic algorithm for the approximate solution of combinatorial optimization problems that has been inspired by the foraging behavior of real ant colonies. ACO has strong robustness and easy to combine with other methods in optimization, but it has the shortcomings of stagnation that limits the wide application to the various areas. In this paper, a hybrid ACO with Differential Evolution (DE) algorithm was proposed to overcome the above-mentioned limitations, and this algorithm was named DEACO. Considering the importance of ACO pheromone trail for ants exploring the candidate paths, DE was applied to optimize the pheromone trail in the basic ACO model. In this way, a reasonable pheromone trail between two neighboring cities can be formed, so as to lead the ants to find out the optimum tour. The proposed algorithm is tested with the Traveling Salesman Problem (TSP), and the experimental results demonstrate that the proposed DEACO is a feasible and effective ACO model in solving complex optimization problems.