The genetic algorithm is the intelligence computational method widely used in solving the optimization problems, however many current genetic algorithms has premature convergence and stagnation behavior while solving complex question. This may because the algorithm had lost the ability of discovering that new or potential genetic material before convergence limited its search to solution space with wide range. Enlightened by hierarchical fair competition widely existed in nature, this paper has studied a model based on Pyramid model of hierarchical fair competition which uses pipeline structure divided sub-population rank according to fitness gradient, guarantee the recent discovery potential genetic material to obtain full development by maintaining a certain global selection pressure under reducing the local selection pressure of sub-population. Unlike traditional genetic algorithm's strategy which attempts to jumps out local optima region from high evolutional population included a high similarity "building block", the Pyramid model continuously maintain the sub-population with the medium fitness value to ensure keeping the multiplicity of the population by the strategy which the new local optima is bottom-up bred and processed unceasingly. Finally, this paper confirms the validity of the algorithm and the multiplicity of the population by HIFF128/256 problems based on binary encoding, and compared the improved algorithm from the solution quality and standard deviation of the population with traditional genetic algorithm, proves this improved algorithm can effectively alleviate premature convergence problem of the traditional genetic algorithm.
The process of retrieving desired or similar images from a large collection of images on the basis of features is referred as Content Based Image Retrieval (CBIR). In this paper, a integrated CBIR system is proposed using combined features and weighted similarity. The features include visual features of color, texture and shape and key text metadata. Some experimental simulations have been presented to show the accuracy and efficiency of the proposed retrieval method.
In order to improve the solution accuracy and the convergence of Population Migration Algorithm (PMA) and avoiding its prematurity, in this paper, we combined chaos theory with PMA. By introducing logistic mapping of chaos theory into PMA, we use the ergodicity, randomicity and regularity of chaos theory to attain an improved algorithm. The experimental results show that: by introducing the ergodicity, randomicity and regularity of chaos theory into PMA, the solution accuracy and the convergence property of PMA can effectively improve and effectively avoid prematurity phenomenon. The improved algorithm performs very well.
Seismic data is generated by a sharp pulse, which transforms into to Earth and is reflected by the layer status in the Earth. The Data is 3D or 2D. Because of transforming through the Earth, Seismic data has wide main lobe and strong side lobe, which is different from that of the sharp pulse. In order to recover the character that is similar to that of sharp pulse, the 2D and 3D seismic data is usually processed by the deconvolution. The data includes down going data and up going data mainly. The deconvolution is done by down going data in the general procedure. In this paper, it is done by up going data statistical distinctively. The new methods can get stable deconvolution operator and deconvolution result. It can be used to process the data that is contaminated high-frequency noise. The new approach can get well data to be used in the following process flow and bring a better result to interpretation and application.
