انجمن کامپیوتر ایران

Job-shop scheduling problem is demonstrated as one of the NP-complete problems. To solve this problem, we propose two types of hybrid shuffled frog leaping algorithm. Hybrid algorithms are generated by combining the shuffled frog leaping and a local search method. Also a new local search method by combining two other simple local searches is proposed. The obtained results demonstrate that our proposed hybrid algorithms have a better performance than their nonhybrid competitors. Also a comparison among proposed hybrid shuffled frog leaping and hybrid genetic algorithms demonstrate that the hybrid shuffled frog leaping algorithms can be generated a better schedule than their genetic algorithm competitors. A caparison of the best obtained results with the results reported in the considered literature shows that our proposed algorithms have a comparable performance.

In this paper, we present a novel hybrid algorithm for binarization of badly illuminated document images. This algorithm locally enhances the document image and makes the gray levels of text and background pixels separable. Afterward a simple global binarization algorithm binarizes the enhanced image. The enhancement process is a novel method that uses a separate transformation function to map the gray level of each pixel into a new domain. For each pixel, the transformation function is determined using its neighboring pixels gray level. The proposed binarization algorithm is robust for wide variety of degraded document images. Evaluation over a set of degraded document images illustrates the effectiveness of our proposed binarization algorithm.

PSO, like many stochastic search methods, is very sensitive to efficient parameter setting. As modifying a single parameter may result in a large effect. In this paper, we propose a new a new learning automatabased approach for adaptive PSO parameter selection. In this approach three learning automata are utilized to determine values of each parameter for updating particles velocity namely inertia weight, cognitive and social components. Experimental results show that the proposed algorithms compared to other schemes such as SPSO, PSO-IW, PSO TVAC, PSO-LP, DAPSO, GPSO, and DCPSO have the same or even higher ability to find better local minima. In addition, proposed algorithms converge to stopping criteria significantly faster than most of the PSO algorithms.

Though numerous approaches have been proposed for face recognition. In this paper we propose a novel face recognition approach based on adaptively weighted patch Local Statistic in Multi dimensional (LMDS) when only one exemplar image per person is available. In this approach, a face image is decomposed into a set of equal-sized patches in a nonoverlapping way. In order to obtain Local Multi Dimensional Statistic Features in each patch, we calculated mean and standard deviation of all pixels along some directions. An adaptively weighting scheme is used to assign proper weights to each LMDS features to adjust the contribution of each local area of a face in terms of the quantity of identity information that a patch contains. An extensive experimental investigation is conducted using AR face databases covering face recognition under controlled/ideal conditions and different facial expressions. The system performance is compared with the performance of four benchmark approaches. The encouraging experimental results demonstrate that our approach can be used for face recognition and patch-based local statistic features provides a novel way for face.

Reversible Cellular Automata is applicable in cryptographic functions. A reversible CA can be obtained by employing reversible rules. In this paper, we propose 65280 two-dimensional reversible CA rules by compounding one-dimensional reversible rules. We produce these rules by alternately applying onedimensional CA rule f1 to the rows and applying another rule f2 to the columns of the configuration matrix of twodimensional CA. In addition to describing these rules, we consider the required cryptographic properties such as completeness, strict avalanche criteria, non-linearity, and differential-profile flatness for our proposed approach. According to the obtained results, 9463 rules are specified as appropriate rules for the purpose of applying in cryptographic functions. Only 510 rules do not satisfy any of the mentioned cryptographic properties and the other remain rules satisfy some of the properties.