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.

One of the most important phases of speaker indexing is speaker clustering which aims to find the number of speakers in a speech document and merge the speech segments corresponding to a single speaker. The most critical source of problem in speaker clustering is the speech segments duration which may be so short that proper segment modeling becomes hard to achieve. An alternative suggestion in these situations is to adapt global models with new data instead of building the speaker models from the ground. In this paper we investigate two adaptation techniques in eigen-voice space for improving clustering performance especially for shorter speech utterances. These techniques were embedded in a clustering framework and evaluated on a set of domestic conversational speech. We have also compared the proposed methods with some other known techniques. The experiments show a considerable improvement in speaker clustering performance.

In this paper we study combinatorial segment visibility, and show how to compute and maintain it as the observer moves in the plane. There are n non-overlapping convex objects in the plane, and we have a segment observer among them. We first consider static case of the problem, in which the observer and objects are static, and then we study dynamic case of the problem, in which the observer can move among obstacles.