Current Issue : January - March Volume : 2019 Issue Number : 1 Articles : 5 Articles
Teaching-learning-based optimization (TLBO) algorithm is a novel heuristic method which simulates the teaching-learning\nphenomenon of a classroom. However, in the later period of evolution of the TLBO algorithm, the lower exploitation ability and\nthe smaller scope of solutions led to the poor results. To address this issue, this paper proposes a novel version of TLBO that is\naugmented with error correction strategy and Cauchy distribution (ECTLBO) in which Cauchy distribution is utilized to expand\nthe searching space and error correction to avoid detours to achieve more accurate solutions. The experimental results verify that\nthe ECTLBO algorithm has overall better performance than various versions of TLBO and is very competitive with respect to\nother nine original intelligence optimization algorithms. Finally, the ECTLBO algorithm is also applied to path planning of\nunmanned aerial vehicle (UAV), and the promising results show the applicability of the ECTLBO algorithm for problem-solving....
In recent years, Intelligent Transportation Systems (ITS) have developed a lot. More and\nmore sensors and communication technologies (e.g., cloud computing) are being integrated into cars,\nwhich opens up a new design space for vehicular-based applications. In this paper, we present the\nSpatial Optimized Dynamic Path Planning algorithm. Our contributions are, firstly, to enhance the\neffective of loading mechanism for road maps by dividing the connected sub-net, and building a\nspatial index; and secondly, to enhance the effect of the dynamic path planning by optimizing the\nsearch direction. We use the real road network and real-time traffic flow data of Karamay city to\nsimulate the effect of our algorithm. Experiments show that our Spatial Optimized Dynamic Path\nPlanning algorithm can significantly reduce the time complexity, and is better suited for use as a\nreal-time navigation system. The algorithm can achieve superior real-time performance and obtain\nthe optimal solution in dynamic path planning....
The common failure mechanism for brittle rocks is known to be axial splitting\nwhich happens parallel to the direction of maximum compression. One\nof the mechanisms proposed for modelling of axial splitting is the sliding\ncrack or so called, â??wing crackâ? model. Fairhurst-Cook model explains this\nspecific type of failure which starts by a pre-crack and finally breaks the rock\nby propagating 2-D cracks under uniaxial compression. In this paper, optimization\nof this model has been considered and the process has been done by\na complete sensitivity analysis on the main parameters of the model and excluding\nthe trends of their changes and also their limits and â??peak pointsâ?.\nLater on this paper, three artificial intelligence algorithms including Particle\nSwarm Intelligence (PSO), Ant Colony Optimization (ACO) and genetic algorithm\n(GA) has been used and compared in order to achieve optimized sets\nof parameters resulting in near-maximum or near-minimum amounts of\nwedging forces creating a wing crack....
Collaborative filtering algorithm is the most widely used and recommended\nalgorithm in major e-commerce recommendation systems nowadays. Concerning\nthe problems such as poor adaptability and cold start of traditional\ncollaborative filtering algorithms, this paper is going to come up with improvements\nand construct a hybrid collaborative filtering algorithm model\nwhich will possess excellent scalability. Meanwhile, this paper will also optimize\nthe process based on the parameter selection of genetic algorithm and\ndemonstrate its pseudocode reference so as to provide new ideas and methods\nfor the study of parameter combination optimization in hybrid collaborative\nfiltering algorithm....
We consider a sliding window W over a stream of characters from some alphabet of constant\nsize. We want to look up a pattern in the current sliding window content and obtain all positions of\nthe matches. We present an indexed version of the sliding window, based on a suffix tree. The data\nstructure of size ... has optimal time queries ... and amortized constant time updates,\nwhere m is the length of the query string and occ is its number of occurrences....
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