scholarly journals Focused Crawler Strategy Based on Improved Energy Landscape Paving Algorithm

2020 ◽  
Author(s):  
Jingfa Liu ◽  
Wei Zhang ◽  
Zhihe Yang ◽  
Ziang Liu
Keyword(s):  
Global Optimization ◽  
Semantic Analysis ◽  
Energy Landscape ◽  
Optimization Method ◽  
Web Pages ◽  
Web Content ◽  
Local Optima ◽  
Link Structure ◽  
Focused Crawlers ◽  
Energy Landscape Paving

The traditional crawlers have difficulty in implementing semantic analysis. Therefore, the focused crawler technologies with topic preference characteristics have received many attentions in the recent years. To increase the precision of focused crawlers and prevent “topic drifting”, this paper adopts the comprehensive relevancy evaluation (CRE) of hyperlinks based on the combination of web content and link structure. In addition, the improved version of the energy landscape paving (ELP) algorithm that is a class of metropolis-sampling-based global optimization method is proposed to avoid the focused crawler falling into local optima. By incorporating the CRE strategy into the improved ELP, a novel focused crawler strategy denoted by IELP is proposed. The experimental results on rainstorm disasters domain show that the precision of the proposed focused crawler is obviously promoted compared to other focused crawlers in literature, illustrating the ability of the IELP to retrieve topic-related web pages.

Web Recommendation Based on Unified Collaborative Filtering

2011 ◽  
Vol 219-220 ◽  
pp. 887-891
Author(s):  
Jiang Zhong ◽  
Yi Feng Cheng ◽  
Shi Tao Deng
Keyword(s):  
Collaborative Filtering ◽  
Semantic Analysis ◽  
Back Propagation ◽  
Web Usage Mining ◽  
Probabilistic Latent Semantic Analysis ◽  
Web Pages ◽  
Web Content ◽  
Time Prediction ◽  
Web Usage ◽  
Latent Features

Web usage mining technique is widely used for Web recommendation, which customizes Web content to user-preferred style. Traditional techniques of Web usage mining can only discover usage pattern explicitly. In order to employ the users’ feature and web pages’ attributes to get more accuracy recommendation, we propose a unified collaborative filtering model for web recommendation which combined the latent and external features of users and web page through back propagation neural networks. In the algorithm, we employ Probabilistic Latent Semantic Analysis (PLSA) method to get latent features. The main advantages of this technique over standard memory-based methods are the higher accuracy, constant time prediction, and an explicit and compact model representation. The preliminary experimental evaluation shows that substantial improvements in accuracy over existing methods can be obtained.

Energy landscape paving with local search for global optimization of the BLN off-lattice model

2014 ◽  
Vol 64 (4) ◽  
pp. 603-610 ◽  
Author(s):  
Jingfa Liu ◽  
Weibo Huang ◽  
Wenjie Liu ◽  
Beibei Song ◽  
Yuanyuan Sun ◽  
...  
Keyword(s):  
Global Optimization ◽  
Local Search ◽  
Lattice Model ◽  
Energy Landscape ◽  
Energy Landscape Paving

scholarly journals PS-FW: A Hybrid Algorithm Based on Particle Swarm and Fireworks for Global Optimization

2018 ◽  
Vol 2018 ◽  
pp. 1-27 ◽  
Author(s):  
Shuangqing Chen ◽  
Yang Liu ◽  
Lixin Wei ◽  
Bing Guan
Keyword(s):  
Global Optimization ◽  
Hybrid Algorithm ◽  
Optimization Problems ◽  
Novel Mutation ◽  
Particle Swarm ◽  
Iteration Process ◽  
Optimization Method ◽  
High Dimensional ◽  
Local Optima ◽  
Fireworks Algorithm

Particle swarm optimization (PSO) and fireworks algorithm (FWA) are two recently developed optimization methods which have been applied in various areas due to their simplicity and efficiency. However, when being applied to high-dimensional optimization problems, PSO algorithm may be trapped in the local optima owing to the lack of powerful global exploration capability, and fireworks algorithm is difficult to converge in some cases because of its relatively low local exploitation efficiency for noncore fireworks. In this paper, a hybrid algorithm called PS-FW is presented, in which the modified operators of FWA are embedded into the solving process of PSO. In the iteration process, the abandonment and supplement mechanism is adopted to balance the exploration and exploitation ability of PS-FW, and the modified explosion operator and the novel mutation operator are proposed to speed up the global convergence and to avoid prematurity. To verify the performance of the proposed PS-FW algorithm, 22 high-dimensional benchmark functions have been employed, and it is compared with PSO, FWA, stdPSO, CPSO, CLPSO, FIPS, Frankenstein, and ALWPSO algorithms. Results show that the PS-FW algorithm is an efficient, robust, and fast converging optimization method for solving global optimization problems.

scholarly journals Real-Coded Genetic Algorithm for Solving Generalized Polynomial Programming Problems

2007 ◽  
Vol 11 (4) ◽  
pp. 358-364 ◽  
Author(s):  
Jui-Yu Wu ◽  
◽  
Yun-Kung Chung
Keyword(s):  
Genetic Algorithm ◽  
Global Optimization ◽  
Global Solution ◽  
Solution Space ◽  
Inequality Constraints ◽  
Optimization Method ◽  
Local Optima ◽  
Polynomial Programming ◽  
Generalized Polynomial ◽  
Real Coded Genetic Algorithm

Generalized polynomial programming (GPP) is a nonlinear programming (NLP) method based on a nonconvex objective function, which is subject to nonconvex inequality constraints. Hence, a GPP problem has multiple local optima in its constrained solution space. General NLP techniques use local optimization, and therefore do not easily solve GPP problems. Some deterministic global optimization approaches have been developed to overcome this drawback of NLP methods. Although these approaches yield a global solution to a GPP problem, they can be mathematically tedious. Therefore, this study presents a real-coded genetic algorithm (RGA), which is a stochastic global optimization method, to find a global solution to a GPP problem. The proposed RGA is used to solve a set of GPP problems. The best solution obtained by the RGA is compared with the known global solution to each test problem. Numerical results show that the proposed RGA converges to a global solution to a GPP problem.

scholarly journals A Kriging-Assisted Multi-Objective Constrained Global Optimization Method for Expensive Black-Box Functions

Mathematics ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 149
Author(s):  
Yaohui Li ◽  
Jingfang Shen ◽  
Ziliang Cai ◽  
Yizhong Wu ◽  
Shuting Wang
Keyword(s):  
Global Optimization ◽  
Pareto Frontier ◽  
Kriging Model ◽  
Optimization Method ◽  
Function Evaluation ◽  
Sampling Point ◽  
Constrained Global Optimization ◽  
Multi Objective ◽  
Sample Data ◽  
Sampling Points

The kriging optimization method that can only obtain one sampling point per cycle has encountered a bottleneck in practical engineering applications. How to find a suitable optimization method to generate multiple sampling points at a time while improving the accuracy of convergence and reducing the number of expensive evaluations has been a wide concern. For this reason, a kriging-assisted multi-objective constrained global optimization (KMCGO) method has been proposed. The sample data obtained from the expensive function evaluation is first used to construct or update the kriging model in each cycle. Then, kriging-based estimated target, RMSE (root mean square error), and feasibility probability are used to form three objectives, which are optimized to generate the Pareto frontier set through multi-objective optimization. Finally, the sample data from the Pareto frontier set is further screened to obtain more promising and valuable sampling points. The test results of five benchmark functions, four design problems, and a fuel economy simulation optimization prove the effectiveness of the proposed algorithm.

Semantic Web mining for Content-Based Online Shopping Recommender Systems

2019 ◽  
Vol 15 (4) ◽  
pp. 41-56 ◽  
Author(s):  
Ibukun Tolulope Afolabi ◽  
Opeyemi Samuel Makinde ◽  
Olufunke Oyejoke Oladipupo
Keyword(s):  
Semantic Web ◽  
Recommender Systems ◽  
Web Mining ◽  
Online Shopping ◽  
Semantic Analysis ◽  
Second Phase ◽  
Web Content ◽  
Textual Data ◽  
Content Mining ◽  
Bayes Algorithm

Currently, for content-based recommendations, semantic analysis of text from webpages seems to be a major problem. In this research, we present a semantic web content mining approach for recommender systems in online shopping. The methodology is based on two major phases. The first phase is the semantic preprocessing of textual data using the combination of a developed ontology and an existing ontology. The second phase uses the Naïve Bayes algorithm to make the recommendations. The output of the system is evaluated using precision, recall and f-measure. The results from the system showed that the semantic preprocessing improved the recommendation accuracy of the recommender system by 5.2% over the existing approach. Also, the developed system is able to provide a platform for content-based recommendation in online shopping. This system has an edge over the existing recommender approaches because it is able to analyze the textual contents of users feedback on a product in order to provide the necessary product recommendation.

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