A new focused crawler using an improved tabu search algorithm incorporating ontology and host information

To solve the problems of incomplete topic description and repetitive crawling of visited hyperlinks in traditional focused crawling methods,in this paper,we propose a novel focused crawler using an improved tabu search algorithm with domain ontology and host information(FCITS_OH),where a domain ontology is constructed by formal concept analysis to describe topics at the semantic and knowledge levels.To avoid crawling visited hyperlinks and expand the search range,we present an improved tabu search(ITS)algorithm and the strategy of host information memory.In addition,a comprehensive priority evaluation method based on Web text and link structure is designed to improve the assessment of topic relevance for unvisited hyperlinks.Experimental results on both tourism and rainstorm disaster domains show that the proposed focused crawlers overmatch the traditional focused crawlers for different performance metrics.

A Hybrid Multi-Objective Evolutionary Algorithm for Optimal Groundwater Management under Variable Density Conditions

In this paper, a new hybrid multi-objective evolutionary algorithm （MOEA）, the niched Pareto tabu search combined with a genetic algorithm （NPTSGA）, is proposed for the management of groundwater resources under variable density conditions. Relatively few MOEAs can possess global search ability contenting with intensified search in a local area. Moreover, the overall searching ability of tabu search （TS） based MOEAs is very sensitive to the neighborhood step size. The NPTSGA is developed on the thought of integrating the genetic algorithm （GA） with a TS based MOEA, the niched Pareto tabu search （NPTS）, which helps to alleviate both of the above difficulties. Here, the global search ability of the NPTS is improved by the diversification of candidate solutions arising from the evolving genetic algorithm population. Furthermore, the proposed methodology coupled with a density-dependent groundwater flow and solute transport simulator, SEAWAT, is developed and its performance is evaluated through a synthetic seawater intrusion management problem. Optimization results indicate that the NPTSGA offers a tradeoff between the two conflicting objectives. A key conclusion of this study is that the NPTSGA keeps the balance between the intensification of nondomination and the diversification of near Pareto-optimal solutions along the tradeoff curves and is a stable and robust method for implementing the multi-objective design of variable-density groundwater resources.

A Case Study of 3D Protein Structure Prediction with Genetic Algorithm and Tabu Search

This paper describes a case study of 3D protein structure prediction of six sequences from protein data bank （PDB） by genetic algorithm and tabu search （GATS）, where off-lattice AB model is considered as a simplified model of protein structure. The lowest-energy values required for forming the native conformation of proteins are searched by GATS, and then the coarse structures （i.e., simplified structure） of the proteins are obtained according to the multiple angle parameters corresponding to the lowest energies. All the coarse structures form single hydrophobic cores surrounded by hydrophilic residues, which stay on the right side of the actual characteristic of protein structure. It demonstrates that this approach can predict the 3D protein structure effectively.

Application of Interval Algorithm in Rural Power Network Planning

Rural power network planning is a complicated nonlinear optimized combination problem which based on load forecasting results, and its actual load is affected by many uncertain factors, which influenced optimization results of rural power network planning. To solve the problems, the interval algorithm was used to modify the initial search method of uncertainty load mathematics model in rural network planning. Meanwhile, the genetic/tabu search combination algorithm was adopted to optimize the initialized network. The sample analysis results showed that compared with the certainty planning, the improved method was suitable for urban medium-voltage distribution network planning with consideration of uncertainty load and the planning results conformed to the reality.

RTS-PGATS based approach for data-intensive scheduling in data grids

Grid computing is the combination of com- puter resources in a loosely coupled, heterogeneous, and geographically dispersed environment. Grid data are the data used in grid computing, which consists of large-scale data-intensive applications, producing and consuming huge amounts of data, distributed across a large number of machines. Data grid computing composes sets of independent tasks each of which require massive distributed data sets that may each be replicated on different resources. To reduce the completion time of the application and improve the performance of the grid, appropriate computing resources should be selected to execute the tasks and appropriate storage resources selected to serve the files required by the tasks. So the problem can be broken into two sub-problems： selection of storage resources and assignment of tasks to computing resources. This paper proposes a scheduler, which is broken into three parts that can run in parallel and uses both parallel tabu search and a parallel genetic algorithm. Finally, the proposed algorithm is evaluated by comparing it with other related algorithms, which target minimizing makespan. Simulation results show that the proposed approach can be a good choice for scheduling large data grid applications.

Free-floating bike-sharing green relocation problem considering greenhouse gas emissions

This paper introduces the problem of green bike relocation considering greenhouse gas emissions in free-floating bike-sharing systems(FFBSSs)and establishes a mathematical model of the problem.This model minimizes the total imbalance degree of bikes in the FFBSS and the greenhouse gas emissions generated by relocation in the FFBSS.Before the relocation phase,the FFBSS is divided into multiple relocation areas using a two-layer clustering method to reduce the scale of the relocation problem.In the relocation phase,the relocation route problem is converted into a pickup and delivery vehicle-routing problem.Then,an adaptive variable neighbourhood tabu search algorithm with a three-dimensional tabu list is proposed,which can simultaneously solve the relocation problem and the routing problem.A computational study based on the actual FFBSS used in Shanghai shows that this method can effectively solve the green relocation problem of FFBSSs.

Optimal location of interline power flow controller for controlling multi transmission line： A new integrated technique

In this paper, an interline power flow controller （IPFC） is used for controlling multi transmission lines. However, the optimal placement of IPFC in the transmis-sion line is a major problem. Thus, we use a combination of tabu search （TS） algorithm and artificial neural network （ANN） in the proposed method to find out the best placement locations for IPFC in a given multi transmission line system. TS algorithm is an optimization algorithm and we use it in the proposed method to determine the optimum bus combination using line data. Then, using the optimum bus combination, the neural network is trained to find out the best placement locations for IPFC. Finally, IPFC is connected at the best locations indicated by the neural network. Furthermore, using Newton-Raphson load flow algorithm, the transmission line loss of the IPFC connected bus is analyzed. The proposed methodology is implemen- ted in MATLAB working platform and tested on the IEEE-14 bus system. The output is compared with the genetic algorithm （GA） and general load flow analysis. The results are validated with Levenberg-Marquardt back propagation and gradient descent with momentum network training algorithm.