High Search Precision Research Articles

Optimizing the Process Parameters for Eco-Friendly Minimum Quantity Lubrication-Turning of AISI 4340 Alloy with Nano-Lubricants Using a Grey Wolf Optimization Approach

Optimization of turning process parameters in minimum quantity lubrication (MQL)-assisted mode is obligatory for enhanced efficiency and product integrity. However, little attention has been paid to analyzing situations where high search precision is needed when evaluating the optimal turning process parameters. This article applies the grey wolf optimization (GWO) approach to optimize the turning of parameters AISI 4340 alloy to enhance cutting force, surface roughness and tool wear. Based on the literature data, turning was conducted with MQL-assisted CuO and Al2O3 nanofluids. The problem was formulated by mimicking six wolves in six different objective functions. The objective functions have the responses as the dependent variables and the parameters including cutting speed, feed and cutting depth as independent variables. The hunting behavior of the wolves as they encircle the prey is interpreted to the machining task optimization. It involves three hierarchically-evaluated guides- the alpha, beta and delta wolves- positioned optimally and other wolves are updated accordingly. The cutting speed, feed and cutting depth are bound in the lower and upper limits as 80 and 140m/min, 0.05 and 0.20m/m/rev and 0.1 and 0.4mm, respectively. The grey wolf optimization algorithm optimizes the parameters to yield the cutting force, surface roughness and tool wear using Al2O3 as 199.50N, -23.54mm and 0.06mm, respectively. For the CuO, the corresponding cutting force, surface roughness and tool wear, the CuO, Al2O3 and CuO nano lubricants produced the best results. However, for mass production, selective use of CuO and Al2O3 should be made. The usefulness of this research endeavor is to help process engineers to make decisions in producing low-cost components in manufacturing.

Practical Multi-Keyword Ranked Search with Time- Based Access Control over Encrypted Cloud Data

The recent trends suggest that there is an increase in the inclination towards storing data in the cloud due to explosive and massive growth of the volume of the data in the cloud computing environment. It helps them to reduce their computational and storage costs but also undeniably brought in concerns about security and privacy as the owners of the highly sensitive data lose control of it directly. The sensitive data could include electronic-based medical records, confidential fiscal documents, etc. An increased distrust about storage of files in a third-party service provider of cloud resources would contradict the very same reason for which cloud storage facilities were introduced. That’s because we cannot deny the fact that cloud based storage systems offer on- demand and ubiquitous access to flexible storage and computational resources. The keyword ranked search methodologies used in the existing systems mainly focus on enhancing and enriching the efficiency of searching the files and their respective functionalities but a lack of straight forward analysis of security and issues with providing access control have not been addressed. To address these disadvantages, in this paper, we propose an efficient Multi-Keyword Ranked Search scheme with Fine-grained access control (MRSF).MRSF is a methodology which can combine matching of coordinates technique with Term Frequency-Inverse Document Frequency (TF-IDF) to thereby achieve a highly precise retrieval of any cipher text of interest. It also improves the secure k-nearest neighbors (kNN) method. By utilizing an access strategy which is polynomial based, it can effectively refine the search privileges of the users’. Professional security analysis proves that MRSF is secure with respect to safeguarding the secrecy of outsourced data and the privacy of tokens and indices. Along with this enhanced methodology of ranked search scheme, a time limit based access control feature has also been proposed to ensure that the adaptive attackers are stalled from giving prolonged access to the data files. Session expiry will ensure security of data and that is to be achieved by providing a time window for the file retrieval. Extensive experiments also show that MRSF reaches higher search precision and many more functionalities when compared to the existing systems.

Reliability analysis and power quality improvement model using enthalpy based grey wolf optimizer

With the modern era of a restructured power system environment, there are various power quality issues faced when it is being transferred within different utilities. To overcome these issues and deliver maximum effective power maintaining its quality in terms of Voltage Stability, Losses, distortion in the signal received etc. is the major challenge. This paper presents the enthalpy based Grey Wolf Optimisation (GWO) model and aims to solve the issues of the node voltage deviation power system, feeder power losses, power factor, and total harmonic distortion in a power system. The results of the model presented is compared with different conventional algorithms and found to be most suitable due to its simplicity, faster convergence and high search precision. The analysis of the presented method is performed on IEEE 30 and 57 test bus systems in MATLAB/SIMULINK environment. When compared with the conventional models, our proposed method shows 0.49, 0.69 and 1.41% better fitness accuracy for IEEE 30 and 3.23, 2.37 and 1.45% better for IEEE 57 than PSO, ABC and GA respectively. The proposed method has proved to be effective in terms of all the objectives stated above and attaining the maximum quality of power.

A modified particle swarm optimization algorithm based on velocity updating mechanism

Abstract Due to less parameters and simple operations in PSO, PSO has attracted the attention of many researchers. However, it amy fall into local optimum and the search precision is not high. Therefore, this paper introduces an improved PSO (CNPSO). There are two new formulas: (1) when the individual is not the best particle, the gbest is replaced by a particle, which is selected from a set based on the probability calculation. Such mechanism can help the algorithm to escape local position. (2) when the individual is the best particle, it is combined with a randomly selected particle to generate a convex combination, after that opposite learning is adopted to get a reverse solution. This operation can maintain the diversity of population. Finally, CNPSO is compared with several algorithms in three experiments, and used to optimize the spring design problem. The results indicate CNPSO has good performance and high search precision.

Distributed data-dependent locality sensitive hashing

Locality sensitive hashing (LSH) is a popular algorithm for approximate nearest neighbour (ANN) search. As LSH partitions vector space uniformly and the distribution of vectors is usually non-uniform, it poorly fits real datasets and has limited efficiency. In this paper, we propose a novel data-dependent locality sensitive hashing (DP-LSH) algorithm, which has a two-level structure. In the first level, we train a number of cluster centres, and use the centres to divide the dataset. So the vectors in each cluster have near uniform distribution. In the second level, we construct LSH tables for each cluster. Given a query, we first determine a few clusters that it belongs to, and perform ANN search in the corresponding LSH tables. Furthermore, we present an optimised distributed scheme and a distributed DP-LSH algorithm. Experimental results on the reference datasets show that the search speed of DP-LSH can be increased by 48 times compared to E2LSH, while keeping high search precision; and the distributed DP-LSH can further improve search efficiency.

Distributed data-dependent locality sensitive hashing

Locality sensitive hashing (LSH) is a popular algorithm for approximate nearest neighbour (ANN) search. As LSH partitions vector space uniformly and the distribution of vectors is usually non-uniform, it poorly fits real datasets and has limited efficiency. In this paper, we propose a novel data-dependent locality sensitive hashing (DP-LSH) algorithm, which has a two-level structure. In the first level, we train a number of cluster centres, and use the centres to divide the dataset. So the vectors in each cluster have near uniform distribution. In the second level, we construct LSH tables for each cluster. Given a query, we first determine a few clusters that it belongs to, and perform ANN search in the corresponding LSH tables. Furthermore, we present an optimised distributed scheme and a distributed DP-LSH algorithm. Experimental results on the reference datasets show that the search speed of DP-LSH can be increased by 48 times compared to E2LSH, while keeping high search precision; and the distributed DP-LSH can further improve search efficiency.

A New Bionic Swarm Intelligence Optimization: Construction and Application of Modified Moth-Flame Optimization Algorithm

The Moth-flame optimization algorithm is a new bionic swarm intelligence algorithm. But the moth’s behavior has a large number of random states and need to repeatedly test in the algorithm, which takes longer. In this paper, the basic principle of the Moth-flame algorithm is analyzed deeply, and proposed a modified Moth-flame algorithm. Its core is to improve and optimize the adaptive mechanism for the number of flames, and to change the flame adaptive mechanism along the straight line to decrease along the curve, so as to improve the convergence speed of the adaptive flame number; Given the ability of study to the moths when moths update position, that all moths update the position with reference to the best flame, so as to improve the search accuracy. By testing 8 classical test functions and 1 engineering example, it is proved that the modified Moth-flame algorithm has the advantages of faster convergence speed, higher search precision and avoiding local optima. The significant computational efficiency and precision of the improved moth-flame algorithm can be used to improve the ability to solve practical engineering problems.

Distributed high-dimensional similarity search approach for large-scale wireless sensor networks

Similarity search in high-dimensional space has become increasingly important in many wireless sensor network applications. However, existing approaches to similarity search is based on the premise that sensed data are centralized to deal with, or sensed data are simple enough to be stored in a relational database. Different from the previous work, we propose a distributed approximate similarity search algorithm to retrieve similar high-dimensional sensed data for query in wireless sensor networks. First, the sensors are divided into several clusters using the distributed clustering method. Furthermore, the sink transmits the compressed hash code set to the cluster heads. Finally, the estimated similarity score is compared with a specified threshold to filter out irrelevant sensed data. Therefore, the higher search precision and energy efficiency can be achieved. Extensive simulation results show that the proposed algorithms provide significant performance gains in terms of precision and energy efficiency compared with the existing algorithms.

Improved Differential Evolution Algorithm based on Dynamic Adaptive Strategies and Control Parameters

To solve the slow convergence speed, low precision in later period and tedious parameter setting of differential evolution when applied to complex optimization functions, an improved differential evolution algorithm (dn-DADE) based on dynamic adaptive strategy is proposed. Firstly, the elite solutions of current population are utilized in the new mutation strategy (DE/current-to-dnbest/1) to guide the search direction, and then these optional elite solutions tend to the global optimal solution in the late stage of evolution to balance the diversity of population and convergence speed. Secondly, the adaptive update strategies of scaling factor and crossover factor are designed for control parameter values self-adapting at different search stages, thus improve the stability and robustness of the algorithm. A set of 14 benchmark functions is adopted to test the performance of the proposed algorithm. The results show that dn-DADE algorithm has the advantages of remarkable optimizing ability, higher search precision, faster convergence speed and outperforms several state-of-the-art improved differential evolution algorithms in terms of the main performance indexes.

Bi-Level Locality Sensitive Hashing Index Based on Clustering

Locality sensitive hashing is the most popular algorithm for approximate nearest neighbor search. As LSH partitions vector space uniformly and the distribution of vectors is usually non-uniform, it poorly fits real dataset and has limited search performance. In this paper, we propose a new Bi-level locality sensitive hashing algorithm, which has two-level structures to perform approximate nearest neighbor search in high dimensional spaces. In the first level, we train a number of cluster centers, then use the cluster centers to divide the dataset into many clusters and the vectors in each cluster has near uniform distribution. In the second level, we construct locality sensitive hashing tables for each cluster. Given a query, we determine a few clusters that it belongs to with high probability, and then perform approximate nearest neighbor search in the corresponding locality sensitive hash tables. Experimental results on the dataset of 1,000,000 vectors show that the search speed can be increased by 48 times compared to Euclidean locality sensitive hashing, while keeping high search precision.

Model of Improving Searching Precision For Interactive Users with Searching Requirements

This paper presents a model based on analysis on interactive users search needs. This model changes from the analysis on user's behavior to the one on search content, which reduces to violate users privacy, and enables users to actively participate in determining the needs of search from the client end in the search engine, in addition to establishment of search demand model based on a large number of user feedback data mining in the search engine subsystem, as a result of higher search precision with less client spending and higher user s participation.

An Improved Adaptive Genetic Algorithm Based on Hormone Modulation Mechanism for Job-shop Scheduling Problem

An improved adaptive genetic algorithm (IAGA) for solving the minimum makespan problem of job shop scheduling problem (JSP) is presented. Though the traditional genetic algorithm (GA) exhibits implicit parallelism and can retain useful redundant information about what is learned from previous searches by its representation in individuals in the population, yet GA may lose solutions and substructures due to the disruptive effects of genetic operators and is not easy to regulate GA’s convergence. The proposed IAGA is inspired from hormone modulation mechanism, and then the adaptive crossover probability and adaptive mutation probability are designed. The proposed IAGA is characterized by simplifying operations, high search precision, overcoming premature phenomenon and slow evolution. The proposed method by employing operation-based encoding is effectively applied to solve a dynamic job-shop scheduling problem (DJSP) and a complicated contrastive experiment of JSP in manufacturing system. Meanwhile, in order to ensure to create a feasible solution, a new method for crossover operation is adopted, named, partheno-genetic operation (PGO). The computational results validate the effectiveness of the proposed IAGA, which can not only find optimal or close-to-optimal solutions but can also obtain both better and more robust results than the existing genetic algorithms reported recently in the literature. By employing IAGA, machines can be used more efficiently, which means tasks can be allocated appropriately, production efficiency can be improved, and the production cycle can be shortened efficiently.

An improved adaptive genetic algorithm based on hormone modulation mechanism for job-shop scheduling problem

An improved adaptive genetic algorithm (IAGA) for solving the minimum makespan problem of job-shop scheduling problem (JSP) is presented. Though the traditional genetic algorithm (GA) exhibits implicit parallelism and can retain useful redundant information about what is learned from previous searches by its representation in individuals in the population, yet GA may lose solutions and substructures due to the disruptive effects of genetic operators and is not easy to regulate GA’s convergence. The proposed IAGA is inspired from hormone modulation mechanism, and then the adaptive crossover probability and adaptive mutation probability are designed. The proposed IAGA is characterized by simplifying operations, high search precision, overcoming premature phenomenon and slow evolution. The proposed method by employing operation-based encoding is effectively applied to solve a dynamic job-shop scheduling problem (DJSP) and a complicated contrastive experiment of JSP in manufacturing system. Meanwhile, in order to ensure to create a feasible solution, a new method for crossover operation is adopted, named, partheno-genetic operation (PGO). The computational results validate the effectiveness of the proposed IAGA, which can not only find optimal or close-to-optimal solutions but can also obtain both better and more robust results than the existing genetic algorithms reported recently in the literature. By employing IAGA, machines can be used more efficiently, which means that tasks can be allocated appropriately, production efficiency can be improved, and the production cycle can be shortened efficiently.

SKIP: an efficient search mechanism in unstructured P2P networks

Search efficiency and accuracy of resource are important considerations for search algorithm in peer-to-peer (P2P) network. Most search algorithms use flooding among neighbor nodes to search relevant resource. However, this usually causes great amount of redundant messages, which results in high search costs and low search precision. In this paper, we use vector space model (VSM) and relevance ranking algorithms to construct overlay network, and a novel search mechanism search with K-iteration preference (SKIP) based on semantic group for P2P networks is proposed to efficiently solve these problems. The key idea of SKIP is to reorder the semantic neighbors of nodes according to relevant scores and to utilize preference selection during the process of query. We analysis and implement the scheme and reveal that the SKIP provides a low overhead on topology maintenance, which can be effectively used in P2P searching and verify it outperformance in higher precision and lower search cost by comparing with current semantic-based searching mechanism gnutella-like efficient searching system (GES).

A novel web query automatic expansion based on rough set

One of important reasons caused low precision was presented, which was due to inaccurate express of the query. So a new method of automatic query expansion based on tolerance rough was put forward. In the algorithm, the uncertain connection between query terms and retrial documents was described as term tolerance class. The upper approximation set of query sentence was considered as query expansion. The new additional terms were also given weight numbers. The results of experiment on collection of Google 5 000 Web pages showed that the approach was effective on query expansion and high search precision was gained.