Ontology Modelling for Metallurgy as a Domain and Retrieval Using Particle Swarm Optimization

2021 ◽  
pp. 272-301
Author(s):  
Gerard Deepak ◽  
Ayush Kumar A. ◽  
Santhanavijayan A. ◽  
Sheeba J. Priyadarshini
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Ontology Development ◽  
Swarm Optimization ◽  
Entity Retrieval ◽  
The Relationship ◽  
The Web

Ontologies are the entities that are built to gather knowledge from the chosen domain and make it available to both machines and humans. In this chapter, an ontology that structures all the processes involved in the metallurgical extraction of the metals has been modelled. Ontology Development 101 methodology with minor modifications has been adopted to model the ontologies for metallurgy as the principal domain, and the processes involved in the various fields of metallurgy have been modelled into an ontology by defining the classes and the relationship between them. The model is implemented using Protégé and quantitatively evaluated using the semiotics approach. To comprehensively demonstrate the applicability of the developed ontology, an algorithm for retrieval of entities from the web incorporating particle swarm optimization and that makes use of the modelled ontology has been proposed. Finally, the quality of the modelled ontology and as well as the performance efficiency of the entity retrieval has been quantitatively evaluated.

scholarly journals Service Composition in IoT using Genetic algorithm and Particle swarm optimization

2020 ◽  
Vol 10 (1) ◽  
pp. 56-64 ◽  
Author(s):  
Neeti Kashyap ◽  
A. Charan Kumari ◽  
Rita Chhikara
Keyword(s):  
Genetic Algorithm ◽  
Quality Of Service ◽  
Particle Swarm Optimization ◽  
Service Composition ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Critical Issue ◽  
Swarm Optimization

AbstractWeb service compositions are commendable in structuring innovative applications for different Internet-based business solutions. The existing services can be reused by the other applications via the web. Due to the availability of services that can serve similar functionality, suitable Service Composition (SC) is required. There is a set of candidates for each service in SC from which a suitable candidate service is picked based on certain criteria. Quality of service (QoS) is one of the criteria to select the appropriate service. A standout amongst the most important functionality presented by services in the Internet of Things (IoT) based system is the dynamic composability. In this paper, two of the metaheuristic algorithms namely Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are utilized to tackle QoS based service composition issues. QoS has turned into a critical issue in the management of web services because of the immense number of services that furnish similar functionality yet with various characteristics. Quality of service in service composition comprises of different non-functional factors, for example, service cost, execution time, availability, throughput, and reliability. Choosing appropriate SC for IoT based applications in order to optimize the QoS parameters with the fulfillment of user’s necessities has turned into a critical issue that is addressed in this paper. To obtain results via simulation, the PSO algorithm is used to solve the SC problem in IoT. This is further assessed and contrasted with GA. Experimental results demonstrate that GA can enhance the proficiency of solutions for SC problem in IoT. It can also help in identifying the optimal solution and also shows preferable outcomes over PSO.

scholarly journals Convergence Analysis of Particle Swarm Optimizer and Its Improved Algorithm Based on Velocity Differential Evolution

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Hongtao Ye ◽  
Wenguang Luo ◽  
Zhenqiang Li
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Particle Velocity ◽  
Particle Swarm ◽  
Search Space ◽  
Particle Swarm Optimizer ◽  
Swarm Optimization ◽  
Relationship Of ◽  
The Relationship ◽  
Improved Algorithm

This paper presents an analysis of the relationship of particle velocity and convergence of the particle swarm optimization. Its premature convergence is due to the decrease of particle velocity in search space that leads to a total implosion and ultimately fitness stagnation of the swarm. An improved algorithm which introduces a velocity differential evolution (DE) strategy for the hierarchical particle swarm optimization (H-PSO) is proposed to improve its performance. The DE is employed to regulate the particle velocity rather than the traditional particle position in case that the optimal result has not improved after several iterations. The benchmark functions will be illustrated to demonstrate the effectiveness of the proposed method.

Markov Chain-Like Model for Prediction Service Based on Improved Hierarchical Particle Swarm Optimization Cluster Algorithm

2016 ◽  
Vol 26 (04) ◽  
pp. 653-674 ◽  
Author(s):  
Zhifang Liao ◽  
Min Liu ◽  
Tianhui Song ◽  
Li Kuang ◽  
Yan Zhang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Markov Chain ◽  
Clustering Algorithms ◽  
Particle Swarm ◽  
Heterogeneous Data ◽  
Swarm Optimization ◽  
Log Data ◽  
Web Log ◽  
Proposed Model ◽  
The Web

Since web pages visited by users contain a variety of data resources and the clustering algorithms frequently used for web data do not take the heterogeneous nature into account when processing the heterogeneous data, this paper proposes a new algorithm, namely IHPSOC algorithm, to cluster web log data on the basis of web log mining. Based on particle swarm optimization (PSO), IHPSOC algorithm clusters the web log data through particle swarm iteration. Based on clustering results, this paper establishes Markov chain-like models which create a corresponding Markov chain for users in each different category so as to predict the web resources in users’ need. The results of the experiments show that the proposed model gives better predication.

scholarly journals Matching Sensor Ontologies with Simulated Annealing Particle Swarm Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Hai Zhu ◽  
Xingsi Xue ◽  
Aifeng Geng ◽  
He Ren
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Particle Swarm ◽  
Location Based Services ◽  
Sensor Data ◽  
Ontology Matching ◽  
Ontology Alignment ◽  
Matching Problem ◽  
Swarm Optimization

In recent years, innovative positioning and mobile communication techniques have been developing to achieve Location-Based Services (LBSs). With the help of sensors, LBS is able to detect and sense the information from the outside world to provide location-related services. To implement the intelligent LBS, it is necessary to develop the Semantic Sensor Web (SSW), which makes use of the sensor ontologies to implement the sensor data interoperability, information sharing, and knowledge fusion among intelligence systems. Due to the subjectivity of sensor ontology engineers, the heterogeneity problem is introduced, which hampers the communications among these sensor ontologies. To address this problem, sensor ontology matching is introduced to establish the corresponding relationship between different sensor terms. Among all ontology matching technologies, Particle Swarm Optimization (PSO) can represent a contributing method to deal with the low-quality ontology alignment problem. For the purpose of further enhancing the quality of matching results, in our work, sensor ontology matching is modeled as the meta-matching problem firstly, and then based on this model, aiming at various similarity measures, a Simulated Annealing PSO (SAPSO) is proposed to optimize their aggregation weights and the threshold. In particular, the approximate evaluation metrics for evaluating quality of alignment without reference are proposed, and a Simulated Annealing (SA) strategy is applied to PSO’s evolving process, which is able to help the algorithm avoid the local optima and enhance the quality of solution. The well-known Ontology Alignment Evaluation Initiative’s benchmark (OAEI’s benchmark) and three real sensor ontologies are used to verify the effectiveness of SAPSO. The experimental results show that SAPSO is able to effectively match the sensor ontologies.

scholarly journals Experimental Analysis of Time Complexity and Solution Quality of Swarm Intelligence Algorithm

2020 ◽  
Author(s):  
Mohammed Ajuji ◽  
Aliyu Abubakar ◽  
Datti, Useni Emmanuel
Keyword(s):  
Particle Swarm Optimization ◽  
Time Complexity ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Worst Case ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Nature Inspired Algorithms

Nature-inspired algorithms are very popular tools for solving optimization problems inspired by nature. However, there is no guarantee that optimal solution can be obtained using a randomly selected algorithm. As such, the problem can be addressed using trial and error via the use of different optimization algorithms. Therefore, the proposed study in this paper analyzes the time-complexity and efficacy of some nature-inspired algorithms which includes Artificial Bee Colony, Bat Algorithm and Particle Swarm Optimization. For each algorithm used, experiments were conducted several times with iterations and comparative analysis was made. The result obtained shows that Artificial Bee Colony outperformed other algorithms in terms of the quality of the solution, Particle Swarm Optimization is time efficient while Artificial Bee Colony yield a worst case scenario in terms of time complexity.

scholarly journals Optimization Algorithm for Multiple Phases Sectionalized Modulation Jamming Based on Particle Swarm Optimization

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 160 ◽  
Author(s):  
Jiawei Jiang ◽  
Yanhong Wu ◽  
Hongyan Wang ◽  
Yakun Lv ◽  
Lei Qiu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Fitness Function ◽  
Particle Swarm ◽  
Random Parameters ◽  
Simulation Experiments ◽  
Swarm Optimization ◽  
Fitness Value ◽  
The Relationship ◽  
Jamming Effect

Due to the difficulty in deducing the corresponding relationship between results and parameter settings of multiple phases sectionalized modulation (MPSM) jamming, a problem occurs when obtaining the optimal local suppression jamming effect, which limits the practical application of MPSM jamming. The traditional method struggles to meet the requirements by setting fixed parameters or random parameters. Therefore, an optimization algorithm for MPSM jamming based on particle swarm optimization (PSO) is proposed in this study to produce the optimal local suppression jamming effect and determine its corresponding parameter settings. First, we analyzed the relationship between the degree of mismatch and local suppression jamming effect. Then, we set appropriate fitness function and fitness value. Finally, we used PSO to calculate parameter settings of a section situation and phase situation, which minimizes the fitness function and fitness value. The optimization algorithm avoids the tremendous computation of traversing all parameter settings, is stable, the results are repeatable, and the algorithm provides the optimal local suppression jamming effect under different conditions. The simulation experiments demonstrate the feasibility and effectiveness of the optimization algorithm.

scholarly journals Evaluation of cloud computing resource scheduling based on improved optimization algorithm

2020 ◽  
Author(s):  
Huafeng Yu
Keyword(s):  
Cloud Computing ◽  
Particle Swarm Optimization ◽  
Virtual Machines ◽  
Particle Swarm ◽  
Resource Scheduling ◽  
Optimal Solution ◽  
Operation Time ◽  
Swarm Optimization ◽  
Ipso Algorithm

Abstract Cloud computing, as a new computing mode in recent years, has been pursued by many users who have computational requirements, and the service quality of cloud computing depends largely on the efficiency of resource scheduling. In this study, an improved particle swarm optimization (IPSO) algorithm was proposed to improve the efficiency of resource scheduling, and simulation experiments were carried out on the IPSO algorithm and the traditional particle swarm optimization using CloudSim simulation platform. The phenomenon of premature appeared with the increase of the number of iterations, and the globally optimal solution was not found. The IPSO algorithm was more efficient in exploring the globally optimal solution, and the phenomenon of premature did not appear. As the number of tasks increased, the operation time of both algorithms increased, but the IPSO algorithm increased more slowly. The IPSO algorithm had more advantages when there were a large amount of tasks. Virtual machines in the two algorithms had different loads, and the load of the virtual machine in the IPSO algorithm was more balanced.

A Modified Particle Swarm Optimization Algorithm with Cases Studies

2011 ◽  
Vol 268-270 ◽  
pp. 823-828
Author(s):  
Cheng Chien Kuo ◽  
Hung Cheng Chen ◽  
Teng Fa Taso ◽  
Chin Ming Chiang
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Solution Quality ◽  
Swarm Optimization ◽  
Fast Calculation ◽  
Modified Particle Swarm Optimization ◽  
Stochastic Optimization Algorithms

s paper presents a hybrid algorithm, the “particle swarm optimization with simulated annealing behavior (SA-PSO)” algorithm, which combines the advantages of good solution quality in simulated annealing and fast calculation in particle swarm optimization. As stochastic optimization algorithms are sensitive to its parameters, this paper introduces criteria in selecting parameters to improve solution quality. To prove the usability and effectiveness of the proposed algorithm, simulations are performed using 20 different mathematical optimized functions of different dimensions. The results made from different algorithms are then compared between the quality of the solution, the efficiency of searching for the solution and the convergence characteristics. According to the simulation results, SA-PSO obtained higher efficiency, better quality and faster convergence speed than other compared algorithms.

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