Analytics for the Quality of Fertility Data using Particle 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.

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Matching Sensor Ontologies with Simulated Annealing Particle Swarm Optimization

Mobile Information Systems ◽

10.1155/2021/5510055 ◽

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.

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

10.20944/preprints202007.0517.v1 ◽

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.

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Evaluation of cloud computing resource scheduling based on improved optimization algorithm

Complex & Intelligent Systems ◽

10.1007/s40747-020-00163-2 ◽

2020 ◽

Cited By ~ 1

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.

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Enhancing clustering quality of geo-demographic analysis using context fuzzy clustering type-2 and particle swarm optimization

Applied Soft Computing ◽

10.1016/j.asoc.2014.04.025 ◽

2014 ◽

Vol 22 ◽

pp. 566-584 ◽

Cited By ~ 26

Author(s):

Le Hoang Son

Keyword(s):

Particle Swarm Optimization ◽

Fuzzy Clustering ◽

Particle Swarm ◽

Demographic Analysis ◽

Swarm Optimization ◽

Clustering Quality

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A Modified Particle Swarm Optimization Algorithm with Cases Studies

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.268-270.823 ◽

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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A Particle Swarm Optimization Algorithm Based on Simulated Annealing

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.989-994.2301 ◽

2014 ◽

Vol 989-994 ◽

pp. 2301-2305 ◽

Cited By ~ 4

Author(s):

Zi Chao Yan ◽

Yang Shen Luo

Keyword(s):

Particle Swarm Optimization ◽

Simulated Annealing ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Global Optimum ◽

Population Diversity ◽

Swarm Optimization ◽

Simulated Analysis ◽

Metropolis Criterion

The passage aims at solving the problems resulted from the optimized process of Particle Swarm Optimization (PSO), which might reduce the population diversity, cause the algorithm to convergence too early, etc. A whole new mutable simulated annealing particle swarm optimization is proposed based on the combine of the simulated annealing mechanism and mutation. This new algorithm substitutes the Metropolis criterion in the simulated annealing mechanism for mutagenic factors in the process of mutation, which both ensures the diversity of the particle swarm, and ameliorates the quality of the swarm, so that this algorithm would convergence to the global optimum. According to the result of simulated analysis, this hybrid algorithm maintains the simplicity of the particle swarm optimization, improves its capability of global optimization, and finally accelerates the convergence and enhances the precision of this algorithm.

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