A New Particle Swarm Optimization-Based Strategy for Cost-Effective Data Placement in Scientific Cloud Workflows

2014 ◽  
pp. 115-120 ◽  
Author(s):  
Xuejun Li ◽  
Yang Wu ◽  
Fei Ma ◽  
Erzhou Zhu ◽  
Futian Wang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Cost Effective ◽  
Data Placement ◽  
Swarm Optimization

scholarly journals Optimization of DNA Sensor Model Based Nanostructured Graphene Using Particle Swarm Optimization Technique

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Hediyeh Karimi ◽  
Rubiyah Yusof ◽  
Rasoul Rahmani ◽  
Mohammad Taghi Ahmadi
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Cost Effective ◽  
Dna Sensor ◽  
Dna Sensors ◽  
Dna Adsorption ◽  
Swarm Optimization ◽  
Particle Swarm Optimization Technique ◽  
Optimized Model

It has been predicted that the nanomaterials of graphene will be among the candidate materials for postsilicon electronics due to their astonishing properties such as high carrier mobility, thermal conductivity, and biocompatibility. Graphene is a semimetal zero gap nanomaterial with demonstrated ability to be employed as an excellent candidate for DNA sensing. Graphene-based DNA sensors have been used to detect the DNA adsorption to examine a DNA concentration in an analyte solution. In particular, there is an essential need for developing the cost-effective DNA sensors holding the fact that it is suitable for the diagnosis of genetic or pathogenic diseases. In this paper, particle swarm optimization technique is employed to optimize the analytical model of a graphene-based DNA sensor which is used for electrical detection of DNA molecules. The results are reported for 5 different concentrations, covering a range from 0.01 nM to 500 nM. The comparison of the optimized model with the experimental data shows an accuracy of more than 95% which verifies that the optimized model is reliable for being used in any application of the graphene-based DNA sensor.

scholarly journals Optimized Production of Biodiesel Using Internet of Things Sensed Temperature with Hybrid Particle Swarm Optimization

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
G. Loganathan ◽  
M. Kannan
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Cost Effective ◽  
Biodiesel Production ◽  
Biofuel Production ◽  
Swarm Optimization ◽  
Hybrid Particle ◽  
Hybrid Particle Swarm Optimization ◽  
Fitness Value ◽  
Increase Productivity

Biofuel production offers a non-fossil fuel that can be utilized in modern engines without any redesign. Regardless of receiving rising attention, many researchers have explored microalgae-based biofuel production and found biodiesel production is cost-effective compared to petroleum-centered conventional fuels. The primary reason is that the lipid accumulation of microalgae is possible. An efficient technique is proposed for optimized biodiesel manufacturing with microalgae through an IoT device with the hybrid particle swarm optimization (HPSO) algorithm for elapsing such drawbacks. First, the component of biodiesel is determined. Then, from the components, the temperature value is sensed through the IoT device. Based on the obtained temperature, the reaction parameters are optimized with HPSO to increase productivity and reduce cost. Finally, we observed performance and comparative analysis. The experimental results contrasted with the existent particle swarm optimization (PSO) and genetic algorithm (GA) concerning iteration’s temperature, concentration, production, and fitness. The present HPSO algorithm has differed from the existing PSO and GA concerning IoT sensed temperature and production function. Fitness value and instance concentration are the performance parameters. It varies based on the iteration values. Thus, the proposed optimized biodiesel production is advanced when weighed down with the top-notch methods.

Review on Particle Swarm Optimization Approach for Optimizing Wellbore Trajectory

2020 ◽  
pp. 290-307 ◽  
Author(s):  
Kallol Biswas ◽  
Pandian M. Vasant ◽  
Moacyr Batholomeu Laruccia ◽  
José Antonio Gámez Vintaned ◽  
Myo M. Myint
Keyword(s):  
Particle Swarm Optimization ◽  
Trajectory Optimization ◽  
Complex Structure ◽  
Particle Swarm ◽  
Cost Effective ◽  
Pso Algorithm ◽  
Computational Time ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Flexible Control

Due to a variety of possible good types and so many complex drilling variables and constraints, optimization of the trajectory of a complex wellbore is very challenging. There are several types of wells, such as directional wells, horizontal wells, redrilling wells, complex structure wells, cluster wells, and extended reach wells. This reduction of the wellbore length helps to establish cost-effective approaches that can be utilized to resolve a group of complex trajectory optimization challenges. For efficient performance (i.e., quickly locating global optima while taking the smallest amount of computational time), we have to identify flexible control parameters. This research will try to develop a review of the various (particle swarm optimization) PSO algorithm used to optimize deviated wellbore trajectories. This chapter helps to find out optimal wellbore trajectory optimization algorithms that can close the technology gap by giving a useful method. This method can generate a solution automatically.

Distributed Query Plan Generation using Particle Swarm Optimization

2013 ◽  
Vol 4 (3) ◽  
pp. 58-82 ◽  
Author(s):  
T.V. Vijay Kumar ◽  
Amit Kumar ◽  
Rahul Singh
Keyword(s):  
Particle Swarm Optimization ◽  
Query Processing ◽  
Particle Swarm ◽  
Cost Effective ◽  
Search Space ◽  
Database Systems ◽  
Swarm Optimization ◽  
Query Plan ◽  
Plan Generation ◽  
Distributed Query

A large number of queries are posed on databases spread across the globe. In order to process these queries efficiently, optimal query processing strategies that generate efficient query processing plans are being devised. In distributed relational database systems, due to replication of relations at multiple sites, the relations required to answer a query may necessitate accessing of data from multiple sites. This leads to an exponential increase in the number of possible alternative query plans for processing a query. Though it is not computationally feasible to explore all possible query plans in such a large search space, the query plan that provides the most cost-effective option for query processing is considered necessary and should be generated for a given query. In this paper, an attempt has been made to generate such optimal query plans using Set based Comprehensive Learning Particle Swarm Optimization (S-CLPSO). Experimental comparisons of this algorithm with the GA based distributed query plan generation algorithm shows that for higher number of relations, the S-CLPSO based algorithm is able to generate comparatively better quality Top-K query plans.

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