Cost optimization in cloud provisioning using Particle Swarm Optimization

2012 ◽  
Author(s):  
Nuttapong Netjinda ◽  
Booncharoen Sirinaovakul ◽  
Tiranee Achalakul
Keyword(s):  
Particle Swarm Optimization ◽  
Cost Optimization ◽  
Particle Swarm ◽  
Swarm Optimization

scholarly journals Cost optimization of sodium hypochlorite bleaching washing for denim by combining ensemble of surrogates with particle swarm optimization

2021 ◽  
Vol 16 ◽  
pp. 155892502110223
Author(s):  
Jie Xu ◽  
Feng Liu ◽  
Zhenglei He ◽  
Zongao Zhang ◽  
Sheng Li
Keyword(s):  
Particle Swarm Optimization ◽  
Sodium Hypochlorite ◽  
Production Cost ◽  
Orthogonal Experiment ◽  
Cost Optimization ◽  
Particle Swarm ◽  
Process Variables ◽  
Swarm Optimization ◽  
Washing Process ◽  
Ensemble Of Surrogates

Sodium hypochlorite bleaching washing process has been broadly carried out in denim garment industrial production. However, the quantitative relationships between process variables and bleaching performances have not been illustrated explicitly. Hence, it is impractical to determine values of the variables that can achieve the optimal production cost while satisfying the requirements of customers. This paper proposes an optimization methodology by combining ensemble of surrogates (ESs) with particle swarm optimization (PSO) to optimize production cost of chlorine bleaching for denim. The methodology starts from the data collections by conducting a Taguchi L25 (56) orthogonal experiment with the process variables and metrics for evaluating bleaching performances. Based on the data, the quantitative relationships are separately constructed by using RBFNN, SVR, RF and ensemble of them. Then, accuracies of the surrogates are evaluated and it proves that the ESs outperforms the others. Later, the production cost optimization model is proposed and PSO is utilized to solve it, while a case study is given to depict the optimization process and verify the effectiveness of the proposed hybrid ESs-PSO approach. Overall, the ESs-PSO approach shows great capability of optimizing production cost of sodium hypochlorite bleaching washing for denim.

The Optimization of Project Time-Cost Based on Particle Swarm Optimization Algorithm

2013 ◽  
Vol 791-793 ◽  
pp. 1423-1426
Author(s):  
Hai Min Wei ◽  
Rong Guang Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Cost Optimization ◽  
Particle Swarm ◽  
Implementation Process ◽  
Pso Algorithm ◽  
Project Schedule ◽  
Swarm Optimization ◽  
Schedule Optimization ◽  
Schedule Management

Project schedule management is the management to each stage of the degree of progress and project final deadline in the project implementation process. Its purpose is to ensure that the project can meet the time constraints under the premise of achieving its overall objectives.When the progress of schedule found deviation in the process of schedule management ,the progress of the plan which have be advanced previously need to adjust.This article mainly discussed to solve the following two questions:establish the schedule optimization model by using the method of linear;discuss the particle swarm optimization (PSO) algorithm and its parameters which have effect on the algorithm:Particle swarm optimization (PSO) algorithm is presented in the time limited project and the application of a cost optimization.

Optimization of Shell and Tube Heat Exchanger Using Alpha Tuning Elephant Herding Optimization (EHO) Technique

2021 ◽  
Vol 52 ◽  
pp. 92-101
Author(s):  
Jiten Makadia ◽  
C.D. Sankhavara
Keyword(s):  
Particle Swarm Optimization ◽  
Heat Exchanger ◽  
Optimization Problems ◽  
Cost Optimization ◽  
Real Life ◽  
Particle Swarm ◽  
Benchmark Problems ◽  
Swarm Optimization ◽  
Tube Heat Exchanger ◽  
Shell And Tube

Swarm Intelligence algorithms like PSO (Particle Swarm Optimization), ACO (Ant Colony Optimization), ABC (Artificial Bee Colony), Glow-worm swarm Optimization, etc. have been utilized by researchers for solving optimization problems. This work presents the application of a novel modified EHO (Elephant Herding Optimization) for cost optimization of shell and tube heat exchanger. A comparison of the results obtained by EHO in two benchmark problems shows that it is superior to those obtained with genetic algorithm and particle swarm optimization. The overall cost reduction is 13.3 % and 9.68% for both the benchmark problem compared to PSO. Results indicate that EHO can be effectively utilized for solving real-life optimization problems.

Optimization Problem Research for Machining Accuracy and Cost of CNC Lathe Based on Adaptive Particle Swarm Optimization

2012 ◽  
Vol 605-607 ◽  
pp. 1448-1452
Author(s):  
Wei Wei Liu ◽  
Ye Zheng
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Model ◽  
Manufacturing Industry ◽  
Cost Optimization ◽  
Particle Swarm ◽  
Machining Accuracy ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Cnc Lathe ◽  
Relationship Of

Based on CNC lathe product’s technical and economic properties demand for manufacturing industry of CNC equipment, this thesis studied the mutual relationship of machining accuracy and cost. According to adaptive particle swarm optimization, a optimization model of the two parts was established. The case of a CNC lathe shows that the optimization model has the advantages of rapid searching, high accuracy, effective and provides a new way for the machining accuracy and cost optimization.

scholarly journals Review on the cost optimization of microgrids via particle swarm optimization

2019 ◽  
Vol 11 (1) ◽  
pp. 73-89 ◽  
Author(s):  
Sengthavy Phommixay ◽  
Mamadou Lamine Doumbia ◽  
David Lupien St-Pierre
Keyword(s):  
Particle Swarm Optimization ◽  
Performance Optimization ◽  
Optimal Power Flow ◽  
Cost Optimization ◽  
Particle Swarm ◽  
Optimal Operation ◽  
Optimization Techniques ◽  
Energy Management Strategy ◽  
Swarm Optimization ◽  
The Cost

AbstractEconomic analysis is an important tool in evaluating the performances of microgrid (MG) operations and sizing. Optimization techniques are required for operating and sizing an MG as economically as possible. Various optimization approaches are applied to MGs, which include classic and artificial intelligence techniques. Particle swarm optimization (PSO) is one of the most frequently used methods for cost optimization due to its high performance and flexibility. PSO has various versions and can be combined with other intelligent methods to realize improved performance optimization. This paper reviews the cost minimization performances of various economic models that are based on PSO with regard to MG operations and sizing. First, PSO is described, and its performance is analyzed. Second, various objective functions, constraints and cost functions that are used in MG optimizations are presented. Then, various applications of PSO for MG sizing and operations are reviewed. Additionally, optimal operation costs that are related to the energy management strategy, unit commitment, economic dispatch and optimal power flow are investigated.

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