A Bee Colony based optimization approach for simultaneous job scheduling and data replication in grid environments

2013 ◽  
Vol 40 (6) ◽  
pp. 1564-1578 ◽  
Author(s):  
Javid Taheri ◽  
Young Choon Lee ◽  
Albert Y. Zomaya ◽  
Howard Jay Siegel
Keyword(s):  
Job Scheduling ◽  
Data Replication ◽  
Optimization Approach ◽  
Bee Colony ◽  
Grid Environments

scholarly journals μ-Synthesis for Fractional-Order Robust Controllers

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 911
Author(s):  
Vlad Mihaly ◽  
Mircea Şuşcă ◽  
Dora Morar ◽  
Mihai Stănese ◽  
Petru Dobra
Keyword(s):  
Control Problem ◽  
Fractional Order ◽  
Design Procedure ◽  
High Order ◽  
Numerical Control ◽  
Performance Criteria ◽  
Iteration Algorithm ◽  
Optimization Approach ◽  
Cnc Machine ◽  
Bee Colony

The current article presents a design procedure for obtaining robust multiple-input and multiple-output (MIMO) fractional-order controllers using a μ-synthesis design procedure with D–K iteration. μ-synthesis uses the generalized Robust Control framework in order to find a controller which meets the stability and performance criteria for a family of plants. Because this control problem is NP-hard, it is usually solved using an approximation, the most common being the D–K iteration algorithm, but, this approximation leads to high-order controllers, which are not practically feasible. If a desired structure is imposed to the controller, the corresponding K step is a non-convex problem. The novelty of the paper consists in an artificial bee colony swarm optimization approach to compute the nearly optimal controller parameters. Further, a mixed-sensitivity μ-synthesis control problem is solved with the proposed approach for a two-axis Computer Numerical Control (CNC) machine benchmark problem. The resulting controller using the described algorithm manages to ensure, with mathematical guarantee, both robust stability and robust performance, while the high-order controller obtained with the classical μ-synthesis approach in MATLAB does not offer this.

scholarly journals Optimal distributed generation in green building assessment towards line loss reduction for Malaysian public hospital

2019 ◽  
Vol 8 (4) ◽  
Author(s):  
Mohd Effendi Amran ◽  
Mohd Nabil Muhtazaruddin ◽  
Nurul Aini Bani ◽  
Hazilah Mad Kaidi ◽  
Mohamad Zaki Hassan ◽  
...  
Keyword(s):  
Distributed Generation ◽  
Public Hospital ◽  
Green Building ◽  
Energy Performance ◽  
Optimization Approach ◽  
Loss Reduction ◽  
Voltage Profile ◽  
Bee Colony ◽  
Line Loss ◽  
Current Setting

This paper presents an optimization approach for criteria setting of Renewable Distributed Generation (DG) in the Green Building Rating System (GBRS). In this study, the total line loss reduction is analyzed and set as the main objective function in the optimization process which then a reassessment of existing criteria setting for renewable energy (RE) is proposed towards lower loss outcome. Solar photovoltaic (PV)-type DG unit (PV-DG) is identified as the type of DG used in this paper. The proposed PV-DG optimization will improve the sustainable energy performance of the green building by total line losses reduction within accepted lower losses region using Artificial bee colony (ABC) algorithm. The distribution network uses bus and line data setup from selected one of each three levels of Malaysian public hospital. MATLAB simulation result shows that the PV-DG expanding capacity towards optimal scale and location provides a better outcome in minimizing total line losses within an appropriate voltage profile as compared to the current setting of PV-DG imposed in selected GBRS. Thus, reassessment of RE parameter setting and the proposed five rankings with new PV-DG setting for public hospital provides technical justification and give the best option to the green building developer for more effective RE integration.

Resource Provisioning in the Cloud

2014 ◽  
pp. 589-612
Author(s):  
Ming Mao ◽  
Marty Humphrey
Keyword(s):  
Job Scheduling ◽  
Data Transfer ◽  
Resource Provisioning ◽  
Research Trends ◽  
Future Research ◽  
Application Performance ◽  
Grid Environments ◽  
Solution Methods ◽  
The One ◽  
Cloud Users

It is a challenge to provision and allocate resources in the Cloud so as to meet both the performance and cost goals of Cloud users. For a Cloud consumer, the ability to acquire and release resources dynamically and trivially in the Cloud, while being a powerful and useful aspect, complicates the resource provisioning and allocation task in the Cloud. While on the one hand, resource under-provisioning may hurt application performance and deteriorate service quality; on the other hand, resource over-provisioning could cost users more and offset Cloud advantages. Although resource management and job scheduling have been studied extensively in the Grid environments and the Cloud shares many common features with the Grid, the mapping from user objectives to resource provisioning and allocation in the Cloud has many challenges due to the seemingly unlimited resource pools, virtualization, and isolation features provided by the Cloud. This chapter focuses on surveying the research trends in resource provisioning in the Cloud based on several factors such as the type of the workload, the VM heterogeneity, data transfer requirements, solution methods, and optimization goals and constraints, and attempts to provide guidelines for future research.

Lifetime Maximization of Target-Covered WSN Using Computational Swarm Intelligence

2019 ◽  
pp. 383-425
Author(s):  
Roselin Jones
Keyword(s):  
Critical Points ◽  
Residual Energy ◽  
Optimization Approach ◽  
Single Node ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Dynamic Deployment ◽  
Coverage Hole ◽  
Bee Colony Optimization

In target-covered WSN, all critical points (CPs) are to be monitored effectively. Even a single node failure may cause coverage hole reducing the lifetime of the network. The sensor has non-rechargeable battery, and hence, energy supervision is inevitable. To maximize the lifetime of the WSN with guaranteed coverage and effective battery utilization, the activities of the sensors are to be scheduled and also the sensors may be repositioned towards the critical points. This chapter proposes an energy-efficient coverage-based artificial bee colony optimization (EEC-ABC) approach that exploits the intelligent foraging behavior of honeybee swarms to solve EEC problem to maximize the lifetime of the WSN. It also adheres to quality of service metrics such as coverage, residual energy, and lifetime. Similarly, energy-balanced dynamic deployment (EB-DD) optimization approach is proposed to heal the coverage hole to maximize the lifetime of the WSN. It positions the self-deployable mobile sensors towards the CPs to balance their energy density and thus enhances the lifetime of the network.

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