scholarly journals Stochastic Models for Optimizing Availability, Cost and Sustainability of Data Center Power Architectures through Genetic Algorithm

2019 ◽  
Vol 26 (2) ◽  
pp. 27-44
Author(s):  
Márcio Sergio Soares Austregésilo ◽  
Gustavo Callou
Keyword(s):  
Genetic Algorithm ◽  
Case Studies ◽  
Data Center ◽  
Electric Energy ◽  
Optimal Solution ◽  
Stochastic Petri Nets ◽  
Optimization Approach ◽  
Optimization Strategy ◽  
Electric Energy Consumption ◽  
Typical Data

In recent years, the growth of information technology has required higher reliability, accessibility, collaboration, availability, and a reduction of costs on data centers due to factors such as social network, cloud computing, and e-commerce. These systems require redundant mechanisms on the data center infrastrucutre to achieve high availability, which may increase the electric energy consumption, impacting in both the sustainability and cost. This work proposes a multi-objective optimization approach, based on Genetic Algorithms, to optimize cost, sustainability and availability of data center power infrastructures. The main goal is to maximize availability and minimize cost and exergy consumed (adopted to estimate the environmental impacts). In order to compute such metrics, this work adopts the energy flow model (EFM), reliability block diagrams (RBD) and stochastic petri nets (SPN). Two case studies are conducted to show the applicability of the proposed strategy: (i) takes into account 5 typical data center architectures that were optimized to conduct the validation process of the proposed strategy; (ii) uses the optimization strategy in two architectures classified by ANSI / TIA-942 (TIER I and II). In both case studies, significant improvements were achieved in the results, which were very close to the optimum one that was obtained by a brute force algorithm that analyzes all the possibilities and returns the optimal solution. It is worth mentioning that the time used to obtain the results using the genetic algorithm approach was significantly lower (6,763,260 times), in comparison with the strategy which combines all the possible combinations to obtain the optimal result.

scholarly journals A New Optimization Approach for Maximizing the Photovoltaic Panel Power Based on Genetic Algorithm and Lagrange Multiplier Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Mahdi M. M. El-Arini ◽  
Ahmed M. Othman ◽  
Ahmed Fathy
Keyword(s):  
Genetic Algorithm ◽  
Output Power ◽  
Lagrange Multiplier ◽  
Electric Energy ◽  
Optimal Solution ◽  
Maximum Efficiency ◽  
Initial Population ◽  
Optimization Approach ◽  
Optimal Point ◽  
Pv Panel

In recent years, the solar energy has become one of the most important alternative sources of electric energy, so it is important to operate photovoltaic (PV) panel at the optimal point to obtain the possible maximum efficiency. This paper presents a new optimization approach to maximize the electrical power of a PV panel. The technique which is based on objective function represents the output power of the PV panel and constraints, equality and inequality. First the dummy variables that have effect on the output power are classified into two categories: dependent and independent. The proposed approach is a multistage one as the genetic algorithm, GA, is used to obtain the best initial population at optimal solution and this initial population is fed to Lagrange multiplier algorithm (LM), then a comparison between the two algorithms, GA and LM, is performed. The proposed technique is applied to solar radiation measured at Helwan city at latitude 29.87°, Egypt. The results showed that the proposed technique is applicable.

A MultiDisciplinary Optimization Approach for Sizing Vertical Lift Aircraft

2021 ◽  
Author(s):  
Ananth Sridharan ◽  
Bharath Govindarajan
Keyword(s):  
Optimal Solution ◽  
Multidisciplinary Optimization ◽  
Shape Design ◽  
Optimization Approach ◽  
Optimization Strategy ◽  
Clock Time ◽  
Element Analysis ◽  
Analysis And Design ◽  
Design Variables ◽  
Vertical Lift

This paper presents an approach to reframe the sizing problem for vertical-lift unmanned aerial vehicles (UAVs) as an optimization problem and obtains a weight-optimal solution with up to two orders of magnitude of savings in wall clock time. Because sizing is performed with higher fidelity models and design variables from several disciplines, the Simultaneous Analysis aNd Design (SAND) approach from fixed-wing multidisciplinary optimization literature is adapted for the UAV sizing task. Governing equations and disciplinary design variables that are usually self-contained within disciplines (airframe tube sizes, trim variables, and trim equations) are migrated to the sizing optimizer and added as design variables and (in)equality constraints. For sizing consistency, the iterative weight convergence loop is replaced by a coupling variable and associated equality consistency constraint for the sizing optimizer. Cruise airspeed is also added as a design variable and driven by the sizing optimizer. The methodology is demonstrated for sizing a package delivery vehicle (a lift-augment quadrotor biplane tailsitter) with up to 39 design variables and 201 constraints. Gradient-based optimizations were initiated from different starting points; without blade shape design in sizing, all processes converged to the same minimum, indicating that the design space is convex for the chosen bounds, constraints, and objective function. Several optimization schemes were investigated by moving combinations of relevant disciplines (airframe sizing with finite element analysis, vehicle trim, and blade aerodynamic shape design) to the sizing optimizer. The biggest advantage of the SAND strategy is its scope for parallelization, and the inherent ability to drive the design away from regions where disciplinary analyses (e.g., trim) cannot find a solution, obviating the need for ad hoc penalty functions. Even in serial mode, the SAND optimization strategy yields results in the shortest wall clock time compared to all other approaches.

Multi-Objective Optimization for a Novel Electrostatic-Feedback Micro-Sensor Based on Genetic Algorithm

2007 ◽  
Author(s):  
Yongquan Wang ◽  
Hualing Chen ◽  
Zhiying Ou ◽  
Xueming He
Keyword(s):  
Genetic Algorithm ◽  
Multidisciplinary Design Optimization ◽  
Optimal Solution ◽  
Optimization Approach ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Micro Sensor ◽  
Energy Relations

In this paper, we present the multi-objective optimization for an entire microsystem, a novel capacitive electrostatic feedback accelerometer. From the energy relations of the coupled electrostatic-field, the dynamic model of the system is constructed. Aiming at the global performance, a multi-objective optimization model, where sensitivity, resolution and damping resonant frequency are selected as objectives, is established based on the concept of multidisciplinary design optimization (MDO). Genetic algorithm (GA) is used to solve this problem, and compared with a traditional optimization approach, sequence quadratic programming (SQP). Both the two algorithms can achieve our aim commendably, and the optimal solution given by GA is more satisfied. The research provides us a good foundation to develop the stochastic and implicit parallel properties of GA to obtain Pareto optimal solutions.

Virtual Machine Migrating Algorithm Based on Genetic Algorithm in Cloud Data Center

2014 ◽  
Vol 513-517 ◽  
pp. 2031-2034
Author(s):  
Hui Zhang ◽  
Yong Liu
Keyword(s):  
Genetic Algorithm ◽  
Resource Utilization ◽  
Virtual Machine ◽  
Data Center ◽  
Virtual Machines ◽  
Heuristic Algorithms ◽  
Optimal Solution ◽  
Virtual Machine Migration ◽  
Cloud Data Center ◽  
Cloud Data

Virtual machine migration is an effective method to improve the resource utilization of cloud data center. The common migration methods use heuristic algorithms to allocation virtual machines, the solution results is easy to fall into local optimal solution. Therefore, an algorithm called Migrating algorithm based on Genetic Algorithm (MGA) is introduced in this paper, which roots from genetic evolution theory to achieve global optimal search in the map of virtual machines to target nodes, and improves the objective function of Genetic Algorithm by setting the resource utilization of virtual machine and target node as an input factor into the calculation process. There is a contrast between MGA, Single Threshold (ST) and Double Threshold (DT) through simulation experiments, the results show that the MGA can effectively reduce migrations times and the number of host machine used.

scholarly journals Machine Reliability Optimization by Genetic Algorithm Approach

2020 ◽  
pp. 35-40
Author(s):  
Ngnassi Djami Aslain Brisco ◽  
Nzie Wolfgang ◽  
Doka Yamigno Serge
Keyword(s):  
Genetic Algorithm ◽  
Heuristic Method ◽  
Reliability Assessment ◽  
Optimal Solution ◽  
Assessment Method ◽  
Optimization Approach ◽  
Reliability Optimization ◽  
Machine Reliability ◽  
Long Time ◽  
Genetic Algorithm Approach

To define the reliability network of a system (machine), we start with a set of components arranged in an appropriate topology (series, parallel, or parallel-series), choose the best terms of the ratio performance / cost, and gather by links with the aim to combine them. This process requires a long time and effort, given the very large number of possible combinations, which becomes tedious for the analyst. For this reason, it is essential to use an appropriate optimization approach when designing any product. However, before trying to optimize, it is necessary to have a reliability assessment method. The objective of this paper is to display a meta-heuristic method, which is sustained on the genetic algorithm (GA) to improve the machines reliability. To achieve this objective, a methodology that consists of presenting the functionalities of genetic algorithms is developed. The result achieved is the proposal of a reliability network for the optimal solution.

Multidisciplinary Design Optimization of Small Canister-Launched Space Launch Vehicle Using Genetic Algorithm

2013 ◽  
Vol 302 ◽  
pp. 583-588 ◽  
Author(s):  
Fredy M. Villanueva ◽  
Lin Shu He ◽  
Da Jun Xu
Keyword(s):  
Genetic Algorithm ◽  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Optimal Solution ◽  
Optimization Method ◽  
Launch Vehicle ◽  
Low Earth Orbit ◽  
Multidisciplinary Design ◽  
Optimization Approach ◽  
Design Variables

A multidisciplinary design optimization approach of a three stage solid propellant canister-launched launch vehicle is considered. A genetic algorithm (GA) optimization method has been used. The optimized launch vehicle (LV) is capable of delivering a microsatellite of 60 kg. to a low earth orbit (LEO) of 600 km. altitude. The LV design variables and the trajectory profile variables were optimized simultaneously, while a depleted shutdown condition was considered for every stage, avoiding the necessity of a thrust termination device, resulting in reduced gross launch mass of the LV. The results show that the proposed optimization approach was able to find the convergence of the optimal solution with highly acceptable value for conceptual design phase.

A hybrid optimization strategy for the maintenance of the wheels of metro vehicles: Vehicle turning, wheel re-profiling, and multi-template use

2017 ◽  
Vol 232 (3) ◽  
pp. 832-841 ◽  
Author(s):  
Wei Zhu ◽  
Di Yang ◽  
Jun Huang
Keyword(s):  
Optimal Solution ◽  
Hybrid Optimization ◽  
Optimization Approach ◽  
Optimization Strategy ◽  
Wheel Wear ◽  
Rail Systems ◽  
Common Strategy ◽  
Shanghai Metro ◽  
Open Nature

The wheel–rail contact relationship has a great impact on the security and reliability of metro vehicles in service. In particular, wear modeling and maintenance optimization of the wheels play significant roles with regard to both safety and cost. However, it is difficult to provide a satisfactory model of wheel wear because of the open nature of real wheel–rail systems and the constantly varying environmental conditions in which they operate. Historically, re-profiling, which also has its limitation to some extent, was adopted as a common strategy to restore the original profiles of the worn wheels. Acknowledging that re-profiling is not the only strategy for dealing with wheel wear, the authors of this study have developed a more advanced optimization approach that includes two more strategies, namely, vehicle turning and multi-template use, to give as near an optimal solution as possible. Vehicle turning refers to the reversal of the vehicle’s orientation on the rail, whereas multi-template use refers to the situation where different re-profiling templates are used alternately. In this paper, re-profiling, vehicle turning, and multi-template use have been discussed separately. Then a hybrid optimization strategy for the maintenance of the wheels of metro vehicles has been proposed, with the aim of maximizing the wheel life while minimizing the relevant costs. An initial case study on the Shanghai Metro system shows that the proposed approach is able to provide a more reasonable solution for the optimization of the maintenance strategies.

Intelligent Optimization of Urban Real-Time Traffic Route Based on DNA Non-Dominated Sorting Genetic Algorithm

2015 ◽  
Vol 713-715 ◽  
pp. 1746-1749
Author(s):  
Min Zhu ◽  
Bo Su ◽  
Gang Min Ning
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Complexity Analysis ◽  
Fitness Function ◽  
Optimal Solution ◽  
Urban Traffic ◽  
Route Optimization ◽  
Optimization Strategy ◽  
Traffic Condition ◽  
Real Time Traffic

The urban traffic condition is changed timely, so the traditional serial algorithm cannot satisfy the requirement of traffic scale and condition changes. Therefore, this paper proposes a DNA non-dominated sorting genetic algorithm for route optimization problem of multi-objects. First, through Pareto frontiers solution set optimization and algorithm complexity analysis, we determine the multi-objects problem to be optimized. Then we convert the problem into optimization problem of single-object fitness function, namely the elite populations optimization strategy, through which we can obtain the optimal solution of timely traffic condition.

scholarly journals Optimal Conductor Size Selection in Distribution Networks with High Penetration of Distributed Generation Using Adaptive Genetic Algorithm

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2065 ◽  
Author(s):  
Zhenghui Zhao ◽  
Joseph Mutale
Keyword(s):  
Genetic Algorithm ◽  
Distributed Generation ◽  
Distribution Systems ◽  
Distribution Network ◽  
Distribution Networks ◽  
Adaptive Genetic Algorithm ◽  
Optimization Approach ◽  
Optimization Strategy ◽  
Size Selection ◽  
High Penetration

The widespread deployment of distributed generation (DG) has significantly impacted the planning and operation of current distribution networks. The environmental benefits and the reduced installation cost have been the primary drivers for the investment in large-scale wind farms and photovoltaics (PVs). However, the distribution network operators (DNOs) face the challenge of conductor upgrade and selection problems due to the increasing capacity of DG. In this paper, a hybrid optimization approach is introduced to solve the optimal conductor size selection (CSS) problem in the distribution network with high penetration of DGs. An adaptive genetic algorithm (AGA) is employed as the primary optimization strategy to find the optimal conductor sizes for distribution networks. The aim of the proposed approach is to minimize the sum of life-cycle cost (LCC) of the selected conductor and the total energy procurement cost during the expected operation periods. Alternating current optimal power flow (AC-OPF) analysis is applied as the secondary optimization strategy to capture the economic dispatch (ED) and return the results to the primary optimization process when a certain conductor arrangement is assigned by AGA. The effectiveness of the proposed algorithm for optimal CSS is validated through simulations on modified IEEE 33-bus and IEEE 69-bus distribution systems.

A Cooperative Scheduling Model for Subway Trains

2012 ◽  
Author(s):  
Xiang Li ◽  
Xin Yang ◽  
Hongwei Wang ◽  
Shuai Su ◽  
Wenzhe Sun
Keyword(s):  
Time Window ◽  
Programming Model ◽  
Electric Energy ◽  
Optimal Solution ◽  
Optimization Approach ◽  
Trip Time ◽  
Proposed Model ◽  
Time Window Constraints ◽  
Braking Phase ◽  
Cooperative Scheduling

For subway systems, the energy put into accelerating the trains can be reconverted into electric energy by using the motors as generators during braking phase. Generally speaking, except a small part is used for on-board purposes, most of the recovery energy is transmitted backwards along the conversion chain and fed back into the catenary. However, since the low catenary voltage DC systems, the transmission losses are very high. In order to improve the utilization of recovery energy, this paper proposes an optimization approach to cooperate the acceleration and brake times of successive trains such that the recovery energy from the braking train can be directly used by the accelerating train. First, we formulate a quadratic programming model to optimize the cooperative degree with trip time constraint and time window constraints. Furthermore, we solve the optimal solution by using the Kuhn-Tucker conditions. Finally, we present a numerical example based on the operation data from Beijing Yizhuang subway line of China, which illustrates that the proposed model can improve the cooperative degree by 9.08%.

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