Optimizing the Data Center Energy Consumption Using a Particle Swarm Optimization-Based Approach

2016 ◽  
pp. 176-189 ◽  
Author(s):  
Cristina Bianca Pop ◽  
Viorica Rozina Chifu ◽  
Ioan Salomie Adrian Cozac ◽  
Marcel Antal ◽  
Claudia Pop
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Consumption ◽  
Data Center ◽  
Particle Swarm ◽  
Swarm Optimization

scholarly journals A Multi-Objective Particle Swarm Optimization for Trajectory Planning of Fruit Picking Manipulator

Agronomy ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2286
Author(s):  
Xiaoman Cao ◽  
Hansheng Yan ◽  
Zhengyan Huang ◽  
Si Ai ◽  
Yongjun Xu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Consumption ◽  
Evaluation Method ◽  
Technical Problem ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Advantages And Disadvantages ◽  
Optimal Evaluation

Stable, efficient and lossless fruit picking has always been a difficult problem, perplexing the development of fruit automatic picking technology. In order to effectively solve this technical problem, this paper establishes a multi-objective trajectory model of the manipulator and proposes an improved multi-objective particle swarm optimization algorithm (represented as GMOPSO). The algorithm combines the methods of mutation operator, annealing factor and feedback mechanism to improve the diversity of the population on the basis of meeting the stable motion, avoiding the local optimal solution and accelerating the convergence speed. By adopting the average optimal evaluation method, the robot arm motion trajectory has been testified to constructively fulfill the picking standards of stability, efficiency and lossless. The performance of the algorithm is verified by ZDT1~ZDT3 benchmark functions, and its competitive advantages and disadvantages with other multi-objective evolutionary algorithms are further elaborated. In this paper, the algorithm is simulated and verified by practical experiments with the optimization objectives of time, energy consumption and pulsation. The simulation results show that the solution set of the algorithm is close to the real Pareto frontier. The optimal solution obtained by the average optimal evaluation method is as follows: the time is 34.20 s, the energy consumption is 61.89 °/S2 and the pulsation is 72.18 °/S3. The actual test results show that the trajectory can effectively complete fruit picking, the average picking time is 25.5 s, and the success rate is 96.67%. The experimental results show that the trajectory of the manipulator obtained by GMOPSO algorithm can make the manipulator run smoothly and facilitates efficient, stable and nondestructive picking.

scholarly journals Eagle Strategy with Cauchy Mutation Particle Swarm Optimization for Energy Management in Cloud Computing

2020 ◽  
Vol 13 (6) ◽  
pp. 42-51
Author(s):  
Archana Kollu ◽  
◽  
Sucharita Vadlamudi ◽  
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Consumption ◽  
Energy Management ◽  
Optimization Technique ◽  
Service Level Agreement ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Cloud Data ◽  
Cauchy Mutation ◽  
Eagle Strategy

Energy management of the cloud datacentre is a challenging task, especially when the cloud server receives a number of the user’s request simultaneously. This requires an efficient method to optimally allocate the resources to the users. Resource allocation in cloud data centers need to be done in optimized manner for conserving energy keeping in view of Service Level Agreement (SLA). We propose, Eagle Strategy (ES) based Modified Particle Swarm Optimization (ES-MPSO) to minimize the energy consumption and SLA violation. The Eagle Strategy method is applied due to its efficient local optimization technique. The Cauchy Mutation method which schedules the task effectively and minimize energy consumption, is applied to the proposed ES-MPSO method for improving the convergence performance. The simulation result shows that the energy consumption of ES-MPSO is 42J and Particle Swarm Optimization (PSO) is 51J. The proposed method ES-MPSO achieves higher efficiency compared to the PSO method in terms of energy management and SLA.

scholarly journals A Multi-Objective and Multi-Dimensional Optimization Scheduling Method Using a Hybrid Evolutionary Algorithms with a Sectional Encoding Mode

2019 ◽  
Vol 11 (5) ◽  
pp. 1329 ◽  
Author(s):  
Wenxiang Xu ◽  
Shunsheng Guo
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Energy Consumption ◽  
Evolutionary Algorithms ◽  
Particle Swarm ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Discrete Particle ◽  
Dimensional Optimization ◽  
Matching Relation

Aimed at the problem of the green scheduling problem with automated guided vehicles (AGVs) in flexible manufacturing systems (FMS), the multi-objective and multi-dimensional optimal scheduling process is defined while considering energy consumption and multi-function of machines. The process is a complex and combinational process, considering this characteristic, a mathematical model was developed and integrated with evolutionary algorithms (EAs), which includes a sectional encoding genetic algorithm (SE-GA), sectional encoding discrete particle swarm optimization (SE-DPSO) and hybrid sectional encoding genetic algorithm and discrete particle swarm optimization (H-SE-GA-DPSO). In the model, the encoding of the algorithms was divided into three segments for different optimization dimensions with the objective of minimizing the makespan and energy consumption of machines and the number of AGVs. The sectional encoding described the sequence of operations of related jobs, the matching relation between transfer tasks and AGVs (AGV-task), and the matching relation between operations and machines (operation-machine) respectively for multi-dimensional optimization scheduling. The effectiveness of the proposed three EAs was verified by a typical experiment. Besides, in the experiment, a comparison among SE-GA, SE-DPSO, H-SE-GA-DPSO, hybrid genetic algorithm and particle swarm optimization (H-GA-PSO) and a tabu search algorithm (TSA) was performed. In H-GA-PSO and TSA, the former just takes the sequence of operations into account, and the latter takes both the sequence of operations and the AGV-task into account. According to the result of the comparison, the superiority of H-SE-GA-DPSO over the other algorithms was proved.

Electric Vehicle Transmission Gear Ratio Optimization Based on Particle Swarm Optimization

2012 ◽  
Vol 187 ◽  
pp. 20-26 ◽  
Author(s):  
Qiang Gu ◽  
Xiu Sheng Cheng
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Consumption ◽  
Electric Vehicle ◽  
Particle Swarm ◽  
Gear Ratio ◽  
Swarm Optimization ◽  
Driving Range ◽  
Ratio Optimization ◽  
Power Component ◽  
Transmission Gear

The driving range of electric vehicles is less than traditional vehicles due to the restriction of energy storage. It is raising the efficiency of each power component that is one of increasing electric vehicle driving range methods. A particle swarm optimization is used to optimize transmission gear ratio on established electric vehicle power component models. A simulation that simulates the energy consumption of vehicle after gear ratio optimization is given to compare with the actual energy consumption data of the vehicle before gear ratio optimization. The results show that the energy consumption and driving range of the latter are better than the former therefore this optimization is valid.

scholarly journals WSN Node Optimal Deployment Algorithm Based on Adaptive Binary Particle Swarm Optimization

2021 ◽  
Vol 1 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Yujiang Li ◽  
Jinghua Cao
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Particle Swarm ◽  
Wireless Sensor ◽  
Binary Particle Swarm Optimization ◽  
Network Coverage ◽  
Swarm Optimization ◽  
Network Nodes ◽  
Optimal Deployment

In order to optimize the deployment of wireless sensor network nodes, and avoid network energy consumption increase due to node redundancy and uneven coverage, the multi-objective mathematical optimization problem of area coverage is transformed into a function problem. Aiming at network coverage rate, node dormancy rate and network coverage uniformity, the idea of genetic algorithm mutation is introduced based on the discrete binary particle swarm optimization and the global optimal speed is mutated to avoid the algorithm falling into the local optimal solution. In order to further improve the optimization ability of the algorithm, the adaptive learning factor and inertia weight are introduced to obtain the optimal deployment algorithm of wireless sensor network nodes. The experimental results show that the algorithm can reduce the number of active nodes efficiently, improve coverage uniformity, reduce network energy consumption and prolong network lifetime under the premise that the coverage rate is greater than 90%, and compared with an algorithm called coverage configuration protocol, an algorithm called finding the minimum working sets in wireless sensor networks, and an algorithm called binary particle swarm optimization-g in literature, the number of active nodes in this algorithm is reduced by about 36%, 30% and 23% respectively.

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