scholarly journals A multi-objective algorithm for virtual machine placement in cloud environments using a hybrid of particle swarm optimization and flower pollination optimization

2022 ◽  
Vol 8 ◽  
pp. e834
Author(s):  
Sara Mejahed ◽  
M Elshrkawey
Keyword(s):  
Particle Swarm Optimization ◽  
Virtual Machine ◽  
Hybrid Algorithm ◽  
Fitness Function ◽  
Particle Swarm ◽  
Virtual Machine Placement ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Flower Pollination ◽  
Best Fit

The demand for virtual machine requests has increased recently due to the growing number of users and applications. Therefore, virtual machine placement (VMP) is now critical for the provision of efficient resource management in cloud data centers. The VMP process considers the placement of a set of virtual machines onto a set of physical machines, in accordance with a set of criteria. The optimal solution for multi-objective VMP can be determined by using a fitness function that combines the objectives. This paper proposes a novel model to enhance the performance of the VMP decision-making process. Placement decisions are made based on a fitness function that combines three criteria: placement time, power consumption, and resource wastage. The proposed model aims to satisfy minimum values for the three objectives for placement onto all available physical machines. To optimize the VMP solution, the proposed fitness function was implemented using three optimization algorithms: particle swarm optimization with Lévy flight (PSOLF), flower pollination optimization (FPO), and a proposed hybrid algorithm (HPSOLF-FPO). Each algorithm was tested experimentally. The results of the comparative study between the three algorithms show that the hybrid algorithm has the strongest performance. Moreover, the proposed algorithm was tested against the bin packing best fit strategy. The results show that the proposed algorithm outperforms the best fit strategy in total server utilization.

scholarly journals Virtual Machine Placement Using Energy Efficient Particle Swarm Optimization in Cloud Datacenter

2021 ◽  
Vol 21 (1) ◽  
pp. 62-72
Author(s):  
R. B. Madhumala ◽  
Harshvardhan Tiwari ◽  
Verma C. Devaraj
Keyword(s):  
Particle Swarm Optimization ◽  
Virtual Machine ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimization Techniques ◽  
Virtual Machine Placement ◽  
Placement Problem ◽  
Swarm Optimization ◽  
Cloud Datacenter

Abstract Efficient resource allocation through Virtual machine placement in a cloud datacenter is an ever-growing demand. Different Virtual Machine optimization techniques are constructed for different optimization problems. Particle Swam Optimization (PSO) Algorithm is one of the optimization techniques to solve the multidimensional virtual machine placement problem. In the algorithm being proposed we use the combination of Modified First Fit Decreasing Algorithm (MFFD) with Particle Swarm Optimization Algorithm, used to solve the best Virtual Machine packing in active Physical Machines to reduce energy consumption; we first screen all Physical Machines for possible accommodation in each Physical Machine and then the Modified Particle Swam Optimization (MPSO) Algorithm is used to get the best fit solution.. In our paper, we discuss how to improve the efficiency of Particle Swarm Intelligence by adapting the efficient mechanism being proposed. The obtained result shows that the proposed algorithm provides an optimized solution compared to the existing algorithms.

scholarly journals A Multi-Objective Permanent Basic Farmland Delineation Model Based on Hybrid Particle Swarm Optimization

2020 ◽  
Vol 9 (4) ◽  
pp. 243 ◽  
Author(s):  
Hua Wang ◽  
Wenwen Li ◽  
Wei Huang ◽  
Ke Nie
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Research Area ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Model Based ◽  
Hybrid Particle Swarm Optimization ◽  
General Rules ◽  
Set Up

The delimitation of permanent basic farmland is essentially a multi-objective optimization problem. The traditional demarcation methods cannot simultaneously take into account the requirements of cultivated land quality and the spatial layout of permanent basic farmland, and it cannot balance the relationship between agriculture and urban development. This paper proposed a multi-objective permanent basic farmland delimitation model based on an immune particle swarm optimization algorithm. The general rules for delineating the permanent basic farmland were defined in the model, and the delineation goals and constraints have been formally expressed. The model introduced the immune system concepts to complement the existing theory. This paper describes the coding and initialization methods for the algorithm, particle position and speed update mechanism, and fitness function design. We selected Xun County, Henan Province, as the research area and set up control experiments that aligned with the different targets and compared the performance of the three models of particle swarm optimization (PSO), artificial immune algorithm (AIA), and the improved AIA-PSO in solving multi-objective problems. The experiments proved the feasibility of the model. It avoided the adverse effects of subjective factors and promoted the scientific rationality of the results of permanent basic farmland delineation.

A Multi-Objective Hybrid Algorithm for Optimization of Grid Structures

2018 ◽  
Vol 10 (01) ◽  
pp. 1850009 ◽  
Author(s):  
Zhe Xiong ◽  
Xiao-Hui Li ◽  
Jing-Chang Liang ◽  
Li-Juan Li
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Convergence Rate ◽  
Pareto Optimality ◽  
Hybrid Algorithm ◽  
Particle Swarm ◽  
Mathematical Functions ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Structural Problems

In this study, a novel multi-objective hybrid algorithm (MHGH, multi-objective HPSO-GA hybrid algorithm) is developed by crossing the heuristic particle swarm optimization (HPSO) algorithm with a genetic algorithm (GA) based on the concept of Pareto optimality. To demonstrate the effectiveness of the MHGH, the optimizations of four unconstrained mathematical functions and four constrained truss structural problems are tested and compared to the results using several other classic algorithms. The results show that the MHGH improves the convergence rate and precision of the particle swarm optimization (PSO) and increases its robustness.

scholarly journals Task-Scheduling Based on Multi-Objective Particle Swarm Optimization in Spatial Crowdsourcing

2019 ◽  
Vol 8 (1) ◽  
pp. 45-57
Author(s):  
Afra A. Alabbadi and Maysoon F. Abulkhair Afra A. Alabbadi and Maysoon F. Abulkhair
Keyword(s):  
Particle Swarm Optimization ◽  
Task Scheduling ◽  
Optimization Problem ◽  
Fitness Function ◽  
Particle Swarm ◽  
Physical World ◽  
Spatial Crowdsourcing ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Future Work

As a result of the rapid growth of internet and smartphone technology, a novel platform that attracts individuals and groups known as crowdsourcing emerged. Crowdsourcing is an outsourcing platform that facilitates the accomplishment of costly tasks that consume long periods of time when traditional methods are used. Spatial crowdsourcing (SC) is based on location; it introduces a new framework for the physical world that enables a crowd to complete spatialtemporal tasks. The primary issue in SC is the assignment and scheduling of a set of available tasks to a set of proper workers based on different factors, such as the location of the task, the distance between task location and hired worker location, temporal conditions, and incentive rewards. In the real-world, SC applications need to optimize multi-objectives simultaneously to exploit the utility of SC, and these objectives can be in conflict. However, there are few studies that address this multi-objective optimization problem within a SC environment. Thus, the authors propose a multi-objective task scheduling optimization problem in SC that aims to maximize the number of completed tasks, minimize total travel cost, and ensure worker workload balance. To solve this problem, we developed a method that adapts the multi-objective particle swarm optimization (MOPSO) algorithm based on a proposed novel fitness function. The experiments were conducted with both synthetic and real datasets; the experimental results show that this approach provides acceptable initial results. As future work, we plan to improve the effectiveness of our proposed algorithm by integrating a simple ranking strategy based on task entropy and expected travel costs to enhance MOPSO performance.

Application of Synergistic Optimization Method by Maximin Fitness Function Strategy Based Multi-Objective Particle Swarm Optimization Algorithm in Metal Bellows Structural Design

2013 ◽  
Vol 722 ◽  
pp. 550-556
Author(s):  
Xiao Mo Yu ◽  
Ai Ling Qin ◽  
Jia Hai Xue ◽  
Jun Ke Ye ◽  
Wen Jing Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Fitness Function ◽  
Particle Swarm ◽  
Structure Design ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Metal Bellows ◽  
Function Strategy

In this paper, the forming process is applied to the structure design of the metal bellows for the synergistic optimization.With bellows minimum overall stiffness and minimum weight for the optimization objectives to establish multi-objective optimization design model, using the Maximin fitness function strategy based multi-objective particle swarm optimization algorithm and introduce the multiple subgroup cooperative search strategy by master-slave clustering to get the optimized solution at the same time. The algorithm is applied to the synergistic optimization of the metal bellows structure design. The results show that the convergent speed of the algorithm is fast and can effectively approximate the actual bellows structure design, and provide users with more practical and intuitive effectively design scheme.

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