Implementation of an Intelligent Hybrid Simulation System for Node Placement Problem in WMNs Considering Particle Swarm Optimization and Simulated Annealing

2017 ◽  
Author(s):  
Shinji Sakamoto ◽  
Ryoichiro Obukata ◽  
Tetsuya Oda ◽  
Leonard Barolli ◽  
Makoto Ikeda
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Particle Swarm ◽  
Hybrid Simulation ◽  
Simulation System ◽  
Placement Problem ◽  
Swarm Optimization ◽  
Node Placement

A New Methodology for the Reliability Based Particle Swarm Optimization with Simulated Annealing

2011 ◽  
Vol 274 ◽  
pp. 101-111 ◽  
Author(s):  
Norelislam Elhami ◽  
Rachid Ellaia ◽  
Mhamed Itmi
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Numerical Models ◽  
Limit State ◽  
Particle Swarm ◽  
Geometrical Parameters ◽  
Rectangular Plates ◽  
Swarm Optimization ◽  
Reliability Methods ◽  
State Functions

This paper presents a new methodology for the Reliability Based Particle Swarm Optimization with Simulated Annealing. The reliability analysis procedure couple traditional and modified first and second order reliability methods, in rectangular plates modelled by an Assumed Modes approach. Both reliability methods are applicable to the implicit limit state functions through numerical models, like those based on the Assumed Mode Method. For traditional reliability approaches, the algorithms FORM and SORM use a Newton-Raphson procedure for estimate design point. In modified approaches, the algorithms are based on heuristic optimization methods such as Particle Swarm Optimization and Simulated Annealing Optimization. Numerical applications in static, dynamic and stability problems are used to illustrate the applicability and effectiveness of proposed methodology. These examples consist in a rectangular plates subjected to in-plane external loads, material and geometrical parameters which are considered as random variables. The results show that the predicted reliability levels are accurate to evaluate simultaneously various implicit limit state functions with respect to static, dynamic and stability criterions.

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.

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