scholarly journals Combined Data with Particle Swarm Optimization for Structural Damage Detection

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Fei Kang ◽  
Junjie Li ◽  
Sheng Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Damage Detection ◽  
Structural Damage ◽  
Detection Method ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Simulated Annealing Genetic Algorithm ◽  
Combined Data ◽  
Better Than

This paper proposes a damage detection method based on combined data of static and modal tests using particle swarm optimization (PSO). To improve the performance of PSO, some immune properties such as selection, receptor editing, and vaccination are introduced into the basic PSO and an improved PSO algorithm is formed. Simulations on three benchmark functions show that the new algorithm performs better than PSO. The efficiency of the proposed damage detection method is tested on a clamped beam, and the results demonstrate that it is more efficient than PSO, differential evolution, and an adaptive real-parameter simulated annealing genetic algorithm.

scholarly journals An Investigation of Particle Swarm Optimization Topologies in Structural Damage Detection

2021 ◽  
Vol 11 (11) ◽  
pp. 5144
Author(s):  
Xiao-Lin Li ◽  
Roger Serra ◽  
Julien Olivier
Keyword(s):  
Particle Swarm Optimization ◽  
Damage Detection ◽  
Structural Damage ◽  
Heuristic Algorithms ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Structural Damage Detection ◽  
Engineering Structures ◽  
Swarm Optimization ◽  
Global Topology

In the past few decades, vibration-based structural damage detection (SDD) has attracted widespread attention. Using the response data of engineering structures, the researchers have developed many methods for damage localization and quantification. Adopting meta-heuristic algorithms, in which particle swarm optimization (PSO) is the most widely used, is a popular approach. Various PSO variants have also been proposed for improving its performance in SDD, and they are generally based on the Global topology. However, in addition to the Global topology, other topologies are also developed in the related literature to enhance the performance of the PSO algorithm. The effects of PSO topologies depend significantly on the studied problems. Therefore, in this article, we conduct a performance investigation of eight PSO topologies in SDD. The success rate and mean iterations that are obtained from the numerical simulations are considered as the evaluation indexes. Furthermore, the average rank and Bonferroni-Dunn’s test are further utilized to perform the statistic analysis. From these analysis results, the Four Clusters are shown to be the more favorable PSO topologies in SDD.

ANALYSIS OF MUTATION OPERATORS ON QUANTUM-BEHAVED PARTICLE SWARM OPTIMIZATION ALGORITHM

2009 ◽  
Vol 05 (02) ◽  
pp. 487-496 ◽  
Author(s):  
WEI FANG ◽  
JUN SUN ◽  
WENBO XU
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Novel Mutation ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Mutation Operator ◽  
Fundamental Theory ◽  
Swarm Optimization ◽  
Mutation Operators ◽  
Better Than

Mutation operator is one of the mechanisms of evolutionary algorithms (EAs) and it can provide diversity in the search and help to explore the undiscovered search place. Quantum-behaved particle swarm optimization (QPSO), which is inspired by fundamental theory of PSO algorithm and quantum mechanics, is a novel stochastic searching technique and it may encounter local minima problem when solving multi-modal problems just as that in PSO. A novel mutation mechanism is proposed in this paper to enhance the global search ability of QPSO and a set of different mutation operators is introduced and implemented on the QPSO. Experiments are conducted on several well-known benchmark functions. Experimental results show that QPSO with some of the mutation operators is proven to be statistically significant better than the original QPSO.

Tasks mapping in the network on a chip using an improved optimization algorithm

2020 ◽  
Vol 16 (2) ◽  
pp. 165-182
Author(s):  
Mehdi Darbandi ◽  
Amir Reza Ramtin ◽  
Omid Khold Sharafi
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Computational Time ◽  
Swarm Optimization ◽  
Content Type ◽  
Better Than

Purpose A set of routers that are connected over communication channels can from network-on-chip (NoC). High performance, scalability, modularity and the ability to parallel the structure of the communications are some of its advantages. Because of the growing number of cores of NoC, their arrangement has got more valuable. The mapping action is done based on assigning different functional units to different nodes on the NoC, and the way it is done contains a significant effect on implementation and network power utilization. The NoC mapping issue is one of the NP-hard problems. Therefore, for achieving optimal or near-optimal answers, meta-heuristic algorithms are the perfect choices. The purpose of this paper is to design a novel procedure for mapping process cores for reducing communication delays and cost parameters. A multi-objective particle swarm optimization algorithm standing on crowding distance (MOPSO-CD) has been used for this purpose. Design/methodology/approach In the proposed approach, in which the two-dimensional mesh topology has been used as base construction, the mapping operation is divided into two stages as follows: allocating the tasks to suitable cores of intellectual property; and plotting the map of these cores in a specific tile on the platform of NoC. Findings The proposed method has dramatically improved the related problems and limitations of meta-heuristic algorithms. This algorithm performs better than the particle swarm optimization (PSO) and genetic algorithm in convergence to the Pareto, producing a proficiently divided collection of solving ways and the computational time. The results of the simulation also show that the delay parameter of the proposed method is 1.1 per cent better than the genetic algorithm and 0.5 per cent better than the PSO algorithm. Also, in the communication cost parameter, the proposed method has 2.7 per cent better action than a genetic algorithm and 0.16 per cent better action than the PSO algorithm. Originality/value As yet, the MOPSO-CD algorithm has not been used for solving the task mapping issue in the NoC.

On PSO Based BP Neural Network

2014 ◽  
Vol 602-605 ◽  
pp. 3518-3521
Author(s):  
Peng Hu ◽  
Xiao Quan Song
Keyword(s):  
Neural Network ◽  
Numerical Simulation ◽  
Particle Swarm Optimization ◽  
Bp Neural Network ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Better Than

Particle swarm optimization (PSO) based BP neural network is introduced , which is superior to the traditional BP neural network . The traditional BP neural network and PSO algorithm is illustrated respectively, and introduces how to apply PSO algorithm in BP neural network. The numerical simulation proves that PSO-BP neural network performs better than traditional BP neural network.

Particle Swarm Optimization Based Dimension Synthesis of the Experimental Mechanism for Elastic Bungee Jumping Ropes

2011 ◽  
Vol 84-85 ◽  
pp. 214-218
Author(s):  
Fu Xiang Zhang ◽  
Wen Zhong Li
Keyword(s):  
Particle Swarm Optimization ◽  
Optimal Algorithm ◽  
Synthesis Method ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Swing Angle ◽  
Drawing Method ◽  
Better Than ◽  
Optimal Calculation

In order to provide an experimental machine for elastic bungee jumping ropes, a prototype of experimental mechanism was designed, and its principles were analyzed. A dimension synthesis method of the experimental mechanism based on the particle swarm optimization (PSO) was brought forward. The aim of optimization was to find the optimized parameters of the mechanism by which the elastic bungee jumping ropes were pulled at the minimum swing angle. An optimization program of the PSO algorithm in the Matlab environment was developed and the optimal calculation was done. The result proved the validity of the algorithm. The calculation result showed that the optimal algorithm made the elastic bungee jumping ropes pulled at the minimum swing angle of only 8.783 degree, which was better than that of the handwork drawing method used by an engineer, so the parameters got by the PSO method can

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