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.

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.

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.

Regularization Strategies for Contiguous and Noncontiguous Damage Detection of Structures

2020 ◽  
pp. 2140001
Author(s):  
Ziwei Luo ◽  
Ling Yu
Keyword(s):  
Numerical Simulation ◽  
Particle Swarm Optimization ◽  
Damage Detection ◽  
Comparative Studies ◽  
Structural Damage ◽  
Simulation Models ◽  
Objective Functions ◽  
Structural Damage Detection ◽  
Swarm Optimization ◽  
Sparsest Solutions

Regularization strategies have attracted attention in the structural damage detection (SDD) field. However, there is lack of studies on regularization strategies for damage patterns in the existing methods. This paper proposes regularization strategies for contiguous and noncontiguous damages of structures and performs comparative studies. The objective functions are first defined to consider effects of strategies on SDD by adding distinct norm penalties, and then are solved by the particle swarm optimization (PSO). Three numerical simulation models are employed to assess the applicability of three strategies. The results show that the [Formula: see text] norm regularization is suitable for detecting multiple damages, the [Formula: see text] norm regularization performs well in contiguous damages, and the sparsest solutions can be obtained by the [Formula: see text] norm regularization.

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