Multi-objective Operation Mode Optimization of Medium Voltage Distribution Networks based on Improved Binary Particle Swarm Optimization

2019 ◽  
Author(s):  
Cai He ◽  
Dan Li
Keyword(s):  
Particle Swarm Optimization ◽  
Operation Mode ◽  
Particle Swarm ◽  
Distribution Networks ◽  
Binary Particle Swarm Optimization ◽  
Voltage Distribution ◽  
Swarm Optimization ◽  
Medium Voltage ◽  
Multi Objective

Multi-objective optimization of water distribution networks using particle swarm optimization

2021 ◽  
Vol 218 ◽  
pp. 18-31
Author(s):  
Douglas F. Surco ◽  
Diogo H. Macowski ◽  
Flávia A.R. Cardoso ◽  
Thelma P.B. Vecchi ◽  
Mauro A.S.S. Ravagnani
Keyword(s):  
Particle Swarm Optimization ◽  
Water Distribution ◽  
Particle Swarm ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Multi Objective

Analysis of Micro Array Gene Expression Data Using Multi-Objective Binary Particle Swarm Optimization with Fuzzy Weighted Clustering (MOBPSO-FWC) Technique

2020 ◽  
Vol 10 (5) ◽  
pp. 1049-1056
Author(s):  
M. Kalaiarasu ◽  
J. Anitha
Keyword(s):  
Gene Expression ◽  
Particle Swarm Optimization ◽  
Gene Expression Data ◽  
Expression Profiles ◽  
Particle Swarm ◽  
Binary Particle Swarm Optimization ◽  
Expression Data ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Weighted Clustering

In the rapidly advancing field of genomics, microarray technologies have turned into a ground-breaking system on simultaneous monitoring the expression patterns of multiple genes under various arrangements of constraints. A fundamental errand is to propose diagnostic techniques to distinguish cluster of genes comparative expression designs and are initiated by comparative conditions. And furthermore, the relating investigation has issue is to cluster multi-condition gene expression data. To overcome these issues, the vast measure of data obtained by this technology, resort to clustering methods that distinguish clusters of genes of share similar expression profiles. The motivation of this work is to introduce a clustering method in microarray gene expression data analysis. Multi-Objective Binary Particle Swarm Optimization with Fuzzy Weighted Clustering (MOBPSOFWC) algorithm is proposed to analyze gene expression data. In high dimensionality, a quick heuristic based pre-processing technique is employed to diminish some of the basic domain features from the initial feature set. Since these pre-processed and reduced features are still high dimensional, the proposed MOBPSO algorithm is implemented in MATLAB tool used for finding further feature subsets. The investigative are directed to distinguish the execution of the proposed work with existing clustering approaches.

Fuzzy Multi-Objective Association Rule Mining Using Evolutionary Computation

2017 ◽  
pp. 119-148
Author(s):  
Ganghishetti Pradeep ◽  
Vadlamani Ravi
Keyword(s):  
Particle Swarm Optimization ◽  
Association Rule ◽  
Association Rule Mining ◽  
Particle Swarm ◽  
Binary Particle Swarm Optimization ◽  
Threshold Accepting ◽  
Rule Mining ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Association Rule Miner

In this chapter, we model association rule mining as a Fuzzy multi-objective global optimization problem by considering several measures of strength such as support, confidence, coverage, comprehensibility, leverage, interestingness, lift and conviction by utilizing various fuzzy aggregator operators. In this, pdel, each measure has its own level of significance. Three fuzzy multi-objective association rule mining techniques viz., Fuzzy Multi-objective Binary Particle Swarm Optimization based association rule miner (FMO-BPSO), a hybridized Fuzzy Multi-objective Binary Firefly Optimization and Threshold Accepting based association rule miner (FMO-BFFOTA), hybridized Fuzzy Multi-objective Binary Particle Swarm Optimization and Threshold Accepting based association rule miner (FMO-BPSOTA) have been proposed. These three algorithms have been tested on various datasets such as book, food, bank, grocery, click stream and bakery datasets along with three fuzzy aggregate operators. From these experiments, we can conclude that Fuzzy-And outperforms all the other operators.

scholarly journals Impedance Estimation with an Enhanced Particle Swarm Optimization for Low-Voltage Distribution Networks

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1167 ◽  
Author(s):  
Daisuke Kodaira ◽  
Jingyeong Park ◽  
Sung Kim ◽  
Soohee Han ◽  
Sekyung Han
Keyword(s):  
Particle Swarm Optimization ◽  
Low Voltage ◽  
Particle Swarm ◽  
Estimation Algorithm ◽  
Distribution Networks ◽  
Estimation Methods ◽  
Local Optimum ◽  
Voltage Distribution ◽  
Swarm Optimization ◽  
Enhanced Particle Swarm Optimization

Many researchers in recent years have studied voltage deviation issues in distribution networks. Characterizing the impedance between consuming nodes in a network is the key to controlling the network voltage. Existing impedance estimation methods are faced with three challenges: time synchronized measurement, a generalization of the network model, and convergence of the optimization for objective functions. This paper extends an existing impedance estimation algorithm by introducing an enhanced particle swarm optimization (PSO). To overcome this method’s local optimum problem, we propose adaptive inertia weights. Also, our proposed method is based on a new general model for a low voltage distribution network with non-synchronized measurements. In the case study, the improved impedance estimation algorithm realizes better accuracy than the existing method.

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