scholarly journals Greedy Binary Particle Swarm Optimization for multi-Objective Constrained Next Release Problem

2019 ◽  
Vol 9 (5) ◽  
pp. 561-568 ◽  
Author(s):  
A. Hamdy ◽  
◽  
A. A. Mohamed ◽  
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Binary Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Next Release Problem

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.

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