scholarly journals Data clustering and imputing using a two-level multi-objective genetic algorithm (GA): A case study of maintenance cost data for tunnel fans

2018 ◽  
Vol 5 (1) ◽  
pp. 1513304 ◽  
Author(s):  
Yamur K. Aldouri ◽  
Hassan Al-Chalabi ◽  
Liangwei Zhang ◽  
Qiang Shawn Cheng
Keyword(s):  
Genetic Algorithm ◽  
Data Clustering ◽  
Cost Data ◽  
Maintenance Cost ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

Broiler management using fuzzy multi-objective genetic algorithm: A case study

2020 ◽  
Vol 233 ◽  
pp. 103941
Author(s):  
Erfan Khosravani Moghadam ◽  
Mohammad Sharifi ◽  
Shahin Rafiee ◽  
Claus Aage Grøn Sørensen
Keyword(s):  
Genetic Algorithm ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

Multi-Objective Genetic Algorithm for Robust Clustering with Unknown Number of Clusters

2012 ◽  
Vol 3 (1) ◽  
pp. 1-20
Author(s):  
Amit Banerjee
Keyword(s):  
Genetic Algorithm ◽  
Data Clustering ◽  
Optimal Number ◽  
Least Trimmed Squares ◽  
Cluster Assignment ◽  
Objective Criterion ◽  
Number Of Clusters ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Optimal Number Of Clusters

In this paper, a multi-objective genetic algorithm for data clustering based on the robust fuzzy least trimmed squares estimator is presented. The proposed clustering methodology addresses two critical issues in unsupervised data clustering – the ability to produce meaningful partition in noisy data, and the requirement that the number of clusters be known a priori. The multi-objective genetic algorithm-driven clustering technique optimizes the number of clusters as well as cluster assignment, and cluster prototypes. A two-parameter, mapped, fixed point coding scheme is used to represent assignment of data into the true retained set and the noisy trimmed set, and the optimal number of clusters in the retained set. A three-objective criterion is also used as the minimization functional for the multi-objective genetic algorithm. Results on well-known data sets from literature suggest that the proposed methodology is superior to conventional fuzzy clustering algorithms that assume a known value for optimal number of clusters.

scholarly journals Fuzzy Multi-Objective Genetic Algorithm Based Resource Constrained Time-Cost Trade-Off Model under Uncertain Environment

2021 ◽  
Vol 9 (8) ◽  
pp. 1126-1133
Author(s):  
Ashish Sharma
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Construction Projects ◽  
Risk Level ◽  
Pareto Optimal ◽  
Time Cost ◽  
Trade Off ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

Abstract: In every construction project, the time and cost are the two most important objectives/factors to be considered. Clients and contractors should strive to optimize the project time and cost to maximize the return. Resources are also one of the major constraints of the construction projects. In recent years, several studies have been conducted to optimize the time and cost of project under constraint conditions of resources. Since most studies assume the time and cost as deterministic parameters, uncertainties should be considered in estimating the time and cost of the project's activities when minimizing the duration and cost of the project. For this purpose, this paper embeds the fuzzy logic to handle the uncertainties in estimating the time and cost. Besides, the multi-objective genetic algorithm (MOGA) is used to develop the resourceconstrained time-cost trade-off model. Alpha-cut approach is utilized to define the accepted risk level of decision maker. The efficiency of the proposed model is demonstrated through solvinga case study project of highway construction. The results of case study project provide a set of Pareto-optimal solutions. The developed model encourage the decision making process by choosing specified risk levels and utilizing the related Pareto-front. Keywords: Construction projects, time-cost trade-off, uncertainties, fuzzy logic, MOGA,Pareto-optimal solution.

Multi-objective optimization of safety instrumented systems maintenance strategy: a case study

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hafed Touahar ◽  
Nouara Ouazraoui ◽  
Nor El Houda Khanfri ◽  
Mourad Korichi ◽  
Bilal Bachi ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Life Cycle ◽  
Environmental Risks ◽  
Life Cycle Costs ◽  
Maintenance Strategy ◽  
Maintenance Strategies ◽  
Content Type ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

PurposeThe main objective of safety instrumented systems (SISs) is to maintain a safe condition of a facility if hazardous events occur. However, in some cases, SIS's can be activated prematurely, these activations are characterized in terms of frequency by a Spurious Trip Rate (STR) and their occurrence leads to significant technical, economic and even environmental losses. This work aims to propose an approach to optimize the performances of the SIS by a multi-objective genetic algorithm. The optimization of SIS performances is performed using the multi-objective genetic algorithm by minimizing their probability of failure on demand PFDavg, Spurious Trip Rate (STR) and Life Cycle Costs (LCCavg). A set of constraints related to maintenance costs have been established. These constraints imply specific maintenance strategies which improve the SIS performances and minimize the technical, economic and environmental risks related to spurious shutdowns. Validation of such an approach is applied to an Emergency Shutdown (ESD) of the blower section of an industrial facility (RGTE- In Amenas).Design/methodology/approachThe optimization of SIS performances is performed using the multi-objective genetic algorithm by minimizing their probability of failure on demand PFDavg, Spurious Trip Rate (STR) and Life Cycle Costs (LCCavg). A set of constraints related to maintenance costs have been established. These constraints imply specific maintenance strategies which improve the SIS performances and minimize the technical, economic and environmental risks related to spurious shutdowns. Validation of such an approach is applied to an Emergency Shutdown (ESD) of the blower section of an industrial facility (RGTE- In Amenas).FindingsA case study concerning a safety instrumented system implemented in the RGTE facility has shown the great applicability of the proposed approach and the results are encouraging. The results show that the selection of a good maintenance strategy allows a very significant minimization of the PFDavg, the frequency of spurious trips and Life Cycle Costs of SIS.Originality/valueThe maintenance strategy defined by the system designer can be modified and improved during the operational phase, in particular safety systems. It constitutes one of the least expensive investment strategies for improving SIS performances. It has allowed a considerable minimization of the SIS life cycle costs; PFDavg and the frequency of spurious trips.

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