scholarly journals Advances in data-driven analyses and modelling using EPR-MOGA

2009 ◽  
Vol 11 (3-4) ◽  
pp. 225-236 ◽  
Author(s):  
O. Giustolisi ◽  
D. A. Savic
Keyword(s):  
Genetic Algorithm ◽  
Polynomial Regression ◽  
Variable Number ◽  
Sequential Search ◽  
Multi Objective ◽  
Polynomial Coefficients ◽  
Evolutionary Polynomial Regression ◽  
Multi Objective Genetic Algorithm ◽  
Regression Techniques

Evolutionary Polynomial Regression (EPR) is a recently developed hybrid regression method that combines the best features of conventional numerical regression techniques with the genetic programming/symbolic regression technique. The original version of EPR works with formulae based on true or pseudo-polynomial expressions using a single-objective genetic algorithm. Therefore, to obtain a set of formulae with a variable number of pseudo-polynomial coefficients, the sequential search is performed in the formulae space. This article presents an improved EPR strategy that uses a multi-objective genetic algorithm instead. We demonstrate that multi-objective approach is a more feasible instrument for data analysis and model selection. Moreover, we show that EPR can also allow for simple uncertainty analysis (since it returns polynomial structures that are linear with respect to the estimated coefficients). The methodology is tested and the results are reported in a case study relating groundwater level predictions to total monthly rainfall.

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

scholarly journals Asset deterioration analysis using multi-utility data and multi-objective data mining

2009 ◽  
Vol 11 (3-4) ◽  
pp. 211-224 ◽  
Author(s):  
D. A. Savic ◽  
O. Giustolisi ◽  
D. Laucelli
Keyword(s):  
Data Mining ◽  
Polynomial Regression ◽  
Single Case ◽  
Model Parameters ◽  
Physical Systems ◽  
Multi Objective ◽  
Evolutionary Polynomial Regression ◽  
Physically Based ◽  
Physical Laws

Physically-based models derive from first principles (e.g. physical laws) and rely on known variables and parameters. Because these have physical meaning, they also explain the underlying relationships of the system and are usually transportable from one system to another as a structural entity. They only require model parameters to be updated. Data-driven or regressive techniques involve data mining for modelling and one of the major drawbacks of this is that the functional form describing relationships between variables and the numerical parameters is not transportable to other physical systems as is the case with their classical physically-based counterparts. Aimed at striking a balance, Evolutionary Polynomial Regression (EPR) offers a way to model multi-utility data of asset deterioration in order to render model structures transportable across physical systems. EPR is a recently developed hybrid regression method providing symbolic expressions for models and works with formulae based on pseudo-polynomial expressions, usually in a multi-objective scenario where the best Pareto optimal models (parsimony versus accuracy) are selected from data in a single case study. This article discusses the improvement of EPR in dealing with multi-utility data (multi-case study) where it has been tried to achieve a general model structure for asset deterioration prediction across different water systems.

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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