A novel approach for neuro-fuzzy system-based multi-objective optimization to capture inherent fuzziness in engineering processes

Das, Amit Kumar; Pratihar, Dilip Kumar

doi:10.1016/j.knosys.2019.03.017

Cited by 15 publications

(8 citation statements)

References 34 publications

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“…In this study, by performing a knowledge discovery and data weighting style, the nondominated solutions are analyzed using data mining techniques to gain a deep understanding of the metal cutting process. Das and Pratihar [24] presented an approach to increase the accuracy of the solutions of multiobjective optimization evaluation algorithms. In this study, after obtaining a set of Pareto points using a weighted multiobjective evaluation algorithm, it is used in a neural system.…”

Section: Literature Reviewmentioning

confidence: 99%

Simulation‐Based Multiobjective Optimization of Open‐Pit Mine Haulage System: A Modified‐NBI Method and Meta Modeling Approach

2022

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A large number of engineering problems involve several conflicting objectives, which today are often solved through expensive simulation calculations. Methods based on meta-models are one of the approaches to solving this group of problems. In this paper, multiobjective optimization in the extraction system of a copper open-pit mine complex is presented by the modified-NBI optimization method and regression meta-model. For this purpose, two objective functions of maximizing the amount of total extraction, which is the sum of the extraction of sulfide, oxide, low-grade ores, and waste in this mine, and minimizing the transport time of haulage according to the limitation of its storage capacity, transport equipment, and budget are considered. The Central Composite Design (CCD) method is used to build the Design of Experiments (DOE) for the design variables. The considered design variables are the number of trucks of 120 tons, 240 tons, 35 tons, and 100 tons. The number of targets considered in each design combination is considered the response surface. The suitable meta-model to maximize the total extraction rate and minimize the transport time of the haulage, two modified functions of nonlinear regression have been determined. The accuracy of the models for selection has been done using PRESS and R 2 statistics. The most common PRESS error has also been used to validate the meta-models. Then the multiobjective optimization problem was solved using the modified-NBI method. Finally, Pareto and optimal solutions using the proposed approach were presented and discussed.

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Section: Literature Reviewmentioning

confidence: 99%

Simulation‐Based Multiobjective Optimization of Open‐Pit Mine Haulage System: A Modified‐NBI Method and Meta Modeling Approach

2022

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“…e detailed survey of NF models from 2000 to 2017 for classification is described in [37]. Das and Pratihar [38] used neuro-fuzzy with multiobjective optimization techniques to inherent fuzziness in the manufacturing process.Škrjanc et al [39] addressed a review on evolving neuro-fuzzy and fuzzy rule-based models used in real-world environments for classification, clustering, regression, and system identification. In the data analysis process, dimensionality reduction techniques such as feature selection and feature reduction are used in the preprocessing [40] stage in which the original features are transformed into either original feature or transformed features.…”

Section: Literature Surveymentioning

confidence: 99%

A Hybrid Neuro-Fuzzy and Feature Reduction Model for Classification

Das

Naik

Behera

2020

Advances in Fuzzy Systems

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The evolvement of the fuzzy system has shown influential and successful in many universal approximation capabilities and applications. This paper proposes a hybrid Neuro-Fuzzy and Feature Reduction (NF-FR) model for data analysis. This proposed NF-FR model uses a feature-based class belongingness fuzzification process for all the patterns. During the fuzzification process, all the features are expanded based on the number of classes available in the dataset. It helps to deal with the uncertainty issues and assists the Artificial Neural Network- (ANN-) based model to achieve better performance. However, the complexity of the problem increases due to this expansion of input features in the fuzzification process. These expanded features may not always contribute significantly to the model. To overcome this problem, feature reduction (FR) is used to filter out the insignificant features, resulting the network less computational cost. These reduced significant features are used in the ANN-based model to classify the data. The effectiveness of this proposed model is tested and validated with ten benchmark datasets (both balanced and unbalanced) to demonstrate the performance of the proposed NF-FR model. The performance comparison of the NF-FR model with other counterparts has been carried out based on various performance measures such as classification accuracy, root means square error, precision, recall, and f-measure for quantitative analysis of the results. The obtained simulated results have been tested using the Friedman, Holm, and ANOVA tests under the null hypothesis for statistical validity and correctness proof of the results. The result analysis and statistical analysis show that the NF-FR model has achieved a considerable improvement in accuracy and is found to be efficient in eliminating redundant and noisy information.

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“…He et al reformulate the original large-scale multi-objective optimization problem into a low-dimensional single-objective optimization problem via problem reformulation, and proposed a large-scale multi-objective optimization framework (LSMOF) [18]. Through training the trade-off solutions to a neuro-fuzzy system (NFS), Das and Pratihar proposed a novel approach for neuro-fuzzy system-based multi-objective optimization to capture inherent fuzziness in engineering processes [19].…”

Section: State Of the Artmentioning

confidence: 99%

“…During each iteration, each particle i in the swarm is updated in the jth dimension using the velocity update formula (18) and the position update formula (19).…”

Section: A the Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Multi-Objective Particle Swarm Optimization Based on Fuzzy Optimality

Shen

2019

IEEE Access

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In order to overcome the limitations of the Pareto optimality in solving multi-objective optimization problems, a new optimality definition, fuzzy optimality is proposed, which considered both of the numbers of improved objectives and the extent of the improvements. Then, the fuzzy optimalitybased multi-objective particle swarm optimization algorithm is presented. It inherits the basic structure of the particle swarm optimization and evaluates the particles by the fuzzy optimality. The numerical experiments are carried out on 6 representative test functions, and the results show that the proposed fuzzy optimality based multi-objective particle swarm optimization algorithm shows better performance on aspects of quality of solutions, robustness, and computational complexity, compared with the results of the NSGA-II and MOPSO. Finally, the efficacy and practicality of the proposed approach are validated in the APU fuel consumption and emissions multi-objective optimization problem.

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A novel approach for neuro-fuzzy system-based multi-objective optimization to capture inherent fuzziness in engineering processes

Cited by 15 publications

References 34 publications

Simulation‐Based Multiobjective Optimization of Open‐Pit Mine Haulage System: A Modified‐NBI Method and Meta Modeling Approach

Simulation‐Based Multiobjective Optimization of Open‐Pit Mine Haulage System: A Modified‐NBI Method and Meta Modeling Approach

A Hybrid Neuro-Fuzzy and Feature Reduction Model for Classification

Multi-Objective Particle Swarm Optimization Based on Fuzzy Optimality

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