Multi-objective differential evolution algorithm with fuzzy inference-based adaptive mutation factor for Pareto optimum design of suspension system

Jamali, Ali; Mallipeddi, Rammohan; Salehpour, M.; Bagheri, Ahmad

doi:10.1016/j.swevo.2020.100666

Cited by 26 publications

(13 citation statements)

References 21 publications

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“…In the past decades, evolutionary algorithms have attracted increasing interest for the solution of multiobjective optimization problems and a large number of multiobjective evolutionary algorithms (MOEAs) have been developed, including genetic algorithm [44]- [46], differential evolution [47], [48], particle swarm optimization [41], [49], [50], and memetic algorithm [51]- [53]. The goal of MOEA is to reach a good distribution of Pareto solutions with good convergence and diversity.…”

Section: Evolutionary Algorithmmentioning

confidence: 99%

Endmember Bundle Extraction Based on Multiobjective Optimization

Liu

Zhu

2021

IEEE Trans. Geosci. Remote Sensing

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A number of endmember extraction methods have been developed to identify pure pixels in hyperspectral images (HSIs). The majority of them use only one spectrum to represent one kind of material, which ignores the spectral variability problem that particularly characterizes a HSI with high spatial resolution. Only a few algorithms have been developed to identify multiple endmembers representing the spectral variability within each class, called endmember bundle extraction (EBE). This article introduces multiobjective particle swarm optimization for the identification of multiple endmember spectra with variability. Unlike existing convex geometry-based EBE methods, which operate on a single geometry of the dataspace, the proposed method divides the observed data into subsets along the spectral dimension and simultaneously operates on multiple dataspaces to obtain candidate endmembers based on multiobjective particle swarm optimization. The candidate endmembers are then refined by spatial post-processing and sequential forward floating selection to produce the final result. Experiments are conducted on both synthetic and real hyperspectral data to demonstrate the effectiveness of the proposed method in comparison with several state-of-the-art methods.

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Section: Evolutionary Algorithmmentioning

confidence: 99%

Endmember Bundle Extraction Based on Multiobjective Optimization

Liu

Zhu

2021

IEEE Trans. Geosci. Remote Sensing

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“…Experiment 5 The algorithm performance experiment is carried out to verify the performance of IMOA in solving the multi-objective FJSP-VB. The algorithm is compared with the non-dominated sorting genetic algorithm-II (NSGA-II) [2] and the multi-objective differential evolution (MODE) algorithm [28]. NSGA-II and MODE algorithms have good performance in solving multi-objective problems.…”

Section: Purpose Of Experimentsmentioning

confidence: 99%

An improved multi-objective optimization algorithm for solving flexible job shop scheduling problem with variable batches

Peng

Zirun

et al. 2021

J. of Syst. Eng. Electron.

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In order to solve the flexible job shop scheduling problem with variable batches, we propose an improved multiobjective optimization algorithm, which combines the idea of inverse scheduling. First, a flexible job shop problem with the variable batches scheduling model is formulated. Second, we propose a batch optimization algorithm with inverse scheduling in which the batch size is adjusted by the dynamic feedback batch adjusting method. Moreover, in order to increase the diversity of the population, two methods are developed. One is the threshold to control the neighborhood updating, and the other is the dynamic clustering algorithm to update the population. Finally, a group of experiments are carried out. The results show that the improved multi-objective optimization algorithm can ensure the diversity of Pareto solutions effectively, and has effective performance in solving the flexible job shop scheduling problem with variable batches.

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“…To this end, one can use GMDH-type NNs with multi-objective optimization and SVD to overcome both overfitting and singularity. In the proposed algorithm, we used the fuzzy adaptive mutation proposed by some authors in to reach global optimum solutions [46]. As shown in Fig.…”

Section: A Off-line Modelling Based On Deterministic Datamentioning

confidence: 99%

A Novel Hybrid Machine Learning Algorithm for Limited and Big Data Modeling With Application in Industry 4.0

et al. 2020

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To meet the challenges of manufacturing smart products, the manufacturing plants have been radically changed to become smart factories underpinned by industry 4.0 technologies. The transformation is assisted by employment of machine learning techniques that can deal with modelling both big or limited data. This manuscript reviews these concepts and present a case study that demonstrates the use of a novel intelligent hybrid algorithms for Industry 4.0 applications with limited data. In particular, an intelligent algorithm is proposed for robust data modelling of nonlinear systems based on input-output data. In our approach, a novel hybrid data-driven combining the Group-Method of Data-Handling and Singular-Value Decomposition is adapted to find an offline deterministic model combined with Pareto multi-objective optimization to overcome the overfitting issue. An Unscented-Kalman-Filter is also incorporated to update the coefficient of the deterministic model and increase its robustness against data uncertainties. The effectiveness of the proposed method is examined on a set of real industrial measurements. INDEX TERMS Industry 4.0; Big data modelling; Limited data modelling, Multi-objective optimization. NOMENCLATURE CNN

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Multi-objective differential evolution algorithm with fuzzy inference-based adaptive mutation factor for Pareto optimum design of suspension system

Cited by 26 publications

References 21 publications

Endmember Bundle Extraction Based on Multiobjective Optimization

Endmember Bundle Extraction Based on Multiobjective Optimization

An improved multi-objective optimization algorithm for solving flexible job shop scheduling problem with variable batches

A Novel Hybrid Machine Learning Algorithm for Limited and Big Data Modeling With Application in Industry 4.0

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