The Development and Investigation of the Efficiency of the Differential Evolution Algorithm for Solving Multi-Objective Optimization Problems

Erokhin, Danil; Ахмедова, Ш А

doi:10.31772/2587-6066-2019-20-2-134-143

Cited by 3 publications

(2 citation statements)

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“…These modifications were based on two well-known schemes: Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D) [16] and Non-dominated Sorting Genetic Algorithm (NSGA) [17]. Proposed modifications were called DE+MOEA/D and DE+NSGA respectively [18].…”

Section: Automatically Generated Neural Networkmentioning

confidence: 99%

Automatically Generated Data Mining Tools for Complex System Operator's Condition Detection Using Non-Contact Vital Sensing

Akhmedova

Stanovov

Vishnevskaya

et al. 2021

IEICE Trans. Commun.

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This study is focused on the automated detection of a complex system operator's condition. For example, in this study a person's reaction while listening to music (or not listening at all) was determined. For this purpose various well-known data mining tools as well as ones developed by authors were used. To be more specific, the following techniques were developed and applied for the mentioned problems: artificial neural networks and fuzzy rule-based classifiers. The neural networks were generated by two modifications of the Differential Evolution algorithm based on the NSGA and MOEA/D schemes, proposed for solving multiobjective optimization problems. Fuzzy logic systems were generated by the population-based algorithm called Co-Operation of Biology Related Algorithms or COBRA. However, firstly each person's state was monitored. Thus, databases for problems described in this study were obtained by using non-contact Doppler sensors. Experimental results demonstrated that automatically generated neural networks and fuzzy rule-based classifiers can properly determine the human condition and reaction. Besides, proposed approaches outperformed alternative data mining tools. However, it was established that fuzzy rule-based classifiers are more accurate and interpretable than neural networks. Thus, they can be used for solving more complex problems related to the automated detection of an operator's condition.

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Section: Automatically Generated Neural Networkmentioning

confidence: 99%

Automatically Generated Data Mining Tools for Complex System Operator's Condition Detection Using Non-Contact Vital Sensing

Akhmedova

Stanovov

Vishnevskaya

et al. 2021

IEICE Trans. Commun.

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“…When solving practical problems of multi-criteria optimization containing conflicting optimality criteria, in order to obtain better results when using the DE algorithm, its modified solutions are developed by selecting parameters, applied mutation operators, schemes for accounting for objective functions, criteria associated with variables and their constraints [15,16,17].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Differential Evolution for Non-Smooth Optimization Problems

Egorova,

Kazakovtsev,

Krutikov

et al. 2023

FU Math Inform

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Solving high dimentional, multimodal, non-smooth global optimization problems faces challenges concerning quality of solution, computational costs or even the impossibility of solving the problem. Evolutionary algorithms, in particular, differential evolution algorithm proved itself as good method of global optimization. On the other side, approach based on subgradient methods are good for optimizing non-smooth functions. Combination of these two approaches enables to improve the quality of the algorithm, using the best features of both methods. In this paper, a new hybrid evolutionary approach based on differential evolution and subgradient algorithm as the local search procedure is proposed. Behavior of the proposed SSGDE algorithm was studied in a numerical experiment on three groups of generated tests. Comparison of the new hybrid algorithm with the pure DE approach showed the advantage of the SSGDE. It has been experimentally established that the proposed method finds the global minimum in the best way for all considered dimensions of the problem with respect to the differential evolution method. The SSGDE algorithm showed the best results with a significant increase in the number of functions.

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Automatically Generated Neural Networks for Human Condition Detection Using Non-Contact Vital Sensing

Akhmedova

Erokhin

Stanovov

et al. 2020

2020 14th International Symposium on Medical Information Communication Technology (ISMICT)

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The Development and Investigation of the Efficiency of the Differential Evolution Algorithm for Solving Multi-Objective Optimization Problems

Cited by 3 publications

References 0 publications

Automatically Generated Data Mining Tools for Complex System Operator's Condition Detection Using Non-Contact Vital Sensing

Automatically Generated Data Mining Tools for Complex System Operator's Condition Detection Using Non-Contact Vital Sensing

A Hybrid Differential Evolution for Non-Smooth Optimization Problems

Automatically Generated Neural Networks for Human Condition Detection Using Non-Contact Vital Sensing

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