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Algorithms
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Differential Evolution for Manufacturing and Production Engineering Applications
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Differential Evolution for Manufacturing and Production Engineering Applications

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: closed (15 May 2023) | Viewed by 6666

Special Issue Editors

Dr. Anand J Kulkarni

E-Mail Website
Guest Editor
Institute of Artificial Intelligence MIT World Peace University, 124 Paud Road, Kothrud, Pune 411038, India
Interests: optimization algorithms; multi-objective optimization; discrete and combinatorial optimization; differential evolution
Prof. Dr. Luigi Benedicenti

E-Mail Website
Guest Editor
Faculty of Computer Science, University of New Brunswick, 550 Windsor Street, Fredericton, NB E3B 5A3, Canada
Interests: emotional contagion in software engineering; agile software processes; decision methods and conflict resolution
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Several approximation optimization algorithms have been proposed in manufacturing and production engineering applications so far. These approaches have been instrumental in solving several complex interdisciplinary problems; one such popular evolutionary methodology is the Differential Evolution (DE) algorithm. The DE has several advantages: it is derivative-free, resorts to directional searches and, importantly, can efficiently handle continuous, discrete and combinatorial problems. So far, several variations of the DE have been proposed and applied to a variety of problems across several domains; however, its application in manufacturing and production is less explored. The manufacturing domain is at the center of economic development of every country where efficient and optimum utilization of resources is critical. This Special Issue calls for contributions focusing on the various related aspects of the Differential Evolution (DE) algorithm as well as its hybrid, including (but not limited to) the following areas:

• Manufacturing processes;
• Design of machine elements;
• Productivity of the machining processes;
• Production planning and scheduling;
• Flexible manufacturing;
• Supply chain management;
• Robotics and automation.

Dr. Anand J Kulkarni
Prof. Dr. Benedicenti Luigi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • manufacturing processes
  • design of machine elements
  • productivity of the machining processes
  • production planning and scheduling
  • flexible manufacturing
  • supply-chain management
  • robotics and automation

Published Papers (3 papers)

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Research

17 pages, 1251 KiB  
Article
Crossover Rate Sorting in Adaptive Differential Evolution
by Vladimir Stanovov, Lev Kazakovtsev and Eugene Semenkin
Algorithms 2023, 16(3), 133; https://doi.org/10.3390/a16030133 - 2 Mar 2023
Viewed by 1421
Abstract
Differential evolution (DE) is a popular and efficient heuristic numerical optimization algorithm that has found many applications in various fields. One of the main disadvantages of DE is its sensitivity to parameter values. In this study, we investigate the effect of the previously [...] Read more.
Differential evolution (DE) is a popular and efficient heuristic numerical optimization algorithm that has found many applications in various fields. One of the main disadvantages of DE is its sensitivity to parameter values. In this study, we investigate the effect of the previously proposed crossover rate sorting mechanism on modern versions of DE. The sorting of the crossover rates, generated by a parameter adaptation mechanism prior to applying them in the crossover operation, enables the algorithm to make smaller changes to better individuals, and larger changes to worse ones, resulting in better exploration and exploitation. The experiments in this study were performed on several modern algorithms, namely L-SHADE-RSP, NL-SHADE-RSP, NL-SHADE-LBC and L-NTADE and two benchmark suites of test problems, CEC 2017 and CEC 2022. It is shown that crossover rate sorting does not result in significant additional computational efforts, but may improve results in certain scenarios, especially for high-dimensional problems. Full article
(This article belongs to the Special Issue Differential Evolution for Manufacturing and Production Engineering Applications)
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20 pages, 3002 KiB  
Article
Novel MIA-LSTM Deep Learning Hybrid Model with Data Preprocessing for Forecasting of PM2.5
by Gaurav Narkhede, Anil Hiwale, Bharat Tidke and Chetan Khadse
Algorithms 2023, 16(1), 52; https://doi.org/10.3390/a16010052 - 12 Jan 2023
Cited by 6 | Viewed by 2053
Abstract
Day by day pollution in cities is increasing due to urbanization. One of the biggest challenges posed by the rapid migration of inhabitants into cities is increased air pollution. Sustainable Development Goal 11 indicates that 99 percent of the world’s urban population breathes [...] Read more.
Day by day pollution in cities is increasing due to urbanization. One of the biggest challenges posed by the rapid migration of inhabitants into cities is increased air pollution. Sustainable Development Goal 11 indicates that 99 percent of the world’s urban population breathes polluted air. In such a trend of urbanization, predicting the concentrations of pollutants in advance is very important. Predictions of pollutants would help city administrations to take timely measures for ensuring Sustainable Development Goal 11. In data engineering, imputation and the removal of outliers are very important steps prior to forecasting the concentration of air pollutants. For pollution and meteorological data, missing values and outliers are critical problems that need to be addressed. This paper proposes a novel method called multiple iterative imputation using autoencoder-based long short-term memory (MIA-LSTM) which uses iterative imputation using an extra tree regressor as an estimator for the missing values in multivariate data followed by an LSTM autoencoder for the detection and removal of outliers present in the dataset. The preprocessed data were given to a multivariate LSTM for forecasting PM2.5 concentration. This paper also presents the effect of removing outliers and missing values from the dataset as well as the effect of imputing missing values in the process of forecasting the concentrations of air pollutants. The proposed method provides better results for forecasting with a root mean square error (RMSE) value of 9.8883. The obtained results were compared with the traditional gated recurrent unit (GRU), 1D convolutional neural network (CNN), and long short-term memory (LSTM) approaches for a dataset of the Aotizhonhxin area of Beijing in China. Similar results were observed for another two locations in China and one location in India. The results obtained show that imputation and outlier/anomaly removal improve the accuracy of air pollution forecasting. Full article
(This article belongs to the Special Issue Differential Evolution for Manufacturing and Production Engineering Applications)
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14 pages, 2387 KiB  
Article
Monte Carlo Simulation Affects Convergence of Differential Evolution: A Case of Optical Response Modeling
by Denis D. Chesalin, Andrei P. Razjivin, Alexey S. Dorokhov and Roman Y. Pishchalnikov
Algorithms 2023, 16(1), 3; https://doi.org/10.3390/a16010003 - 20 Dec 2022
Cited by 1 | Viewed by 2135
Abstract
It is known that the protein surrounding, as well as solvent molecules, has a significant influence on optical spectra of organic pigments by modulating the transition energies of their electronic states. These effects manifest themselves by a broadening of the spectral lines. Most [...] Read more.
It is known that the protein surrounding, as well as solvent molecules, has a significant influence on optical spectra of organic pigments by modulating the transition energies of their electronic states. These effects manifest themselves by a broadening of the spectral lines. Most semiclassical theories assume that the resulting lineshape of an electronic transition is a combination of homogeneous and inhomogeneous broadening contributions. In the case of the systems of interacting pigments such as photosynthetic pigment–protein complexes, the inhomogeneous broadening can be incorporated in addition to the homogeneous part by applying the Monte Carlo method (MCM), which implements the averaging over static disorder of the transition energies. In this study, taking the reaction center of photosystem II (PSIIRC) as an example of a quantum optical system, we showed that differential evolution (DE), a heuristic optimization algorithm, used to fit the experimentally measured data, produces results that are sensitive to the settings of MCM. Applying the exciton theory to simulate the PSIIRC linear optical response, the number of minimum required MCM realizations for the efficient performance of DE was estimated. Finally, the real linear spectroscopy data of PSIIRC were fitted using DE considering the necessary modifications to the implementation of the optical response modeling procedures. Full article
(This article belongs to the Special Issue Differential Evolution for Manufacturing and Production Engineering Applications)
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