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Mathematics
Special Issues
Heuristic Optimization and Machine Learning
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Heuristic Optimization and Machine Learning

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 2379

Special Issue Editor

Dr. Antonio Bolufé-Röhler

E-Mail Website
Guest Editor
School of Mathematical and Computational Sciences, University of Prince Edward Island, 550 University Ave, Charlottetown, PE C1A 4P3, Canada
Interests: heuristic optimization; machine learning; artificial intelligence

Special Issue Information

Dear Colleagues,

During the past several decades, heuristic optimization has proven to be effective in solving a broad variety of complex optimization problems; at the same time, machine learning became a transformational force in computer science and data analytics. Currently, the combination of heuristic optimization with machine learning is a rapidly developing field and one of the most successful trends in optimization.

The aim of this Special Issue is to explore this integration from a broad perspective, bringing together the latest research achievements of scholars studying and developing theoretical and practical applications in both fields. Topics include, but are not limited to:

  • Machine learning techniques for improving heuristic and metaheuristic optimization;
  • Evolutionary algorithms for generating artificial neural networks, parameters, and rules (neuro-evolution);
  • Evolutionary unsupervised learning;
  • Evolutionary deep learning;
  • Data-driven heuristic optimization;
  • Representation learning applied to landscape data;
  • Learnheuristics and meta-learning;
  • Machine learning for automatic algorithm selection and configuration;
  • Transfer of approaches between machine learning and optimization;
  • Analysis of heuristic optimization using machine learning methods.

Dr. Antonio Bolufé-Röhler
Guest Editor

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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

  • heuristic optimization；
  • metaheuristics；
  • machine learning；
  • deep learning；
  • reinforcement learning；
  • neuro-evolution；
  • learnheuristics；
  • evolutionary algorithms；
  • global optimization；
  • combinatorial optimization

Published Papers (2 papers)

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Research

22 pages, 848 KiB  
Article
An Integrated Model of Deep Learning and Heuristic Algorithm for Load Forecasting in Smart Grid
by Hisham Alghamdi, Ghulam Hafeez, Sajjad Ali, Safeer Ullah, Muhammad Iftikhar Khan, Sadia Murawwat and Lyu-Guang Hua
Mathematics 2023, 11(21), 4561; https://doi.org/10.3390/math11214561 - 06 Nov 2023
Cited by 2 | Viewed by 1053
Abstract
Accurate load forecasting plays a crucial role in the effective energy management of smart cities. However, the smart cities’ residents’ load profile is nonlinear, having high volatility, uncertainty, and randomness. Forecasting such nonlinear profiles requires accurate and stable prediction models. On this note, [...] Read more.
Accurate load forecasting plays a crucial role in the effective energy management of smart cities. However, the smart cities’ residents’ load profile is nonlinear, having high volatility, uncertainty, and randomness. Forecasting such nonlinear profiles requires accurate and stable prediction models. On this note, a prediction model has been developed by combining feature preprocessing, a multilayer perceptron, and a genetic wind-driven optimization algorithm, namely FPP-MLP-GWDO. The developed hybrid model has three parts: (i) feature preprocessing (FPP), (ii) a multilayer perceptron (MLP), and (iii) a genetic wind-driven optimization (GWDO) algorithm. The MLP is the key part of the developed model, which uses a multivariate autoregressive algorithm and rectified linear unit (ReLU) for network training. The developed hybrid model known as FPP-MLP-GWDO is evaluated using Dayton Ohio grid load data regarding aspects of accuracy (the mean absolute percentage error (MAPE), Theil’s inequality coefficient (TIC), and the correlation coefficient (CC)) and convergence speed (computational time (CT) and convergence rate (CR)). The findings endorsed the validity and applicability of the developed model compared to other literature models such as the feature selection–support vector machine–modified enhanced differential evolution (FS-SVM-mEDE) model, the feature selection–artificial neural network (FS-ANN) model, the support vector machine–differential evolution algorithm (SVM-DEA) model, and the autoregressive (AR) model regarding aspects of accuracy and convergence speed. The findings confirm that the developed FPP-MLP-GWDO model achieved an accuracy of 98.9%, thus surpassing benchmark models such as the FS-ANN (96.5%), FS-SVM-mEDE (97.9%), SVM-DEA (97.5%), and AR (95.7%). Furthermore, the FPP-MLP-GWDO significantly reduced the CT (299s) compared to the FS-SVM-mEDE (350s), SVM-DEA (240s), FS-ANN (159s), and AR (132s) models. Full article
(This article belongs to the Special Issue Heuristic Optimization and Machine Learning)
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19 pages, 888 KiB  
Article
Surrogate-Assisted Automatic Parameter Adaptation Design for Differential Evolution
by Vladimir Stanovov and Eugene Semenkin
Mathematics 2023, 11(13), 2937; https://doi.org/10.3390/math11132937 - 30 Jun 2023
Cited by 2 | Viewed by 706
Abstract
In this study, parameter adaptation methods for differential evolution are automatically designed using a surrogate approach. In particular, Taylor series are applied to model the searched dependence between the algorithm’s parameters and values, describing the current algorithm state. To find the best-performing adaptation [...] Read more.
In this study, parameter adaptation methods for differential evolution are automatically designed using a surrogate approach. In particular, Taylor series are applied to model the searched dependence between the algorithm’s parameters and values, describing the current algorithm state. To find the best-performing adaptation technique, efficient global optimization, a surrogate-assisted optimization technique, is applied. Three parameters are considered: scaling factor, crossover rate and population decrease rate. The learning phase is performed on a set of benchmark problems from the CEC 2017 competition, and the resulting parameter adaptation heuristics are additionally tested on CEC 2022 and SOCO benchmark suites. The results show that the proposed approach is capable of finding efficient adaptation techniques given relatively small computational resources. Full article
(This article belongs to the Special Issue Heuristic Optimization and Machine Learning)
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