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Symmetry
Special Issues
Evolutionary Computation, Metaheuristics, Nature-Inspired Algorithms, and Symmetry
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Evolutionary Computation, Metaheuristics, Nature-Inspired Algorithms, and Symmetry

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 669

Special Issue Editors

Dr. Yirui Wang

E-Mail Website
Guest Editor
Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China
Interests: evolutionary computation; search problems; chaos; neuralnets; optimisation; backpropagation; covariance matrices
Prof. Dr. Shangce Gao

E-Mail Website
Guest Editor
Faculty of Engineering, University of Toyama, Toyama-shi 930-8555, Japan
Interests: artificial intelligence; computational intelligence; neural networks
Special Issues, Collections and Topics in MDPI journals
Dr. Yang Yu

E-Mail Website
Guest Editor
College of Automation & College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
Interests: evolutionary computation; neural nets; search problems

Special Issue Information

Dear Colleagues,

Computational intelligence is an important branch of artificial intelligence. Nowadays, evolutionary computation as a part of computational intelligence is widely used to solve various numerical problems and real-world engineering problems. Its application and development bring a great contribution to the optimization domain. Thus, it is of great interest to investigate the role and significance of evolutionary computation, metaheuristics, and nature-inspired algorithms in optimizing distinctive problems such as model symmetry/asymmetry, model architecture and hyperparameters, numerical functions, and industrial processing.

This Special Issue aims to bring together both experts and newcomers from either academia or industry to discuss new and existing issues concerning evolutionary computation and optimization. The research topics include single-objective optimization, multi-objective optimization, combinatorial optimization, and real-world problems, as well as industrial control, job-shop scheduling, pattern recognition, and computer vision. There is no limit on the number of pages, but the submissions must demonstrate an understanding of the theme and contribute to the specified topic.

Dr. Yirui Wang
Dr. Shangce Gao
Dr. Yang Yu
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. Symmetry 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 2400 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

  • evolutionary computation
  • metaheuristics
  • nature-inspired algorithms
  • single-objective optimization
  • multi-objective optimization
  • real-world engineering application
  • intelligent systems
  • Artificial Intelligence

Published Papers (1 paper)

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Research

15 pages, 3489 KiB  
Article
Short-Term Electrical Load Forecasting Using an Enhanced Extreme Learning Machine Based on the Improved Dwarf Mongoose Optimization Algorithm
by Haocheng Wang, Yu Zhang and Lixin Mu
Symmetry 2024, 16(5), 628; https://doi.org/10.3390/sym16050628 - 18 May 2024
Viewed by 249
Abstract
Accurate short-term electrical load forecasting is crucial for the stable operation of power systems. Given the nonlinear, periodic, and rapidly changing characteristics of short-term power load forecasts, this paper introduces a novel forecasting method employing an Extreme Learning Machine (ELM) enhanced by an [...] Read more.
Accurate short-term electrical load forecasting is crucial for the stable operation of power systems. Given the nonlinear, periodic, and rapidly changing characteristics of short-term power load forecasts, this paper introduces a novel forecasting method employing an Extreme Learning Machine (ELM) enhanced by an improved Dwarf Mongoose Optimization Algorithm (Local escape Dwarf Mongoose Optimization Algorithm, LDMOA). This method addresses the significant prediction errors of conventional ELM models and enhances prediction accuracy. The enhancements to the Dwarf Mongoose Optimization Algorithm include three key modifications: initially, a dynamic backward learning strategy is integrated at the early stages of the algorithm to augment its global search capabilities. Subsequently, a cosine algorithm is employed to locate new food sources, thereby expanding the search scope and avoiding local optima. Lastly, a “madness factor” is added when identifying new sleeping burrows to further widen the search area and effectively circumvent local optima. Comparative analyses using benchmark functions demonstrate the improved algorithm’s superior convergence and stability. In this study, the LDMOA algorithm optimizes the weights and thresholds of the ELM to establish the LDMOA-ELM prediction model. Experimental forecasts utilizing data from China’s 2016 “The Electrician Mathematical Contest in Modeling” demonstrate that the LDMOA-ELM model significantly outperforms the original ELM model in terms of prediction error and accuracy. Full article
(This article belongs to the Special Issue Evolutionary Computation, Metaheuristics, Nature-Inspired Algorithms, and Symmetry)
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