Mathematical Optimization & Evolutionary Computing

A special issue of Mathematics (ISSN 2227-7390).

Deadline for manuscript submissions: 20 September 2024 | Viewed by 467

Special Issue Editor


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Guest Editor
Department of Design Engineering and Maths, Middlesex University, London NW4 4BT, UK
Interests: applied mathematics; algorithm development; computational intelligence; engineering optimisation; mathematical modelling; optimisation and swarm intelligence

Special Issue Information

Dear Colleagues,

Mathematical optimization is important from the perspectives of both theoretical interests and applications. Many applications in science and engineering as well as industry are closely related to highly nonlinear optimization problems. Finding solutions to such optimization problems requires sophisticated algorithms and techniques, and evolutionary computing includes a class of such powerful optimization techniques. This Special Issue will focus on both the theoretical analysis of algorithms and the application of evolutionary computing techniques.

Interested researchers are invited to contribute in (but not limited to) the following areas:

  • Mathematical optimization, algorithms and techniques;
  • Mathematical and statistical foundations of optimization; 
  • Analysis of algorithms, such as convergence and stability;
  • Evolutionary algorithms and their applications;
  • New algorithms and hybrid algorithms;
  • Applications in science and engineering;
  • Numerical experiments and case studies in industry.

Dr. Xin-She Yang
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

  • mathematical optimization
  • evolutionary computing
  • numerical experiments

Published Papers (1 paper)

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Research

31 pages, 2035 KiB  
Article
An Underwater Passive Electric Field Positioning Method Based on Scalar Potential
by Yi Zhang, Cong Chen, Jiaqing Sun, Mingjie Qiu and Xu Wu
Mathematics 2024, 12(12), 1832; https://doi.org/10.3390/math12121832 - 12 Jun 2024
Viewed by 313
Abstract
In order to fulfill the practical application demands of precisely localizing underwater vehicles using passive electric field localization technology, we propose a scalar-potential-based method for the passive electric field localization of underwater vehicles. This method is grounded on an intelligent differential evolution algorithm [...] Read more.
In order to fulfill the practical application demands of precisely localizing underwater vehicles using passive electric field localization technology, we propose a scalar-potential-based method for the passive electric field localization of underwater vehicles. This method is grounded on an intelligent differential evolution algorithm and is particularly suited for use in three-layer and stratified oceanic environments. Firstly, based on the potential distribution law of constant current elements in a three-layer parallel stratified ocean environment, the mathematical positioning model is established using the mirror method. Secondly, the differential evolution (DE) algorithm is enhanced with a parameter-adaptive strategy and a boundary mutation processing mechanism to optimize the key objective function in the positioning problem. Additionally, the simulation experiments of the current element in the layered model prove the effectiveness of the proposed positioning method and show that it has no special requirements for the sensor measurement array, but the large range and moderate number of sensors are beneficial to improve the positioning effect. Finally, the laboratory experiments on the positioning method proposed in this paper, involving underwater simulated current elements and underwater vehicle tracks, were carried out successfully. The results indicate that the positioning method proposed in this paper can achieve the performance requirements of independent initial value, strong anti-noise capabilities, rapid positioning speed, easy implementation, and suitability in shallow sea environments. These findings suggest a promising practical application potential for the proposed method. Full article
(This article belongs to the Special Issue Mathematical Optimization & Evolutionary Computing)
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