Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.5
2.7
Journals
Applied Sciences
Special Issues
Multi-objective Optimization: Techniques and Applications
share announcement

Multi-objective Optimization: Techniques and Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 536

Special Issue Editors

Dr. Douglas Alexandre Gomes Vieira

E-Mail Website
Guest Editor
Graduate Program in Mathematical Modeling, Federal Center of Technological Education of Minas Gerais, Belo Horizonte 30421-169, Brazil
Interests: math
Dr. Lisboa Adriano Chaves

E-Mail Website
Guest Editor
Graduate Program in Mathematical Modeling, Federal Center of Technological Education of Minas Gerais, Belo Horizonte 30421-169, Brazil
Interests: multiobjective optimization; machine learning; digital twin

Special Issue Information

Dear Colleagues,

This Special Issue presents a broad array of methodologies and applications for multiobjective optimization and decision-making. This includes innovative algorithms such as deterministic, linear, convex, non-linear, stochastic, and combinatorial algorithms, among others. Real-world applications in fields like artificial intelligence, machine learning, supply chain optimization, logistics, risk analysis, resource allocation, deficit allocation, portfolio management, sustainability, and renewable energy, among others, are of interest.

Dr. Douglas Alexandre Gomes Vieira
Dr. Lisboa Adriano Chaves
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. Applied Sciences 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 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

  • multi-objective optimization
  • decision making
  • deterministic optimization
  • combinatorial optimization
  • linear optimization

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

13 pages, 3835 KiB  
Article
An Improved Evolutionary Multi-Objective Clustering Algorithm Based on Autoencoder
by Mingxin Qiu, Yingyao Zhang, Shuai Lei and Miaosong Gu
Appl. Sci. 2024, 14(6), 2454; https://doi.org/10.3390/app14062454 - 14 Mar 2024
Viewed by 409
Abstract
Evolutionary multi-objective clustering (EMOC) algorithms have gained popularity recently, as they can obtain a set of clustering solutions in a single run by optimizing multiple objectives. Particularly, in one type of EMOC algorithm, the number of clusters k is taken as one of [...] Read more.
Evolutionary multi-objective clustering (EMOC) algorithms have gained popularity recently, as they can obtain a set of clustering solutions in a single run by optimizing multiple objectives. Particularly, in one type of EMOC algorithm, the number of clusters k is taken as one of the multiple objectives to obtain a set of clustering solutions with different k. However, the numbers of clusters k and other objectives are not always in conflict, so it is impossible to obtain the clustering solutions with all different k in a single run. Therefore, evolutionary multi-objective k-clustering (EMO-KC) has recently been proposed to ensure this conflict. However, EMO-KC could not obtain good clustering accuracy on high-dimensional datasets. Moreover, EMO-KC’s validity is not ensured as one of its objectives (SSDexp, which is transformed from the sum of squared distances (SSD)) could not be effectively optimized and it could not avoid invalid solutions in its initialization. In this paper, an improved evolutionary multi-objective clustering algorithm based on autoencoder (AE-IEMOKC) is proposed to improve the accuracy and ensure the validity of EMO-KC. The proposed AE-IEMOKC is established by combining an autoencoder with an improved version of EMO-KC (IEMO-KC) for better accuracy, where IEMO-KC is improved based on EMO-KC by proposing a scaling factor to help effectively optimize the objective of SSDexp and introducing a valid initialization to avoid the invalid solutions. Experimental results on several datasets demonstrate the accuracy and validity of AE-IEMOKC. The results of this paper may provide some useful information for other EMOC algorithms to improve accuracy and convergence. Full article
(This article belongs to the Special Issue Multi-objective Optimization: Techniques and Applications)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop