Optimization and Machine Learning
A special issue of AppliedMath (ISSN 2673-9909).
Deadline for manuscript submissions: 30 August 2024 | Viewed by 344
Special Issue Editor
Special Issue Information
Dear Colleagues,
We are pleased to invite you to submit your research to be considered for publication in a Special Issue of AppliedMath, focused on the latest advances in optimization and machine learning. The goal of this Special Issue is to showcase the latest advances in this field and to provide a platform for researchers to share their promising findings.
Optimization and machine learning (ML) have become two of the most popular issues in the last decade. ML provides a variety of tricks for data preprocessing, feature extraction, model selection, etc., whereas optimization algorithms offer elementary techniques for the construction of mathematical models and parameter fitting of ML techniques. Arising from the deep integration of optimization and ML, excellent optimization-based ML algorithms and efficient ML-assisted optimization algorithms can be developed to address the challenges of scientific research and engineering applications in the big data era.
In this Special Issue, we invite and welcome reviews and original papers about theoretical and practical studies of ML and optimization algorithms, including excellent ML algorithms based on novel optimization techniques and optimization algorithms promoted by ML strategies.
Dr. Yu Chen
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. AppliedMath is an international peer-reviewed open access quarterly 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 1000 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
- optimization
- combinatorial optimization
- evolutionary optimization
- swarm intelligence
- metaheuristics
- machine learning
- reinforcement learning
- transfer learning
- deep learning
- data-driven optimization
- large-scale optimization
- multi-objective optimization
- evolutionary multi-task optimization
- evolutionary deep learning
Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: A Multi-objective Random Drift Particle Swarm Optimization
Authors: Liwei Li; Min Shan; Jun Sun; Vasile Palade; Xiaojun Wu
Affiliation: Coventry University, UK; Jiangnan University China
Abstract: This paper proposes, this paper proposes a multi-objective random drift particle swarm optimization algorithm with a dual-archive mechanism (MORDPSO-DA). First, Random Drift particle swarm optimization algorithm is applied to the multi-objective optimization by adopting a multi-scale chaotic variational operation on the particles to enhance the global search ability of the particles. Then a dual-archive mechanism is proposed to establish an auxiliary archive with a capacity threshold in addition to the main archive external to the non-dominated solution set. The particles deleted from the main archive are censored according to the congestion distance and are saved in the auxiliary archive. When the capacity of the auxiliary archive reaches the threshold, the particles in the auxiliary archive are compared with those in the main archive, and the main archive is updated according to the congestion distance, with the particles beneficial to the diversity of the solution being retained. The proposed MORDPSO-DA is tested on the ZDT and DTLZ benchmark test functions, with the results showing that the algorithm achieved better results in terms of IGD, GD, and SP than the other compared methods.
