Latest Advances and Applications of Multi-Objective Optimization Techniques

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

Deadline for manuscript submissions: closed (10 April 2023) | Viewed by 2160

Special Issue Editors


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Guest Editor
School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi'an 710072, China
Interests: multi-objective optimization; social computing
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Artificial Intelligence, Optics and Electronics, Northwestern Polytechnical University, Xi’an 710072, China
Interests: data-driven modeling; social networks analysis; application of machine learning approaches
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is devoted to the latest advances and applications of multi-objective optimization techniques in various research areas. As human society develops, various optimization algorithms are designed and widely applied in different areas, such as heuristic algorithms, collaborative game algorithms, multi-time intervals algorithms, etc. Various multi-objective optimization techniques have been developed to prevent falling into local optima and derive desired solutions. When facing conflicting objectives, evolutionary multi-objective optimization techniques efficiently address these complicated scenarios with black-box search/optimization. Furthermore, as artificial intelligence evolves, the optimization of machine learning architectures has become a subject of particular interest. This Special Issue focuses on the latest advances and applications of multi-objective optimization for various real-world theoretical and practical issues. This SI will further contribute to the body of literature on social, mechanical, biomedical, aeronautical, and aerospace engineering, aiming to bring together scholars, researchers, industry personnel, academicians, and individuals in these fields to promote the exchange of novel ideas and findings.

Potential topics include, but are not limited to, the following:

  • Multi-agent systems;
  • Social computation;
  • Data-driven multi-objective computation;
  • High-dimensional and many-objective algorithms;
  • Evolutionary learning for combinatorial optimization;
  • Transport scheduling;
  • Automated heuristic design;
  • Data-driven multi-objective optimization;
  • Parallelized multi-objective optimization;
  • Many-objective multi-objective optimization;
  • Large-scale multi-objective optimization;
  • Machine learning architecture optimization;
  • Application of multi-objective optimization bioinformatics, intelligent transportation, smart city, smart sensor networks, cybersecurity, and other critical application areas.

Prof. Dr. Chao Gao
Prof. Dr. Peican Zhu
Prof. Dr. Lianbo Ma
Guest Editors

Manuscript Submission Information

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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.

Published Papers (1 paper)

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Research

21 pages, 1353 KiB  
Article
Optimal Defense Strategy Selection Algorithm Based on Reinforcement Learning and Opposition-Based Learning
by Yiqun Yue, Yang Zhou, Lijuan Xu and Dawei Zhao
Appl. Sci. 2022, 12(19), 9594; https://0-doi-org.brum.beds.ac.uk/10.3390/app12199594 - 24 Sep 2022
Cited by 1 | Viewed by 1264
Abstract
Industrial control systems (ICS) are facing increasing cybersecurity issues, leading to enormous threats and risks to numerous industrial infrastructures. In order to resist such threats and risks, it is particularly important to scientifically construct security strategies before an attack occurs. The characteristics of [...] Read more.
Industrial control systems (ICS) are facing increasing cybersecurity issues, leading to enormous threats and risks to numerous industrial infrastructures. In order to resist such threats and risks, it is particularly important to scientifically construct security strategies before an attack occurs. The characteristics of evolutionary algorithms are very suitable for finding optimal strategies. However, the more common evolutionary algorithms currently used have relatively large limitations in convergence accuracy and convergence speed, such as PSO, DE, GA, etc. Therefore, this paper proposes a hybrid strategy differential evolution algorithm based on reinforcement learning and opposition-based learning to construct the optimal security strategy. It greatly improved the common problems of evolutionary algorithms. This paper first scans the vulnerabilities of the water distribution system and generates an attack graph. Then, in order to solve the balance problem of cost and benefit, a cost–benefit-based objective function is constructed. Finally, the optimal security strategy set is constructed using the algorithm proposed in this paper. Through experiments, it is found that in the problem of security strategy construction, the algorithm in this paper has obvious advantages in convergence speed and convergence accuracy compared with some other intelligent strategy selection algorithms. Full article
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