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Current Topics in Evolutionary Computation and Multi-Objective Optimization
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Current Topics in Evolutionary Computation and Multi-Objective Optimization

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E2: Control Theory and Mechanics".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 1353

Special Issue Editors

Dr. Wenhua Li

E-Mail Website
Guest Editor
College of Systems Engineering, National University of Defense Technology (NUDT), Changsha 410073, China
Interests: evolutionary computation; microgrid; multi-objective optimization
Dr. Kaiwen Li

E-Mail Website
Guest Editor
College of Systems Engineering, National University of Defense Technology (NUDT), Changsha 410073, China
Interests: evolutionary computation; swarm intelligence; multi-objective optimization

Special Issue Information

Dear Colleagues,

Evolutionary Computation and Multi-Objective Optimization have emerged as pivotal methodologies for addressing complex optimization problems across various domains. These techniques provide robust frameworks for modeling and solving real-world problems that often involve multiple conflicting objectives, requiring sophisticated strategies to find optimal trade-offs.

The theoretical advancements and practical applications of Evolutionary Computation and Multi-Objective Optimization span a wide range of fields, including but not limited to engineering design, economics, logistics, artificial intelligence, and bioinformatics. The unique ability of these methods to handle large, complex, and dynamic problem spaces makes them invaluable tools for researchers and practitioners alike.

This Special Issue aims to gather innovative research and significant advancements in the field of Evolutionary Computation and Multi-Objective Optimization. We invite contributions that explore novel algorithms, theoretical insights, and practical applications. Special attention will be given to the following:

  • The development of new evolutionary algorithms and optimization techniques.
  • Theoretical analysis and performance evaluation of existing and newly proposed methods.
  • Applications of these techniques to real-world problems, demonstrating their practical impact.
  • Hybrid approaches that combine evolutionary computation with other optimization methods.
  • Case studies and empirical research that highlight the effectiveness of these approaches in various domains.

By bringing together cutting-edge research and diverse perspectives, this Special Issue seeks to advance the state-of-the-art in Evolutionary Computation and Multi-Objective Optimization, fostering innovation and collaboration within the research community.

We look forward to your valuable contributions to this Special Issue.

Dr. Wenhua Li
Dr. Kaiwen Li
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. 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

  • evolutionary algorithms
  • multi-objective optimization
  • optimization techniques
  • theoretical analysis
  • real-world applications

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Published Papers (3 papers)

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22 pages, 1402 KiB  
Article
Dual-Population Cooperative Correlation Evolutionary Algorithm for Constrained Multi-Objective Optimization
by Junming Chen, Yanxiu Wang, Zichun Shao, Hui Zeng and Siyuan Zhao
Mathematics 2025, 13(9), 1441; https://doi.org/10.3390/math13091441 - 28 Apr 2025
Viewed by 50
Abstract
When addressing constrained multi-objective optimization problems (CMOPs), the key challenge lies in achieving a balance between the objective functions and the constraint conditions. However, existing evolutionary algorithms exhibit certain limitations when tackling CMOPs with complex feasible regions. To address this issue, this paper [...] Read more.
When addressing constrained multi-objective optimization problems (CMOPs), the key challenge lies in achieving a balance between the objective functions and the constraint conditions. However, existing evolutionary algorithms exhibit certain limitations when tackling CMOPs with complex feasible regions. To address this issue, this paper proposes a constrained multi-objective evolutionary algorithm based on a dual-population cooperative correlation (CMOEA-DCC). Under the CMOEA-DDC framework, the system maintains two independently evolving populations: the driving population and the conventional population. These two populations share information through a collaborative interaction mechanism, where the driving population focuses on objective optimization, while the conventional population balances both objectives and constraints. To further enhance the performance of the algorithm, a shift-based density estimation (SDE) method is introduced to maintain the diversity of solutions in the driving population, while a multi-criteria evaluation metric is adopted to improve the feasibility quality of solutions in the normal population. CMOEA-DDC was compared with seven representative constrained multi-objective evolutionary algorithms (CMOEAs) across various test problems and real-world application scenarios. Through an in-depth analysis of a series of experimental results, it can be concluded that CMOEA-DDC significantly outperforms the other competing algorithms in terms of performance. Full article
(This article belongs to the Special Issue Current Topics in Evolutionary Computation and Multi-Objective Optimization)
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22 pages, 2107 KiB  
Article
Feedback Tracking Constraint Relaxation Algorithm for Constrained Multi-Objective Optimization
by Yuling Lai, Junming Chen, Yile Chen, Hui Zeng and Jialin Cai
Mathematics 2025, 13(4), 629; https://doi.org/10.3390/math13040629 - 14 Feb 2025
Viewed by 455
Abstract
In practical applications, constrained multi-objective optimization problems (CMOPs) often fail to achieve the desired results when dealing with CMOPs with different characteristics. Therefore, to address this drawback, we designed a constraint multi-objective evolutionary algorithm based on feedback tracking constraint relaxation, referred to as [...] Read more.
In practical applications, constrained multi-objective optimization problems (CMOPs) often fail to achieve the desired results when dealing with CMOPs with different characteristics. Therefore, to address this drawback, we designed a constraint multi-objective evolutionary algorithm based on feedback tracking constraint relaxation, referred to as CMOEA-FTR. The entire search process of the algorithm is divided into two stages: In the first stage, the constraint boundaries are adaptively adjusted based on the feedback information from the population solutions, guiding the boundary solutions towards neighboring solutions and tracking high-quality solutions to obtain the complete feasible region, thereby promoting the population to approach the unconstrained Pareto front (UPF). The obtained feasible solutions are stored in an archive and continuously updated to promote the diversity and convergence of the population. In the second stage, the scaling of constraint boundaries is stopped, and a new dominance criterion is established to obtain high-quality parents, thereby achieving the complete constrained Pareto front (CPF). Additionally, we customized an elite mating pool selection, an archive updating strategy, and an elite environmental selection truncation mechanism to maintain a balance between diversity and convergence. To validate the performance of CMOEA-FTR, we conducted comparative experiments on 44 benchmark test problems and 16 real-world application cases. The statistical IGD and HV metrics indicate that CMOEA-FTR outperforms seven other CMOEAs. Full article
(This article belongs to the Special Issue Current Topics in Evolutionary Computation and Multi-Objective Optimization)
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29 pages, 12703 KiB  
Article
An Improved Constrained Multiobjective Optimization for Energy Multimodal Transport Among Clustering Islands
by Xu Yang, Fuxing Zhang and Honglei Miao
Mathematics 2024, 12(24), 3926; https://doi.org/10.3390/math12243926 - 13 Dec 2024
Viewed by 621
Abstract
Clustering islands located close to each other and sharing some common characteristics offer diverse and unique opportunities for tourism, trade, and research, and especially take a crucial part in the military. Remote from inland, islands have relatively limited resources, which makes them dependent [...] Read more.
Clustering islands located close to each other and sharing some common characteristics offer diverse and unique opportunities for tourism, trade, and research, and especially take a crucial part in the military. Remote from inland, islands have relatively limited resources, which makes them dependent on imported energy sources such as oil and gas or renewable energy. However, there are few studies about the energy security of clustering islands. To this end, this study proposes a novel energy optimization framework that aims to optimize the use of their different types of energy among clustering islands and improve the stability of the whole energy internet via a multilayer transportation network. The transportation network also enables islands to serve as emergency power sources for each other in some emergency situations. Specifically, we construct an assignment model that considers multimodal transport, multiobjective, and multiple constraints. To address this issue, we develop an unconstrained-individuals guiding constrained multiobjective optimization algorithm, named uiCMOA. Experimental results demonstrate the effectiveness of the transportation network and the efficiency of the proposed algorithm. Full article
(This article belongs to the Special Issue Current Topics in Evolutionary Computation and Multi-Objective Optimization)
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