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Mathematics
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Evolutionary Multi-Criteria Optimization: Methods and Applications
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Evolutionary Multi-Criteria Optimization: Methods and Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Fuzzy Sets, Systems and Decision Making".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 6933

Special Issue Editors

Dr. Rui Wang

E-Mail Website
Guest Editor
College of System Engineering, National University of Defense Technology, Changsha 410073, China
Interests: many-objective optimization; evolutionary computation; machine learning; data minning; swarm robotics
Dr. Shi Cheng

E-Mail Website
Guest Editor
School of Computer Science, Shaanxi Normal University, Xi’an 710119, China
Interests: swarm intelligence; evolutionary algorithms; big data analytics; particle swarm optimization; brain storm optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Complex problems usually require the simultaneous consideration of multiple performance criteria within multidisciplinary environments. Since the middle of the 1990s, population-based heuristic approaches have been widely used in the field of evolutionary multi-criterion optimization (EMO) to address such problems. This is evidenced by the rapidly growing number of research publications and by the availability of many related software tools. Recently, EMO researchers have understood the need to develop and integrate decision making into EMO, and the need for cross-fertilization between EMO and the multiple-criteria decision making (MCDM) communities has become apparent.

The main aim of this Special Issue is to bring together both experts and newcomers to discuss new and existing issues in these areas, and in particular to continue the integration and blending of ideas between EMO and MCDM researchers, and to stimulate engagement with the user community.

Scope and topics

Full papers are invited which discuss recent advances in the development and application of evolutionary multi-criterion optimization (EMO) approaches, new horizons for multiple-criteria decision making, which may be pathfinders for a step-change in multidisciplinary decision making, showcase developments in the multiple-criteria decision making (MCDM) communities that have potential for blending with EMO themes, or consider hybrid EMO-MCDM methods and applications. In addition, application papers in the area of hybrid renewable energy system optimal design and management are highly encouraged.

You are invited to submit papers that are unpublished original work for this Special Issue. The topics include, but are not limited to:

  1. Interactive multi-objective optimization.
  2. Pareto optimal knee front search.
  3. Hybrid EMO-MCDM methodologies.
  4. Theoretical aspects of EMO and MCDM methodologies.
  5. Multiple-criteria decision aiding.
  6. Preference modeling.
  7. Multiple-criteria choice, ranking, and sorting.
  8. Multiple-objective continuous and combinatorial optimization.
  9. Evolutionary many-objective optimization.
  10. Multiple attribute utility theory.
  11. Outranking methods.
  12. Goal programming.
  13. Multiple-objective metaheuristics.
  14. Fuzzy multiple-criteria decision making.
  15. Data-driven and model-based multi-objective optimization.
  16. Dynamic multi-objective optimization.
  17. Applications of EMO and MCDM in government, business, industry and interdisciplinary sciences.

Dr. Rui Wang
Dr. Shi Cheng
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

  • Multi/many-objective optimization
  • Evolutionary computation
  • Preference modeling
  • Combinatorial optimization
  • Metaheuristics
  • Fuzzy decision making
  • Data-driven optimization

Published Papers (5 papers)

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Research

20 pages, 1300 KiB  
Article
Methods of Fuzzy Multi-Criteria Decision Making for Controlling the Operating Modes of the Stabilization Column of the Primary Oil-Refining Unit
by Batyr Orazbayev, Yerbol Ospanov, Valentina Makhatova, Lazzat Salybek, Zhanat Abdugulova, Zhumazhan Kulmagambetova, Salamat Suleimenova and Kulman Orazbayeva
Mathematics 2023, 11(13), 2820; https://0-doi-org.brum.beds.ac.uk/10.3390/math11132820 - 23 Jun 2023
Viewed by 931
Abstract
Many technological systems are characterized by fuzzy initial information, necessary for the development of their models, optimization and control of operating modes. Therefore, the purpose of this study is to formulate decision making problems for optimizing and controlling operating modes of such systems [...] Read more.
Many technological systems are characterized by fuzzy initial information, necessary for the development of their models, optimization and control of operating modes. Therefore, the purpose of this study is to formulate decision making problems for optimizing and controlling operating modes of such systems in a fuzzy environment and to develop methods for solving them. The developed heuristic methods of fuzzy multi-criteria decision making are based on the modification and combination of different principles of optimality. The proposed methods based on system models, knowledge and experience of the decision maker allow iterative improvement and effective decision making. On the basis of experimental–statistical methods, methods of expert evaluation, statistical and fuzzy models of the stabilization column have been developed. The conditions for judging the fuzzy model’s effectiveness are determined and investigated. Using the proposed heuristic method based on the main criterion and maximin, the problem of two-criterion optimization of the stabilization column parameters in a fuzzy environment is solved. The results obtained confirm the advantages of the proposed method of fuzzy decision making in comparison with the results of known methods. The developed heuristic methods differ from known ones because they allow making adequate decisions in a fuzzy environment by maximizing the use of the collected fuzzy information. Full article
(This article belongs to the Special Issue Evolutionary Multi-Criteria Optimization: Methods and Applications)
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16 pages, 4574 KiB  
Article
Bilevel Optimal Sizing and Operation Method of Fuel Cell/Battery Hybrid All-Electric Shipboard Microgrid
by Hao Jin and Xinhang Yang
Mathematics 2023, 11(12), 2728; https://0-doi-org.brum.beds.ac.uk/10.3390/math11122728 - 16 Jun 2023
Cited by 3 | Viewed by 1177
Abstract
The combination of transportation electrification and clean energy in the shipping industry has been a hot topic, and related applications of hybrid all-electric ships (AESs) have emerged recently. However, it has been found that ship efficiency will be negatively impacted by improper component [...] Read more.
The combination of transportation electrification and clean energy in the shipping industry has been a hot topic, and related applications of hybrid all-electric ships (AESs) have emerged recently. However, it has been found that ship efficiency will be negatively impacted by improper component size and operation strategy. Therefore, the bilevel optimal sizing and operation method for the fuel cell/battery hybrid AES is proposed in this paper. This method optimizes the sizing of the AES while considering joint optimal energy management and voyage scheduling. The sizing problem is formulated at the upper level, and the joint scheduling problem is described at the lower level. Then, multiple cases are simulated to verify the effectiveness of the proposed method on a passenger ferry, and the results show that a 5.3% fuel saving and 5.2% total cost reduction can be achieved. Correspondingly, the ship’s energy efficiency is improved. This approach also can be used in similar vessels to enhance their overall performance and sustainability. Full article
(This article belongs to the Special Issue Evolutionary Multi-Criteria Optimization: Methods and Applications)
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29 pages, 7723 KiB  
Article
MSGWO-MKL-SVM: A Missing Link Prediction Method for UAV Swarm Network Based on Time Series
by Mingyu Nan, Yifan Zhu, Jie Zhang, Tao Wang and Xin Zhou
Mathematics 2022, 10(14), 2535; https://0-doi-org.brum.beds.ac.uk/10.3390/math10142535 - 21 Jul 2022
Cited by 2 | Viewed by 1142
Abstract
Missing link prediction technology (MLP) is always a hot research area in the field of complex networks, and it has been extensively utilized in UAV swarm network reconstruction recently. UAV swarm is an artificial network with strong randomness, in the face of which [...] Read more.
Missing link prediction technology (MLP) is always a hot research area in the field of complex networks, and it has been extensively utilized in UAV swarm network reconstruction recently. UAV swarm is an artificial network with strong randomness, in the face of which prediction methods based on network similarity often perform poorly. To solve those problems, this paper proposes a Multi Kernel Learning algorithm with a multi-strategy grey wolf optimizer based on time series (MSGWO-MKL-SVM). The Multiple Kernel Learning (MKL) method is adopted in this algorithm to extract the advanced features of time series, and the Support Vector Machine (SVM) algorithm is used to determine the hyperplane of threshold value in nonlinear high dimensional space. Besides that, we propose a new measurable indicator of Multiple Kernel Learning based on cluster, transforming a Multiple Kernel Learning problem into a multi-objective optimization problem. Some adaptive neighborhood strategies are used to enhance the global searching ability of grey wolf optimizer algorithm (GWO). Comparison experiments were conducted on the standard UCI datasets and the professional UAV swarm datasets. The classification accuracy of MSGWO-MKL-SVM on UCI datasets is improved by 6.2% on average, and the link prediction accuracy of MSGWO-MKL-SVM on professional UAV swarm datasets is improved by 25.9% on average. Full article
(This article belongs to the Special Issue Evolutionary Multi-Criteria Optimization: Methods and Applications)
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30 pages, 2646 KiB  
Article
An Improved Intelligent Auction Mechanism for Emergency Material Delivery
by Jie Zhang, Yifan Zhu, Tao Wang, Weiping Wang, Rui Wang and Xiaobo Li
Mathematics 2022, 10(13), 2184; https://0-doi-org.brum.beds.ac.uk/10.3390/math10132184 - 23 Jun 2022
Cited by 2 | Viewed by 1284
Abstract
Emergency material delivery is vital to disaster emergency rescue. Herein, the framework of the emergency material delivery system (EMDS) with the unmanned aerial vehicle (UAV) as the vehicle is proposed, and the problem is modeled into a multi-trip time-dependent dynamic vehicle routing problem [...] Read more.
Emergency material delivery is vital to disaster emergency rescue. Herein, the framework of the emergency material delivery system (EMDS) with the unmanned aerial vehicle (UAV) as the vehicle is proposed, and the problem is modeled into a multi-trip time-dependent dynamic vehicle routing problem with split-delivery (MTTDDVRP-SD) in combination with the rescue reality, which provides decision support for planning disaster relief material. Due to the universality of dynamic interference in the process of material delivery, an optimization algorithm based on the traditional intelligent auction mechanism is proposed to avoid system performance degradation or even collapse. The algorithm adds pre-authorization and sequential auction mechanisms to the traditional auction mechanism, where the pre-authorization mechanism improves the capability performance of the system when there is no interference during the rescue process and the sequential auction mechanism improves the resilience performance of the system when it faces interferences. Finally, considering three types of interference comprehensively, which includes new task generations, task unexpected changes and UAV’s number decreases, the proposed algorithm is compared with DTAP (DTA based on sequential single item auctions) and CBBA-PR (consensus-based bundle algorithms-partial replanning) algorithms under different dynamic interference intensity scenarios for simulation experimental from two perspectives of the capability performance and resilience performance. The results of Friedman’s test with 99% confidence interval indicate that the proposed algorithm can effectively improve the capability performance and resilience performance of EMDS. Full article
(This article belongs to the Special Issue Evolutionary Multi-Criteria Optimization: Methods and Applications)
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19 pages, 1759 KiB  
Article
Multi-Objective Optimal Sizing of HRES under Multiple Scenarios with Undetermined Probability
by Kaiwen Li, Yuanming Song and Rui Wang
Mathematics 2022, 10(9), 1508; https://0-doi-org.brum.beds.ac.uk/10.3390/math10091508 - 01 May 2022
Cited by 2 | Viewed by 1355
Abstract
In recent years, technologies for renewable energy utilization have been booming. Hybrid renewable energy systems (HRESs), integrating multiple energy sources to mitigate the unstable, unpredictable, and intermittent characteristics of a single renewable energy source, have become increasingly popular. However, due to the inherent [...] Read more.
In recent years, technologies for renewable energy utilization have been booming. Hybrid renewable energy systems (HRESs), integrating multiple energy sources to mitigate the unstable, unpredictable, and intermittent characteristics of a single renewable energy source, have become increasingly popular. However, due to the inherent intermittency and uncertainty of renewable energies, the impact of uncertain factors on the capacity optimization of HRESs needs to be considered. In the traditional scenario-based planning method, when dealing with uncertain factors, the probability corresponding to the scenario is difficult to determine. Furthermore, when applying the robust optimization method, it is difficult to fully use existing data to describe uncertain parameters in the form of intervals. To tackle these difficulties, this study proposes a probability undetermined scenario-based sizing model (PUSS model) for stand-alone HRES configuration optimization and a multi-objective evolutionary algorithm as the problem solver. The solution set obtained by the method covers multiple possible values of scenario probability combinations and can provide decision-makers with an overview of alternatives for HRES sizing under different power supply pressures. Based on the real environment data and load data of a certain place, the proposed model and algorithm are applied to sizing a typical HRES comprising wind generators, solar photovoltaic panels, energy-storage devices, and diesel generators. The experimental results show that the proposed PUSS method is both effective and efficient. Full article
(This article belongs to the Special Issue Evolutionary Multi-Criteria Optimization: Methods and Applications)
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