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Multi-objective Optimization: Techniques and Applications
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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: 31 May 2025 | Viewed by 5051

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

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Keywords

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

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Published Papers (5 papers)

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Research

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21 pages, 2008 KiB  
Article
Addressing the Global Logistics Performance Index Rankings with Methodological Insights and an Innovative Decision Support Framework
by Željko Stević, Nazlı Ersoy, Enes Emre Başar and Mahmut Baydaş
Appl. Sci. 2024, 14(22), 10334; https://doi.org/10.3390/app142210334 - 10 Nov 2024
Viewed by 564
Abstract
This study examines the Logistics Performance Index (LPI) rankings developed by the World Bank from a methodological perspective and proposes an alternative decision support framework. LPI serves as an interactive tool that helps countries identify challenges, innovative solutions, and opportunities in their trade [...] Read more.
This study examines the Logistics Performance Index (LPI) rankings developed by the World Bank from a methodological perspective and proposes an alternative decision support framework. LPI serves as an interactive tool that helps countries identify challenges, innovative solutions, and opportunities in their trade and logistics sectors. In this study, the efficiency of logistics operations in 118 countries was evaluated using an integrated multi-criteria decision-making (MCDM) model objectively weighted by the Entropy method. Countries were ranked using the MCRAT, SAW, TOPSIS, and FUCA methods. According to the findings, large datasets provide more robust insights for sensitivity analyses, and wider weighting coefficient combinations make the data more meaningful. In addition, it is suggested to use low-compensation methods instead of classical additive methods for LPI. Unlike other studies in literature, this research applied an innovative sensitivity analysis to test the robustness of the model and comprehensively examined the effects of weighting techniques based on over 2500 different MCDM results. The findings suggest that the FUCA method should be recommended to decision-makers for calculating LPI rankings due to its simplicity, practicality, low compensatory power, and low sensitivity. This study offers methodological improvements when evaluating logistics performance and provides significant contributions to decision-making processes. The findings are expected to provide a valuable resource for policymakers and businesses in understanding a country’s position in global competition, as well as serving as a reference for researchers evaluating the logistics performance of countries. Full article
(This article belongs to the Special Issue Multi-objective Optimization: Techniques and Applications)
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16 pages, 3774 KiB  
Article
An Adaptive Multi-Objective Genetic Algorithm for Solving Heterogeneous Green City Vehicle Routing Problem
by Wanqiu Zhao, Xu Bian and Xuesong Mei
Appl. Sci. 2024, 14(15), 6594; https://doi.org/10.3390/app14156594 - 28 Jul 2024
Cited by 1 | Viewed by 1161
Abstract
Intelligent scheduling plays a crucial role in minimizing transportation expenses and enhancing overall efficiency. However, most of the existing scheduling models fail to comprehensively account for the requirements of urban development, as exemplified by the vehicle routing problem with time windows (VRPTW), which [...] Read more.
Intelligent scheduling plays a crucial role in minimizing transportation expenses and enhancing overall efficiency. However, most of the existing scheduling models fail to comprehensively account for the requirements of urban development, as exemplified by the vehicle routing problem with time windows (VRPTW), which merely specifies the minimization of path length. This paper introduces a new model of the heterogeneous green city vehicle routing problem with time windows (HGCVRPTW), addressing challenges in urban logistics. The HGCVRPTW model considers carriers with diverse attributes, recipients with varying tolerance for delays, and fluctuating road congestion levels impacting carbon emissions. To better deal with the HGCVRPTW, an adaptive multi-objective genetic algorithm based on the greedy initialization strategy (AMoGA-GIS) is proposed, which includes the following three advantages. Firstly, considering the impact of initial information on the search process, a greedy initialization strategy (GIS) is proposed to guide the overall evolution during the initialization phase. Secondly, the adaptive multiple mutation operators (AMMO) are introduced to improve the diversity of the population at different evolutionary stages according to their success rate of mutation. Moreover, we built a more tailored testing dataset that better aligns with the challenges faced by the HGCVRPTW. Our extensive experiments affirm the competitive performance of the AMoGA-GIS by comparing it with other state-of-the-art algorithms and prove that the GIS and AMMO play a pivotal role in advancing algorithmic capabilities tailored to the HGCVRPTW. Full article
(This article belongs to the Special Issue Multi-objective Optimization: Techniques and Applications)
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20 pages, 7157 KiB  
Article
Multi-Objective Ship Route Optimisation Using Estimation of Distribution Algorithm
by Roman Dębski and Rafał Dreżewski
Appl. Sci. 2024, 14(13), 5919; https://doi.org/10.3390/app14135919 - 6 Jul 2024
Viewed by 819
Abstract
The paper proposes an innovative adaptation of the estimation of distribution algorithm (EDA), intended for multi-objective optimisation of a ship’s route in a non-stationary environment (tidal waters). The key elements of the proposed approach—the adaptive Markov chain-based path generator and the dynamic programming-based [...] Read more.
The paper proposes an innovative adaptation of the estimation of distribution algorithm (EDA), intended for multi-objective optimisation of a ship’s route in a non-stationary environment (tidal waters). The key elements of the proposed approach—the adaptive Markov chain-based path generator and the dynamic programming-based local search algorithm—are presented in detail. The experimental results presented indicate the high effectiveness of the proposed algorithm in finding very good quality approximations of optimal solutions in the Pareto sense. Critical for this was the proposed local search algorithm, whose application improved the final result significantly (the Pareto set size increased from five up to nine times, and the Pareto front quality just about doubled). The proposed algorithm can also be applied to other domains (e.g., mobile robot path planning). It can be considered a framework for (simulation-based) multi-objective optimal path planning in non-stationary environments. Full article
(This article belongs to the Special Issue Multi-objective Optimization: Techniques and Applications)
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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 892
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)
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Review

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25 pages, 1890 KiB  
Review
Multidisciplinary Optimization of Aircraft Aerodynamics for Distributed Propulsion Configurations
by Shaojun Luo, Tian Zi Eng, Zhili Tang, Qianrong Ma, Jinyou Su and Gabriel Bugeda
Appl. Sci. 2024, 14(17), 7781; https://doi.org/10.3390/app14177781 - 3 Sep 2024
Viewed by 886
Abstract
The combination of different aerodynamic configurations and propulsion systems, namely, aero-propulsion, affects flight performance differently. These effects are closely related to multidisciplinary collaborative aspects (aerodynamic configuration, propulsion, energy, control systems, etc.) and determine the overall energy consumption of an aircraft. The potential benefits [...] Read more.
The combination of different aerodynamic configurations and propulsion systems, namely, aero-propulsion, affects flight performance differently. These effects are closely related to multidisciplinary collaborative aspects (aerodynamic configuration, propulsion, energy, control systems, etc.) and determine the overall energy consumption of an aircraft. The potential benefits of distributed propulsion (DP) involve propulsive efficiency, energy-saving, and emissions reduction. In particular, wake filling is maximized when the trailing edge of a blended wing body (BWB) is fully covered by propulsion systems that employ boundary layer ingestion (BLI). Nonetheless, the thrust–drag imbalance that frequently arises at the trailing edge, excessive energy consumption, and flow distortions during propulsion remain unsolved challenges. These after-effects imply the complexity of DP systems in multidisciplinary optimization (MDO). To coordinate the different functions of the aero-propulsive configuration, the application of MDO is essential for intellectualized modulate layout, thrust manipulation, and energy efficiency. This paper presents the research challenges of ultra-high-dimensional optimization objectives and design variables in the current literature in aerodynamic configuration integrated DP. The benefits and defects of various coupled conditions and feasible proposals have been listed. Contemporary advanced energy systems, propulsion control, and influential technologies that are energy-saving are discussed. Based on the proposed technical benchmarks and the algorithm of MDO, the propulsive configuration that might affect energy efficiency is summarized. Moreover, suggestions are drawn for forthcoming exploitation and studies. Full article
(This article belongs to the Special Issue Multi-objective Optimization: Techniques and Applications)
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