Relevant bibliographies by topics / Nsga-Iii

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Nsga-Iii'

Author: Grafiati
Published: 15 February 2025

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Journal articles on the topic "Nsga-Iii"

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Ariza Vesga, Luis Felipe, Johan Sebastián Eslava Garzón, and Rafael Puerta Ramirez. "EF1-NSGA-III: An evolutionary algorithm based on the first front to obtain non-negative and non-repeated extreme points." Ingeniería e Investigación 40, no. 3 (October 21, 2020): 55–69. http://dx.doi.org/10.15446/inginvestig.v40n3.82906.

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Multi-Objective and Many-objective Optimization problems have been extensively solved through evolutionary algorithms over a few decades. Despite the fact that NSGA-II and NSGA-III are frequently employed as a reference for a comparative evaluation of new evolutionary algorithms, the latter is proprietary. In this paper, we used the basic framework of the NSGA-II, which is very similar to the NSGA-III, with significant changes in its selection operator. We took the first front generated at the non-dominating sort procedure to obtain nonnegative and nonrepeated extreme points. This opensource version of the NSGA-III is called EF1-NSGA-III, and its implementation does not start from scratch; that would be reinventing the wheel. Instead, we took the NSGA-II code from the authors in the repository of the Kanpur Genetic Algorithms Laboratory to extend the EF1-NSGA-III. We then adjusted its selection operator from diversity, based on the crowding distance, to the one found on reference points and preserved its parameters. After that, we continued with the adaptive EF1-NSGA-III (A-EF1-NSGA-III), and the efficient adaptive EF1-NSGA-III (A2-EF1-NSGA-III), while also contributing to explain how to generate different types of reference points. The proposed algorithms resolve optimization problems with constraints of up to 10 objective functions. We tested them on a wide range of benchmark problems, and they showed notable improvements in terms of convergence and diversity by using the Inverted Generational Distance (IGD) and the HyperVolume (HV) performance metrics. The EF1-NSGA-III aims to resolve the power consumption for Centralized Radio Access Networks and the BiObjective Minimum DiameterCost Spanning Tree problems.
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Awad, Mahmoud, Mohamed Abouhawwash, and H. N. Agiza. "On NSGA-II and NSGA-III in Portfolio Management." Intelligent Automation & Soft Computing 32, no. 3 (2022): 1893–904. http://dx.doi.org/10.32604/iasc.2022.023510.

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Sun, Xingping, Ye Wang, Hongwei Kang, Yong Shen, Qingyi Chen, and Da Wang. "Modified Multi-Crossover Operator NSGA-III for Solving Low Carbon Flexible Job Shop Scheduling Problem." Processes 9, no. 1 (December 29, 2020): 62. http://dx.doi.org/10.3390/pr9010062.

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Low carbon manufacturing has received increasingly more attention in the context of global warming. The flexible job shop scheduling problem (FJSP) widely exists in various manufacturing processes. Researchers have always emphasized manufacturing efficiency and economic benefits while ignoring environmental impacts. In this paper, considering carbon emissions, a multi-objective flexible job shop scheduling problem (MO-FJSP) mathematical model with minimum completion time, carbon emission, and machine load is established. To solve this problem, we study six variants of the non-dominated sorting genetic algorithm-III (NSGA-III). We find that some variants have better search capability in the MO-FJSP decision space. When the solution set is close to the Pareto frontier, the development ability of the NSGA-III variant in the decision space shows a difference. According to the research, we combine Pareto dominance with indicator-based thought. By utilizing three existing crossover operators, a modified NSGA-III (co-evolutionary NSGA-III (NSGA-III-COE) incorporated with the multi-group co-evolution and the natural selection is proposed. By comparing with three NSGA-III variants and five multi-objective evolutionary algorithms (MOEAs) on 27 well-known FJSP benchmark instances, it is found that the NSGA-III-COE greatly improves the speed of convergence and the ability to jump out of local optimum while maintaining the diversity of the population. From the experimental results, it can be concluded that the NSGA-III-COE has significant advantages in solving the low carbon MO-FJSP.
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Zou, Ying, Zuguo Chen, Shangyang Zhu, and Yingcong Li. "NSGA-III-Based Production Scheduling Optimization Algorithm for Pressure Sensor Calibration Workshop." Electronics 13, no. 14 (July 19, 2024): 2844. http://dx.doi.org/10.3390/electronics13142844.

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Although the NSGA-III algorithm is able to find the global optimal solution and has a good effect on the workshop scheduling optimization, the limitations in population diversity, convergence ability and local optimal solutions make it not applicable to certain situations. Thus, an improved NSGA-III workshop scheduling optimization algorithm is proposed in this work. It aims to address these limitations of the NSGA-III algorithm in processing workshop scheduling optimization. To solve the problem of individual elimination in the traditional NSGA-III algorithm, chaotic mapping is introduced in the improved NSGA-III algorithm to generate new offspring individuals and add the selected winning individuals to the offspring population as the parent population for the next iteration. The proposed algorithm was applied to a pressure sensor calibration workshop. A comparison with the traditional NSGA-III algorithm was conducted through a simulation analysis. The results show that the proposed algorithm can obtain a better convergence performance, improve the optimization ability and avoid falling into local optimal solutions.
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Geng, Huantong, Zhengli Zhou, Junye Shen, and Feifei Song. "A Dual-Population-Based NSGA-III for Constrained Many-Objective Optimization." Entropy 25, no. 1 (December 21, 2022): 13. http://dx.doi.org/10.3390/e25010013.

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The main challenge for constrained many-objective optimization problems (CMaOPs) is how to achieve a balance between feasible and infeasible solutions. Most of the existing constrained many-objective evolutionary algorithms (CMaOEAs) are feasibility-driven, neglecting the maintenance of population convergence and diversity when dealing with conflicting objectives and constraints. This might lead to the population being stuck at some locally optimal or locally feasible regions. To alleviate the above challenges, we proposed a dual-population-based NSGA-III, named DP-NSGA-III, where the two populations exchange information through the offspring. The main population based on the NSGA-III solves CMaOPs and the auxiliary populations with different environment selection ignore the constraints. In addition, we designed an ε-constraint handling method in combination with NSGA-III, aiming to exploit the excellent infeasible solutions in the main population. The proposed DP-NSGA-III is compared with four state-of-the-art CMaOEAs on a series of benchmark problems. The experimental results show that the proposed evolutionary algorithm is highly competitive in solving CMaOPs.
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Muteba Mwamba, John Weirstrass, Leon Mishindo Mbucici, and Jules Clement Mba. "Multi-Objective Portfolio Optimization: An Application of the Non-Dominated Sorting Genetic Algorithm III." International Journal of Financial Studies 13, no. 1 (January 28, 2025): 15. https://doi.org/10.3390/ijfs13010015.

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This study evaluates the effectiveness of the Non-dominated Sorting Genetic Algorithm III (NSGA-III) in comparison to the traditional Mean–Variance optimization method for financial portfolio management. Leveraging a dataset of global financial assets, we applied both approaches to optimize portfolios across multiple objectives, including risk, return, skewness, and kurtosis. The findings reveal that NSGA-III significantly outperforms the Mean–Variance method by generating a more diverse set of Pareto-optimal portfolios. Portfolios optimized with NSGA-III exhibited superior performance, achieving higher Sharpe ratios, more favorable skewness, and reduced kurtosis, indicating a better balance between risk and return. Moreover, NSGA-III’s capability to handle conflicting objectives underscores its utility in navigating complex financial environments and enhancing portfolio resilience. In contrast, while the Mean–Variance method effectively balances risk and return, it demonstrates limitations in addressing higher-order moments of the return distribution. These results emphasize the potential of NSGA-III as a robust and comprehensive tool for portfolio optimization in modern financial markets characterized by multifaceted objectives.
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Qu, Zhanghao, Peng Zhang, Yaohua Hu, Huanbo Yang, Taifeng Guo, Kaili Zhang, and Junchang Zhang. "Optimal Design of Agricultural Mobile Robot Suspension System Based on NSGA-III and TOPSIS." Agriculture 13, no. 1 (January 14, 2023): 207. http://dx.doi.org/10.3390/agriculture13010207.

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The stability of vehicles is influenced by the suspension system. At present, there are many studies on the suspension of traditional passenger vehicles, but few are related to agricultural mobile robots. There are structural differences between the suspension system of agricultural mobile robots and passenger vehicles, which requires structural simplification and modelling concerning suspension of agricultural mobile robots. This study investigates the optimal design for an agricultural mobile robot’s suspension system designed based on a double wishbone suspension structure. The dynamics of the quarter suspension system were modelled based on Lagrange’s equation. In our work, the non-dominated sorting genetic algorithm III (NSGA-III) was selected for conducting multi-objective optimization of the suspension design, combined with the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) to choose the optimal combination of parameters in the non-dominated solution set obtained by NSGA-III. We compared the performance of NSGA-III with that of other multi-objective evolutionary algorithms (MOEAs). Compared with the second-scoring solution, the score of the optimal solution obtained by NSGA-III increased by 4.92%, indicating that NSGA-III has a significant advantage in terms of the solution quality and robustness for the optimal design of the suspension system. This was verified by simulation in Adams that our method, which utilizes multibody dynamics, NSGA-III and TOPSIS, is feasible to determine the optimal design of a suspension system for an agricultural mobile robot.
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Yi-Hui Chen, Yi-Hui Chen, Heng-Zhou Ye Yi-Hui Chen, and Feng-Yi Huang Heng-Zhou Ye. "The Configuration Design of Electronic Products Based on improved NSGA-III with Information Feedback Models." 電腦學刊 33, no. 4 (August 2022): 081–94. http://dx.doi.org/10.53106/199115992022083304007.

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<p>The configuration of electronic products is an important means to meet the diverse and personalized needs of users and achieve mass customization, and one of its goals is to recommend an excellent bill of material to users according to users&rsquo; individualized needs and preferences. Current research describes the configuration of electronic products as a single-objective optimization model, which suffers from the problems of single recommended configuration and difficulty in meeting the dynamic adjustment of user preferences. Therefore, we describe it as a multi-objective optimization model and propose an NSGA-III-FR algorithm to solve the model. In order to balance the convergence and diversity of the algorithm, NSGA-III-FR has made two improvements on the basis of NSGA-III with information feedback models: introducing adaptive parameters to balance NSGA-III-F1 and NSGA-III-R1; and using Angle Penalized Distance (APD) to improve the niche technology. The experimental results show that our modified meth-od can achieve better performance compared with the other three algorithms.</p> <p>&nbsp;</p>
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Thawkar, Shankar, Law Kumar Singh, and Munish Khanna. "Multi-objective techniques for feature selection and classification in digital mammography." Intelligent Decision Technologies 15, no. 1 (March 24, 2021): 115–25. http://dx.doi.org/10.3233/idt-200049.

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Feature selection is a crucial stage in the design of a computer-aided classification system for breast cancer diagnosis. The main objective of the proposed research design is to discover the use of multi-objective particle swarm optimization (MOPSO) and Nondominated sorting genetic algorithm-III (NSGA-III) for feature selection in digital mammography. The Pareto-optimal fronts generated by MOPSO and NSGA-III for two conflicting objective functions are used to select optimal features. An artificial neural network (ANN) is used to compute the fitness of objective functions. The importance of features selected by MOPSO and NSGA-III are assessed using artificial neural networks. The experimental results show that MOPSO based optimization is superior to NSGA-III. MOPSO achieves high accuracy with a 55% feature reduction. MOPSO based feature selection and classification deliver an efficiency of 97.54% with 98.22% sensitivity, 96.82% specificity, 0.9508 Cohen’s kappa coefficient, and area under curve AZ= 0.983 ± 0.003.
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Trưởng, Nguyễn Huy, and Dinh-Nam Dao. "New hybrid between NSGA-III with multi-objective particle swarm optimization to multi-objective robust optimization design for Powertrain mount system of electric vehicles." Advances in Mechanical Engineering 12, no. 2 (February 2020): 168781402090425. http://dx.doi.org/10.1177/1687814020904253.

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In this study, a new methodology, hybrid NSGA-III with multi-objective particle swarm optimization (HNSGA-III&MOPSO), has been developed to design and achieve cost optimization of Powertrain mount system stiffness parameters. This problem is formalized as a multi-objective optimization problem involving six optimization objectives: mean square acceleration and mean square displacement of the Powertrain mount system. A hybrid HNSGA-III&MOPSO is proposed with the integration of multi-objective particle swarm optimization and a genetic algorithm (NSGA-III). Several benchmark functions are tested, and results reveal that the HNSGA-III&MOPSO is more efficient than the typical multi-objective particle swarm optimization, NSGA-III. Powertrain mount system stiffness parameter optimization with HNSGA-III&MOPSO is simulated, respectively. It proved the potential of the HNSGA-III&MOPSO for Powertrain mount system stiffness parameter optimization problem. The amplitude of the acceleration of the vehicle frame decreased by 22.8%, and the amplitude of the displacement of the vehicle frame reduced by 12.4% compared to the normal design case. The calculation time of the algorithm HNSGA-III&MOPSO is less than the algorithm NSGA-III, that is, 5 and 6 h, respectively, compared to the algorithm multi-objective particle swarm optimization.
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Dissertations / Theses on the topic "Nsga-Iii"

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Al, Samrout Marwa. "Approches mono et bi-objective pour l'optimisation intégrée des postes d'amarrage et des grues de quai dans les opérations de transbordement." Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMLH21.

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Le transport maritime international est vital pour le commerce mondial, représentant plus de 85 % des échanges, avec 10,5 milliards de tonnes transportées chaque année. Ce mode de transport est le plus économique et durable, contribuant seulement à 2,6 % des émissions de CO2. En France, le secteur maritime représente 1,5 % du PIB et près de 525 000 emplois. Les ports maritimes, cruciaux pour la chaîne logistique, facilitent le transbordement des marchandises et adoptent de plus en plus des solutions numériques basées sur l'intelligence artificielle pour améliorer leur efficacité. La France compte onze Grands ports maritimes, dont sept en Métropole. La thèse se concentre sur l’optimisation des terminaux à conteneurs pour améliorer l’efficacité et la performance des ports.Ce mémoire aborde la problématique de la planification des postes d’accostage et de l’activation des portiques dans les terminaux à conteneurs des ports maritimes, en réponse aux changements récents dans la logistique maritime, tels que l’arrivée de méga-navires et l’automatisation. Il souligne les lacunes dans la littérature existante et propose une analyse approfondie des défis actuels. Le document se divise en trois chapitres : Le premier chapitre explore l’histoire de la conteneurisation, les types de conteneurs, et les défis de la planification opérationnelle. Il se concentre sur le problème d’attribution des postes d’amarrage (BAP), ses méthodes de résolution et l’intégration de l’intelligence artificielle (IA) pour optimiser les processus logistiques. Le 2ème chapitre introduit le problème d'allocation dynamique avec transbordement ship-to-ship. Il propose un programme linéaire en nombres entiers mixtes (MILP) pour optimiser l’ordonnancement d’accostage et le transbordement entre navires. L’objectif est de réduire les temps de séjour des navires dans le terminal, ainsi que les pénalités dues aux retards des navires, et de décider du mode de transbordement nécessaire. La méthode combine une heuristique de type packing et un algorithme génétique amélioré, démontrant une efficacité dans la réduction des temps de séjour des navires. Nous avons effectué une analyse statistique pour identifier les paramètres de contrôle efficaces du GA, puis nous avons appliqué cet algorithme avec les paramètres de contrôle déterminés pour réaliser des expériences numériques sur des instances générées aléatoirement. De plus, nous avons réalisé une étude comparative afin d’évaluer différents opérateurs de croisement, en utilisant le test d’analyse de variance (ANOVA). Ensuite, nous avons présenté une série d’exemples basés sur des données aléatoires, résolus à l’aide du solveur CPLEX, afin de confirmer la validité du modèle proposé. La méthode proposée est capable de résoudre le problème dans un temps de calcul acceptable pour des instances de taille moyenne et grande. Le dernier chapitre présente un problème intégré d’allocation des postes d’amarrage et des grues, avec un focus sur le transbordement ship-to-ship. Trois approches sont proposées . La première approche utilise l'algorithme génétique NSGA-III, complété par une analyse statistique pour optimiser les paramètres et évaluer différents opérateurs de croisement. En analysant des données de la base AIS, des tests numériques montrent l’efficacité de cette méthode au port du Havre, avec des résultats satisfaisants et un temps de calcul raisonnable.La deuxième approche implique deux modèles de régression, Gradient Boosting Regression (GBR) et Random Forest Regression (RFR), entraînés sur des caractéristiques sélectionnées. La méthodologie inclut des étapes de prétraitement et l'optimisation des hyperparamètres. Bien que NSGA-III offre la meilleure précision, il nécessite un temps d'exécution plus long. En revanche, GBR et RFR, bien que légèrement moins précis, améliorent l’efficacité, soulignant le compromis entre précision et temps d'exécution dans les applications pratiques
International maritime transport is vital for global trade, representing over 85% of exchanges, with 10.5 billion tons transported each year. This mode of transport is the most economical and sustainable, contributing only 2.6% of CO2 emissions. In France, the maritime sector accounts for 1.5% of GDP and nearly 525,000 jobs. Maritime ports, crucial for the logistics chain, facilitate the transshipment of goods and increasingly adopt digital solutions based on artificial intelligence to improve their efficiency. France has eleven major seaports, seven of which are located in mainland France.The thesis focuses on optimizing container terminals to enhance the efficiency and performance of ports. It addresses the issues of berth allocation planning and crane activation in container terminals in response to recent changes in maritime logistics, such as the arrival of mega-ships and automation. It highlights gaps in the existing literature and offers an in-depth analysis of current challenges. The document is divided into three chapters:The first chapter explores the history of containerization, types of containers, and challenges in operational planning. It focuses on the berth allocation problem (BAP), its resolution methods, and the integration of artificial intelligence (AI) to optimize logistical processes. The second chapter introduces the dynamic allocation problem with ship-to-ship transshipment. It proposes a mixed-integer linear program (MILP) to optimize the berthing schedule and transshipment between vessels. The objective is to reduce vessel stay times in the terminal, as well as penalties due to vessel delays, and to determine the necessary transshipment method. The method combines a packing-type heuristic and an improved genetic algorithm, demonstrating effectiveness in reducing vessel stay times. We conducted a statistical analysis to identify effective control parameters for the GA, then applied this algorithm with the determined control parameters to perform numerical experiments on randomly generated instances. Additionally, we conducted a comparative study to evaluate different crossover operators using ANOVA. We then presented a series of examples based on random data, solved using the CPLEX solver, to confirm the validity of the proposed model. The proposed method is capable of solving the problem in an acceptable computation time for medium and large instances. The final chapter presents an integrated berth and crane allocation problem, focusing on ship-to-ship transshipment. Three approaches are proposed. The first approach uses the NSGA-III genetic algorithm, supplemented by a statistical analysis to optimize parameters and evaluate different crossover operators. By analyzing AIS database data, numerical tests demonstrate the effectiveness of this method at the port of Le Havre, yielding satisfactory results within a reasonable computation time. The second approach involves two regression models, Gradient Boosting Regression (GBR) and Random Forest Regression (RFR), trained on selected features. The methodology includes preprocessing steps and hyperparameter optimization. While NSGA-III achieves the highest accuracy, it requires a longer execution time. In contrast, although GBR and RFR are slightly less precise, they significantly improve efficiency, highlighting the trade-off between accuracy and execution time in practical applications
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Book chapters on the topic "Nsga-Iii"

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Khettabi, Imen, Lyes Benyoucef, and Mohamed Amine Boutiche. "Multi-objective Sustainable Process Plan Generation for RMS: NSGA-III vs New NSGA-III." In Modelling, Computation and Optimization in Information Systems and Management Sciences, 170–81. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92666-3_15.

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Blank, Julian, Kalyanmoy Deb, and Proteek Chandan Roy. "Investigating the Normalization Procedure of NSGA-III." In Lecture Notes in Computer Science, 229–40. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12598-1_19.

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Haider, Christian, and Gabriel Kronberger. "Shape-Constrained Symbolic Regression with NSGA-III." In Computer Aided Systems Theory – EUROCAST 2022, 164–72. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-25312-6_19.

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Santoshkumar, Balija, Kalyanmoy Deb, and Lei Chen. "Eliminating Non-dominated Sorting from NSGA-III." In Lecture Notes in Computer Science, 71–85. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27250-9_6.

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Ghosh, Tamal, and Kristian Martinsen. "NSGA III for CNC End Milling Process Optimization." In Communications in Computer and Information Science, 185–95. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4301-2_16.

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Bekhit, Mahmoud, Ahmed Fathalla, Esraa Eldesouky, and Ahmad Salah. "Multi-objective VNF Placement Optimization with NSGA-III." In Proceedings of the 2023 International Conference on Advances in Computing Research (ACR’23), 481–93. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33743-7_39.

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dos Santos, Francisco, Lino A. Costa, and Leonilde Varela. "Performance Comparison of NSGA-II and NSGA-III on Bi-objective Job Shop Scheduling Problems." In Communications in Computer and Information Science, 531–43. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53025-8_36.

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Makkar, Priyanka, Sunil Sikka, and Anshu Malhotra. "Empirical Evaluation of NSGA II, NSGA III, and MOEA/D Optimization Algorithms on Multi-objective Target." In Advances in Intelligent Systems and Computing, 23–31. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1740-9_3.

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Gonçalves, Richard A., Lucas M. Pavelski, Carolina P. de Almeida, Josiel N. Kuk, Sandra M. Venske, and Myriam R. Delgado. "Adaptive Operator Selection for Many-Objective Optimization with NSGA-III." In Lecture Notes in Computer Science, 267–81. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54157-0_19.

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Ding, Rui, Hongbin Dong, Jun He, Xianbin Feng, Xiaodong Yu, and Lijie Li. "U-NSGA-III: An Improved Evolutionary Many-Objective Optimization Algorithm." In Communications in Computer and Information Science, 24–35. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2826-8_3.

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Conference papers on the topic "Nsga-Iii"

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Haddad, Anouar, Takwa Tlili, Issam Nouaouri, and Saoussen Krichen. "Solving the multi-objective ambulance routing problem using NSGA III." In 2024 10th International Conference on Control, Decision and Information Technologies (CoDIT), 2296–300. IEEE, 2024. http://dx.doi.org/10.1109/codit62066.2024.10708304.

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Chu, Wang, Liu Zhongze, Cao Shunxiang, Hu Kai, Zhang Yibo, Xiang Dong, and Zhou Weiye. "An Improved RRT* Algorithm for Multi-Objective Optimization Based on NSGA-III." In 2024 8th International Conference on Robotics and Automation Sciences (ICRAS), 55–65. IEEE, 2024. http://dx.doi.org/10.1109/icras62427.2024.10654473.

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Agarwal, Aakansha, and Satyasai Jagannath Nanda. "Dynamic NSGA-III with KRR-ANOVA Kernel Predictor for In-Motion Sonar Image Segmentation." In 2024 IEEE Congress on Evolutionary Computation (CEC), 1–8. IEEE, 2024. http://dx.doi.org/10.1109/cec60901.2024.10612074.

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Poor, Masoud Kermani, Shahryar Rahnamayan, Azam Asilian Bidgoli, and Mehran Ebrahimi. "Exploring Long-term Memory in Evolutionary Multi-objective Algorithms: A Case Study with NSGA-III." In 2024 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), 864–70. IEEE, 2024. http://dx.doi.org/10.1109/ccece59415.2024.10667327.

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Sampaio, Sávio Menezes, Altino Dantas, and Celso G. Camilo-Junior. "IVF/NSGA-III - In Vitro Fertilization Method Coupled to NSGA-III." In 2023 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2023. http://dx.doi.org/10.1109/cec53210.2023.10254062.

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Wangsom, Peerasak, Pascal Bouvry, and Kittichai Lavangnananda. "Extreme Solutions NSGA-III (E-NSGA-III) for Scientific Workflow Scheduling on Cloud." In 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2018. http://dx.doi.org/10.1109/icmla.2018.00184.

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Goncalves, Richard A., Carolina P. Almeida, Lucas M. Pavelski, Sandra M. Venske, Josiel N. Kuk, and Aurora T. Pozo. "Adaptive Operator Selection in NSGA-III." In 2016 5th Brazilian Conference on Intelligent Systems (BRACIS). IEEE, 2016. http://dx.doi.org/10.1109/bracis.2016.042.

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Wolschick, Lucas, Paulo Cesar Gonçalves, João Choma Neto, Willian Marques Freire, Aline Maria Malachini Miotto Amaral, and Thelma Elita Colanzi. "Evaluating the performance of NSGA-II and NSGA-III on Product Line Architecture Design." In Simpósio Brasileiro de Componentes, Arquiteturas e Reutilização de Software, 11–20. Sociedade Brasileira de Computação, 2024. http://dx.doi.org/10.5753/sbcars.2024.3830.

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Product Line Architecture (PLA) design can be modeled as an optimization problem to be solved with search-based algorithms. PLA design optimization has successfully been done using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) in scenarios involving up to three objectives, which involve software metrics for properties such as feature modularization, PLA extensibility, and cohesion. As many of these properties may be desired in a PLA at the same time, more than three objectives might need to be optimized simultaneously. The Non-Dominated Sorting Genetic Algorithm III (NSGA-III) was designed to solve problems impacted by more than three objectives, named many-objective problems, so it might suit this need. However, NSGA-III has not yet been applied in the context of PLA design. In this sense, this study aims to compare the performance of NSGA-II and NSGA-III for PLA design to uncover which algorithm best fits this problem. To accomplish this goal, we implemented a specialized version of NSGA-III and then ran experiments using both algorithms to optimize eight PLAs with three, four, and five objectives. We evaluate the algorithms’ performance via quality indicators commonly used in search-based software engineering. The empirical results point out that: (i) NSGA-III had a slightly better performance than NSGA-II when optimizing four or five objectives in the context of our study; (ii) NSGA-II was the best or the algorithms tied when the PLA given as input is easier to optimize due to reduced solution space.
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9

Wietheger, Simon, and Benjamin Doerr. "A Mathematical Runtime Analysis of the Non-dominated Sorting Genetic Algorithm III (NSGA-III)." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/628.

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The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is the most prominent multi-objective evolutionary algorithm for real-world applications. While it performs evidently well on bi-objective optimization problems, empirical studies suggest that it is less effective when applied to problems with more than two objectives. A recent mathematical runtime analysis confirmed this observation by proving the NGSA-II for an exponential number of iterations misses a constant factor of the Pareto front of the simple 3-objective OneMinMax problem. In this work, we provide the first mathematical runtime analysis of the NSGA-III, a refinement of the NSGA-II aimed at better handling more than two objectives. We prove that the NSGA-III with sufficiently many reference points - a small constant factor more than the size of the Pareto front, as suggested for this algorithm - computes the complete Pareto front of the 3-objective OneMinMax benchmark in an expected number of O(n log n) iterations. This result holds for all population sizes (that are at least the size of the Pareto front). It shows a drastic advantage of the NSGA-III over the NSGA-II on this benchmark. The mathematical arguments used here and in the previous work on the NSGA-II suggest that similar findings are likely for other benchmarks with three or more objectives.
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10

Silva, Rheidner, and André Britto. "NSGA-III com Adaptação dos Pontos de Referência." In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/eniac.2019.9312.

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Algoritmos Evolucionários Multi-objetivo enfrentam dificuldades na resolução de problemas com mais de 3 objetivos, os chamados Problemas de Otimização com Muitos Objetivos. Novos algoritmos então surgiram para contornar este problema, entre eles NSGA-III, que explora o conceito de pontos de referência para realizar a seleção de soluções. Porém, certas limitações ainda são apresentadas pelo algoritmo que pode ser melhorado. Assim, este trabalho propõe dois processos de adaptação dos pontos de referência no algoritmo NSGA-III, baseado no algoritmo MOEA/D-AWA. Os algoritmos com os dois processos propostos foram avaliados, buscando verificar se o processo de adaptação proposto melhora o desempenho do NSGA-III.
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