A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems
IEEE Access, 2021 This paper provides an extensive review of the popular multi-objective optimization algorithm NSGA-II for selected combinatorial optimization problems viz.
Shanu Verma, Millie Pant, Vaclav Snasel
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Self-adaptive polynomial mutation in NSGA-II
Soft Computing, 2023 Evolutionary multi-objective optimization is a field that has experienced a rapid growth in the last two decades. Although an important number of new multi-objective evolutionary algorithms have been designed and implemented by the scientific community, the popular Non-Dominated Sorting Genetic Algorithm (NSGA-II) remains as a widely used baseline for ...
Jose L. Carles-Bou, S. F. Galán
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Proposal and Comparative Study of Evolutionary Algorithms for Optimum Design of a Gear System [PDF]
IEEE Access, 2020 This paper proposes a novel metaheuristic framework using a Differential Evolution (DE) algorithm with the Non-dominated Sorting Genetic Algorithm-II (NSGA-II).
Maximo Mendez +3 more
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Improved NSGA-II and its application in BIW structure optimization
Advances in Mechanical Engineering, 2023 Based on the crowding distance algorithm of Non-Dominated Sorting Genetic Algorithm-II (NSGA-II), three improved algorithms are proposed: side length optimization strategy, diagonal optimization strategy, center optimization strategy.
Xiao Wu +5 more
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Optimizing Ontology Alignment through Improved NSGA-II
Discrete Dynamics in Nature and Society, 2020 Over the past decades, a large number of complex optimization problems have been widely addressed through multiobjective evolutionary algorithms (MOEAs), and the knee solutions of the Pareto front (PF) are most likely to be fitting for the decision maker
Yikun Huang, Xingsi Xue, Chao Jiang
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Research on Multi-Objective Low-Carbon Flexible Job Shop Scheduling Based on Improved NSGA-II
MachinesTo optimize the production scheduling of a flexible job shop, this paper, based on the NSGA-II algorithm, proposes an adaptive simulated annealing non-dominated sorting genetic algorithm II with enhanced elitism (ASA-NSGA-EE) that establishes a multi ...
Zheyu Mei, Yujun Lu, Liye Lv
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Optimization of rotor-side controller parameters in doubly fed induction generators based on an improved NSGA-II. [PDF]
PLoS ONEHerein, an advanced control strategy to enhance the operational stability of wind turbine generators during grid-voltage surges is presented. In particular, a multiobjective optimization framework based on an improved nondominated sorting genetic ...
Yanling Lv, Xiang Zhao, Zexin Mou
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Is NSGA-II Ready for Large-Scale Multi-Objective Optimization?
Mathematical and Computational Applications, 2022 NSGA-II is, by far, the most popular metaheuristic that has been adopted for solving multi-objective optimization problems. However, its most common usage, particularly when dealing with continuous problems, is circumscribed to a standard algorithmic ...
Antonio J. Nebro +3 more
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An Improved NSGA-II and its Application for Reconfigurable Pixel Antenna Design [PDF]
Radioengineering, 2014 Based on the elitist non-dominated sorting genetic algorithm (NSGA-II) for multi-objective optimization problems, an improved scheme with self-adaptive crossover and mutation operators is proposed to obtain good optimization performance in this paper ...
Y. L. Li, W. Shao, J. T. Wang, H. Chen
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The First Proven Performance Guarantees for the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) on a Combinatorial Optimization Problem [PDF]
International Joint Conference on Artificial Intelligence, 2023 The Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is one of the most prominent algorithms to solve multi-objective optimization problems. Recently, the first mathematical runtime guarantees have been obtained for this algorithm, however only for ...
Sacha Cerf +4 more
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