Results 11 to 20 of about 5,089,183 (236)
IEEE Open Journal of the Communications Society, 2022 Optimum controller placement in the presence of several conflicting objectives has received significant attention in the Software-Defined Wide Area Network (SD-WAN) deployment.
Oladipupo Adekoya, Adel Aneiba
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PLoS ONE, 2021 In order to solve the problem of unbalanced workload of employees in parallel flow shop scheduling, a method of job standard balance is proposed to describe the work balance of employees. The minimum delay time of completion and the imbalance of employee
Zhengyu Hu +3 more
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Mathematics, 2023 Recently, many-objective optimization problems (MaOPs) have become a hot issue of interest in academia and industry, and many more many-objective evolutionary algorithms (MaOEAs) have been proposed.
Yingxin Zhang, Gaige Wang, Hongmei Wang
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Journal of Marine Science and Engineering, 2022 As maritime transportation develops, the pressure of port traffic increases. To improve the management of ports and the efficiency of their operations, vessel scheduling must be optimized.
Xing Jiang +5 more
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NSGA-II With Simple Modification Works Well on a Wide Variety of Many-Objective Problems
IEEE Access, 2020 In the last two decades, the non-dominated sorting genetic algorithm II (NSGA-II) has been the most widely-used evolutionary multi-objective optimization (EMO) algorithm.
Lie Meng Pang, Hisao Ishibuchi, Ke Shang
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Multiobjectivization with NSGA-ii on the noiseless BBOB testbed [PDF]
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation, 2013 The idea of multiobjectivization is to reformulate a single-objective problem as a multiobjective one. In one of the scarce studies proposing this idea for problems in continuous domains, the distance to the closest neighbor (DCN) in the population of a multiobjective algorithm has been used as the additional (dynamic) second objective.
Tran, Thanh-Do +2 more
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Runtime Analysis for the NSGA-II: Proving, Quantifying, and Explaining the Inefficiency for Many Objectives [PDF]
IEEE Transactions on Evolutionary Computation, 2022 The nondominated sorting genetic algorithm II (NSGA-II) is one of the most prominent algorithms to solve multiobjective optimization problems. Despite numerous successful applications, several studies have shown that the NSGA-II is less effective for ...
Weijie Zheng, Benjamin Doerr
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Improving NSGA-II with an adaptive mutation operator [PDF]
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers, 2009 The performance of a Multiobjective Evolutionary Algorithm (MOEA) is crucially dependent on the parameter setting of the operators. The most desired control of such parameters presents the characteristic of adaptiveness, i.e., the capacity of changing the value of the parameter, in distinct stages of the evolutionary process, using feedbacks from the ...
Arthur Gonçalves Carvalho +1 more
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Automatic configuration of NSGA-II with jMetal and irace [PDF]
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2019 Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
Antonio J. Nebro +3 more
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IEEE Access, 2020 This paper develops an improved non dominated sorting genetic algorithm II (NSGA-II) based on objective importance vector γ, abbreviated as γ-NSGA-II. Different importance levels for the multiple objectives are incorporated in the objective
Lu Zhang +5 more
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