Results 31 to 40 of about 5,089,183 (236)
Improved Crowding Distance for NSGA-II
CoRR, 2018 EC course ...
Xiangxiang Chu, Xinjie Yu
openaire +2 more sources
Case Studies in Thermal Engineering, 2023 A performance prediction model is built utilizing Slipcevie method and the experimental verification results show that the average errors of Q, ΔPt, ΔPs are 4.4%, 5.3%, 6.0%, respectively. Through parametric study of Ddo_i, Ddb, Nb by Sobol’ method based
Zhe Xu +5 more
doaj +1 more source
Ingeniería e Investigación, 2020 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 ...
Luis Felipe Ariza Vesga +2 more
doaj +1 more source
Supplier Selection using NSGA-II Technique
International Journal of Web Portals, 2012 In modern manufacturing industries, supplier selection is increasingly recognized as a critical decision in supply chain management. Supplier selection problem is a typical multiple criteria decision making problem involving a number of different and usually conflicting objectives.
Vladimir Rankovic +5 more
openaire +2 more sources
NSGA-PINN: A Multi-Objective Optimization Method for Physics-Informed Neural Network Training
Algorithms, 2023 This paper presents NSGA-PINN, a multi-objective optimization framework for the effective training of physics-informed neural networks (PINNs). The proposed framework uses the non-dominated sorting genetic algorithm (NSGA-II) to enable traditional ...
Binghang Lu, Christian Moya, Guang Lin
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Dynamic multi‐objective optimisation of complex networks based on evolutionary computation
IET Networks, EarlyView., 2022 Abstract As the problems concerning the number of information to be optimised is increasing, the optimisation level is getting higher, the target information is more diversified, and the algorithms are becoming more complex; the traditional algorithms such as particle swarm and differential evolution are far from being able to deal with this situation ...
Linfeng Huang
wiley +1 more source
Mathematical runtime analysis for the non-dominated sorting genetic algorithm II (NSGA-II) [PDF]
Artificial Intelligence, 2021 The non-dominated sorting genetic algorithm II (NSGA-II) is the most intensively used multi-objective evolutionary algorithm (MOEA) in real-world applications.
Weijie Zheng, Benjamin Doerr
semanticscholar +1 more source
Application of a fast and elitist multi-objective genetic algorithm to Reactive Power Dispatch [PDF]
Serbian Journal of Electrical Engineering, 2009 This paper presents an Elitist Non-Dominated Sorting Genetic Algorithm version II (NSGA-II), for solving the Reactive Power Dispatch (RPD) problem. The optimal RPD problem is a nonlinear constrained multi-objective optimization problem where the real ...
Subramanian Ramesh +2 more
doaj +1 more source
An improved knowledge-informed NSGA-II for multi-objective land allocation (MOLA)
Geo-spatial Information Science, 2018 Multi-objective land allocation (MOLA) can be regarded as a spatial optimization problem that allocates appropriate use to certain land units subjecting to multiple objectives and constraints.
Mingjie Song, Dongmei Chen
doaj +1 more source
Speeding Up the NSGA-II With a Simple Tie-Breaking Rule [PDF]
AAAI Conference on Artificial IntelligenceThe non-dominated sorting genetic algorithm II (NSGA-II) is the most popular multi-objective optimization heuristic. Recent mathematical runtime analyses have detected two shortcomings in discrete search spaces, namely, that the NSGA-II has difficulties ...
Benjamin Doerr +2 more
semanticscholar +1 more source