Performance of NSGA-III on Multi-objective Combinatorial Optimization Problems Heavily Depends on Its Implementations-Reference-Cited by-同舟云学术

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Performance of NSGA-III on Multi-objective Combinatorial Optimization Problems Heavily Depends on Its Implementations

Published:2024-07-14 Issue: Volume:1 Page:511-519
ISSN:
Container-title:Proceedings of the Genetic and Evolutionary Computation Conference
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Author:

Gong Cheng¹²³^ORCID,Nan Yang²^ORCID,Pang Meng²^ORCID,Ishibuchi Hisao²^ORCID,Zhang Qingfu¹³^ORCID

Affiliation:

1. Department of Computer Science, City University of Hong Kong, Hong Kong, China

2. Southern University of Science and Technology, Shenzhen, China

3. The City University of Hong Kong Shenzhen Research Institute, Shenzhen, China

Funder

Guangdong Provincial Key Laboratory

Research Grants Council of the Hong Kong Special Administrative Region, China

National Natural Science Foundation of China

Publisher

ACM

Reference29 articles.

1. Solving efficiently the 0–1 multi-objective knapsack problem

2. Evolutionary Many-Objective Algorithms for Combinatorial Optimization Problems: A Comparative Study

3. SMS-EMOA: Multiobjective selection based on dominated hypervolume

4. Pymoo: Multi-Objective Optimization in Python

5. Investigating the Normalization Procedure of NSGA-III

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Three Objectives Degrade the Convergence Ability of Dominance-Based Multi-objective Evolutionary Algorithms;Lecture Notes in Computer Science;2024

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