A reference vector adaptive strategy for balancing diversity and convergence in many-objective evolutionary algorithms-Reference-Cited by-同舟云学术

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A reference vector adaptive strategy for balancing diversity and convergence in many-objective evolutionary algorithms

Published:2022-07-20 Issue:7 Volume:53 Page:7423-7438
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Zhang Lin,Wang Liping^ORCID,Pan Xiaotian,Qiu Qicang

Funder

natural science foundation of zhejiang province

natural science foundation of jilin province

cao guangbiao high science and technology foundation, zhejiang university

fujian provincial department of science and technology

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

https://link.springer.com/content/pdf/10.1007/s10489-022-03545-w.pdf

Reference41 articles.

1. Alkebsi K, Du W (2021) Surrogate-assisted multi-objective particle swarm optimization for the operation of co2 capture using vpsa. Energy 224(3):120078

2. Tawhid M A, Savsani V (October 2018) A novel multi-objective optimization algorithm based on artificial algae for multi-objective engineering design problems. Appl Intell 48(10):3762?3781. https://doi.org/10.1007/s10489-018-1170-x

3. Mirjalili S, Jangir P, Saremi S (January 2017) Multi-objective ant lion optimizer: A multi-objective optimization algorithm for solving engineering problems. Appl Intell 46(1):79?-95. https://doi.org/10.1007/s10489-016-0825-8

4. Khan I, Maiti M K, Basuli K (2020) Multi-objective traveling salesman problem: an abc approach. Appl Intell, 2

5. Li H, Deb K, Zhang Q, Suganthan P N, Chen L (2019) Comparison between moea/d and nsga-iii on a set of many and multi-objective benchmark problems with challenging difficulties. Swarm and Evolutionary Computation

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