Truncation-learning-driven surrogate assisted social learning particle swarm optimization for computationally expensive problem-Reference-Cited by-同舟云学术

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Truncation-learning-driven surrogate assisted social learning particle swarm optimization for computationally expensive problem

Published:2020-12 Issue: Volume:97 Page:106812
ISSN:1568-4946
Container-title:Applied Soft Computing
language:en
Short-container-title:Applied Soft Computing

Author:

Yu Haibo,Kang Li,Tan Ying,Sun Chaoli,Zeng Jianchao

Funder

Key Research and Development Program Projects in Shanxi Province

Shanxi Province Science Foundation for Youths, China

Publisher

Elsevier BV

Subject

Software

Reference86 articles.

1. Data-driven evolutionary optimization: an overview and case studies;Jin;IEEE Trans. Evol. Comput.,2018

2. Surrogate-assisted multicriteria optimization: Complexities, prospective solutions, and business case;Allmendinger;J. Multi-Criteria Decis. Anal.,2017

3. Meta-Heuristic Algorithms in Car Engine design: A literature survey;Tayarani-N;IEEE Trans. Evol. Comput.,2015

4. Surrogate-assisted evolutionary computation: Recent advances and future challenges;Jin;Swarm Evol. Comput.,2011

5. On evolutionary optimization with Approximate Fitness Functions;Jin,2000

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