An effective ensemble framework for Many-Objective optimization based on AdaBoost and K-means clustering-Reference-Cited by-同舟云学术

An effective ensemble framework for Many-Objective optimization based on AdaBoost and K-means clustering

Published:2023-10 Issue: Volume:227 Page:120278
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Palakonda Vikas,Kang Jae-Mo^ORCID,Jung Heechul^ORCID

Funder

IITP

NRF

Ministry of Science, ICT and Future Planning

National Research Foundation of Korea

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference69 articles.

1. D2MOPSO: MOPSO based on decomposition and dominance with archiving using crowding distance in objective and solution spaces;Al Moubayed;Evolutionary computation,2014

2. HypE: An algorithm for fast hypervolume-based many-objective optimization;Bader;Evolutionary computation,2011

3. Fast density clustering strategies based on the k-means algorithm;Bai;Pattern Recognition,2017

4. Finding acceptable solutions in the pareto-optimal range using multiobjective genetic algorithms Soft computing in engineering design and manufacturing;Bentley,1998

5. An external archive guided multiobjective evolutionary algorithm based on decomposition for combinatorial optimization;Cai;IEEE Transactions on Evolutionary Computation,2014

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