Pareto-aware strategies for faster convergence in multi-objective multi-scale search optimization-Reference-Cited by-同舟云学术

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Pareto-aware strategies for faster convergence in multi-objective multi-scale search optimization

Published:2018-07 Issue: Volume:454-455 Page:1-15
ISSN:0020-0255
Container-title:Information Sciences
language:en
Short-container-title:Information Sciences

Author:

Wong C.S.Y.,Al-Dujaili Abdullah,Suresh S.^ORCID,Sundararajan N.

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Computer Science Applications,Theoretical Computer Science,Control and Systems Engineering,Software

Reference29 articles.

1. A. Al-Dujaili, S. Suresh, BMOBench: black-box multi-objective optimization benchmarking platform, arXiv:1605.07009 (2016).

2. Dividing rectangles attack multi-objective optimization;Al-Dujaili,2016

3. A naive multi-scale search algorithm for global optimization problems;Al-Dujaili;Inf. Sci.,2016

4. Multi-objective simultaneous optimistic optimization;Al-Dujaili;Inf. Sci.,2018

5. MSO: a framework for bound-constrained black-box global optimization algorithms;Al-Dujaili;J. Global Optim.,2016

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1. A new resource allocation strategy based on the relationship between subproblems for MOEA/D;Information Sciences;2019-10

2. An adaptation reference-point-based multiobjective evolutionary algorithm;Information Sciences;2019-07

3. Preselection via classification: A case study on evolutionary multiobjective optimization;Information Sciences;2018-10

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