Extending the push and pull search framework with boundary search for constrained multi-objective optimization-Reference-Cited by-同舟云学术

Extending the push and pull search framework with boundary search for constrained multi-objective optimization

Published:2022-07-09 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Genetic and Evolutionary Computation Conference Companion
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Author:

Wisløff Erling¹,Aarsnes Marius¹,Ripon Kazi Shah Nawaz²,Haddow Pauline¹

Affiliation:

1. Norweg. Univ. of Science and Tech., Trondheim, Norway

2. Østfold University College, Halden, Norway

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3520304.3528950

Reference10 articles.

1. An improved epsilon constraint-handling method in MOEA/D for CMOPs with large infeasible regions

2. Zhun Fan , Wenji Li , Xinye Cai , Hui Li , Caimin Wei , Qingfu Zhang , Kalyanmoy Deb , and Erik Goodman . 2019. Push and pull search for solving constrained multi-objective optimization problems. Swarm and evolutionary computation 44 ( 2019 ), 665--679. Zhun Fan, Wenji Li, Xinye Cai, Hui Li, Caimin Wei, Qingfu Zhang, Kalyanmoy Deb, and Erik Goodman. 2019. Push and pull search for solving constrained multi-objective optimization problems. Swarm and evolutionary computation 44 (2019), 665--679.

3. Feasibility Structure Modeling: An Effective Chaperone for Constrained Memetic Algorithms

4. Guillermo Leguizamon and Carlos A Coello Coello . 2008. Boundary search for constrained numerical optimization problems with an algorithm inspired by the ant colony metaphor . IEEE transactions on evolutionary computation 13, 2 ( 2008 ), 350--368. Guillermo Leguizamon and Carlos A Coello Coello. 2008. Boundary search for constrained numerical optimization problems with an algorithm inspired by the ant colony metaphor. IEEE transactions on evolutionary computation 13, 2 (2008), 350--368.

5. Boundary Search for Constrained Numerical Optimization Problems