An Experimental Investigation of Variation Operators in Reference-Point Based Many-Objective Optimization-Reference-Cited by-同舟云学术

An Experimental Investigation of Variation Operators in Reference-Point Based Many-Objective Optimization

Published:2015-07-11 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation
language:
Short-container-title:

Author:

Yuan Yuan¹,Xu Hua¹,Wang Bo¹

Affiliation:

1. Tsinghua University, Beijing, China

Funder

National S&T Major Projects of China

National Basic Research Program of China (973 Program)

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/2739480.2754655

Reference33 articles.

1. Simulated binary crossover for continuous search space;Agrawal R. B.;Complex Systems,1994

2. HypE: An Algorithm for Fast Hypervolume-Based Many-Objective Optimization

3. An algorithm for many-objective optimization with reduced objective computations: A study in differential evolution;Bandyopadhyay S.;IEEE Transactions on Evolutionary Computation, in press.

4. Application of Differential Evolution Algorithm for Transient Stability Constrained Optimal Power Flow

5. A Jumping Gene Paradigm for Evolutionary Multiobjective Optimization

Cited by 20 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Constrained Multi-Objective Optimization With Deep Reinforcement Learning Assisted Operator Selection;IEEE/CAA Journal of Automatica Sinica;2024-04

2. Evolutionary Many‐objective Optimization: Difficulties, Approaches, and Discussions;IEEJ Transactions on Electrical and Electronic Engineering;2023-03-17

3. A third generation genetic algorithm NSGAIII for task scheduling in cloud computing;Journal of King Saud University - Computer and Information Sciences;2022-10

4. Hybrid multi-objective optimization algorithm based on angle competition and neighborhood protection mechanism;Applied Intelligence;2022-08-10

5. On the importance of isolated infeasible solutions in the many-objective constrained NSGA-III;Knowledge-Based Systems;2021-09