A T-cell algorithm for solving dynamic optimization problems-Reference-Cited by-同舟云学术

A T-cell algorithm for solving dynamic optimization problems

Published:2011-09 Issue:17 Volume:181 Page:3614-3637
ISSN:0020-0255
Container-title:Information Sciences
language:en
Short-container-title:Information Sciences

Author:

Aragón Victoria S.,Esquivel Susana C.,Coello Coello Carlos A.

Publisher

Elsevier BV

Subject

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

Reference32 articles.

1. Victoria S. Aragón, Susana C. Esquivel, Carlos A. Coello Coello, Artificial immune system for solving global optimization problems, in: XIV Congreso Argentino en Ciencias de la Computacion (CACIC 2008), Chilecito, La Rioja, Argentina, 2008, pp. 637–647.

2. Immune K-means and negative selection algorithms for data analysis;Bereta;Information Sciences,2009

3. Multiswarms, exclusion, and anti-convergence in dynamic environments;Blackwell;IEEE Transactions on Evolutionary Computation,2006

4. Memory enhanced evolutionary algorithms for changing optimization problems;Branke,1999

5. Evolutionary Optimization in Dynamic Environments;Branke,2002

Cited by 14 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. CaAIS: Cellular Automata-Based Artificial Immune System for Dynamic Environments;Algorithms;2023-12-30

2. A survey of artificial immune algorithms for multi-objective optimization;Neurocomputing;2022-06

3. Q-learning and hyper-heuristic based algorithm recommendation for changing environments;Engineering Applications of Artificial Intelligence;2021-06

4. Quantum particles-enhanced multiple Harris Hawks swarms for dynamic optimization problems;Expert Systems with Applications;2021-04

5. A cluster-based clonal selection algorithm for optimization in dynamic environment;Swarm and Evolutionary Computation;2019-11