Cultured differential evolution for constrained optimization
By Ricardo Landa Becerra, Carlos Artemio Coello Coello. 2006
335 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/6f9d7dd00e57381cdc6fe094c6583d7e45a94940
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Ponnuthurai Nagaratnam Suganthan
- Swagatam Das
- Yong Wang
- Zixing Cai
- Efrén Mezura-Montes
- Seyedali Mirjalili
- Carlos Artemio Coello Coello
- Hui Liu
In Computational Theory and Mathematics
- Ponnuthurai Nagaratnam Suganthan
- Swagatam Das
- Yong Wang
- Zixing Cai
- Efrén Mezura-Montes
- Seyedali Mirjalili
- Carlos Artemio Coello Coello
- Hui Liu
Citations in the corpus
- Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems (2011, neutral): “This problem was solved by the different optimization methods: Multimem-bered Evolutionary Strategy (M-ES) [14], Particle Evolutionary Swarm Optimization (PESO) [15], Cultural Differential Evolution (CDE) [16], Co-evolutionary Differential Evolution (CoDE) [17] and Artificial Bee Colony (ABC) [18].”
- Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization (2010, methodology): “PSO-DE CRGA [8] SAPF [4] CDE [5] CULDE [23] CPSO-GD [15] SMES [10]”
- Differential Evolution: A Survey of the State-of-the-Art (2011, methodology): “Examples of such combined techniques are the vector evaluated DE [70] by Parsopoulos et al and the work of LandaBecerra and Coello Coello [42] where they hybridized the ε-constraint technique [S71] with a single-objective evolutionary optimizer: the cultured DE [43].”
- Constraint-handling in nature-inspired numerical optimization: Past, present and future (2011, methodology): “Landa and Coello [75] also adopted the feasibility rules in an approach in which a cultural DE-based mechanism was developed, with the aim of incorporating knowledge from the problem into the search process when solving CNOPs.”