Constrained Optimization by the ε Constrained Differential Evolution with Gradient-Based Mutation and Feasible Elites

By Tetsuyuki Takahama, Setsuko Sakai. International Conference on Evolutionary Computation, 2006

489 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/f560e968790115336b518f021dee5874991ae096

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

1. Ponnuthurai Nagaratnam Suganthan
2. Swagatam Das
3. Efrén Mezura-Montes
4. Yong Wang
5. R. Venkata Rao
6. Carlos Artemio Coello Coello
7. Zhi-hui Zhan
8. Vimal J. Savsani

In Computational Theory and Mathematics

1. Ponnuthurai Nagaratnam Suganthan
2. Swagatam Das
3. Efrén Mezura-Montes
4. Yong Wang
5. Carlos Artemio Coello Coello
6. R. Venkata Rao
7. Vimal J. Savsani
8. Rammohan Mallipeddi

Citations in the corpus

• Constraint-handling in nature-inspired numerical optimization: Past, present and future (2011, methodology): “The authors have proposed dynamic mechanisms [135,136] which have allowed these two algorithms to provide competitive results.”
• A survey on evolutionary computation for complex continuous optimization (2021, methodology): “, SP (Tessema and Yen 2009), FR (Deb 2000), ε-constrained method (Takahama and Sakai 2006), and SR (Runarsson and Yao 2000).”
• Teaching–learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems (2012, methodology): “…constraint handling techniques: superiority of feasible solutions (SF) (Deb 2000), self-adaptive penalty approach (SP) (Tessema andYen 2006), ε -constraint technique (EC) (Takahama and Sakai 2006) and stochastic ranking technique (SR) (Runarsson and Yao 2000), are experimented with using TLBO.”
• An improved epsilon constraint-handling method in MOEA/D for CMOPs with large infeasible regions (2017, methodology): “3.1 Epsilon Level Comparison In the epsilon constraint handling approach (Takahama and Sakai (2006)), the relaxation of constraints is controlled by the epsilon level ε.”
• Differential Evolution: A Survey of the State-of-the-Art (2011, methodology): “The εDE algorithm of Takahama and Sakai [97] uses a dynamic control of the allowable constraint violation specified by the ε-level and it achieved the first rank in the CEC 2006 competition on the constrained real-parameter optimization [47].”
• Constrained optimization by the ε constrained differential evolution with an archive and gradient-based mutation (2010, methodology): “The gradient-based mutation [8] is an operation similar to the gradient-based repair method proposed by Chootinan and Chen [33].”
• A Survey on Evolutionary Constrained Multiobjective Optimization (2023, methodology): “2) ε Constrained Method: This method was proposed by Takahama and Sakai [50], which uses a parameter ε to relax constraints. ε is gradually reduced, and when the constraint violation degree of an individual is less than ε , it is considered as a feasible solution.”
• Evolutionary Constrained Multiobjective Optimization: Test Suite Construction and Performance Comparisons (2019, methodology): “, CDP [28], SP [39], SR [40], [42], and ATM [46]) were compared.”
• Ensemble of Constraint Handling Techniques (2010, methodology): “2) Experiment 2: From the experimental results presented in [11], [13], [14], it can be observed that most of the problems of CEC2006 are solved by algorithms using DE as basic search algorithm within 500 000 function evaluations.”