Adaptive boundary constraint-handling scheme for constrained optimization

2018 ◽  
Vol 23 (17) ◽  
pp. 8247-8280 ◽  
Author(s):  
Efrén Juárez-Castillo ◽  
Héctor-Gabriel Acosta-Mesa ◽  
Efrén Mezura-Montes
Keyword(s):  
Constrained Optimization ◽  
Constraint Handling ◽  
Boundary Constraint

Constraint handling in constrained optimization of a storage ring multi-bend-achromat lattice

2021 ◽  
Vol 988 ◽  
pp. 164890 ◽  
Author(s):  
Jianhao Xu ◽  
Penghui Yang ◽  
Gangwen Liu ◽  
Zhenghe Bai ◽  
Weimin Li
Keyword(s):  
Constrained Optimization ◽  
Storage Ring ◽  
Constraint Handling

scholarly journals A Comparison of Constraint Handling Techniques for Dynamic Constrained Optimization Problems

2018 ◽  
Author(s):  
Maria-Yaneli Ameca-Alducin ◽  
Maryam Hasani-Shoreh ◽  
Wilson Blaikie ◽  
Frank Neumann ◽  
Efren Mezura-Montes
Keyword(s):  
Constrained Optimization ◽  
Optimization Problems ◽  
Constraint Handling ◽  
Constrained Optimization Problems

A novel constraint-handling technique based on dynamic weights for constrained optimization problems

2017 ◽  
Vol 22 (12) ◽  
pp. 3919-3935 ◽  
Author(s):  
Chaoda Peng ◽  
Hai-Lin Liu ◽  
Fangqing Gu
Keyword(s):  
Constrained Optimization ◽  
Optimization Problems ◽  
Constraint Handling ◽  
Constrained Optimization Problems ◽  
Handling Technique

scholarly journals The Performance of Penalty Methods on Tree-Seed Algorithm for Numerical Constrained Optimization Problems

2020 ◽  
Vol 17 (5) ◽  
pp. 799-807 ◽  
Author(s):  
Ahmet Cinar ◽  
Mustafa Kiran
Keyword(s):  
Constrained Optimization ◽  
Optimization Problems ◽  
Constraint Handling ◽  
Constrained Optimization Problems ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Unconstrained Optimization Problems ◽  
Handling Methods ◽  
State Of Art ◽  
Tree Seed

The constraints are the most important part of many optimization problems. The metaheuristic algorithms are designed for solving continuous unconstrained optimization problems initially. The constraint handling methods are integrated into these algorithms for solving constrained optimization problems. Penalty approaches are not only the simplest way but also as effective as other constraint handling techniques. In literature, there are many penalty approaches and these are grouped as static, dynamic and adaptive. In this study, we collect them and discuss the key benefits and drawbacks of these techniques. Tree-Seed Algorithm (TSA) is a recently developed metaheuristic algorithm, and in this study, nine different penalty approaches are integrated with the TSA. The performance of these approaches is analyzed on well-known thirteen constrained benchmark functions. The obtained results are compared with state-of-art algorithms like Differential Evolution (DE), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Genetic Algorithm (GA). The experimental results and comparisons show that TSA outperformed all of them on these benchmark functions

Constraint Handling in Bayesian Optimization -- A Comparative Study of Support Vector Machine, Augmented Lagrangian and Expected Feasible Improvement

2021 ◽  
Author(s):  
Yuan Jin ◽  
Zheyi Yang ◽  
Shiran Dai ◽  
Yann Lebret ◽  
Olivier Jung
Keyword(s):  
Support Vector Machine ◽  
Comparative Study ◽  
Constrained Optimization ◽  
Augmented Lagrangian ◽  
Optimization Problems ◽  
Bayesian Optimization ◽  
Constraint Handling ◽  
Sequential Optimization ◽  
Support Vector ◽  
Complex Constraints

Abstract Many engineering problems involve complex constraints which can be computationally costly. To reduce the overall numerical cost, such constrained optimization problems are solved via surrogate models constructed on a Design of Experiment (DoE). Meanwhile, complex constraints may lead to infeasible initial DoE, which can be problematic for subsequent sequential optimization. In this study, we address constrained optimization problem in a Bayesian optimization framework. A comparative study is conducted to evaluate the performance of three approaches namely Expected Feasible Improvement (EFI) and slack Augmented Lagrangian method (AL) and Expected Improvement with Probabilistic Support Vector Machine in constraint handling with feasible or infeasible initial DoE. AL is capable to start sequential optimization with infeasible initial DoE, while EFI requires extra a priori enrichment to find at least one feasible sample. Empirical experiments are performed on both analytical functions and a low pressure turbine disc design problem. Through these benchmark problems, EFI and AL are shown to have overall similar performance in problems with inequality constraints. However, the performance of EIPSVM is affected strongly by the corresponding hyperparameter values. In addition, we show evidences that with an appropriate handling of infeasible initial DoE, EFI does not necessarily underperform compared with AL solving optimization problems with mixed inequality and equality constraints.

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