Landscape changes and the performance of Mapping Based Constraint handling methods

1998 ◽  
pp. 221-229
Author(s):  
Dae Gyu Kim ◽  
Phil Husbands
Keyword(s):  
Constraint Handling ◽  
Landscape Changes ◽  
Handling Methods

Other constraint-handling methods

2004 ◽  
Author(s):  
Zbigniew Michalewicz
Keyword(s):  
Constraint Handling ◽  
Handling Methods

Comparison of Constraint-Handling Mechanisms for the (1,λ)-ES on a Simple Constrained Problem

2016 ◽  
Vol 24 (1) ◽  
pp. 1-23 ◽  
Author(s):  
Michael Hellwig ◽  
Dirk V. Arnold
Keyword(s):  
Linear Constraint ◽  
Constraint Handling ◽  
Theoretical Knowledge ◽  
Zeroth Order ◽  
Order Model ◽  
Step Size ◽  
Constrained Problem ◽  
Repair Mechanisms ◽  
Handling Methods ◽  
Feasible Solutions

This paper investigates constraint-handling techniques used in nonelitist single-parent evolution strategies for the problem of maximizing a linear function with a single linear constraint. Two repair mechanisms are considered, and the analytical results are compared to those of earlier repair approaches in the same fitness environment. The first algorithm variant applies reflection to initially infeasible candidate solutions, and the second repair method uses truncation to generate feasible solutions from infeasible ones. The distributions describing the strategies’ one-generation behavior are calculated and used in a zeroth-order model for the steady state attained when operating with fixed step size. Considering cumulative step size adaptation, the qualitative differences in the behavior of the algorithm variants can be explained. The approach extends the theoretical knowledge of constraint-handling methods in the field of evolutionary computation and has implications for the design of constraint-handling techniques in connection with cumulative step size adaptation.

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

Other constraint-handling methods

2004 ◽  
Author(s):  
Zbigniew Michalewicz
Keyword(s):  
Constraint Handling ◽  
Handling Methods

Component-Based Ranking Strategy for Evolutionary Optimization with Sparse Constraints

2014 ◽  
Vol 556-562 ◽  
pp. 3925-3929
Author(s):  
Yu Wu ◽  
Jing Wei Liu ◽  
Yue Hong Xie
Keyword(s):  
Evolutionary Optimization ◽  
Experimental Results ◽  
Constraint Handling ◽  
Ranking Strategy ◽  
Handling Methods ◽  
Feasible Solutions ◽  
Better Than

Most constraint-handling methods in constrained evolutionary optimization usually take advantage of only the valuable information of feasible solutions, while they don’t exploit adequately the information from infeasible ones. In this paper, a concept of “feasible component” is introduced to recognize the characteristics of diverse information extracted from infeasible solutions. Then a component-based ranking strategy is proposed for evolutionary optimization with sparse constraints by integrating feasible components and the idea of stochastic ranking. Experimental results on several problems with sparse constraints show that the component-based ranking strategy performs better than the stochastic ranking.

Adaptive Constraint Handling in Optimization of Complex Structures by Using Machine Learning

2021 ◽  
Author(s):  
Yuecheng Cai ◽  
Jasmin Jelovica
Keyword(s):  
Machine Learning ◽  
Pareto Front ◽  
Search Algorithms ◽  
Constraint Handling ◽  
Computational Time ◽  
Complex Structures ◽  
Computationally Efficient ◽  
Nsga Ii ◽  
Slowing Down ◽  
Handling Methods

Abstract Optimization of complex systems requires robust and computationally efficient global search algorithms. Constraints make this a very difficult task, significantly slowing down an algorithm, and can even prevent finding the true Pareto front. This study continues the development of a recently proposed repair approach that exploits infeasible designs to increase computational efficiency of a prominent genetic algorithm, and to find a wider spread of the Pareto front. This paper proposes adaptive and automatized discovery of sensitivity of constraints to variables, i.e. the link, which needed direct designer’s input in the previous version of the repair approach. This is achieved by using machine learning in the form of artificial neural networks (ANN). A surrogate model is afterwards utilized in optimization based on ANN. The proposed approach is used for the recently proposed constraint handling implemented into NSGA-II optimization algorithm. The proposed framework is compared with two other constraint handling methods. The performance is analyzed on a structural optimization of a 178 m long chemical tanker which needs to fulfil class society’s criteria for strength. The results show that the proposed framework is competitive in terms of convergence and spread of the front. This is achieved while discovering the link automatically using ANN, without an input from a user. In addition, computational time is reduced by 60%.

Other constraint-handling methods

2000 ◽  
pp. 69-74
Author(s):  
Zbigniew Michalewicz
Keyword(s):  
Constraint Handling ◽  
Handling Methods
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