A novel boundary constraint-handling technique for constrained numerical optimization problems

2015 ◽  
Author(s):  
Efren Juarez-Castillo ◽  
Nancy Perez-Castro ◽  
Efren Mezura-Montes
Keyword(s):  
Numerical Optimization ◽  
Optimization Problems ◽  
Constraint Handling ◽  
Boundary Constraint ◽  
Handling Technique

An Improved Centroid-Based Boundary Constraint-Handling Method in Differential Evolution for Constrained Optimization

2017 ◽  
Vol 31 (11) ◽  
pp. 1759023 ◽  
Author(s):  
Efrén Juárez-Castillo ◽  
Nancy Pérez-Castro ◽  
Efrén Mezura-Montes
Keyword(s):  
Differential Evolution ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Search Space ◽  
Population Based ◽  
Constraint Handling ◽  
Feasible Region ◽  
Boundary Constraint ◽  
Decision Variables ◽  
Handling Method

Differential Evolution (DE) is a population-based Evolutionary Algorithm (EA) for solving optimization problems over continuous spaces. Many optimization problems are constrained and have a bounded search space from which some vectors leave when the mutation operator of DE is applied. Therefore, it is necessary the use of a boundary constraint-handling method (BCHM) in order to repair the invalid mutant vectors. This paper presents a generalized and improved version of the Centroid BCHM in order to keep the search within the valid ranges of decision variables in constrained numerical optimization problems (CNOPs), which has been tested on a robust and comprehensive set of experiments that include a variant of DE specialized in dealing with CNOPs. This new version, named Centroid [Formula: see text], relocates the mutant vector in the centroid formed by K random vectors and one vector taken from the population that is within or near the feasible region. The results show that this new version has a major impact on the algorithm’s performance, and it is able to promote better final results through the improvement of both, the approach to the feasible region and the ability to generate better solutions.

E-BRM: A constraint handling technique to solve optimization problems with evolutionary algorithms

2018 ◽  
Vol 72 ◽  
pp. 14-29 ◽  
Author(s):  
Max de Castro Rodrigues ◽  
Solange Guimarães ◽  
Beatriz Souza Leite Pires de Lima
Keyword(s):  
Evolutionary Algorithms ◽  
Optimization Problems ◽  
Constraint Handling ◽  
Handling Technique

A rank-based constraint handling technique for engineering design optimization problems solved by genetic algorithms

2017 ◽  
Vol 187 ◽  
pp. 77-87 ◽  
Author(s):  
Rafael de Paula Garcia ◽  
Beatriz Souza Leite Pires de Lima ◽  
Afonso Celso de Castro Lemonge ◽  
Breno Pinheiro Jacob
Keyword(s):  
Genetic Algorithms ◽  
Design Optimization ◽  
Engineering Design ◽  
Optimization Problems ◽  
Constraint Handling ◽  
Engineering Design Optimization ◽  
Handling Technique

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

Adaptively Allocating Constraint-Handling Techniques for Constrained Multi-objective Optimization Problems

2021 ◽  
pp. 2159032
Author(s):  
Ning Yang ◽  
Hai-Lin Liu
Keyword(s):  
Optimization Problems ◽  
Performance Measure ◽  
Experimental Results ◽  
The Other ◽  
Constraint Handling ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Selection Operators ◽  
Handling Technique ◽  
Feasible Solutions

For solving constrained multi-objective optimization problems (CMOPs), an effective constraint-handling technique (CHT) is of great importance. Recently, many CHTs have been proposed for solving CMOPs. However, no single CHT can outperform all kinds of CMOPs. This paper proposes an algorithm, namely, ACHT-M2M, which adaptively allocates the existing CHTs in an M2M framework for solving CMOPs. To be more specific, a CMOP is first decomposed into several constrained multi-objective optimization subproblems by ACHT-M2M. Each subproblem has a subpopulation in a subregion. CHT for each subregion is adaptively allocated according to a proposed composite performance measure. Population for the next generation is selected from subregions by selection operators with different CHTs and the obtained nondominated feasible solutions in each generation are used to update a predefined archive. ACHT-M2M assembles the advantages of different CHTs and makes them cooperate with each other. The proposed ACHT-M2M is finally compared with the other 12 representative algorithms on benchmark CMOPs and the experimental results further confirm the effectiveness of ACHT-M2M for solving CMOPs.

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