Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art
CAC Coello - Computer methods in applied mechanics and …, 2002 - Elsevier
This paper provides a comprehensive survey of the most popular constraint-handling
techniques currently used with evolutionary algorithms. We review approaches that go from …
techniques currently used with evolutionary algorithms. We review approaches that go from …
Particle swarm optimization for single objective continuous space problems: a review
MR Bonyadi, Z Michalewicz - Evolutionary computation, 2017 - ieeexplore.ieee.org
Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review Page 1
Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review …
Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review …
A multi-stage evolutionary algorithm for multi-objective optimization with complex constraints
Constrained multi-objective optimization problems (CMOPs) are difficult to handle because
objectives and constraints need to be considered simultaneously, especially when the …
objectives and constraints need to be considered simultaneously, especially when the …
[图书][B] Genetic algorithms and engineering optimization
A comprehensive guide to a powerful new analytical tool by two of its foremost innovators
The past decade has witnessed many exciting advances in the use of genetic algorithms …
The past decade has witnessed many exciting advances in the use of genetic algorithms …
A constrained multiobjective evolutionary algorithm with detect-and-escape strategy
Overall constraint violation functions are commonly used in multiobjective evolutionary
algorithms (MOEAs) for handling constraints. Constraints could cause these algorithms stuck …
algorithms (MOEAs) for handling constraints. Constraints could cause these algorithms stuck …
An effective co-evolutionary particle swarm optimization for constrained engineering design problems
Many engineering design problems can be formulated as constrained optimization
problems. So far, penalty function methods have been the most popular methods for …
problems. So far, penalty function methods have been the most popular methods for …
Improved accelerated PSO algorithm for mechanical engineering optimization problems
NB Guedria - Applied Soft Computing, 2016 - Elsevier
This paper introduces an improved accelerated particle swarm optimization algorithm
(IAPSO) to solve constrained nonlinear optimization problems with various types of design …
(IAPSO) to solve constrained nonlinear optimization problems with various types of design …
Constrained optimization by the ε constrained differential evolution with an archive and gradient-based mutation
T Takahama, S Sakai - IEEE congress on evolutionary …, 2010 - ieeexplore.ieee.org
The ε constrained method is an algorithm transformation method, which can convert
algorithms for unconstrained problems to algorithms for constrained problems using the ε …
algorithms for unconstrained problems to algorithms for constrained problems using the ε …
Penalty function methods for constrained optimization with genetic algorithms
Ö Yeniay - Mathematical and computational Applications, 2005 - mdpi.com
Genetic Algorithms are most directly suited to unconstrained optimization. Application of
Genetic Algorithms to constrained optimization problems is often a challenging effort …
Genetic Algorithms to constrained optimization problems is often a challenging effort …
Society and civilization: an optimization algorithm based on the simulation of social behavior
The ability to mutually interact is a fundamental social behavior in all human and insect
societies. Social interactions enable individuals to adapt and improve faster than biological …
societies. Social interactions enable individuals to adapt and improve faster than biological …