Multiobjective evolutionary algorithms: A survey of the state of the art
A multiobjective optimization problem involves several conflicting objectives and has a set of
Pareto optimal solutions. By evolving a population of solutions, multiobjective evolutionary …
Pareto optimal solutions. By evolving a population of solutions, multiobjective evolutionary …
[BOOK][B] Evolutionary algorithms for solving multi-objective problems
CAC Coello - 2007 - Springer
Problems with multiple objectives arise in a natural fashion in most disciplines and their
solution has been a challenge to researchers for a long time. Despite the considerable …
solution has been a challenge to researchers for a long time. Despite the considerable …
Constraint-handling in nature-inspired numerical optimization: past, present and future
In their original versions, nature-inspired search algorithms such as evolutionary algorithms
and those based on swarm intelligence, lack a mechanism to deal with the constraints of a …
and those based on swarm intelligence, lack a mechanism to deal with the constraints of a …
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 …
A multiobjective optimization-based evolutionary algorithm for constrained optimization
A considerable number of constrained optimization evolutionary algorithms (COEAs) have
been proposed due to increasing interest in solving constrained optimization problems …
been proposed due to increasing interest in solving constrained optimization problems …
Combining multiobjective optimization with differential evolution to solve constrained optimization problems
During the past decade, solving constrained optimization problems with evolutionary
algorithms has received considerable attention among researchers and practitioners. Cai …
algorithms has received considerable attention among researchers and practitioners. Cai …
An adaptive tradeoff model for constrained evolutionary optimization
In this paper, an adaptive tradeoff model (ATM) is proposed for constrained evolutionary
optimization. In this model, three main issues are considered:(1) the evaluation of infeasible …
optimization. In this model, three main issues are considered:(1) the evaluation of infeasible …
Decomposition-based multiobjective optimization for constrained evolutionary optimization
Pareto dominance-based multiobjective optimization has been successfully applied to
constrained evolutionary optimization during the last two decades. However, as another …
constrained evolutionary optimization during the last two decades. However, as another …
Constrained optimization based on hybrid evolutionary algorithm and adaptive constraint-handling technique
A novel approach to deal with numerical and engineering constrained optimization
problems, which incorporates a hybrid evolutionary algorithm and an adaptive constraint …
problems, which incorporates a hybrid evolutionary algorithm and an adaptive constraint …
Constrained optimization by radial basis function interpolation for high-dimensional expensive black-box problems with infeasible initial points
RG Regis - Engineering Optimization, 2014 - Taylor & Francis
This article develops two new algorithms for constrained expensive black-box optimization
that use radial basis function surrogates for the objective and constraint functions. These …
that use radial basis function surrogates for the objective and constraint functions. These …