In constrained optimisation, the augmented Lagrangian method is considered as one of the most effective and efficient methods. This paper studies the behaviour of augmented Lagrangian function (ALF) in the solution space and then proposes an improved augmented Lagrangian method. We have shown that our proposed method can overcome some of the drawbacks of the conventional augmented Lagrangian method. With the improved augmented Lagrangian approach, this paper then proposes a cooperative coevolutionary differential evolution algorithm for solving constrained optimisation problems. The proposed algorithm is evaluated on a set of 24 well-known benchmark functions and five practical engineering problems. Experimental results demonstrate that the...
While coevolution has many parallels to natural evolution, methods other than those based on evoluti...
Coevolutionary algorithms are a variant of evolutionary algorithms which are aimed for the solution ...
International audienceWe consider the task of design optimization, where the constraint is a state e...
AbstractWe present a new hybrid method for solving constrained numerical and engineering optimizatio...
Engineering designs can involve multiple stages, where at each stage, the design models are incremen...
The Differential Evolution (DE) algorithm is widely used for real-world global optimisation problems...
International audienceIn this paper, we investigate a non-elitist Evolution Strategy designed to han...
Constrained Optimal Control Problems are notoriously difficult to solve accurately. Preliminary inve...
Many problems encountered in computer science are best stated in terms of interactions amongst indiv...
International audienceWe analyze linear convergence of an evolution strategy for constrained optimiz...
This paper presents an augmented Lagrangian algorithm to solve continuous constrained global optimi...
Differential evolution (DE) is an evolutionary algorithm widely used to solve optimization problems ...
We consider the task of design optimization, where the constraint is a state equation that can only ...
International audienceWe consider the problem of minimizing a function f subject to a single inequal...
In this paper, a memetic co-evolutionary differential evolution algorithm (MCODE) for constrained op...
While coevolution has many parallels to natural evolution, methods other than those based on evoluti...
Coevolutionary algorithms are a variant of evolutionary algorithms which are aimed for the solution ...
International audienceWe consider the task of design optimization, where the constraint is a state e...
AbstractWe present a new hybrid method for solving constrained numerical and engineering optimizatio...
Engineering designs can involve multiple stages, where at each stage, the design models are incremen...
The Differential Evolution (DE) algorithm is widely used for real-world global optimisation problems...
International audienceIn this paper, we investigate a non-elitist Evolution Strategy designed to han...
Constrained Optimal Control Problems are notoriously difficult to solve accurately. Preliminary inve...
Many problems encountered in computer science are best stated in terms of interactions amongst indiv...
International audienceWe analyze linear convergence of an evolution strategy for constrained optimiz...
This paper presents an augmented Lagrangian algorithm to solve continuous constrained global optimi...
Differential evolution (DE) is an evolutionary algorithm widely used to solve optimization problems ...
We consider the task of design optimization, where the constraint is a state equation that can only ...
International audienceWe consider the problem of minimizing a function f subject to a single inequal...
In this paper, a memetic co-evolutionary differential evolution algorithm (MCODE) for constrained op...
While coevolution has many parallels to natural evolution, methods other than those based on evoluti...
Coevolutionary algorithms are a variant of evolutionary algorithms which are aimed for the solution ...
International audienceWe consider the task of design optimization, where the constraint is a state e...