Firstly formulated to solve unconstrained optimization problems, the common way to solve constrained ones with the metaheuristic particle swarm optimization algorithm (PSO) is represented by adopting some penalty functions. In this paper, a new nonpenalty-based constraint handling approach for PSO is implemented, adopting a supervised classification machine learning method, the support vector machine (SVM). Because of its generality, constraint handling with SVM appears more adaptive both to nonlinear and discontinuous boundary. To preserve the feasibility of the population, a simple bisection algorithm is also implemented. To improve the search performances of the algorithm, a relaxation function of the constraints is also adopted. In th...
O presente trabalho tem como objetivo a otimização de estruturas treliçadas atra-vés de um algoritmo...
A methodology to the multiobjective structural design of buildings based on an improved particle swa...
This paper attempts to improve the computational efficiency of the well known particle swarm optimiz...
Firstly formulated to solve unconstrained optimization problems, the common way to solve constrained...
Structural design optimization has become an extremely challenging and more complex task for most re...
In order to overcome the premature convergence defect of the basic particle swarm optimization (PSO)...
Particle swarm optimization (PSO) is applied to the low-weight design of trusses. The objective func...
p. 1044-1057Particle Swarm Optimization (PSO) is a new paradigm of Swarm Intelligence which is insp...
This paper focuses on optimizing truss structures while propose best PSO variants. Truss optimizatio...
In this paper, an improved bare-bones multi-objective particle swarm algorithm is proposed to solve ...
The purpose of this paper is to show how the search algorithm known as particle swarm optimization p...
Using Particle Swarm Optimization (PSO) for solving nonlinear, multimodal and non-di#erentiable opti...
This study investigates the performances of the integrated particle swarm optimizer (iPSO) algorithm...
Field studies of structural optimization have gained increased attention due to the rapid developmen...
Many engineering optimization problems can be state as function optimization with constrained, intel...
O presente trabalho tem como objetivo a otimização de estruturas treliçadas atra-vés de um algoritmo...
A methodology to the multiobjective structural design of buildings based on an improved particle swa...
This paper attempts to improve the computational efficiency of the well known particle swarm optimiz...
Firstly formulated to solve unconstrained optimization problems, the common way to solve constrained...
Structural design optimization has become an extremely challenging and more complex task for most re...
In order to overcome the premature convergence defect of the basic particle swarm optimization (PSO)...
Particle swarm optimization (PSO) is applied to the low-weight design of trusses. The objective func...
p. 1044-1057Particle Swarm Optimization (PSO) is a new paradigm of Swarm Intelligence which is insp...
This paper focuses on optimizing truss structures while propose best PSO variants. Truss optimizatio...
In this paper, an improved bare-bones multi-objective particle swarm algorithm is proposed to solve ...
The purpose of this paper is to show how the search algorithm known as particle swarm optimization p...
Using Particle Swarm Optimization (PSO) for solving nonlinear, multimodal and non-di#erentiable opti...
This study investigates the performances of the integrated particle swarm optimizer (iPSO) algorithm...
Field studies of structural optimization have gained increased attention due to the rapid developmen...
Many engineering optimization problems can be state as function optimization with constrained, intel...
O presente trabalho tem como objetivo a otimização de estruturas treliçadas atra-vés de um algoritmo...
A methodology to the multiobjective structural design of buildings based on an improved particle swa...
This paper attempts to improve the computational efficiency of the well known particle swarm optimiz...