• Nenhum resultado encontrado

Application of an iterative method and an evolutionary algorithm in fuzzy optimization

N/A
N/A
Protected

Academic year: 2017

Share "Application of an iterative method and an evolutionary algorithm in fuzzy optimization"

Copied!
15
0
0

Texto

Loading

Imagem

Table 1 – Theoretical problems consisting only of inequality constraints.
Table 3 – Maximum and minimum levels of tolerance for the set of constraints.
Table 5 depicts the optimal solutions of the real-world problem in two forms: (i) totally satis- satis-fied constraints; and (ii) totally violated constraints

Referências

Documentos relacionados

he purpose of this section is to present an iterative algorithm for calculating the new equilibrium state of the gas in the inlatable structure chamber ater the

ABSTRACT: An optimization strategy is constructed to solve the aerodynamic and structural optimization problems in the conceptual design of double-swept lying wing

The mathematical programming models proposed were based in two classic methods of resolution of multi‑objective problems: the ε‑restricted multi-objective resolution method

The fluid velocity on the drop interfaces are obtained by an iterative solution of (36) using the GMRES algorithm (a generalization of the conjugate gradient method to

The filled function algorithm is an efficient deterministic global optimization algorithm, and it has been used to solve plenty of optimization problems, such as unconstrained

In Section 4, we define an adapted fuzzy integral based on the Sugeno integral and we propose a numerical integration formula for monotonic and differentiable functions whose range

Method: A neuro-fuzzy system was developed using the NEFCLASS (NEuro Fuzzy CLASSIfication) architecture and an artificial neural network with backpropagation learning