Recently, there has been a renewed interest in multi-objective control system design using parameter search techniques. One multi-objective approach is the method of inequalities, where the problem is formulated as a set of algebraic inequalities which must be satisfied for a successful design. In this report, three algorithms which can be used to solve the method of inequalities are described and compared. Two of the algorithms are based on hill-climbing techniques, whilst the third uses a genetic algorithm approach. The report also serves as an introduction and tutorial into hill-climbing and genetic algorithm techniques for multi-objective design.
Linear feedback designed problems were previously solved using modern optimal control theory not cap...
The optimization of nonlinear controller parameters by Genetic Algorithm (GA) is explored in this p...
In this paper, we study the problem features that may cause a multi-objective genetic algorithm (GA)...
In accordance with the copyright legislation no information derived from the dissertation nor quotat...
In this thesis, the basic principles and concepts of single and multi-objective Genetic Algorithms (...
Optimisation is a challenge for computerized multidisciplinary design. With multidisciplinary design...
Control System Design methods are presented in terms of optimization techniques that incorporate Mu...
A general approach to the determination of approximate solutions of general control problems by expl...
Traditionally the Genetic Algorithm (GA) relies upon the evaluation of a single fitness criterion to...
Creating or preparing Multi-objective formulations are a realistic models for many complex engineeri...
This study presents a method to determine weights of objectives in multi-objective optimization with...
Genetic algorithms (GA'S) are global, parallel, stochastic search methods, founded on Darwinian evol...
This paper introduces a method for constrained optimization using a modified multi-objective algorit...
Feedback controls are usually designed to achieve multiple and often conflicting performance goals. ...
We propose a sequential interactive genetic algorithm (IGA), multi-objective IGA and parallel IGA, a...
Linear feedback designed problems were previously solved using modern optimal control theory not cap...
The optimization of nonlinear controller parameters by Genetic Algorithm (GA) is explored in this p...
In this paper, we study the problem features that may cause a multi-objective genetic algorithm (GA)...
In accordance with the copyright legislation no information derived from the dissertation nor quotat...
In this thesis, the basic principles and concepts of single and multi-objective Genetic Algorithms (...
Optimisation is a challenge for computerized multidisciplinary design. With multidisciplinary design...
Control System Design methods are presented in terms of optimization techniques that incorporate Mu...
A general approach to the determination of approximate solutions of general control problems by expl...
Traditionally the Genetic Algorithm (GA) relies upon the evaluation of a single fitness criterion to...
Creating or preparing Multi-objective formulations are a realistic models for many complex engineeri...
This study presents a method to determine weights of objectives in multi-objective optimization with...
Genetic algorithms (GA'S) are global, parallel, stochastic search methods, founded on Darwinian evol...
This paper introduces a method for constrained optimization using a modified multi-objective algorit...
Feedback controls are usually designed to achieve multiple and often conflicting performance goals. ...
We propose a sequential interactive genetic algorithm (IGA), multi-objective IGA and parallel IGA, a...
Linear feedback designed problems were previously solved using modern optimal control theory not cap...
The optimization of nonlinear controller parameters by Genetic Algorithm (GA) is explored in this p...
In this paper, we study the problem features that may cause a multi-objective genetic algorithm (GA)...