A Multiobjective Genetic Algorithm Applied to Control Optimization

A Multiobjective Genetic Algorithm Applied to Multivariable Control Optimization

Many control problems involve simultaneous optimization of multiple performance measures that are often noncommensurable and competing with each other. Traditionally, classical optimization algorithms based on nonlinear programming or optimal control theory is applied to obtain the solution of such problems using different scalar approaches. The presence of multiple objectives in a problem usua...

A Genetic Algorithm for Multiobjective Structural Optimization

A genetic algorithm for multiobjective optimization is presented which tries to evolve an evenly distributed set of solutions belonging to the Pareto set by: (i) ranking the population according to nondomination properties; (ii) defining a filter to retain Pareto set solutions and (iii) using adequate operators: exclusion, addition and single-objective operator which improves the individuals fr...

A Micro-Genetic Algorithm for Multiobjective Optimization

Genetic Algorithms Applied to Real Time Multiobjective Optimization Problems

Genetic algorithms are often well suited for multiobjective optimization problems. In this work, multiple objectives pertaining to the THUNDER software were concerned to optimize the war results obtained from the software. It is a stochastic, two-sided, analytical simulation of military operations. The simulation is subject to internal unknown noises. Due to these noises and discreetness in the...

The Multiobjective Genetic Algorithm Applied to Benchmark Problems

The multiobjective genetic algorithm (MOGA) has been applied to various real-world problems in a variety of fields, most prominently in control systems engineering, with considerable success. However, a recent empirical analysis of multiobjective evolutionary algorithms (MOEAs) has suggested that a MOGA-based algorithm performed poorly across a diverse set of two-objective test problems. In thi...