Generating Epsilon-Efficient Solutions in Multiobjective Optimization by Genetic Algorithm

Generating epsilon-efficient solutions in multiobjective programming

Scalarization approaches to purposely generating epsilon-efficient solutions of multiobjective programs are investigated and a generic procedure for computing these solutions is proposed and illustrated with an example. Real-life decision making situations in which the solutions are of significance are described.

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

An Elitist Genetic Algorithm for Multiobjective Optimization

Solving multiobjective engineering problems is a very difficult task due to, in general, in these class of problems, the objectives conflict across a high-dimensional problem space. In these problems, there is no single optimal solution, the interaction of multiple objectives gives rise to a set of efficient solutions, known as the Pareto-optimal solutions. During the past decade, Genetic Algor...

Dac - 8576 Multiobjective Optimization by Iterative Genetic Algorithm

This paper presents a method to simultaneously produce multiple solutions to unconstrained multi-objective optimization problems. The proposed methodology uses populations of sets instead of populations of individuals and iterative calls to a Genetic Algorithm (IGA) to obtain a set of solutions spread across the Pareto set in the objective space. The superiority of such an approach to single ru...