Genetic Algorithm Applied on Multiobjective Optimization
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Date
2014-06-17
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Addis Ababa University
Abstract
Multi-objective formulations are a realistic models for many complex optimization prob-
lems. In this project we presented multiobjective optimization problems using genetic
algorithms developed specically for the problems with multiple objectives. Customized
genetic algorithms have been demonstrated to be particularly eective to determine excel-
lent solutions(pareto-optimal points) to the problems. Moreover, in solving multi-objective
problems, designers may be interested in a set of pareto-optimal points instead of a single
point. Since genetic algorithms(GAs) work with a population of points, it seems natural
to use GAs in multi-objective optimization problems to capture a number of solutions si-
multaneously. In this project we also describe the working principle of a binary-coded and
real-parameter genetic algorithm, which is ideally suited to handle problems with a con-
tinuous search space.Moreover, a non-dominated sorting-based multi-objective evolutionary
algorithm (MOEA), called non-dominated sorting genetic algorithm II (NSGA-II), is also
presented.
Keywords: Generic Algorithm, Multi-objective Optimization, Elitism, Pareto optimal so-
lutions, Ordering relation.
Description
Keywords
Generic Algorithm, Multi-Objective Optimization, Elitism, Pareto Optimal So- Lutions, Ordering Relation