Genetic Algorithm Applied on Multiobjective Optimization

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Addis Ababa University


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.



Generic Algorithm, Multi-Objective Optimization, Elitism, Pareto Optimal So- Lutions, Ordering Relation