Genetic Algorithm Applied On Multiobjective Optimization

Multi-objective formulations are a realistic models for many complex optimization prob-rnlems. In this project we presented multiobjective optimization problems using geneticrnalgorithms developed specically for the problems with multiple objectives. Customizedrngenetic algorithms have been demonstrated to be particularly eective to determine excel-rnlent solutions(pareto-optimal points) to the problems. Moreover, in solving multi-objectivernproblems, designers may be interested in a set of pareto-optimal points instead of a singlernpoint. Since genetic algorithms(GAs) work with a population of points, it seems naturalrnto use GAs in multi-objective optimization problems to capture a number of solutions si-rnmultaneously. In this project we also describe the working principle of a binary-coded andrnreal-parameter genetic algorithm, which is ideally suited to handle problems with a con-rntinuous search space.Moreover, a non-dominated sorting-based multi-objective evolutionaryrnalgorithm (MOEA), called non-dominated sorting genetic algorithm II (NSGA-II), is alsornpresented.rnKeywords: Generic Algorithm, Multi-objective Optimization, Elitism, Pareto optimal so-rnlutions, Ordering relation.

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