Evolutionary Methods For Solving Multi-objective Optimization Problem

Evolutionary methods are characterized as a set of solution based algorithms to solve multi-objective optimization problems. Evolutionary algorithms have a potential of finding multiple Pareto optimal solution in a single simulation run. In this report we have considered non-dominated sorting genetic algorithm to solve multi objective optimization problem. We have suggested non-dominated sorting genetic algorithm–II for minimization of the objectives. Non-dominated sorting genetic algorithm–II is fast elitist search algorithm which is based on non-domination rank. Non- domination rank provides chance to the population to be chosen to become parent of the next generation. Selection is based on crowded comparison operator to pick population to variation operator

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