An Approach to Multiobjective Optimization Using Genetic Algorithms


We present a hybrid approach to optimize the counterweight balancing of a robot arm, which uses a combination of a genetic algorithm (GA) with the min-max multiobjective optimization method to get the Pareto optimal set of solutions. This set corresponds to several possible robot designs from which the most appropriate has to be chosen by the designer. Our approach is compared to a more traditional min-max search technique in which a combination of random and sequential search was used to generate the Pareto optimal solutions. Our results show how the GA is able to get solutions with a lower deviation from the ideal vector.