Distributed Genetic Algorithms with a New Sharing Approach in Multiobjective Optimization Problems

1 AbstractIn this paper, a new distributed genetic algorithm for multiobjective optimization problems is proposed. In this approach, the island model is used with a distributed genetic algorithm and an operation of sharing for Pareto-optimum solutions is performed with the total population. In multiobjective optimization problems, the Pareto-optimum solutions should be derived for designers. Because the Paretooptimum solutions are the set of optimum solutions that are in the relationship of trade-o , not only the accuracy but also the diversity of the solutions should be high. The e ect of the distributed populations leads to the high accuracy and the sharing e ect leads to the high diversity of solutions. These e ects are examined and discussed through some numerical examples that have more than three objective functions.