A Multi-objective Genetic Algorithm Approach Based on the Uniform Design Metmod

Many optimization problems in the scientific research and engineering practice can be modeled as multi-objective optimization problems. Effective algorithms for them is of not only important in scientific research, but also valuable in applications. In this paper, a new genetic algorithm for multi-objective optimization problems based on uniform design called BUMOGA is proposed combined with uniform design. The algorithm can find the sparse areas of non-dominated frontier, and explore the sparse area which can make the non-dominated solutions more uniform. The introductions of uniform crossover operator and single point crossover complex operator make up the defects of weak search capabilities of simulated binary crossover operator. The global convergence of the algorithm is proved, and effectiveness of the algorithm is demonstrated by the simulations. The computer simulations for five difficult standard benchmark functions also verify this fact.