Research Of Multi Objective Genetic Algorithm Based On Chaotic Local Search

In real world, there are many multi-objective optimization problems, which are hot spots in the fields of engineering and scientific research. Multi-objective optimization problems are different from single objective optimization, because their optimization objectives are not single. In the multi-objective optimization, the objectives may be consistent or conflict. There are many traditional conventional multiobjective optimization methods, such as target weighting method, the level of priority method, goal programming method, binding method and maximum-minimum law to solve multi-objective optimization problems. Although these algorithms have achieved a certain degree of successful application, but they not suitable for large-scale multi-objective optimization problems because of their many defects.Genetic algorithms as an intelligent algorithm, originated in the1960s, attain a rapid development with a number of academic studies. Genetic algorithm as a probabilistic algorithm that has many features, such as parallelism, universal, global optimization, robustness and simplicity. These characteristics make it have many advantages over traditional methods to solve multi-objective optimization problems. It is a simulation evolutionary mode of biological heredity, mutation and survival of the fittest, by individual selection, crossover and mutation, and constantly evolving, finally getting the optimal individual. There are many researches and applications for Multi-objective genetic algorithm. Many improved methods raised to overcome Genetic algorithm’s shortcomings contribute to the development of genetic algorithms.Chaos is a common phenomenon in the non-linear systems. Chaos has many features, such as non-cyclical, randomness, ergodicity, sensitivity to initial values, fractal and so on. These characteristics make chaos can be used for function optimization. The research of chaos optimization algorithm is also an important branch in the study of chaos and important issue in the application of chaos. The research of chaos optimization algorithm gets a lot of results with the extensive studies and applications. It will also promote research and development of chaos.This paper studies the advantages and disadvantages of Genetic algorithm and chaos optimization algorithm, so does the hybrid genetic algorithm framework and the hybrid mothed for gnetic algorithm and chaos optimization algorithm. Multi-objective chaos genetic algorithm (MOGA-CLS) is presented basing on the characteristic of global optimization of genetic algorithm and the strong local search ability of chaos optimization algorithm. The main idea of MOGA-CLS algorithm is to gets the better solution around the1st and2nd rank of pareto solutions based on chaotic local search, and keep the distribution and diversity of population and pareto solution set with cluster method based on crowding degree. In this paper, MOGA-CLS algorithm is designed effective. The analysis and simulation experiments show the validation of MOGA-CLS algorithm.