Improved Genetic Algorithm And Its Applied Research In The Multi-objective Optimization

Genetic algorithms are new kinds of modern optimization algorithms that are inspired by principle of nature evolution. As new kinds of random search algorithms, they have some advantages over the traditional optimization algorithms, and are of the great importance and have a wide range of applications. The traditional optimization algorithms usually have strict limitation on the functions such as their differentiability, however, evolutionary algorithms do not require the differentiability of the functions and have parallel property, Therefore, they are often be used to solve some complex, large scale, nonlinear and non-differentiable optimization problems.This paper introduces the general situation of Genetic Algorithms and analyzes the implementation steps; discusses the theory foundation about Genetic Algorithms, including Schema theorem, building block hypothesis, implicit parallelism, the transform of Walsh schema and deceptive problem etc.; sums up some typical and latest improvement's strategies. A kind of general improvement in operators of Genetic Algorithm is presented, combining some concrete features, such as non-linear programming, networks path optimization and TSP problem. Simulation results show the improved algorithms are feasible, and enhance the efficiency at the same time.Multi-objective optimization has been a difficult problem and focus for research in fields of science and engineering. There already have a lot of classical methods for solving multi-objective optimization problems before evolutionary algorithms were introduced. Classical multi-objective optimization methods have been thoroughly developed, but there are still lots of shortcomings in solving high dimension and large-scale problems, which can be solved by GAs. To some concrete problems, the key issue in combining Genetic Algorithm with Multi-objective Optimization is how to grade a individual in a population by Multi-objects. This paper presents cone-sorting concept and applies it to practical problem's processing, the simulation results indicate the improved algorithms can figure out the Pareto-optimal solution set, and show the algorithms are practical.