An Efficient Function Optimization Algorithm based on Culture Evolution

Optimization problems arise in many real-world applications. Cultural Algorithms are a class of computational models derived from observing the cultural evolution process in nature, compared with genetic algorithm the cultural algorithms have high convergence speed. Aiming at the disadvantages of basic cultural algorithms like being trapped easily into a local optimum, this paper improves the basic cultural algorithms and proposes a new algorithm to solve the overcomes of the basic cultural algorithms. The new algorithm keeps not only the fast convergence speed characteristic of basic cultural algorithms, but effectively improves the capability of global searching as well. For the case studies, this means has proved to be efficient and the experiment results show that the new means have got the better results.