Cultural Algorithms for Optimization

Abstract

This chapter provides the use of cultural algorithms for both single- and multi-objective optimization. Ravichandran proposed a cultural algorithm based on decomposition to decompose a dynamic multi-objective optimization problem into several sub-problems that are then optimized using information shared by neighboring problems. Although this is clearly a multi-objective optimization algorithm, the author used it to solve single-objective optimization problems. In this case, the authors used all of the previously designed knowledge sources, and they investigated the role of the belief space in the different stages of a dynamic optimization process. The topics covered in this chapter include static and dynamic single-objective optimization as well as multi-objective optimization. Cultural algorithms have been adopted in a wide variety of applications in which they have been used to solve both single- and multi-objective optimization problems. The cultural algorithm is able to produce competitive results while performing a lower number of objective function evaluations than the other algorithms.