Group Leaders Optimization Algorithm

Complexity of global optimization algorithms makes implementation of the algorithms difficult and leads the algorithms to require more computer resources for the optimization process. The ability to explore the whole solution space without increasing the complexity of algorithms has a great importance to not only get reliable results but so also make the implementation of these algorithms more convenient for higher dimensional and complex-real world problems in science and engineering. In this paper, we present a new global optimization algorithm in which the influence of the leaders in social groups is used as an inspiration for the evolutionary technique that is designed into a group architecture similar to the architecture of Cooperative Coevolutionary Algorithms. Therefore, we present the implementation method and the experimental results for the single and multidimensional optimization test problems and a scientific real world problem, the energies and the geometric structures of Lennard-Jones clusters.