Abstract:

The purpose of this study is to compare population methods. We present the genetic algorithms and particle swarm algorithm of nonlinear optimization which include two classes of heuristic algorithms for solving n-dimensional mathematical optimization problems. This work suggests a new hybrid algorithm which is nests particle swarm optimization (PSA) operations in the genetic algorithm (GA). The new hybrid algorithm provides a better convergence between the exploitation compared and exploration of both parent algorithms. However, the existing hybrid algorithms and achieving consistency provide the best accurate results of the optimal solution with relatively small computational cost.