Gray-Encoded Hybrid Accelerating Genetic Algorithm for Global Optimization of Water Environmental Model

This improved algorithm, Gray-encoded hybrid accelerating genetic algorithm (GHAGA), is presented to reduce computational amount and to improve the computational accuracy for the global optimization of water environmental models. The hybrid method combines two algorithms, which are the Gray-encoded genetic algorithm and Hooke-Jeeves algorithm. With the shrinking of searching range, the method gradually directs to optimal result with the excellent individuals obtained by Gray genetic algorithm embedding the Hooke-Jeeves searching operator. The convergence and global optimization of the new genetic algorithm are analyzed. Its global convergence rate is 100%, and the computational velocity is fast for five test functions. And it is efficient for the global optimization in the practical water environmental model on wastewater treatment.