GHHAGA for environmental systems optimization

The global optimization of complicated nonlinear systems is mathematically intractable and such an optimization extensively exists in science and engineering. Once an objective function has many local extreme points, the traditional optimization methods may not obtain the global optimization efficiently. A genetic algorithm (GA) based on the genetic evolution of a species provides a robust procedure to explore broad and promising regions of solutions and to avoid being trapped at the local optimization. However, the computational amount is very large. To reduce computations and to improve the computational accuracy, a method based on the two-point crossover and two-point mutation of the hybrid accelerating genetic algorithm with Hooke–Jeeves searching operator is developed for systems optimization. With the shrinking of searching range, the method gradually directs to optimal result by the excellent individuals obtained by Gray code genetic algorithm embedding with Hooke–Jeeves searching operator and Hooke–Jeeves algorithm. The efficiency of the new algorithm is verified by application of several test functions. The comparison of our GA with six existing other algorithms is presented. This algorithm overcomes the Hamming–Cliff phenomena in other existing genetic methods, and is proved to be very efficient for the given environmental systems optimization.