A Hybrid Optimization Algorithm Based on Genetic Algorithm and Hyper Spherical Search Based on Chaos Theory

A hybrid optimization algorithm based on genetic algorithm and choatic hyper spherical search method is proposed. In the proposed method, in order to increase the efficiency of searching the optimal solution, chaos theory along with  genetic operators have been used. This, not only makes the results of the proposed algorithm definite and decreases their standard deviation, but also resolves the weakness of the hyper spherical search optimization algorithm based on chaos theory including the speed of convergence and the weak performance in some benchmark functions. The simulation results on the standard benchmark functions show that the proposed algorithm not only has faster convergence, but also acts as a more accurate search algorithm to find the optimal solution in comparison to the standard hyper spherical search algorithm, chaotic hyper sherical search algorithm, and some other optimization algorithms such as genetic, particle swarm, and harmony search algorithm.