Group Search Algorithm With Quadratic Interpolation Of The Hybrid Algorithm And Its Application

Group search optimizer (GSO) is a new swarm intelligence optimizer algorithm inspired by animal social behaviors. GSO is based on PS model, and employs the ranger search strategy and the visual scanning mechanism, and it has good global search capability. In this article, we proposed two variants of GSO to improve its search capability, and applied them to solving nonlinear equations.Producers in the group search optimizer like the eyes of animals, which determines the "food" position and the movement direction of scroungers. However, due to the random sample mechanism, the computational efficiency is poor. To improve its search efficiency, a new group search optimizer based on quadratic interpolation method(QIGSO) is proposed, in which the estimated position with quadratic interpolation theory is used to replace the random point in each iteration to increase the speed of convergence. To test the performance of this modification, seven functions are used. Simulation results show that the performance of this modification is superior to the standard group search optimizer algorithm.To design a new variant of GSO to solve the nonlinear equations, the steepest gradient descent method is incorporated into the methodolgy of GSO. As the steepest gradient descent method is a local search algorithm, we mixed it with QIGSO algorithm,then we applied it to solve the nonlinear equations. Finally, a special problem about radar detection is used to test the performance of this variant. Simulation results show it is effective.