Mixed Group Search Optimization Algorithm And Its Application

Group Search Optimization (GSO) is a new population-based swarm intelligent algorithm, firstly proposed in 2006 by S. He, Q. H. Wu and J. R. Saunders. It is inspired by animal seeking behaviors, and incorporated the visional search mechanism. However, compared with the advantages, there exist some shortcomings for GSO. Firstly, it is easily trapped into local optima when dealing with multi-modal problems. Secondly, the computational efficiency is poor in the final stage of GSO. Therefore, in this article, two hybrid algorithms of GSO are proposed aiming to improve the performance.To overcome the premature convergence phenomenon, the metropolis rule is introduced into producer's search pattern to enhance the global search capability. Different from original random search pattern, in this new variant hybrid with Metropolis rule, the producer can accept the bad solution with a certain probability. Simulation results show it superior to the standard version in multi-modal benchmarks.To solve a special kind optimization problem with differential information, the Limited Storage Quasi-Newton Method is hybrid with GSO to increase the local search capability. Numerical results show it is effective. Furthermore, many practical engineering problems can be represented with non-linear equations. In this article, four famous benchmarks are used to test the performances of two hybrid algorithms mentioned before. Simulation results show the second one achieves the best performance when compared with the first one and the standard version.