Composite differential search algorithm

Differential search algorithm(ds) is a relatively newevolutionary algorithminspired by the brownian-like random-walkmovement which is used by an organism to migrate. it has been verified to be more effective than abc,jde,jade,sade,epsde,gsa,pso2011,and cma-es. in this paper,we propose four improved solution search algorithms,namely ds/rand/1, ds/rand/2, ds/current to rand/1, and ds/current to rand/2 to search the new space and enhance the convergence rate for the global optimization problem. in order to verify the performance of different solution search methods,23 benchmark functions are employed. experimental results indicate that the proposed algorithm performs better than,or at least comparable to,the original algorithm when considering the quality of the solution obtained. however,these schemes cannot still achieve the best solution for all functions. in order to further enhance the convergence rate and the diversity of the algorithm,a composite differential search algorithm (cds) is proposed in this paper. this new algorithm combines three new proposed search schemes including ds/rand/1, ds/rand/2, and ds/current to rand/1 with three control parameters using a random method to generate the offspring. experiment results show that cds has a faster convergence rate and better search ability based on the 23 benchmark functions. copyright © 2014 bo liu.