Improvement Of Group Search Optimizer Algorithm

Standard group search optimizer algorithm(GSO)is a new swarm intell igence algorithm,which has a superior performance on high-dimensional function optimization.It is simple and efficient,and easy to implement,but can't avoid entrapments by local minima.In order to enhance its convergence speed and precision,improveme nts on GSO are presented.(1)Inheriting the framework of “producer-scrounger” of GSO,in the light of the purposeless of producer and rangers,an improved group search optimizer algorithm(IGSO)is presented.Tests through 23 benchmark functions on standard GSO and IGSO are carried out independently.The results show that IGSO has a preferable convergence rate and accuracy.(2)In the light of the unicity of producer,a new strategy of multi producer group search optimizer algorithm(MPGSO)is presented.Tests through a set of benchmark functions on standard GSO and MPGSO are carried out independently.The results show that MPGSO has a preferable convergence rate and speed.(3)IGSO and MPGSO are applied to optimize the Butene Alkylation process.The results show that these two algorithms have good robustness and optimization ability in practical problems.