A Novel Binary Spider Monkey Optimization Algorithm for Thinning of Concentric Circular Antenna Arrays

Abstract

ABSTRACTThis paper presents a novel binary algorithm named as binary spider monkey optimization (binSMO) for thinning of concentric circular antenna arrays (CCAA). The proposed algorithm has been adapted from a recently developed nature inspired optimization method, spider monkey optimization (SMO). SMO works in continuous domain and as such is not suitable for application to binary optimization problems. The binSMO algorithm has been proposed with inclusion of logical operators in SMO for binary thinning problem. Thinning of an antenna array reduces the maximum side lobe level (SLL) as well as cost and size of antenna array. Thinning of CCAA can be modelled as 0–1 binary integer optimization problem. The proposed binSMO is used to synthesize CCAA in order to reduce the SLL and at the same time keeping the percentage of thinning equal to or more than the desired level. Simulation examples of two ring and ten ring CCAA have been considered. The novel method binSMO gives reduced SLL as compared to the results available in literature of teacher learning based optimization, biogeography based optimization, modified particle swarm optimization, and firefly algorithm. Moreover, the convergence rate of binSMO is faster than the other methods. The results prove the competence and superiority of binSMO to existing metaheuristic algorithms and it has an ability to become an effective tool for solving binary optimization problems.