Optimal design of linear phase multi-band stop filters using improved cuckoo search particle swarm optimization

Abstract

Filtering and design of suitable filters are the basic requirement in most of modern signal processing networks. In this manuscript, linear phase multiband stop filters (LPMBSFs) are designed using a robust hybrid metaheuristic algorithm called improved cuckoo search particle swarm optimization (ICSPSO) for searching desired impulse responses of the filters. Generally LPMBSFs are considered for simultaneous processing of group of spectra widely used in digital communication and signal processing applications. The window method and the simple frequency sampling method of designing linear phase multiband filters (LPMBFs) have no precise control over the stop band and pass band cut-off frequencies. Thus in order to improve the design strategy of the LPMBSF, optimum design criterion is devised where the error between ideal frequency response and practical frequency response is reduced by incorporating the efficient modern swarm and global evolutionary computation technique. The ICSPSO technique is such an excellent hybrid global random search technique for error minimization, and is structured incorporating the advantages of cuckoo search algorithm into particle swarm optimization (PSO) while the global searching capability is further accelerated by tuning the mutation operation of differential evolution (DE) technique. The quality of the proposed ICSPSO based filter design has been compared with other prominent optimal method of design such as hybrid cuckoo search particle swarm optimization (CSPSO), improved cuckoo search (ICS) optimization, and PSO.