Design of optimal high pass and band stop FIR filters using adaptive Cuckoo search algorithm. (April 2018)

• Record Type: Journal Article
• Authors: Sarangi, Shubhendu Kumar; Panda, Rutuparna; Das, Pradeep Kumar; Abraham, Ajith
• Is Part Of: Engineering applications of artificial intelligence. Volume 70(2017:Oct.)
• Page Start: 67
• Page End: 80
• Publication Date: 2018-04
• Subjects: Optimal FIR filter design -- Adaptive cuckoo search; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2018.01.005
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: This paper presents an efficient design of digital FIR high pass and band stop filters using an adaptive cuckoo search algorithm (ACSA). The important features of ACSA are — (i) the step size is independent and (ii) it is accurately decided from the current fitness value. The step size is decided according to the current fitness value within the iteration process. This increases the convergence speed. The other five global optimizers are also used for optimization. The optimal solutions obtained by the ACSA are compared with the other global optimizers. CEC 2005 benchmark test functions are considered for the comparison. The results are compared in terms of the convergence speed, accuracy, deviation from the desired response, minimum stop-band attenuation and maximum pass-band attenuation. The statistical analysis, i.e. t -Test is performed to claim the superiority of the proposed approach. The simulation results presented in this paper reveal the fact that the performance of the ACSA is better than the other algorithms. Highlights: AI technique is used for design of optimal FIR filters. The adaptive Cuckoo search algorithm is used for function optimization. Results on 14 numbers of CEC 2005 benchmark test functions are presented. Two filter design problems are solved using the proposed idea. Statistical analysis is provided to foster the approach.