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Parvinder Kaur
Mandeep Kaur


:This paper presents the optimal design of fractional delay-Infinite Impulse Response filter (IIR) using a meta-heuristic approach called Modified Cuckoo Search Algorithm (MCSA). The Fractional Delay (FD) filters are used to give fraction of delay to signal and the FD-IIR filters are being used in various applications of signal processing. Coefficients of optimized Fractional Delay-Infinite Impulse Filter (FD-IIR) is calculated using Modified Cuckoo Search Algorithm (MCSA) to match the response of fractional delay IIR filter with ideal response of the filter. Different heuristic optimization algorithms such as Cat, Bat, Genetic Algorithm (GA), Simulated Annealing (SA), Cuckoo Search Algorithm (CSA), Particle Swarm Optimization (PSO) etc. have been used to design optimal fractional delay-IIR filter. FD-IIR filter design is a multimodal design problem. Hence Meta-heuristic optimization algorithm is used in the paper. The proposed algorithm is a modification of cuckoo search algorithm and hence called Modified Cuckoo Search Algorithm. It is a meta-heuristic optimization technique based on population of birds and cuckoos behavior. It is simple and is a global optimization algorithm. The performance of MCSA is compared with genetic algorithm, particle swarm optimization and cuckoo search algorithm. It is found that MCSA provides better results than GA, PSO and CSA. The fitness function used to evaluate the algorithm is Weighted Least Square function. The simulation results show that proposed algorithm, MCSA has less absolute magnitude error. The statistical data analysis also shows that MCSA has higher value of percentage improvement in magnitude error and has faster convergence rate than GA, PSO and CSA in terms of execution time.


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