Abstract
We present a novel hybrid algorithm based on particle swarm optimization (PSO) and simulated annealing (SA) for the design of two-dimensional recursive digital filters. The proposed method, known as SA-PSO, integrates the global search ability of PSO with the local search ability of SA and offsets the weakness of each other. The acceptance criterion of Metropolis is included in the basic algorithm of PSO to increase the swarm’s diversity by accepting sometimes weaker solutions also. The experimental results reveal that the performance of the optimal filter designed by the proposed SA-PSO method is improved. Further, the convergence behavior as well as optimization accuracy of proposed method has been improved significantly and computational time is also reduced. In addition, the proposed SA-PSO method also produces the best optimal solution with lower mean and variance which indicates that the algorithm can be used more efficiently in realizing two-dimensional digital filters.
Highlights
Design of two-dimensional (2D) filters has been considered extensively over the past two decades as it plays a very significant role in the domain of biomedical image processing, satellite imaging, seismic data processing, and so forth [1]
We present a novel hybrid algorithm based on particle swarm optimization (PSO) and simulated annealing (SA) for the design of two-dimensional recursive digital filters
The main problems of infinite impulse response (IIR) filters are that they have a multimodal error surface and they may be unstable in some cases
Summary
Design of two-dimensional (2D) filters has been considered extensively over the past two decades as it plays a very significant role in the domain of biomedical image processing, satellite imaging, seismic data processing, and so forth [1]. Reported work on this problem has applied different optimization techniques, like neural network (NN) [1], genetic algorithm (GA) [2], computer language GENETICA [3], Taguchi-based immune algorithm [4], Bees algorithm [5], and particle swarm optimization (PSO) [6], efficiently. Most of these algorithms exhibit slow convergence to achieve a good nearoptimum solution and are trapped into local optima. These shortcomings can be avoided by introducing SA with PSO because the first one has strong local exploration capabilities and PSO exhibits fast global searching abilities
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