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Digital FIR filter design using fitness based hybrid adaptive differential evolution with particle swarm optimization

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Abstract

This paper presents an efficient way of designing linear phase finite impulse response (FIR) low pass and high pass filters using a novel algorithm ADEPSO. ADEPSO is hybrid of fitness based adaptive differential evolution (ADE) and particle swarm optimization (PSO). DE is a simple and robust evolutionary algorithm but sometimes causes instability problem; PSO is also a simple, population based robust evolutionary algorithm but has the problem of sub-optimality. ADEPSO has overcome the above individual disadvantages faced by both the algorithms and is used for the design of linear phase low pass and high pass FIR filters. The simulation results show that the ADEPSO outperforms PSO, ADE, and DE in combination with PSO not only in magnitude response but also in the convergence speed and thus proves itself to be a promising candidate for designing the FIR filters.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology, Durgapur, West Bengal, India

    Vasundhara, Durbadal Mandal & Rajib Kar

  2. Department of Electrical Engineering, National Institute of Technology, Durgapur, West Bengal, India

    Sakti Prasad Ghoshal

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  1. Vasundhara
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  2. Durbadal Mandal
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  3. Rajib Kar
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  4. Sakti Prasad Ghoshal
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Correspondence to Durbadal Mandal.

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Vasundhara, Mandal, D., Kar, R. et al. Digital FIR filter design using fitness based hybrid adaptive differential evolution with particle swarm optimization. Nat Comput 13, 55–64 (2014). https://doi.org/10.1007/s11047-013-9381-x

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