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Fitness based weighted flower pollination algorithm with mutation strategies for image enhancement

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Abstract

Flower Pollination Algorithm (FPA) is a well-known swarm intelligence optimization algorithm, which has shown an effective performance by solving many optimization problems. The performance of the FPA significantly depends on the balance among exploration and exploitation stages. However, FPA operators may cause false positive optima location in multimodal surfaces. Under such circumstances, modifying the original structure of the FPA can increase the ability of FPA to effectively locate optima in multimodal surfaces. In this study, fitness based dynamic inertia weight and two popular mutation techniques of differential evolution (DE) have been employed to increase the performance of FPA which helps to achieve a higher balance among evolutionary stages and effectively locate optima in multimodal surfaces. The proposed modified FPA (PMFPA) has been employed in image enhancement field to measure the efficiency. The experimental study corroborates the effectiveness of the PMFPA over popular swarm intelligence algorithms, original FPA and some of its variants by producing more robust, scalable and precise results.

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Authors and Affiliations

  1. Department of Computer Science and Application, Midnapore College (Autonomous), Paschim Medinipur, West Bengal, India

    Arunita Das & Krishna Gopal Dhal

  2. Dept. of Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, Kolkata, West Bengal, India

    Swarnajit Ray

  3. Departamento de Electrónica, Universidad de Guadalajara, CUCEI Av. Revolución 1500, 44430, Guadalajara, Mexico

    Jorge Galvez

  4. Department of Engineering and Technological Studies, University of Kalyani, Kalyani, Nadia, India

    Sanjoy Das

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  1. Arunita Das
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  2. Krishna Gopal Dhal
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Correspondence to Krishna Gopal Dhal.

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Das, A., Dhal, K.G., Ray, S. et al. Fitness based weighted flower pollination algorithm with mutation strategies for image enhancement. Multimed Tools Appl 81, 28955–28986 (2022). https://doi.org/10.1007/s11042-022-12879-z

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