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Locally Informed Shuffled Frog Leaping Algorithm

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Proceedings of Sixth International Conference on Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 546))

Abstract

Shuffled Frog-Leaping Algorithm (SFLA) is a memetic meta-heuristic approach for solving complex optimization problems. Like other evolutionary algorithms, it may also suffer from the problem of slow convergence. To elevate the convergence property of the algorithm, locally informed search strategy is incorporated with SFLA. To improve the intensification and diversification capabilities of SFLA, locally informed search strategy is embedded by calculating the mean of local best and one randomly selected neighbour solution of memeplex while updating the position of worst solution in local best updating phase. Similarly, mean of global best and a randomly selected neighbour solution is used to improve the position of worst solution while updating the position of worst solution in global best updating phase. The proposed algorithm is named as Locally Informed Shuffled Frog-Leaping Algorithm (LISFLA). The modified algorithm LISFLA is analysed over 15 distinct benchmark test problems and compared with conventional SFLA, its recent variant, namely Binomial Crossover Embedded Shuffled Frog-Leaping Algorithm (BC-SFLA) and three other nature inspired algorithms, namely Gravitational Search Algorithm (GSA), Differential Evolution (DE) and Biogeography-Based Optimization Algorithm (BBO). The results manifest that LISFLA is an antagonist variant of SFLA.

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Authors and Affiliations

  1. Rajasthan Technical University, Kota, India

    Pragya Sharma, Nirmala Sharma & Harish Sharma

Authors
  1. Pragya Sharma
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  2. Nirmala Sharma
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  3. Harish Sharma
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Corresponding author

Correspondence to Pragya Sharma .

Editor information

Editors and Affiliations

  1. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, India

    Kusum Deep

  2. Department of Mathematics, South Asian University, New Delhi, India

    Jagdish Chand Bansal

  3. Department of Mathematics, National Institute of Technology, Silchar, Silchar, Assam, India

    Kedar Nath Das

  4. School of Mathematics, Thapar Institute of Engineering and Technology University, Patiala, Punjab, India

    Arvind Kumar Lal

  5. School of Mathematics, Thapar University Patiala, Patiala, Punjab, India

    Harish Garg

  6. Department of Mathematics and Computer Science, Liverpool Hope University, Liverpool, United Kingdom

    Atulya K. Nagar

  7. Department of Applied Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, India

    Millie Pant

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Sharma, P., Sharma, N., Sharma, H. (2017). Locally Informed Shuffled Frog Leaping Algorithm. In: Deep, K., et al. Proceedings of Sixth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 546. Springer, Singapore. https://doi.org/10.1007/978-981-10-3322-3_13

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  • DOI: https://doi.org/10.1007/978-981-10-3322-3_13

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3321-6

  • Online ISBN: 978-981-10-3322-3

  • eBook Packages: EngineeringEngineering (R0)

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