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Adaptive Multi-swarm Bat Algorithm (AMBA)

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Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1048))

Abstract

Modified shuffled multi-population bat algorithm (MSMPBat) is a recently proposed swarm algorithm. It divides its population into multiple sub-populations (SPs), each of which uses different parameter settings and evolves independently using an enhanced search mechanism. For information exchange among these SPs, a solution from one SP is copied to the next after every generation. This process leads to duplication of solutions over time. To overcome this drawback, different techniques are introduced. Opposition-based learning is used to generate a diverse starting population. For information exchange, if a solution comes too close to the swarm best, only then it is sent (moved, not copied) to another swarm. Four techniques are proposed to select this second swarm. Initially, the selection probability of each technique is same. The algorithm adaptively updates these probabilities based on their success rate. The swarm which gave up the solution uses a modified opposition-based learning technique to generate a new solution. These changes help to maintain the overall diversity of the population. The proposed approach, namely, adaptive multi-swarm bat algorithm (AMBA), is compared to six algorithms over 20 benchmark functions. Results establish the superiority of adaptive multi-swarm bat algorithm.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Delhi, Delhi, 110007, India

    Reshu Chaudhary

  2. Dyal Singh College, Lodhi Road, Delhi, 110003, India

    Hema Banati

Authors
  1. Reshu Chaudhary
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  2. Hema Banati
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Corresponding author

Correspondence to Reshu Chaudhary .

Editor information

Editors and Affiliations

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

    Kedar Nath Das

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

    Jagdish Chand Bansal

  3. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Kusum Deep

  4. Department of Mathematics, Faculty of Science, Liverpool Hope University, Liverpool, UK

    Atulya K. Nagar

  5. School of Electrical Engineering, VIT University, Vellore, Tamil Nadu, India

    Ponnambalam Pathipooranam

  6. School of Electrical Engineering, VIT University, Vellore, Tamil Nadu, India

    Rani Chinnappa Naidu

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Chaudhary, R., Banati, H. (2020). Adaptive Multi-swarm Bat Algorithm (AMBA). In: Das, K., Bansal, J., Deep, K., Nagar, A., Pathipooranam, P., Naidu, R. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1048. Springer, Singapore. https://doi.org/10.1007/978-981-15-0035-0_66

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