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Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012) pp 323–333Cite as

Artificial bee Colony Algorithm Integrated with Differential Evolution Operators for Product Design and Manufacturing Optimization

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 202))

Abstract

Artificial bee colony (ABC) algorithm is a nature-inspired algorithm that mimics the intelligent foraging behavior of honey bees and it is steadily gaining popularity. It is observed that convergence of ABC algorithm in local minimum is slow. This paper presents an effort to improve the convergence rate of ABC algorithm by integrating differential evolution (DE) operators into it. The proposed ABC-DE algorithm is first tested on three product design optimization problems and the results are compared with co-evolutionary differential evolution (CDE), hybrid particle swarm optimization-differential evolution (PSO-DE) and ABC algorithms. Further, the algorithm is applied on three manufacturing optimization problems, and the results are compared with genetic algorithm (GA), real coded genetic algorithm (RCGA), and RCGA with Laplace Crossover and Power Mutation (LXPM) algorithm and ABC algorithm. Results indicate that ABC-DE algorithm is better than the state of the art algorithms for the aforesaid problems on selected performance metrics.

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Authors and Affiliations

  1. Dayalbagh Educational Institute, Dayalbagh, Agra, India

    R. S. S. Prasanth & K. Hans Raj

Authors
  1. R. S. S. Prasanth
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  2. K. Hans Raj
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Correspondence to R. S. S. Prasanth .

Editor information

Editors and Affiliations

  1. South Asian University, Chankya Puri, New Delhi, 110021, India

    Jagdish Chand Bansal

  2. ABV - IIITM, Gwalior, Gwalior, 474015, Madhya Pradesh, India

    Pramod Singh

  3. , Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, 247667, India

    Kusum Deep

  4. , Department of Paper Technology, Indian Institute of Technology Roorkee, Saharanpur Campus, Roorkee, India

    Millie Pant

  5. , Department of Computer Science, Liverpool Hope University, Office: FML 412, Liverpool, Liverpool, L16 9JD, United Kingdom

    Atulya Nagar

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© 2013 Springer India

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Prasanth, R.S.S., Hans Raj, K. (2013). Artificial bee Colony Algorithm Integrated with Differential Evolution Operators for Product Design and Manufacturing Optimization. In: Bansal, J., Singh, P., Deep, K., Pant, M., Nagar, A. (eds) Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012). Advances in Intelligent Systems and Computing, vol 202. Springer, India. https://doi.org/10.1007/978-81-322-1041-2_28

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  • DOI: https://doi.org/10.1007/978-81-322-1041-2_28

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  • Publisher Name: Springer, India

  • Print ISBN: 978-81-322-1040-5

  • Online ISBN: 978-81-322-1041-2

  • eBook Packages: EngineeringEngineering (R0)

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