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Differential Evolution Strategies for Multi-objective Optimization

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Book cover Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 130))

Abstract

Multi-objective optimization (MOO) using evolutionary algorithms has gained popularity in the recent past due to its ability of producing number of solutions in a single run and handling multiple objectives simultaneously. In this effort, several MOO algorithms are developed. In this manuscript several strategies of multi-objective differential evolution algorithm (namely, MODE-I, MODE-III, elitist MODE and hybrid MODE) are briefly discussed. Three important unconstrained test problems are considered for validating the performance (in terms of Pareto front and convergence & diversity metrics) of strategies of MODE algorithm with other popular algorithms from literature. It is observed that the strategies of MODE algorithm are in general able to produce Pareto front with good convergence to the true Pareto front.

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Author information

Authors and Affiliations

  1. Chemical Engineering Department, Birla Institute of Technology and Science (BITS), Pilani, 33031, India

    Ashish M. Gujarathi

  2. Institute of Engineering and Technology, JKLU, Jaipur, 302026, India

    B. V. Babu

Authors
  1. Ashish M. Gujarathi
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  2. B. V. Babu
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Corresponding author

Correspondence to Ashish M. Gujarathi .

Editor information

Editors and Affiliations

  1. , Department of Mathematics, Indian Institute of Technology Roorkee, Uttarakhand, Roorkee, 247667, India

    Kusum Deep

  2. Department of Mathematics and, Computer Science, Liverpool Hope University, Hope Park, Liverpool, L16 9JD, United Kingdom

    Atulya Nagar

  3. , Department of Paper Technology, Indian Institute of Technology Roorkee, Uttarakhand, Roorkee, 247667, India

    Millie Pant

  4. Information Technology and Management, ABV-Indian Institute of, 109, E- Block, Gwalior, 474010, India

    Jagdish Chand Bansal

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© 2012 Springer India Pvt. Ltd.

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Gujarathi, A.M., Babu, B.V. (2012). Differential Evolution Strategies for Multi-objective Optimization. In: Deep, K., Nagar, A., Pant, M., Bansal, J. (eds) Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011. Advances in Intelligent and Soft Computing, vol 130. Springer, India. https://doi.org/10.1007/978-81-322-0487-9_7

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  • DOI: https://doi.org/10.1007/978-81-322-0487-9_7

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  • Publisher Name: Springer, India

  • Print ISBN: 978-81-322-0486-2

  • Online ISBN: 978-81-322-0487-9

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