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Kursawe and ZDT functions optimization using hybrid micro genetic algorithm (HMGA)

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Abstract

A hybrid micro genetic algorithm (HMGA) is proposed for Pareto optimum search focusing on the Kursawe and ZDT test functions. HMGA is a fusion of the micro genetic algorithm (MGA) and the elitism concept of fast Pareto genetic algorithm. The effectiveness of HMGA in Pareto optimal convergence was investigated with two performance indicators (i.e. generational distance and spacing). To measure HMGA’s performance, a comparison study was conducted between HMGA and MGA. In this work, HMGA is outperformed MGA in the search for Pareto optimal front and capable of solving different difficulty of MOPs.

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Acknowledgments

This research was funded by a Knowledge Transfer Program (KTP) Grant in collaboration with Unimap and Myreka Sdn Bhd.

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Authors and Affiliations

  1. School of Microelectronic Engineering, Universiti Malaysia Perlis, Kampus Alam, Pauh Putra, 02600, Pauh, Perlis, Malaysia

    Wei Jer Lim & Asral Bahari Jambek

  2. Computational Intelligence Research Group, Department of Computing Science and Digital Technologies, Faculty of Engineering and Environment, University of Northumbria, Newcastle, NE1 8ST, UK

    Siew Chin Neoh

Authors
  1. Wei Jer Lim
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  2. Asral Bahari Jambek
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  3. Siew Chin Neoh
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Correspondence to Wei Jer Lim.

Additional information

Communicated by E. Lughofer.

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Lim, W.J., Jambek, A.B. & Neoh, S.C. Kursawe and ZDT functions optimization using hybrid micro genetic algorithm (HMGA). Soft Comput 19, 3571–3580 (2015). https://doi.org/10.1007/s00500-015-1767-5

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