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Ideology algorithm: a socio-inspired optimization methodology

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Abstract

This paper introduces a new socio-inspired metaheuristic technique referred to as ideology algorithm (IA). It is inspired by the self-interested and competitive behaviour of political party individuals which makes them improve their ranking. IA demonstrated superior performance as compared to other well-known techniques in solving unconstrained test problems. Wilcoxon signed-rank test is applied to verify the performance of IA in solving optimization problems. The results are compared with seven well-known and some recently proposed optimization algorithms (PSO, CLPSO, CMAES, ABC, JDE, SADE and BSA). A total of 75 unconstrained benchmark problems are used to test the performance of IA up to 30 dimensions. The results from this study highlighted that the IA outperforms the other algorithms in terms of number function evaluations and computational time. The eminent observed features of the algorithm are also discussed.

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Acknowledgments

The authors would like to thank Frontier Science Research Cluster, University Malaya Research Fund: RG333-15AFR, for supporting this work. The authors would also like to thank anonymous reviewers for comments and suggestions that have resulted in a much improved manuscript.

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Authors and Affiliations

  1. Department of Electrical Engineering, Faculty of Engineering, University Malaya, Kuala Lumpur, Malaysia

    Teo Ting Huan, Jeevan Kanesan & Chuah Joon Huang

  2. Odette School of Business, University of Windsor, 401 Sunset Avenue, Windsor, ON, N9B3P4, Canada

    Anand J. Kulkarni

  3. Department of Mechanical Engineering, Symbiosis Institute of Technology, Symbiosis International University, Pune, MH, 412 115, India

    Anand J. Kulkarni

  4. Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, Auburn, WA, 98071, USA

    Ajith Abraham

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  1. Teo Ting Huan
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  4. Chuah Joon Huang
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Correspondence to Anand J. Kulkarni.

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Huan, T.T., Kulkarni, A.J., Kanesan, J. et al. Ideology algorithm: a socio-inspired optimization methodology. Neural Comput & Applic 28 (Suppl 1), 845–876 (2017). https://doi.org/10.1007/s00521-016-2379-4

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