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Power law-based local search in spider monkey optimisation for lower order system modelling

Author

Listed:
  • Ajay Sharma
  • Harish Sharma
  • Annapurna Bhargava
  • Nirmala Sharma

Abstract

The nature-inspired algorithms (NIAs) have shown efficiency to solve many complex real-world optimisation problems. The efficiency of NIAs is measured by their ability to find adequate results within a reasonable amount of time, rather than an ability to guarantee the optimal solution. This paper presents a solution for lower order system modelling using spider monkey optimisation (SMO) algorithm to obtain a better approximation for lower order systems and reflects almost original higher order system's characteristics. Further, a local search strategy, namely, power law-based local search is incorporated with SMO. The proposed strategy is named as power law-based local search in SMO (PLSMO). The efficiency, accuracy and reliability of the proposed algorithm is tested over 20 well-known benchmark functions. Then, the PLSMO algorithm is applied to solve the lower order system modelling problem.

Suggested Citation

  • Ajay Sharma & Harish Sharma & Annapurna Bhargava & Nirmala Sharma, 2017. "Power law-based local search in spider monkey optimisation for lower order system modelling," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(1), pages 150-160, January.
    • Handle: RePEc:taf:tsysxx:v:48:y:2017:i:1:p:150-160
    DOI: 10.1080/00207721.2016.1165895
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    Cited by:

    1. Vani Agrawal & Ratika Rastogi & D. C. Tiwari, 2018. "Spider Monkey Optimization: a survey," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(4), pages 929-941, August.

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