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

Ajay Sharma, Harish Sharma, Annapurna Bhargava and Nirmala Sharma

International Journal of Systems Science, 2017, vol. 48, issue 1, 150-160

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.

Date: 2017
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Citations: View citations in EconPapers (1)

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DOI: 10.1080/00207721.2016.1165895

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