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A new modified social engineering optimizer algorithm for engineering applications

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Abstract

Nowadays, a great deal of attention is paid to metaheuristic algorithms to reach the approximate solution in an acceptable computational time. As one of the recent-developed successful metaheuristics, Social Engineering Optimizer (SEO) algorithm is according to the inspiration of the rules of social engineering to solve approximate optimization problems. In this research, a Modified Social Engineering Optimizer algorithm (MSEO) by using an adjustment operator is proposed in which there are some assessment criteria for defender and attacker to determine and calculate the weight simultaneously for the first time. This enhancement comprises adding adjustment operators to improve the performance of SEO in terms of search accuracy and running time. Most notably, this operator is utilized to make a better new generation and improve the interaction between the search phases. The adjustment operator strategy is also applied to a novel division based on the best person. As an extensive comparison, the suggested algorithm is tested on fourteen standard benchmark functions and compared with ten well-established and recent optimization algorithms as well as the main version of the SEO algorithm. This algorithm is also tested for sensitivities on the parameters. In this regard, a set of engineering applications were provided to prove and validate the MSEO algorithm for the first time. The experimental outcomes show that the suggested algorithm produces very accurate results which are better than the SEO and other compared algorithms. Most notably, the MSEO provides a very competitive output and a high convergence rate.

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Acknowledgements

This research has been financially supported by The Analytical Center for the Government of the Russian Federation (Agreement No. 70-2021-00143 dd. 01.11.2021, IGK000000D730321P5Q0002).

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The authors have not disclosed any funding.

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Authors and Affiliations

  1. Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, 11, 3rd Street NW, P.O. Box 2259, Auburn, WA, 98071, USA

    Fariba Goodarzian & Ajith Abraham

  2. Department of Logistics, Tourism and Service Management, German University of Technology in Oman (GUtech), Muscat, Oman

    Peiman Ghasemi

  3. Bristol Business School, University of the West of England, Bristol, BS16 1QY, UK

    Vikas Kumar

  4. Center for Artificial Intelligence, Innopolis University, Innopolis, Russia

    Ajith Abraham

Authors
  1. Fariba Goodarzian
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  2. Peiman Ghasemi
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Correspondence to Fariba Goodarzian.

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Goodarzian, F., Ghasemi, P., Kumar, V. et al. A new modified social engineering optimizer algorithm for engineering applications. Soft Comput 26, 4333–4361 (2022). https://doi.org/10.1007/s00500-022-06837-y

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