A new modified social engineering optimizer algorithm for engineering applications

Goodarzian, Fariba; Ghasemi, Peiman; Kumar, Vikas; Abraham, Ajith

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.

• Journal Article Type: Article
• Acceptance Date: Jan 25, 2022
• Online Publication Date: Feb 22, 2022
• Publication Date: 2022-05
• Deposit Date: Feb 8, 2022
• Publicly Available Date: Feb 23, 2023
• Journal: Soft Computing
• Print ISSN: 1432-7643
• Electronic ISSN: 1433-7479
• Publisher: Springer (part of Springer Nature)
• Peer Reviewed: Peer Reviewed
• Volume: 26
• Pages: 4333–4361
• DOI: https://doi.org/10.1007/s00500-022-06837-y
• Keywords: Meta-heuristic algorithms; Modified Social Engineering Optimizer; Benchmark functions; Engineering applications.
• Public URL: https://uwe-repository.worktribe.com/output/9005002