An adaptive enhanced differential evolution strategies for topology robustness in internet of things. (25th October 2021)

• Record Type: Journal Article
• Authors: Qureshi, Talha Naeem; Javaid, Nadeem; Almogren, Ahmad; Khan, Asad Ullah; Almajed, Hisham; Mohiuddin, Irfan
• Is Part Of: International journal of web and grid services. Volume 18:Number 1(2022)
• Page Start: 1
• Page End: 33
• Publication Date: 2021-10-25
• Subjects: data driven -- internet of things -- IoTs -- malicious attacks -- scale-free -- topology robustness; Web services -- Periodicals; Computational grids (Computer systems) -- Periodicals; 006.78
• Languages: English
• ISSNs: 1741-1106

Abstract

In this paper, two algorithms enhanced differential evolution (EDE) and adaptive EDE (AEDE) are proposed. The proposed algorithms improve the robustness of the IoT network without changing the degree distribution of nodes. The EDE algorithm maintains the diversity in a solution space through the tri-vector mutation operation and explores the hidden areas. The crossover phase makes the algorithm's convergence fast towards the global optima. The AEDE dynamically changes the probabilities of multiple operations of the EDE with the changing environment. Also, it maintains the balance between the diversity of solution space and the convergence speed through adaptive probabilities. The EDE performs 7.13%, 31.6% and 41.8% better as compared to GA, SA and HA, respectively. The AEDE outperforms the GA, SA and HA with 11%, 35.3% and 45.4% better efficiency, respectively. The proposed algorithms outperform existing algorithms in terms of robustness and convergence speed.