2018/06/12 by Harshit Mehrotra, Mehrotra, Harshit, Saibal K. Pal +1
Computer Science · #Chaos-based Image/Signal Encryption #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Neural and Evolutionary Computing (cs.NE) #Quantum Computing Algorithms and Architecture
paper · pdf · doi:10.48550/arxiv.1806.04419
openalex publication_date 2018/06/12 · openalex created_date 2022/10/06 · openalex updated_date 2026/07/28
The Grey Wolf Optimizer (GWO) is a swarm intelligence meta-heuristic\nalgorithm inspired by the hunting behaviour and social hierarchy of grey wolves\nin nature. This paper analyses the use of chaos theory in this algorithm to\nimprove its ability to escape local optima by replacing the key parameters by\nchaotic variables. The optimal choice of chaotic maps is then used to apply the\nChaotic Grey Wolf Optimizer (CGWO) to the problem of factoring a large semi\nprime into its prime factors. Assuming the number of digits of the factors to\nbe equal, this is a computationally difficult task upon which the\nRSA-cryptosystem relies. This work proposes the use of a new objective function\nto solve the problem and uses the CGWO to optimize it and compute the factors.\nIt is shown that this function performs better than its predecessor for large\nsemi primes and CGWO is an efficient algorithm to optimize it.\n