  The Grey Wolf Optimizer (GWO) is a swarm intelligence meta-heuristic
algorithm inspired by the hunting behaviour and social hierarchy of grey wolves
in nature. This paper analyses the use of chaos theory in this algorithm to
improve its ability to escape local optima by replacing the key parameters by
chaotic variables. The optimal choice of chaotic maps is then used to apply the
Chaotic Grey Wolf Optimizer (CGWO) to the problem of factoring a large semi
prime into its prime factors. Assuming the number of digits of the factors to
be equal, this is a computationally difficult task upon which the
RSA-cryptosystem relies. This work proposes the use of a new objective function
to solve the problem and uses the CGWO to optimize it and compute the factors.
It is shown that this function performs better than its predecessor for large
semi primes and CGWO is an efficient algorithm to optimize it.
