Optimizing S-box generation based on the Adaptive Agent Heroes and Cowards Algorithm

This paper introduces a new variant of a metaheuristic algorithm based on Agent Heroes and Cowards Algorithm (AHC), called Adaptive Agent Heroes and Cowards Algorithm (AAHC). The main feature of AAHC is the fact that the algorithm allows adaptive assignment of its population agents into cowards and heroes based on the exponential controlling functions. Furthermore, unlike its predecessor, AAHC also permits systematic manipulation of candidate solutions around the global best agent via the swap operator to boost its search intensification process. Meanwhile, to further enhance the diversification its solution, AHC also exploit the Tent map as the pseudo random generator replacement during its initial population initialization. Experimental results based on the generation of 8 x 8 substitution-box demonstrate that the proposed AAHC outperforms other competing metaheuristic algorithms in two main S-box criteria namely nonlinearity and strict avalanche criteria whilst maintaining commendable performances on bits independence criteria, differential approximation probability, linear approximation probability and transparency order.