2D fully chaotic map for image encryption constructed through a quadruple-objective optimization via artificial bee colony algorithm

In this study, a novel 2D fully chaotic map (FULLMAP) derived through a multi-objective optimization strategy with artificial bee colony (ABC) algorithm is introduced for image encryption procedures (IMEPs). First, a model for FULLMAP with eighth decision variables was empirically constituted, and then, the variables were optimally determined using ABC for minimizing a quadruple-objective function composed of Lyapunov exponent (LE), entropy, 0–1 test and correlation coefficient. FULLMAP manifests superior performance in diverse measurements such as bifurcation, 3D phase space, LE, 0–1 test, permutation entropy (PE) and sample entropy (SE). The encryption performance of FULLMAP through an IMEP was verified with respect to various cryptanalyses compared with many reported studies, as well. The main advantage of FULLMAP rather than the optimization-based IMEP studies reported elsewhere is that it need not any optimization in the encryption procedures, and hence, it is faster than the reported procedures. On the other hand, those studies use ciphertext images through IMEPs in every cycle of the optimization. For this reason, they might have long processing time. As a result, the proposed IMEP with FULLMAP demonstrates better cryptanalyses for the most of the compared results.