Security of image communication is more and more important in many applications such as the transmission of military and medical images. To meet the requirement, a new color image encryption algorithm using a new one-dimension (1D) chaotic map was proposed recently, which can resist various attacks because the range of the new chaotic map is larger than that of the previous ones. In our study, the security of the new original method is analyzed and a novel attack method is proposed. It is demonstrated that the scheme is not secure under chosen-plaintext attack, by which the encrypted image can be successfully converted into the corresponding plaintext image without any error.

In this paper, a new image encryption algorithm based on compressive sensing (CS) and M sequence is proposed to decrease the image communication load and improve the security of image communication in the internet of things. Most of the available image encryption schemes are based on chaotic systems to shuffle the image pixels. Before shuffling the image pixels, the random sequence, which is produced by a chaotic system, need to be sorted. This sorting operation is avoided by utilizing a modified linear feedback shift register (LFSR) state sequence. Then, the security of the proposed scheme is improved by combining CS with an improved 1D chaotic system, which is used to construct a measurement matrix. The computational complexity is reduced by the use of the improved 1D chaotic system. Simultaneously, the amount of image data is reduced. Simulation results and performance analyses demonstrate that the proposed encryption scheme can greatly reduce the amount of image data and has good security and robustness.

In order to obtain a Coyote optimization algorithm (COA) is with universal applicability, this paper proposes a novel hybrid algorithm based on COA and Deferential Evolution (DE), named DECOA. Firstly, Global optimum guidance growth scenario of alpha coyotes is introduced into the growth procedure of the alpha coyote of each group, which enables the scenario to approach the global optimum result faster. Secondly, a Gaussian global growth operator is randomly adopted in the growth procedure of ordinary coyotes to make better the global search capacity while preserving the vigorous local search capacity. Thirdly, in order to further improve the social adaptability of coyotes after they grow up, differential expulsion and admission strategy is integrated. Finally, the numbers of the groups are dynamically modified to balance exploitation and exploration. A large number of tests on the benchmark functions from CEC-2017 and CEC-2013 test sets verify the proposed strategy. Especially, compared to many state-of-the-art algorithms, the proposed strategy ranks first 15 times among 29 benchmark functions of CEC-2017 and the average running speed has reached 2.29s. It is extremely important to obtain the PV cell parameters effectively, because the required data to model PV cells will be not provided by the manufacturers. Hence, DECOA is further applied to the scene of parameter estimation of photovoltaic solar cells and modules and the results still indicate that it surpasses the other advanced comparison scenarios.