Xuncai Zhang

A modified invasive weed optimization with crossover operation

Invasive weed optimization, which is inspired from the invasive habits of growth of weeds in nature, is a population-based intelligence algorithm. In this paper, we present invasive weed optimization with crossover operation combining the idea of the invasive weed with concepts from evolutionary algorithms. By applying the crossover operation in invasive weed optimization, it not only discourages premature convergence to local optimum but also explores and exploits the promising regions in the search space effectively. This modified algorithm is tested and compared with the standard invasive weed optimization and PSO. The comparative experiments have been conducted on benchmark test functions; invasive weed optimization with crossover operation is able to obtain the result superior to the standard invasive weed optimization and PSO.

DNA codewords design using the improved NSGA-II algorithms

We have developed an an efficient approach called the improved non-dominated sorting genetic algorithm II (INSGA-II) for designing the library of DNA codewords based on the physicochemical constraints. Sequences that satisfy these constraints can be utilized in computations, various engineering applications such as microarrays, and nano-fabrications. The novelty of our algorithm is that introduced the constraints to the non-dominated sorting process. Experiment results in silico showed that the INSGA-II have higher convergence speed and better population diversity than those of other algorithms, and can provide reliable and effective codewords for controllable DNA computing.

Generalised mathematical model of memristor

To qualify as a memristor, the pinched hysteresis loop of a dynamical system corresponding to a sinusoidal excitation signal must be pinched at the origin, for any amplitude, and for any frequency, as well as for any initial condition of the state variable. The above conditions can be checked by the simulations which should be repeated as many times as possible. However, the times of simulation are limited, the finite results drawn through the simulation are not necessarily reliable. To increase the reliability of the judgment, a generalised mathematical model of memristor is designed in the study, which confirms three fingerprints of memristor. HP memristor, piecewise-linear memristor, memristor with square non-linearity and memristor with cubic non-linearity are included as generalised memristor model special cases. The generalised mathematical model of memristor is applied to distinguish memristor from three mathematical model examples. A generalised mathematical model of memristor is a necessary, but not a sufficient condition for judging whether the dynamical system is or not a memristor, which may save us a lot of time and energy.

Chaotic Image Encryption Algorithm Based on Bit Permutation and Dynamic DNA Encoding

With the help of the fact that chaos is sensitive to initial conditions and pseudorandomness, combined with the spatial configurations in the DNA molecules inherent and unique information processing ability, a novel image encryption algorithm based on bit permutation and dynamic DNA encoding is proposed here. The algorithm first uses Keccak to calculate the hash value for a given DNA sequence as the initial value of a chaotic map; second, it uses a chaotic sequence to scramble the image pixel locations, and the butterfly network is used to implement the bit permutation. Then, the image is coded into a DNA matrix dynamic, and an algebraic operation is performed with the DNA sequence to realize the substitution of the pixels, which further improves the security of the encryption. Finally, the confusion and diffusion properties of the algorithm are further enhanced by the operation of the DNA sequence and the ciphertext feedback. The results of the experiment and security analysis show that the algorithm not only has a large key space and strong sensitivity to the key but can also effectively resist attack operations such as statistical analysis and exhaustive analysis.

Image Encryption Algorithm Based on Hyperchaotic Maps and Nucleotide Sequences Database

Image encryption technology is one of the main means to ensure the safety of image information. Using the characteristics of chaos, such as randomness, regularity, ergodicity, and initial value sensitiveness, combined with the unique space conformation of DNA molecules and their unique information storage and processing ability, an efficient method for image encryption based on the chaos theory and a DNA sequence database is proposed. In this paper, digital image encryption employs a process of transforming the image pixel gray value by using chaotic sequence scrambling image pixel location and establishing superchaotic mapping, which maps quaternary sequences and DNA sequences, and by combining with the logic of the transformation between DNA sequences. The bases are replaced under the displaced rules by using DNA coding in a certain number of iterations that are based on the enhanced quaternary hyperchaotic sequence; the sequence is generated by Chen chaos. The cipher feedback mode and chaos iteration are employed in the encryption process to enhance the confusion and diffusion properties of the algorithm. Theoretical analysis and experimental results show that the proposed scheme not only demonstrates excellent encryption but also effectively resists chosen-plaintext attack, statistical attack, and differential attack.

DNA tile assembly model for 0–1 knapsack problem

Research results shows that reasonable solution for NP-complete problem could be achieved using DNA self-assembly model, in which the parallel computing ability of DNA computation could be get a full play. In DNA computing paradigm, the information is encoded in DNA tiles, which can be self-assembled via sticky-end associations. In this paper, the DNA self-assembly model for 0–1 knapsack problem is constructed. This model is composed of three units: nondeterministic guess system, adder system and comparator system. Results shows that the three systems can be carried out in polynomial time with optimal 0(1) distinct tile types in parallel. All of these demonstrate the feasibility of DNA tiles self-assembly for NP-problems.

A New Image Encryption Algorithm Based on DNA Dynamic Encoding and Hyper-Chaotic System

Aiming at the deficiency of the low sensitivity of DNA encoding and chaotic encryption algorithms to text and key, and the limited encoding rules of DNA, etc. This paper presents a new image encryption algorithm based on DNA dynamic encoding and hyper-chaotic system. Firstly, the algorithm uses the SHA-3 algorithm to process the original image, generate a set of hash values, perform the dynamic encoding of the generated hash values and then carry out XOR operation with the original image, and then the generated hash values through Hamming distance processing to generate the initial value of the hyper-chaotic system. Secondly, the S-box is constructed by the sequence values generated by the hyper-chaotic system, and the XOR-shift manipulation is performed to the image by using the S-box. Finally, the image is scrambled by the hyper-chaotic Chen System. The simulation results and theoretical analysis show that the algorithm improves the sensitivity of key and the security of data transmission, and has better ability of anti-exhaustive attack, statistical attack and differential attack.

A Hybrid IWO Algorithm Based on Lévy Flight

This paper presents a hybrid nature inspired metaheuristic algorithms, which derive from Invasive Weed Optimization (IWO) and Cuckoo Search (CS). Based on the novel and distinct qualifications of IWO and CS, we introduce a hybrid IWO algorithm and try to combine their excellent features in this extended algorithm. The efficiency of this algorithm both in the case of speed of convergence and optimality of the results are compared with IWO algorithm through a number of common multi-dimensional benchmark functions. Finally, experimental results show that the proposed approach can be successfully employed as a fast and global optimization method for a variety of theoretical or practical purposes.

Solving graph vertex coloring problem with microfluidic DNA computer

The hugely storing information ability, parallel computing ability and lower computing energy cost make DNA computing to be a perfect computing paradigm. Nowadays it has been used to solve various computationally hard problems. In order to improve its reliability and simplify operations, microfluidic chips support an effective way to realize an automatable and universal DNA computer. In this paper we introduce microfluidic logic operators, simple fluidic switches and memory. Furthermore, the use of electronic fluidic control components in microfluidic systems will be demonstrated in such way as to perform dynamic operations and programming. Finally a proposal for an actual fluidic computer will be made which solves the graph vertex coloring problems.

3D DNA self-assembly for the maximum clique problem

DNA self-assembly technology has brought some novel inspirations to the development of DNA computing. At present, there are many diversified computational models to solve various NP problems, some of which are very useful to solve complex NP problems. In this paper, we introduce how the 3D self-assembly model to solve the maximum clique problem, with the capacity of DNA molecules in massive parallel computation. In this model, the number of distinct tiles used in the computing is a constant - 15, computation time is θ(n2), and computation space is θ(n3). Our work makes a significant attempt to explore the computational power of 3D DNA self-assembly.