Optimization of Port Emergency Materials Dispatch with Time Windows Constraints under Uncertainties

Uncertainties exist and affect the actual port production. For example, at the beginning of 2020, the sudden outbreak of COVID-19 seriously affected terminal production and increased the short-term pressure of handling at container terminals. Consequently, a large number of containers were stacked at terminals, and the problem of terminal congestion became more serious. To solve the congestion problem of container terminals and ensure the priority dispatch of emergency materials, this study uses the optimized arrival patterns of external trucks and a priority dispatch strategy for emergency materials to establish a bilevel optimization model for container terminals and proposes a chaotic genetic algorithm based on logistic mapping as a solution. Through numerical experiments, the algorithm proposed in this study was compared with the genetic algorithm and adaptive genetic algorithm. The experimental results show that the model and algorithm proposed in this study can effectively reduce the total cost of containers in a terminal while ensuring the priority dispatch of emergency materials, reducing the overlapping part of the time window, optimizing the arrival mode of external trucks, and reducing the waiting time of external trucks, effectively alleviating the terminal congestion problem.

Reversible Data Hiding in a Chaotic Encryption Domain Based on Odevity Verification

On the premise of guaranteeing the visual effect, in order to improve the security of the image containing digital watermarking and restore the carrier image without distortion, reversible data hiding in chaotic encryption domain based on odevity verification was proposed. The original image was scrambled and encrypted by Henon mapping, and the redundancy between the pixels of the encrypted image was lost. Then, the embedding capacity of watermarking can be improved by using odevity verification, and the embedding location of watermarking can be randomly selected by using logistic mapping. When extracting the watermarking, the embedded data was judged according to the odevity of the pixel value of the embedding position of the watermarking, and the carrier image was restored nondestructively by odevity check image. The experimental results show that the peak signal-to-noise ratio (PSNR) of the original image is above 53 decibels after the image is decrypted and restored after embedding the watermarking in the encrypted domain, and the invisibility is good.

Comparative analysis of dynamic state recognition between permutation entropy and weighted permutation entropy

Permutation entropy (PE) and weighted permutation entropy (WPE) are indexes of the complex system. In this paper, the nonlinear sequence of two chaotic states is simulated by Logistic mapping, and the effectiveness of the PE and WPE algorithms in chaotic state recognition is compared and analyzed with different sliding step sizes. The results show that the value of WPE changes more obviously than PE in different chaotic states, and the recognition effect of WPE is better than PE in dynamic states, which can provide theoretical support for further analyzing the complexity characteristics of actual data.

Reversible data hiding in chaotic encryption domain based on odevity verification

On the premise of guaranteeing the visual effect, in order to improve the security of the image containing digital watermarking and restore the carrier image without distortion, reversible data hiding in chaotic encryption domain based on odevity verification was proposed. The original image was scrambled and encrypted by Henon mapping, and the redundancy between the pixels of the encrypted image was lost. Then, the embedding capacity of watermarking can be improved by using odevity verification, and the embedding location of watermarking can be randomly selected by using logistic mapping. When extracting the watermarking, the embedded data was judged according to the odevity of the pixel value of the embedding position of the watermarking, and the carrier image was restored nondestructively by odevity check image. The experimental results show that the peak signal-to-noise ratio (PSNR) of the original image is above 53 decibels after the image is decrypted and restored after embedding the watermarking in the encrypted domain, and the invisibility is good.

Single-threshold Image Segmentation Algorithm Based on Improved Bat Algorithm

The Improved Bat Algorithm (IBA) is proposed for the image segmentation based on the maximum interclass variance method. Firstly, the principle of image segmentation based on the maximum interclass variance method is explained, and secondly, the bat algorithm is improved by using chaotic logistic mapping to initialize the population to improve the diversity of solutions, using adaptive parameter optimization to avoid falling into local optimum, using Monkey algorithm for individual selection, and finally, the image segmentation function in image segmentation is used as the individual fitness function of the bat algorithm for solving. The simulation experiments show that compared with the bat algorithm and the monkey group algorithm, this algorithm has better segmentation effect under different threshold values.

Chaotic Jaya Approaches to Solving Electromagnetic Optimization Benchmark Problems

The Jaya optimization algorithm is a simple, fast, robust, and powerful population-based stochastic metaheuristic that in recent years has been successfully applied in a variety of global optimization problems in various application fields. The essential idea of the Jaya algorithm is that the searching agents try to change their positions toward the best obtained solution by avoiding the worst solution at every generation. The important difference between Jaya and other metaheuristics is that Jaya does not require the tuning of its control, except for the maximum number of iterations and population size parameters. However, like other metaheuristics, Jaya still has the dilemma of an appropriate tradeoff between its exploration and exploitation abilities during the evolution process. To enhance the convergence performance of the standard Jaya algorithm in the continuous domain, chaotic Jaya (CJ) frameworks based on chaotic sequences are proposed in this paper. In order to obtain the performance of the standard Jaya and CJ approaches, tests related to electromagnetic optimization using two different benchmark problems are conducted. These are the Loney’s solenoid benchmark and a brushless direct current (DC) motor benchmark. Both problems are realized to evaluate the effectiveness and convergence rate. The simulation results and comparisons with the standard Jaya algorithm demonstrated that the performance of the CJ approaches based on Chebyshev-type chaotic mapping and logistic mapping can be competitive results in terms of both efficiency and solution quality in electromagnetics optimization.

THE SETS OF JULIA AND MANDELBROT FOR MULTI-DIMENSIONAL CASE OF LOGISTIC MAPPING

The present paper is devoted to investigation of the multidimensional case of the logistic mapping on the plane to itself. In this paper we learnt the properties of the sets of Julia and Mandelbrot for some two-dimensional logistic mappings. The sets of Julia and Mandelbrot help to define asymptotical behavior of the trajectories of certain mappings. The analytical solutions of the equations for finding fixed and periodic points and the computational simulations for describing the sets of Julia and Mandelbrot are the main results of this paper.