A Chaos Sparrow Search Algorithm with Logarithmic Spiral and Adaptive Step for Engineering Problems

The sparrow search algorithm(SSA)is a newly proposed meta-heuristic optimization algorithm based on the sparrowforaging principle.Similar to other meta-heuristic algorithms,SSA has problems such as slowconvergence speed and difficulty in jumping out of the local optimum.In order to overcome these shortcomings,a chaotic sparrow search algorithm based on logarithmic spiral strategy and adaptive step strategy(CLSSA)is proposed in this paper.Firstly,in order to balance the exploration and exploitation ability of the algorithm,chaotic mapping is introduced to adjust the main parameters of SSA.Secondly,in order to improve the diversity of the population and enhance the search of the surrounding space,the logarithmic spiral strategy is introduced to improve the sparrow search mechanism.Finally,the adaptive step strategy is introduced to better control the process of algorithm exploitation and exploration.The best chaotic map is determined by different test functions,and the CLSSA with the best chaotic map is applied to solve 23 benchmark functions and 3 classical engineering problems.The simulation results show that the iterative map is the best chaotic map,and CLSSA is efficient and useful for engineering problems,which is better than all comparison algorithms.

Rapid iterative incremental model of the intermittent chaos of deep hole developing in coal-gas outburst

In view of the occurrence of the coal and gas, outburst coal body separates in series of layer form, and tosses in a series of coal shell, and the morphological characteristics of the holes that formed in the coal layers are very similar to some iterative morphological characteristics of the system state under highly nonlinear condition in chaos theory. Two kinds of morphology as well as their starting and end states are comparatively studied in this paper. The research results indicate that the outburst coal and rock system is in a chaotic state of lower nested hierarchy before outburst, and the process that lots of holes form owing to continuous outburst of a series of coal shells in a short time is in a rhythmical fast iterative stage of intermittent chaos state. And the state of the coal-gas system is in a stable equilibrium state after outburst. The behaviors of outburst occurrence, development and termination, based on the universal properties of various nonlinear mappings in describing complex problems, can be described by iterative operation in mathematics which uses the Logistic function f (x,μ)=μx(1-x) and the composite function F(3, x) = f(3)(x, μ) as kernel function. The primary equation of relative hole depth x and outburst parameter l in kernel function are given in this paper. The given results can deepen and enrich the understanding of physical essence of outburst.

Synchronously scrambled diffuse image encryption method based on a new cosine chaotic map

We present a new cosine chaotic mapping proved by chaos theory test and analysis such that the system has good cryptography properties, wide chaos range, simple structure, and good sensitivity to initial value, and the mapping can meet the needs of chaotic image encryption. Based on the cosine chaotic system, we propose a new encryption method. First,according to the cyclic characteristics of the mapping, the cyclic information wave is simulated. Second, the quasi-Doppler effect is used to synchronously scramble and diffuse the image to obfuscate the original pixel. Finally, the XOR diffusion of image pixels is carried out by information wave to further enhance the encryption effect. Simulation experiment and security analysis show that the algorithm has good security, can resist the common attack mode, and has good efficiency.

Migration time prediction and assessment of toxic fumes under forced ventilation in underground mines

This study aims to predict the migration time of toxic fumes induced by excavation blasting in underground mines.To reduce numerical simulation time and optimize ventilation design,several back propagation neural network(BPNN)models optimized by honey badger algorithm(HBA)with four chaos mapping(CM)functions(i.e.,Chebyshev(Che)map,Circle(Cir)map,Logistic(Log)map,and Piecewise(Pie)map)are developed to predict the migration time.125 simulations by the computational fluid dynamics(CFD)method are used to train and test the developed models.The determination coefficient(R2),the variance accounted for(VAF),the Willmott’s index(WI),the root mean square error(RMSE),the mean absolute percentage error(MAPE),and the sum of squares error(SSE)are utilized to evaluate the model performance.The evaluation results indicate that the CirHBA-BPNN model has achieved the most satisfactory performance by reaching the highest values of R2(0.9945),WI(0.9986),VAF(99.4811%),and the lowest values of RMSE(15.7600),MAPE(0.0343)and SSE(6209.4),respectively.The wind velocity in roadway(Wv)is the most important feature for predicting the migration time of toxic fumes.Furthermore,the intrinsic response characteristic of the optimal model is implemented to enhance the model interpretability and provide reference for the relationship between features and migration time of toxic fumes in ventilation design.

Balancing Exploration–Exploitation of Multi-verse Optimizer for Parameter Extraction on Photovoltaic Models

Extracting photovoltaic(PV)model parameters based on the measured voltage and current information is crucial in the simulation and management of PV systems.To accurately and reliably extract the unknown parameters of different PV models,this paper proposes an improved multi-verse optimizer that integrates an iterative chaos map and the Nelder–Mead simplex method,INMVO.Quantitative experiments verified that the proposed INMVO fueled by both mechanisms has more affluent populations and a more reasonable balance between exploration and exploitation.Further,to verify the feasibility and competitiveness of the proposal,this paper employed INMVO to extract the unknown parameters on single-diode,double-diode,three-diode,and PV module four well-known PV models,and the high-performance techniques are selected for comparison.In addition,the Wilcoxon signed-rank and Friedman tests were employed to test the experimental results statistically.Various evaluation metrics,such as root means square error,relative error,absolute error,and statistical test,demonstrate that the proposed INMVO works effectively and accurately to extract the unknown parameters on different PV models compared to other techniques.In addition,the capability of INMVO to stably and accurately extract unknown parameters was also verified on three commercial PV modules under different irradiance and temperatures.In conclusion,the proposal in this paper can be implemented as an advanced and reliable tool for extracting the unknown parameters of different PV models.Note that the source code of INMVO is available at https://github.com/woniuzuioupao/INMVO.

Multi-UAV Collaborative Trajectory Planning for 3D Terrain Based on CS-GJO Algorithm

Existing solutions for collaborative trajectory planning using multiple UAVs suffer from issues such as low accuracy,instability,and slow convergence.To address the aforementioned issues,this paper introduces a new method for multiple unmanned aerial vehicle(UAV)3D terrain cooperative trajectory planning based on the cuck0o search golden jackal optimization(CS-GJO)algorithm.A model for single UAV trajectory planning and a model for multi-UAV collaborative trajectory planning have been developed,and the problem of solving the models is restructured into an optimization problem.Building upon the original golden jackal optimization,the use of tent chaotic mapping aids in the generation of the golden jackal's inital population,thereby promoting population diversity.Subsequently,the position update strategy of the cuckoo search algorithm is combined for purpose of update the position information of individual golden jackals,effectively preventing the algorithm from getting stuck in local minima.Finally,the corresponding nonlinear control parameter were developed.The new parameters expedite the decrease in the convergence factor during the pre-exploration stage,resulting in an improved overall search speed of the algorithm.Moreover,they attenuate the decrease in the convergence factor during the post-exploration stage,thereby enhancing the algorithm's global search.The experimental results demonstrate that the CS-GJO algorithm efficiently and accurately accomplishes multi-UAV cooperative trajectory planning in a 3D environment.Compared with other comparative algorithms,the CS-GJO algorithm also has better stability,higher optimization accuracy,and faster convergence speed.