A Multi-Strategy-Improved Northern Goshawk Optimization Algorithm for Global Optimization and Engineering Design

Optimization algorithms play a pivotal role in enhancing the performance and efficiency of systems across various scientific and engineering disciplines.To enhance the performance and alleviate the limitations of the Northern Goshawk Optimization(NGO)algorithm,particularly its tendency towards premature convergence and entrapment in local optima during function optimization processes,this study introduces an advanced Improved Northern Goshawk Optimization(INGO)algorithm.This algorithm incorporates a multifaceted enhancement strategy to boost operational efficiency.Initially,a tent chaotic map is employed in the initialization phase to generate a diverse initial population,providing high-quality feasible solutions.Subsequently,after the first phase of the NGO’s iterative process,a whale fall strategy is introduced to prevent premature convergence into local optima.This is followed by the integration of T-distributionmutation strategies and the State Transition Algorithm(STA)after the second phase of the NGO,achieving a balanced synergy between the algorithm’s exploitation and exploration.This research evaluates the performance of INGO using 23 benchmark functions alongside the IEEE CEC 2017 benchmark functions,accompanied by a statistical analysis of the results.The experimental outcomes demonstrate INGO’s superior achievements in function optimization tasks.Furthermore,its applicability in solving engineering design problems was verified through simulations on Unmanned Aerial Vehicle(UAV)trajectory planning issues,establishing INGO’s capability in addressing complex optimization challenges.

Performances of improved Tent chaos-based FM radar signal

A novel algorithm is proposed to solve the poor per- formance problem of the Tent chaos-based frequency modulation （FM） signal for range-Doppler imaging, which takes it into complex multi-segment system by increasing its segments. The simulation results show that the effectiveness of the proposed algorithm, as well as the performance of the improved Tent FM signal is obvious in a multipath or noise propagation environment.

A perturbation method to the tent map based on Lyapunov exponent and its application

Perturbation imposed on a chaos system is an effective way to maintain its chaotic features. A novel parameter perturbation method for the tent map based on the Lyapunov exponent is proposed in this paper. The pseudo-random sequence generated by the tent map is sent to another chaos function - the Chebyshev map for the post processing. If the output value of the Chebyshev map falls into a certain range, it will be sent back to replace the parameter of the tent map. As a result, the parameter of the tent map keeps changing dynamically. The statistical analysis and experimental results prove that the disturbed tent map has a highly random distribution and achieves good cryptographic properties of a pseudo-random sequence. As a result, it weakens the phenomenon of strong correlation caused by the finite precision and effectively compensates for the digital chaos system dynamics degradation.

Discrete-Time Chaotic Circuits for Implementation of Tent Map and Bernoulli Map

Discrete-time chaotic circuit implementations of a tent map and a Bernoulli map using switched-current （SI） techniques are presented. The two circuits can be constructed with 16 MOSFET＇s and 2 capacitors. The simulations and experiments built with commercially available IC＇s for the circuits have demonstrated the validity of the circuit designs. The experiment results also indicate that the proposed circuits are integrable by a standard CMOS technology. The implementations are useful for studies and applications of chaos.

Secure hash function based on chaotic tent map with changeable parameter

A new algorithm for a novel hash function, based on chaotic tent map with changeable parameter, is proposed and analyzed. The one dimensional and piecewise tent map with changeable parameters decided by the position index of blocked messages is chosen, and blocked massages translated into ASCII code values are employed as the iteration time of the chaotic tent map. The final 128-bit hash value is generated by logical XOR operation on intermediate hash values. Theoretical analysis and computer simulation indicate that the proposed algorithm can satisfy the performance requirements of hash function.

Severity Based Light-Weight Encryption Model for Secure Medical Information System

As the amount of medical images transmitted over networks and kept on online servers continues to rise,the need to protect those images digitally is becoming increasingly important.However,due to the massive amounts of multimedia and medical pictures being exchanged,low computational complexity techniques have been developed.Most commonly used algorithms offer very little security and require a great deal of communication,all of which add to the high processing costs associated with using them.First,a deep learning classifier is used to classify records according to the degree of concealment they require.Medical images that aren’t needed can be saved by using this method,which cuts down on security costs.Encryption is one of the most effective methods for protecting medical images after this step.Confusion and dispersion are two fundamental encryption processes.A new encryption algorithm for very sensitive data is developed in this study.Picture splitting with image blocks is nowdeveloped by using Zigzag patterns,rotation of the image blocks,and random permutation for scrambling the blocks.After that,this research suggests a Region of Interest(ROI)technique based on selective picture encryption.For the first step,we use an active contour picture segmentation to separate the ROI from the Region of Background(ROB).Permutation and diffusion are then carried out using a Hilbert curve and a Skew Tent map.Once all of the blocks have been encrypted,they are combined to create encrypted images.The investigational analysis is carried out to test the competence of the projected ideal with existing techniques.

An Improved Honey Badger Algorithm through Fusing Multi-Strategies

TheHoney Badger Algorithm(HBA)is a novelmeta-heuristic algorithm proposed recently inspired by the foraging behavior of honey badgers.The dynamic search behavior of honey badgers with sniffing and wandering is divided into exploration and exploitation in HBA,which has been applied in photovoltaic systems and optimization problems effectively.However,HBA tends to suffer from the local optimum and low convergence.To alleviate these challenges,an improved HBA(IHBA)through fusing multi-strategies is presented in the paper.It introduces Tent chaotic mapping and composite mutation factors to HBA,meanwhile,the random control parameter is improved,moreover,a diversified updating strategy of position is put forward to enhance the advantage between exploration and exploitation.IHBA is compared with 7 meta-heuristic algorithms in 10 benchmark functions and 5 engineering problems.The Wilcoxon Rank-sum Test,Friedman Test and Mann-WhitneyU Test are conducted after emulation.The results indicate the competitiveness and merits of the IHBA,which has better solution quality and convergence traits.The source code is currently available from:https://github.com/zhaotao789/IHBA.

Chaotic Image Encryption Algorithm by Correlating Keys with Plaintext

A novel image encryption scheme based on the modified skew tent map was proposed in this paper. In the key generating procedure, the algorithm generates a plaintext-dependent secret keys set. In the encryption process, the diffusion operation with cipher output feedback is introduced. Thus, cipher-irmge is sensitive to both initial keys and plaintext through only one round diffusion operation. The key space is large. As a resuk, the algorithm can effectively resist differential attacks, statistical attacks, brute-force attacks, known plaintext and chosen plaintext attacks. Perforrmnce test and security analysis demonstrates that this algorithm is eficient and reliable, with high potential to be adopted for secure comnmnications.

Improved Oustaloup approximation of fractional-order operators using adaptive chaotic particle swarm optimization

A rational approximation method of the fractional-order derivative and integral operators is proposed. The turning fre- quency points are fixed in each frequency interval in the standard Oustaloup approximation. In the improved Oustaloup method, the turning frequency points are determined by the adaptive chaotic particle swarm optimization （PSO）. The average velocity is proposed to reduce the iterations of the PSO. The chaotic search scheme is combined to reduce the opportunity of the premature phenomenon. Two fitness functions are given to minimize the zero-pole and amplitude-phase frequency errors for the underlying optimization problems. Some numerical examples are compared to demonstrate the effectiveness and accuracy of this proposed rational approximation method.

Parameter identification of the fractional-order systems based on a modified PSO algorithm

In order to better identify the parameters of the fractional-order system,a modified particle swarm optimization(MPSO)algorithm based on an improved Tent mapping is proposed.The MPSO algorithm is validated with eight classical test functions,and compared with the POS algorithm with adaptive time varying accelerators(ACPSO),the genetic algorithm(GA),a d the improved PSO algorithm with passive congregation(IPSO).Based on the systems with known model structures a d unknown model structures,the proposed algorithm is adopted to identify two typical fractional-order models.The results of parameter identification show that the application of average value of position information is beneficial to making f 11 use of the information exchange among individuals and speeds up the global searching speed.By introducing the uniformity and ergodicity of Tent mapping,the MPSO avoids the extreme v^ue of position information,so as not to fall into the local optimal value.In brief the MPSOalgorithm is an effective a d useful method with a fast convergence rate and high accuracy.

Nonlinear inversion for electrical resistivity tomography based on chaotic DE-BP algorithm

Nonlinear resistivity inversion requires efficient artificial neural network(ANN)model for better inversion results.An evolutionary BP neural network(BPNN)approach based on differential evolution(DE)algorithm was presented,which was able to improve global search ability for resistivity tomography 2-D nonlinear inversion.In the proposed method,Tent equation was applied to obtain automatic parameter settings in DE and the restricted parameter Fcrit was used to enhance the ability of converging to global optimum.An implementation of proposed DE-BPNN was given,the network had one hidden layer with 52 nodes and it was trained on 36 datasets and tested on another 4 synthetic datasets.Two abnormity models were used to verify the feasibility and effectiveness of the proposed method,the results show that the proposed DE-BP algorithm has better performance than BP,conventional DE-BP and other chaotic DE-BP methods in stability and accuracy,and higher imaging quality than least square inversion.

Robust Image Hashing via Random Gabor Filtering and DWT

Image hashing is a useful multimedia technology for many applications,such as image authentication,image retrieval,image copy detection and image forensics.In this paper,we propose a robust image hashing based on random Gabor filtering and discrete wavelet transform(DWT).Specifically,robust and secure image features are first extracted from the normalized image by Gabor filtering and a chaotic map called Skew tent map,and then are compressed via a single-level 2-D DWT.Image hash is finally obtained by concatenating DWT coefficients in the LL sub-band.Many experiments with open image datasets are carried out and the results illustrate that our hashing is robust,discriminative and secure.Receiver operating characteristic(ROC)curve comparisons show that our hashing is better than some popular image hashing algorithms in classification performance between robustness and discrimination.

A multispectral image compression and encryption algorithm based on tensor decomposition and chaos

A multi spectral image compression and encryption algorithm that combines Karhunen-Loeve(KL) transform,tensor decomposition and chaos is proposed for solving the security problem of multi-spectral image compression and transmission.Firstly,in order to eliminate residual spatial redundancy and most of the spectral redundancy,the image is performed by KL transform.Secondly,to further eliminate spatial redundancy and reduce block effects in the compression process,two-dimensional discrete 9/7 wavelet transform is performed,and then Arnold transform and encryption processing on the transformed coefficients are performed.Subsequently,the tensor is decomposed to keep its intrinsic structure intact and eliminate residual space redundancy.Finally,differential pulse filters are used to encode the coefficients,and Tent mapping is used to implement confusion diffusion encryption on the code stream.The experimental results show that the method has high signal-to-noise ratio,fast calculation speed,and large key space,and it is sensitive to keys and plaintexts with a positive effect in spectrum assurance at the same time.

The Centres of Gravity of Periodic Orbits

Let f ： I → I be a continuous map. If P （n, f） = {x ∈I; fn （x） = x} is a finite set for each n ∈ N, then there exits an anticentered map topologically conjugate to f, which partially answers a question of Kolyada and Snoha. Specially, there exits an anticentered map topologically conjugate to the standard tent map.

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.

Hybrid social cognitive optimization algorithm for constrained nonlinear programming

To improve the global convergence speed of social cognitive optimization （SCO） algorithm, a hybrid social cognitive optimization （HSCO） algorithm based on elitist strategy and chaotic optimization is proposed to solve constrained nonlinear programming problems （NLPs）. The proposed algorithm partitions learning agents into three groups in proportion： elite learning agents, chaotic learning agents and common learning agents. The common learning agents work in the search way of traditional SCO, chaotic learning agents search via chaotic search （CS） algorithm based on tent map which helps to avoid the premature convergence, elite learning agents search via elitist selection which helps to improve the global searching performance. Additionally, a chaotic search process is incorporated into local searching operation so as to enhance the local searching efficiency in the neighboring areas of the feasible solutions. Simulation results on a set of benchmark problems show that the proposed algorithm has high optimization efficiency, good global performance, and stable optimization outcomes for constrained NLPs.