An efficient harris hawk optimization algorithm for solving the travelling salesman problem

Gharehchopogh, Farhad Soleimanian; Gharehchopogh, Farhad Soleimanian

doi:10.1007/s10586-021-03304-5

Cited by 61 publications

(24 citation statements)

References 83 publications

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“…This article uses the random numbers generated by the Fortuna Accumulator method of Crypto encryption module in python as plaintexts, and then uses the cryptographic algorithm library in the same module to encrypt the plaintexts to obtain the ciphertexts. The encryption key and initialization vector are generated by the Cipher encryption module of Crypto ( Gharehchopogh & Abdollahzadeh, 2021 ), and we use AES, 3DES, Blowfish, CAST and RC2 from cryptographic algorithm library to generate ciphertexts using a fixed 16-bit string key in ECB mode. We chose five ciphertext sizes: 1, 8, 64, 256, 512 KB, and each with 100 files, totaling 500 files for one cryptographic algorithm, so 2,500 files for five algorithms.…”

Section: Experimental Environmentmentioning

confidence: 99%

A block cipher algorithm identification scheme based on hybrid k-nearest neighbor and random forest algorithm

Yuan

Feng

et al. 2022

PeerJ Computer Science

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Cryptographic algorithm identification, which refers to analyzing and identifying the encryption algorithm used in cryptographic system, is of great significance to cryptanalysis. In order to improve the accuracy of identification work, this article proposes a new ensemble learning-based model named hybrid k-nearest neighbor and random forest (HKNNRF), and constructs a block cipher algorithm identification scheme. In the ciphertext-only scenario, we use NIST randomness test methods to extract ciphertext features, and carry out binary-classification and five-classification experiments on the block cipher algorithms using proposed scheme. Experiments show that when the ciphertext size and other experimental conditions are the same, compared with the baselines, the HKNNRF model has higher classification accuracy. Specifically, the average binary-classification identification accuracy of HKNNRF is 69.5%, which is 13%, 12.5%, and 10% higher than the single-layer support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF) respectively. The five-classification identification accuracy can reach 34%, which is higher than the 21% accuracy of KNN, the 22% accuracy of RF and the 23% accuracy of SVM respectively under the same experimental conditions.

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Section: Experimental Environmentmentioning

confidence: 99%

A block cipher algorithm identification scheme based on hybrid k-nearest neighbor and random forest algorithm

Yuan

Feng

et al. 2022

PeerJ Computer Science

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“…Swarm search algorithms are widely used in robotics, such as inverse solution computation ( Zhao et al, 2022 ), control ( Liu G et al, 2021 ; Wu et al, 2022 ), pose recognition ( Li et al, 2019a ; Tao et al, 2022a ) and other nonlinear problems ( Huang et al, 2019 ; Sun et al, 2020d ; Hao et al, 2022 ). Recently published optimisers ( Ghafori and Gharehchopogh, 2012 ; Abedi and Gharehchopogh, 2020 ; Abdollahzadeh et al, 2021a ; Gharehchopogh et al, 2021a ; Abdollahzadeh et al, 2021b ; Benyamin et al, 2021 ; Gharehchopogh et al, 2021b ; Gharehchopogh and Abdollahzadeh, 2021 ; Goldanloo and Gharechophugh, 2021 ; Mohammadzadeh and Gharehchopogh, 2021 ; Zaman and Gharehchopogh, 2021 ; Gharehchopogh, 2022 ) have achieved good performance but may not suit industrial scenarios with high real-time requirements. The particle swarm optimization algorithm (PSO) is used to search for the global time-optimal trajectory of a spatial robot in conjunction with robot dynamics ( Huang and Xu, 2006 ).…”

Section: Related Workmentioning

confidence: 99%

Time Optimal Trajectory Planing Based on Improved Sparrow Search Algorithm

Zhang

Xiao

Tong

et al. 2022

Front. Bioeng. Biotechnol.

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Complete trajectory planning includes path planning, inverse solution solving and trajectory optimization. In this paper, a highly smooth and time-saving approach to trajectory planning is obtained by improving the kinematic and optimization algorithms for the time-optimal trajectory planning problem. By partitioning the joint space, the paper obtains an inverse solution calculation based on the partitioning of the joint space, saving 40% of the inverse kinematics solution time. This means that a large number of computational resources can be saved in trajectory planning. In addition, an improved sparrow search algorithm (SSA) is proposed to complete the solution of the time-optimal trajectory. A Tent chaotic mapping was used to optimize the way of generating initial populations. The algorithm was further improved by combining it with an adaptive step factor. The experiments demonstrated the performance of the improved SSA. The robot’s trajectory is further optimized in time by an improved sparrow search algorithm. Experimental results show that the method can improve convergence speed and global search capability and ensure smooth trajectories.

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“…These strategies improve the exploration efficiency of the HHO algorithm, and can also avoid the occurrence of local optimal. It has also been furthermore applied in solving other global optimization algorithm such as travelling salesman problems [ 66 , 67 ], multiple objective feature selection problems [ 68 ].…”

Section: Preliminariesmentioning

confidence: 99%

CSCAHHO: Chaotic hybridization algorithm of the Sine Cosine with Harris Hawk optimization algorithms for solving global optimization problems

et al. 2022

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Because of the No Free Lunch (NFL) rule, we are still under the way developing new algorithms and improving the capabilities of the existed algorithms. Under consideration of the simple and steady convergence capability of the sine cosine algorithm (SCA) and the fast convergence rate of the Harris Hawk optimization (HHO) algorithms, we hereby propose a new hybridization algorithm of the SCA and HHO algorithm in this paper, called the CSCAHHO algorithm henceforth. The energy parameter is introduced to balance the exploration and exploitation procedure for individuals in the new swarm, and chaos is introduced to improve the randomness. Updating equations is redefined and combined of the equations in the SCA and HHO algorithms. Simulation experiments on 27 benchmark functions and CEC 2014 competitive functions, together with 3 engineering problems are carried out. Comparisons have been made with the original SCA, HHO, Archimedes optimization algorithm (AOA), Seagull optimization algorithm (SOA), Sooty Tern optimization algorithm (STOA), Arithmetic optimizer (AO) and Chimp optimization algorithm (ChOA). Simulation experiments on either unimodal or multimodal, benchmark or CEC2014 functions, or real engineering problems all verified the better performance of the proposed CSAHHO, such as faster convergence rate, low residual errors, and steadier capability. Matlab code of this algorithm is shared in Gitee with the following address: https://gitee.com/yuj-zhang/cscahho.

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An efficient harris hawk optimization algorithm for solving the travelling salesman problem

Cited by 61 publications

References 83 publications

A block cipher algorithm identification scheme based on hybrid k-nearest neighbor and random forest algorithm

A block cipher algorithm identification scheme based on hybrid k-nearest neighbor and random forest algorithm

Time Optimal Trajectory Planing Based on Improved Sparrow Search Algorithm

CSCAHHO: Chaotic hybridization algorithm of the Sine Cosine with Harris Hawk optimization algorithms for solving global optimization problems

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