This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
C. Wang, Y. Wang, K. Wang, Y. Dong, and Y. Yang, An Improved Hybrid Algorithm Based on Biogeography/Complex and Metropolis for Many-Objective Optimization, Math. Probl. Eng., vol. 2017, no. 1, p. 2462891, 2017.Search in Google Scholar
F. Yu, C. Lu, J. Zhou, L. Yin, and K. Wang, A knowledge-guided bi- population evolutionary algorithm for energy-efficient scheduling of distributed flexible job shop problem, Eng. Appl. Artif. Intell., vol. 128, p. 107458, 2024.Search in Google Scholar
K. Liu et al., Research on fault diagnosis method of vehicle cable terminal based on time series segmentation for graph neural network model, Measurement, p. 114999, 2024.Search in Google Scholar
C. Wang, Z. Wang, S. Zhang, X. Liu, and J. Tan, Reinforced quantum- behaved particle swarm-optimized neural network for cross-sectional distortion prediction of novel variable- diameter-die-formed metal bent tubes, J. Comput. Des. Eng., vol. 10, no. 3, pp. 1060–1079, 2023.Search in Google Scholar
W. Liu, X. Bai, H. Yang, R. Bao, and J. Liu, Tendon driven bistable origami flexible gripper for high-speed adaptive grasping, IEEE Robot. Autom. Lett., 2024.Search in Google Scholar
W. Dang et al., Increasing Text Filtering Accuracy with Improved LSTM, Comput. Informatics, vol. 42, no. 6, pp. 1491–1517, 2023.Search in Google Scholar
B. Cao, Y. Gu, Z. Lv, S. Yang, J. Zhao, and Y. Li, RFID Reader Anticollision Based on Distributed Parallel Particle Swarm Optimization, IEEE Internet Things J., vol. 8, no. 5, pp. 3099–3107, 2020.Search in Google Scholar
R. Wang and R. Zhang, Techno- economic analysis and optimization of hybrid energy systems based on hydrogen storage for sustainable energy utilization by a biological-inspired optimization algorithm, J. Energy Storage, vol. 66, p. 107469, 2023.Search in Google Scholar
H. Jia, S. Shi, D. Wu, H. Rao, J. Zhang, and L. Abualigah, Improve coati optimization algorithm for solving constrained engineering optimization problems, J. Comput. Des. Eng., vol. 10, no. 6, pp. 2223–2250, 2023.Search in Google Scholar
L. Yin, M. Zhuang, J. Jia, and H. Wang, Energy saving in flow-shop scheduling management: an improved multiobjective model based on grey wolf optimization algorithm, Math. Probl. Eng., vol. 2020, pp. 1–14, 2020.Search in Google Scholar
R. Luo, Z. Peng, J. Hu, and B. K. Ghosh, Adaptive optimal control of affine nonlinear systems via identifier– critic neural network approximation with relaxed PE conditions, Neural Networks, vol. 167, pp. 588–600, 2023.Search in Google Scholar
M. Shi, W. Hu, M. Li, J. Zhang, X. Song, and W. Sun, Ensemble regression based on polynomial regression-based decision tree and its application in the in-situ data of tunnel boring machine, Mech. Syst. Signal Process., vol. 188, p. 110022, 2023.Search in Google Scholar
G. Arun and V. Mishra, A review on quantum computing and communication, in 2014 2nd International Conference on Emerging Technology Trends in Electronics, Communication and Networking, IEEE, 2014, pp. 1–5.Search in Google Scholar
R. P. Feynman, Simulating physics with computers, in Feynman and computation, CRC Press, 2018, pp. 133–153.Search in Google Scholar
S. Ramlo, Mixed methods research and quantum theory: Q methodology as an exemplar for complementarity, J. Mix. Methods Res., vol. 16, no. 2, pp. 226–241, 2022.Search in Google Scholar
D. Deutsch and R. Jozsa, Rapid solution of problems by quantum computation, Proc. R. Soc. London. Ser. A Math. Phys. Sci., vol. 439, no. 1907, pp. 553–558, 1992.Search in Google Scholar
M. Cerezo et al., Variational quantum algorithms, Nat. Rev. Phys., vol. 3, no. 9, pp. 625–644, 2021.Search in Google Scholar
P. W. Shor, Algorithms for quantum computation: discrete logarithms and factoring, in Proceedings 35th annual symposium on foundations of computer science, Ieee, 1994, pp. 124–134.Search in Google Scholar
X. Li and Y. Sun, Application of RBF neural network optimal segmentation algorithm in credit rating, Neural Comput. Appl., vol. 33, pp. 8227–8235, 2021.Search in Google Scholar
L. Luan, Z. Wang, and S. Liu, Progress of grover quantum search algorithm, Energy Procedia, vol. 16, pp. 1701–1706, 2012.Search in Google Scholar
L. Zhu et al., Adaptive quantum approximate optimization algorithm for solving combinatorial problems on a quantum computer, Phys. Rev. Res., vol. 4, no. 3, p. 33029, 2022.Search in Google Scholar
X. Xu and Z. Wei, Dynamic pickup and delivery problem with transshipments and LIFO constraints, Comput. Ind. Eng., vol. 175, p. 108835, 2023.Search in Google Scholar
B. Cao et al., Multiobjective 3-D topology optimization of next- generation wireless data center network, IEEE Trans. Ind. Informatics, vol. 16, no. 5, pp. 3597–3605, 2019.Search in Google Scholar
B. S. Yıldız, N. Pholdee, N. Panagant, S. Bureerat, A. R. Yildiz, and S. M. Sait, A novel chaotic Henry gas solubility optimization algorithm for solving real-world engineering problems, Eng. Comput., pp. 1–13, 2021.Search in Google Scholar
B. S. Yıldız, S. Kumar, N. Pholdee, S. Bureerat, S. M. Sait, and A. R. Yildiz, A new chaotic Lévy flight distribution optimization algorithm for solving constrained engineering problems, Expert Syst., vol. 39, no. 8, p. e12992, 2022.Search in Google Scholar
D. Gürses, P. Mehta, S. M. Sait, and A. R. Yildiz, African vultures optimization algorithm for optimization of shell and tube heat exchangers, Mater. Test., vol. 64, no. 8, pp. 1234–1241, 2022.Search in Google Scholar
D. Gürses, P. Mehta, V. Patel, S. M. Sait, and A. R. Yildiz, Artificial gorilla troops algorithm for the optimization of a fine plate heat exchanger, Mater. Test., vol. 64, no. 9, pp. 1325–1331, 2022.Search in Google Scholar
P. Mehta, B. S. Yildiz, S. M. Sait, and A. R. Yildiz, Hunger games search algorithm for global optimization of engineering design problems, Mater. Test., vol. 64, no. 4, pp. 524–532, 2022.Search in Google Scholar
A. Alazeb et al., Remote intelligent perception system for multi-object detection, Front. Neurorobot., vol. 18, p. 1398703, 2024.Search in Google Scholar
Y. Hartmann, H. Liu, and T. Schultz, High-level features for human activity recognition and modeling, in International Joint Conference on Biomedical Engineering Systems and Technologies, Springer, 2022, pp. 141–163.Search in Google Scholar
L. S. Madsen et al., Quantum computational advantage with a programmable photonic processor, Nature, vol. 606, no. 7912, pp. 75–81, 2022.Search in Google Scholar
X. Cai et al., An improved quantum- inspired cooperative co-evolution algorithm with muli-strategy and its application, Expert Syst. Appl., vol. 171, p. 114629, 2021.Search in Google Scholar
W. Ding and J. Wang, A novel approach to minimum attribute reduction based on quantum-inspired self-adaptive cooperative co-evolution, Knowledge-Based Syst., vol. 50, pp. 1–13, 2013.Search in Google Scholar
C. Yu, A. A. Heidari, and H. Chen, A quantum-behaved simulated annealing algorithm-based moth-flame optimization method, Appl. Math. Model., vol. 87, pp. 1–19, 2020.Search in Google Scholar
R. K. Agrawal, B. Kaur, and S. Sharma, Quantum based whale optimization algorithm for wrapper feature selection, Appl. Soft Comput., vol. 89, p. 106092, 2020.Search in Google Scholar
Y. Chen, F. Li, J. Wang, B. Tang, and X. Zhou, Quantum recurrent encoder– decoder neural network for performance trend prediction of rotating machinery, Knowledge-Based Syst., vol. 197, p. 105863, 2020.Search in Google Scholar
J. Chen, X. Qi, L. Chen, F. Chen, and G. Cheng, Quantum-inspired ant lion optimized hybrid k-means for cluster analysis and intrusion detection, Knowledge-Based Syst., vol. 203, p. 106167, 2020.Search in Google Scholar
R. V Casa˜na-Eslava, P. J. G. Lisboa, S. Ortega-Martorell, I. H. Jarman, and J. D. Martín-Guerrero, Probabilistic quantum clustering, Knowledge-Based Syst., vol. 194, p. 105567, 2020.Search in Google Scholar
P. Yan, L. Li, and D. Zeng, Quantum Probability-inspired Graph Attention Network for Modeling Complex Text Interaction, Knowledge-Based Syst., vol. 234, p. 107557, 2021.Search in Google Scholar
W. Deng et al., Quantum differential evolution with cooperative coevolution framework and hybrid mutation strategy for large scale optimization, Knowledge-Based Syst., vol. 224, p. 107080, 2021.Search in Google Scholar
M. Sharma, S. Gupta, H. Aggarwal, T. Aggarwal, D. Gupta, and A. Khanna, Quantum Grey Wolf optimisation and evolutionary algorithms for diagnosis of Alzheimer’s disease, Int. J. Model. Identif. Control, vol. 41, no. 1–2, pp. 53–67, 2022.Search in Google Scholar
N.-R. Zhou, S.-H. Xia, Y. Ma, and Y. Zhang, Quantum particle swarm optimization algorithm with the truncated mean stabilization strategy, Quantum Inf. Process., vol. 21, no. 2, pp. 1–23, 2022.Search in Google Scholar
T. Liu, L. Jiao, W. Ma, J. Ma, and R. Shang, A new quantum-behaved particle swarm optimization based on cultural evolution mechanism for multi-objective problems, Knowledge- Based Syst., vol. 101, pp. 90–99, 2016.Search in Google Scholar
A. M. Anter, H. S. Elnashar, and Z. Zhang, QMVO-SCDL: A new regression model for fMRI pain decoding using quantum-behaved sparse dictionary learning, Knowledge-Based Syst., vol. 252, p. 109323, 2022.Search in Google Scholar
S. Yarkoni, E. Raponi, T. B¨ack, and S. Schmitt, Quantum annealing for industry applications: Introduction and review, Reports Prog. Phys., 2022.Search in Google Scholar
J. Li, B. Xu, Y. Yang, and H. Wu, Quantum ant colony optimization algorithm for AGVs path planning based on Bloch coordinates of pheromones, Nat. Comput., vol. 19, pp. 673–682, 2020.Search in Google Scholar
M. Khishe and M. R. Mosavi, Chimp optimization algorithm, Expert Syst. Appl., 2020, doi: 10.1016/j.eswa.2020.113338.Search in Google Scholar
T. Hu, M. Khishe, M. Mohammadi, G.-R. Parvizi, S. H. T. Karim, and T. A. Rashid, Real-time COVID-19 diagnosis from X-Ray images using deep CNN and extreme learning machines stabilized by chimp optimization algorithm, Biomed. Signal Process. Control, p. 102764, 2021.Search in Google Scholar
A. N. Ahmed, T. Van Lam, N. D. Hung, N. Van Thieu, O. Kisi, and A. El-Shafie, A comprehensive comparison of recent developed meta- heuristic algorithms for streamflow time series forecasting problem, Appl. Soft Comput., vol. 105, p. 107282, 2021.Search in Google Scholar
E. H. Houssein, M. M. Emam, and A. Ali, An efficient multilevel thresholding segmentation method for thermography breast cancer imaging based on improved chimp optimization algorithm, Expert Syst. Appl., p. 115651, 2021, doi: https://doi.org/10.1016/j.eswa.2021.115651.Search in Google Scholar
Y. Tang, S. Liu, Y. Deng, Y. Zhang, L. Yin, and W. Zheng, Construction of force haptic reappearance system based on Geomagic Touch haptic device, Comput. Methods Programs Biomed., vol. 190, p. 105344, 2020.Search in Google Scholar
D. Wu, W. Zhang, H. Jia, and X. Leng, Simultaneous feature selection and support vector machine optimization using an enhanced chimp optimization algorithm, Algorithms, vol. 14, no. 10, p. 282, 2021.Search in Google Scholar
F. Valdez, O. Castillo, and P. Melin, An Exhaustive Review of Bio-Inspired Algorithms and its Applications for Optimization in Fuzzy Clustering, 2021.Search in Google Scholar
S. P. H. Boroujeni and E. Pashaei, Data clustering using chimp optimization algorithm, in 2021 11th international conference on computer engineering and knowledge (ICCKE), IEEE, 2021, pp. 296–301.Search in Google Scholar
L. Zhu, H. Ren, M. Habibi, K. J. Mohammed, and M. A. Khadimallah, Predicting the environmental economic dispatch problem for reducing waste nonrenewable materials via an innovative constraint multi- objective Chimp Optimization Algorithm, J. Clean. Prod., vol. 365, p. 132697, 2022.Search in Google Scholar
T. Sui, D. Marelli, X. Sun, and M. Fu, Multi-sensor state estimation over lossy channels using coded measurements, Automatica, vol. 111, p. 108561, 2020.Search in Google Scholar
X. Xu, C. Wang, and P. Zhou, GVRP considered oil-gas recovery in refined oil distribution: from an environmental perspective, Int. J. Prod. Econ., vol. 235, p. 108078, 2021.Search in Google Scholar
L. Ding et al., Definition and application of variable resistance coefficient for wheeled mobile robots on deformable terrain, IEEE Trans. Robot., vol. 36, no. 3, pp. 894–909, 2020.Search in Google Scholar
M. Khishe and M. R. Mosavi, Classification of underwater acoustical dataset using neural network trained by Chimp Optimization Algorithm, Appl. Acoust., 2020, doi: 10.1016/j.apacoust.2019.107005.Search in Google Scholar
A. Fathy, D. Yousri, A. Y. Abdelaziz, and H. S. Ramadan, Robust approach based chimp optimization algorithm for minimizing power loss of electrical distribution networks via allocating distributed generators, Sustain. Energy Technol. Assessments, vol. 47, p. 101359, 2021.Search in Google Scholar
S. Bhattacharya, S. L. Tripathi, and V. K. Kamboj, Design of tunnel FET architectures for low power application using improved Chimp optimizer algorithm, Eng. Comput., pp. 1–44, 2021.Search in Google Scholar
N. Du, Q. Luo, Y. Du, and Y. Zhou, Color Image Enhancement: A Metaheuristic Chimp Optimization Algorithm, Neural Process. Lett., pp. 1–40, 2022.Search in Google Scholar
Z. Chen, K. Zhang, T. H. T. Chan, X. Li, and S. Zhao, A Novel Hybrid Whale-Chimp Optimization Algorithm for Structural Damage Detection, Appl. Sci., vol. 12, no. 18, p. 9036, 2022.Search in Google Scholar
Y. Yang, Y. Wu, H. Yuan, M. Khishe, and M. Mohammadi, Nodes Clustering and Multi-Hop Routing Protocol Optimization using Hybrid Chimp Optimization and Hunger Games Search Algorithms for Sustainable Energy Efficient Underwater Wireless Sensor Networks, Sustain. Comput.Informatics Syst., p. 100731, 2022.Search in Google Scholar
M. Kaur, R. Kaur, and N. Singh, A novel hybrid of chimp with cuckoo search algorithm for the optimal designing of digital infinite impulse response filter using high-level synthesis, Soft Comput., pp. 1–25, 2022.Search in Google Scholar
M. Kaur, R. Kaur, N. Singh, and G. Dhiman, SChoA: an newly fusion of sine and cosine with chimp optimization algorithm for HLS of datapaths in digital filters and engineering applications, Eng. Comput., 2021, doi: 10.1007/s00366-020-01233-2.Search in Google Scholar
O. A. M. F. Alnaggar, B. N. Jagadale, and S. H. Narayan, MRI Brain Tumor Detection Using Boosted Crossbred Random Forests and Chimp Optimization Algorithm Based Convolutional Neural Networks .Search in Google Scholar
M. E. Zayed et al., Predicting the performance of solar dish Stirling power plant using a hybrid random vector functional link/chimp optimization model, Sol. Energy, vol. 222, pp. 1–17, 2021.Search in Google Scholar
F. Mousavipour and M. R. Mosavi, Sonar Data Classification using Neural Network Trained by Hybrid Dragonfly and Chimp Optimization Algorithms, 2022.Search in Google Scholar
G. Dhiman, SSC: A hybrid nature- inspired meta-heuristic optimization algorithm for engineering applications, Knowledge-Based Syst., vol. 222, p. 106926, 2021.Search in Google Scholar
A. Saffari, S. H. Zahiri, M. Khishe, and seyyed mohammadreza mosavi, Design of a fuzzy model of control parameters of chimp algorithm optimization for automatic sonar targets recognition, IJMT, 2020, [Online]. Available: http://ijmt.iranjournals.ir/article241126.htmlSearch in Google Scholar
H. Jia, K. Sun, W. Zhang, and X. Leng, An enhanced chimp optimization algorithm for continuous optimization domains, Complex Intell. Syst., pp. 1–18, 2021.Search in Google Scholar
M. Khishe, M. Nezhadshahbodaghi, M. R. Mosavi, and D. Martín, A Weighted Chimp Optimization Algorithm, IEEE Access, 2021.Search in Google Scholar
W. Kaidi, M. Khishe, and M. Mohammadi, Dynamic Levy Flight Chimp Optimization, Knowledge- Based Syst., p. 107625, 2021.Search in Google Scholar
G. Hu, W. Dou, X. Wang, and M. Abbas, An enhanced chimp optimization algorithm for optimal degree reduction of Said–Ball curves, Math. Comput. Simul., vol. 197, pp. 207–252, 2022.Search in Google Scholar
Q. Zhang, S. Du, Y. Zhang, H. Wu, K. Duan, and Y. Lin, A Novel Chimp Optimization Algorithm with Refraction Learning and Its Engineering Applications, Algorithms, vol. 15, no. 6, p. 189, 2022.Search in Google Scholar
N. Du, Y. Zhou, W. Deng, and Q. Luo, Improved chimp optimization algorithm for three-dimensional path planning problem, Multimed. Tools Appl., pp. 1–26, 2022.Search in Google Scholar
N. Du, Y. Zhou, Q. Luo, M. Jiang, and W. Deng, Multi-strategy chimp optimization algorithm for global optimization and minimum spanning tree, Soft Comput., pp. 1–28, 2023.Search in Google Scholar
D. H. Wolpert and W. G. Macready, No free lunch theorems for optimization, IEEE Trans. Evol. Comput., vol. 1, no. 1, pp. 67–82, 1997, doi: 10.1109/4235.585893.Search in Google Scholar
L. Liu, M. Khishe, M. Mohammadi, and A. H. Mohammed, Optimization of constraint engineering problems using robust universal learning chimp optimization, Adv. Eng. Informatics, vol. 53, p. 101636, 2022.Search in Google Scholar
S.-P. Gong, M. Khishe, and M. Mohammadi, Niching Chimp Optimization for Constraint Multi-modal Engineering Optimization Problems, Expert Syst. Appl., p. 116887, 2022.Search in Google Scholar
R. Poláková, L-SHADE with competing strategies applied to constrained optimization, in 2017 IEEE congress on evolutionary computation (CEC), IEEE, 2017, pp. 1683–1689.Search in Google Scholar
A. A. Hadi, A. W. Mohamed, and K. M. Jambi, Single-objective real- parameter optimization: Enhanced LSHADE-SPACMA algorithm, in Heuristics for optimization and learning, Springer, 2021, pp. 103–121.Search in Google Scholar
K. Krishnamoorthy, Wilcoxon Signed- Rank Test, in Handbook of Statistical Distributions with Applications, 2020, pp. 339–342. doi: 10.1201/9781420011371-34.Search in Google Scholar
H. Abdi, Holm’s sequential Bonferroni procedure, Encycl. Res. Des., vol. 1, no. 8, pp. 1–8, 2010.Search in Google Scholar
G. A. Mack and J. H. Skillings, A Friedman-type rank test for main effects in a two-factor ANOVA, J. Am. Stat. Assoc., vol. 75, no. 372, pp. 947–951, 1980.Search in Google Scholar
P. N. Price, K. V., Awad, N. H., Ali, M. Z., & Suganthan, Problem definitions and evaluation criteria for the 100-digit challenge special session and competition on single objective numerical optimization. Technical Report., 2018. [Online]. Available: https://personal.ntu.edu.sg/404.htmlSearch in Google Scholar
A. Kumar, G. Wu, M. Z. Ali, R. Mallipeddi, P. N. Suganthan, and S. Das, A test-suite of non-convex constrained optimization problems from the real-world and some baseline results, Swarm Evol. Comput., 2020, doi: 10.1016/j.swevo.2020.100693.Search in Google Scholar
L. Yin, S. Lin, Z. Sun, S. Wang, R. Li, and Y. He, PriMonitor: An adaptive tuning privacy-preserving approach for multimodal emotion detection, World Wide Web, vol. 27, no. 2, pp. 1–28, 2024.Search in Google Scholar
L. Yin, S. Lin, Z. Sun, R. Li, Y. He, and Z. Hao, A game-theoretic approach for federated learning: a trade-off among privacy, accuracy and energy, Digit. Commun. Networks, vol. 10, no. 2, pp. 389–403, 2024.Search in Google Scholar
H. Liu, T. Xue, and T. Schultz, Merged Pitch Histograms and Pitch- duration Histograms., in SIGMAP, 2022, pp. 32–39.Search in Google Scholar
J. Brest, M. S. Maucec, and B. Boskovic, The 100-Digit Challenge: Algorithm jDE100, in 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings, 2019. doi: 10.1109/CEC.2019.8789904.Search in Google Scholar
S. X. Zhang, W. Shing Chan, K. S. Tang, and S. Yong Zheng, Restart based Collective Information Powered Differential Evolution for Solving the 100-Digit Challenge on Single Objective Numerical Optimization, in 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings, 2019. doi: 10.1109/CEC.2019.8790279.Search in Google Scholar
J. F. Yeh, T. Y. Chen, and T. C. Chiang, Modified L-SHADE for Single Objective Real-Parameter Optimization, in 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings, 2019. doi: 10.1109/CEC.2019.8789991.Search in Google Scholar
D. Yazdani, R. Cheng, D. Yazdani, J. Branke, Y. Jin, and X. Yao, A survey of evolutionary continuous dynamic optimization over two decades—Part A, IEEE Trans. Evol. Comput., vol. 25, no. 4, pp. 609–629, 2021.Search in Google Scholar
J. Branke and H. Schmeck, Designing evolutionary algorithms for dynamic optimization problems, Adv. Evol. Comput. theory Appl., pp. 239–262, 2003.Search in Google Scholar
T. Blackwell and J. Branke, Multiswarms, exclusion, and anti- convergence in dynamic environments, IEEE Trans. Evol. Comput., vol. 10, no. 4, pp. 459–472, 2006.Search in Google Scholar
, 