• Kaveh, A., Talatahari, S. & Khodadadi, N. Stochastic paint optimizer: theory and application in civil engineering. Engineering with Computers 1–32 (2020).

  • Abualigah, L. & Diabat, A. Advances in sine cosine algorithm: a comprehensive survey. Artificial Intelligence Review 1–42 (2021).

  • Yang, X.-S. Flower pollination algorithm for global optimization. In International conference on unconventional computing and natural computation, 240–249 (Springer, 2012).

  • Storn, R. & Price, K. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization 11, 341–359 (1997).

    Article 
    MathSciNet 
    MATH 

    Google Scholar
     

  • Mirjalili, S., Mirjalili, S. M. & Lewis, A. Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014).

    Article 

    Google Scholar
     

  • Abualigah, L., Abd Elaziz, M., Sumari, P., Geem, Z. W. & Gandomi, A. H. Reptile search algorithm (rsa): A nature-inspired meta-heuristic optimizer. Expert Systems with Applications 191, 116158 (2022).

    Article 

    Google Scholar
     

  • Heidari, A. A. et al. Harris hawks optimization: Algorithm and applications. Future generation computer systems 97, 849–872 (2019).

    Article 

    Google Scholar
     

  • Abualigah, L. M. & Diabat, A. A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments. Clust. Comput. 24, 205–223 (2021).

    Article 

    Google Scholar
     

  • Salgotra, R. & Singh, U. The naked mole-rat algorithm. Neural Computing and Applications 31, 8837–8857 (2019).

    Article 

    Google Scholar
     

  • Jiang, Y., Luo, Q., Wei, Y., Abualigah, L. et al. An efficient binary gradient-based optimizer for feature selection. Mathematical Biosciences and Engineering 18 (2021).

  • Abualigah, L., Diabat, A., Mirjalili, S., Abd Elaziz, M. & Gandomi, A. H. The arithmetic optimization algorithm. Computer methods in applied mechanics and engineering 376, 113609 (2021).

    Article 
    ADS 
    MathSciNet 
    MATH 

    Google Scholar
     

  • Abualigah, L. et al. Aquila optimizer: A novel meta-heuristic optimization algorithm. Computers & Industrial Engineering 157, 107250 (2021).

    Article 

    Google Scholar
     

  • Lin, Q. et al. A novel artificial bee colony algorithm with local and global information interaction. Applied Soft Computing 62, 702–735 (2018).

    Article 

    Google Scholar
     

  • Brajević, I. et al. Hybrid sine cosine algorithm for solving engineering optimization problems. Mathematics 10, 4555 (2022).

    Article 

    Google Scholar
     

  • Özbay, F. A., Özbay, E. & Gharehchopogh, F. S. An improved artificial rabbits optimization algorithm with chaotic local search and opposition-based learning for engineering problems and its applications in breast cancer problem. CMES-Computer Modeling in Engineering & Sciences 141 (2024).

  • Özbay, F. A. A modified seahorse optimization algorithm based on chaotic maps for solving global optimization and engineering problems. Engineering Science and Technology, an International Journal 41, 101408 (2023).

    Article 

    Google Scholar
     

  • Özbay, F. A. & Özbay, E. A new approach for gender detection from voice data: Feature selection with optimization methods. J Fac Eng Archit Gazi Univ 38, 1179–1192 (2023).


    Google Scholar
     

  • Bakır, H. Enhanced artificial hummingbird algorithm for global optimization and engineering design problems. Advances in Engineering Software 194, 103671 (2024).

    Article 

    Google Scholar
     

  • Bakır, H. A novel artificial hummingbird algorithm improved by natural survivor method. Neural Computing and Applications 36, 16873–16897 (2024).

    Article 

    Google Scholar
     

  • Bakır, H. Dynamic fitness-distance balance-based artificial rabbits optimization algorithm to solve optimal power flow problem. Expert Systems with Applications 240, 122460 (2024).

    Article 

    Google Scholar
     

  • Bakır, H., Duman, S., Guvenc, U. & Kahraman, H. T. Improved adaptive gaining-sharing knowledge algorithm with fdb-based guiding mechanism for optimization of optimal reactive power flow problem. Electrical Engineering 105, 3121–3160 (2023).

    Article 

    Google Scholar
     

  • Salgotra, R. & Singh, U. Application of mutation operators to flower pollination algorithm. Expert Systems with Applications 79, 112–129 (2017).

    Article 

    Google Scholar
     

  • Karaboga, D. & Basturk, B. On the performance of artificial bee colony (abc) algorithm. Applied soft computing 8, 687–697 (2008).

    Article 

    Google Scholar
     

  • Nelder, J. A. & Mead, R. A simplex method for function minimization. The computer journal 7, 308–313 (1965).

    Article 
    MathSciNet 
    MATH 

    Google Scholar
     

  • Yang, X.-S. & Deb, S. Cuckoo search via lévy flights. In 2009 World congress on nature & biologically inspired computing (NaBIC), 210–214 (IEEE, 2009).

  • Salgotra, R., Singh, U. & Saha, S. New cuckoo search algorithms with enhanced exploration and exploitation properties. Expert Systems with Applications 95, 384–420 (2018).

    Article 

    Google Scholar
     

  • Salgotra, R., Singh, U. & Saha, S. Improved cuckoo search with better search capabilities for solving cec2017 benchmark problems. In 2018 IEEE Congress on Evolutionary Computation (CEC), 1–7 (IEEE, 2018).

  • Bansal, J. C. et al. Inertia weight strategies in particle swarm optimization. In 2011 Third world congress on nature and biologically inspired computing, 633–640 (IEEE, 2011).

  • Kennedy, J. Bare bones particle swarms. In Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS’03 (Cat. No. 03EX706), 80–87 (IEEE, 2003).

  • Hallam, J. W., Akman, O. & Akman, F. Genetic algorithms with shrinking population size. Computational Statistics 25, 691–705 (2010).

    Article 
    MathSciNet 
    MATH 

    Google Scholar
     

  • Suganthan, P. N. et al. Problem definitions and evaluation criteria for the cec 2005 special session on real-parameter optimization. KanGAL report 2005005, 2005 (2005).


    Google Scholar
     

  • Elsayed, S. M., Sarker, R. A., Essam, D. L. & Hamza, N. M. Testing united multi-operator evolutionary algorithms on the cec2014 real-parameter numerical optimization. In 2014 IEEE congress on evolutionary computation (CEC), 1650–1657 (IEEE, 2014).

  • Gupta, S., Deep, K. & Engelbrecht, A. P. A memory guided sine cosine algorithm for global optimization. Engineering Applications of Artificial Intelligence 93, 103718 (2020).

    Article 

    Google Scholar
     

  • Bhandari, A. K. A novel beta differential evolution algorithm-based fast multilevel thresholding for color image segmentation. Neural Computing and Applications 32, 4583–4613 (2020).

    Article 

    Google Scholar
     

  • Faramarzi, A., Heidarinejad, M., Stephens, B. & Mirjalili, S. Equilibrium optimizer: A novel optimization algorithm. Knowledge-Based Systems 191, 105190 (2020).

    Article 

    Google Scholar
     

  • Hansen, N., Müller, S. D. & Koumoutsakos, P. Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (cma-es). Evolutionary computation 11, 1–18 (2003).

    Article 
    PubMed 

    Google Scholar
     

  • Khalilpourazari, S. & Pasandideh, S. H. R. Sine–cosine crow search algorithm: theory and applications. Neural Computing and Applications 1–18 (2019).

  • Zhang, J. & Sanderson, A. C. Jade: adaptive differential evolution with optional external archive. IEEE Transactions on evolutionary computation 13, 945–958 (2009).

    Article 

    Google Scholar
     

  • Tanabe, R. & Fukunaga, A. Success-history based parameter adaptation for differential evolution. In 2013 IEEE congress on evolutionary computation, 71–78 (IEEE, 2013).

  • Salgotra, R., Singh, U. & Sharma, S. On the improvement in grey wolf optimization. Neural Computing and Applications 1–40 (2019).

  • Brest, J., Zumer, V. & Maucec, M. S. Self-adaptive differential evolution algorithm in constrained real-parameter optimization. In 2006 IEEE international conference on evolutionary computation, 215–222 (IEEE, 2006).

  • Salgotra, R., Singh, U. & Saha, S. On some improved versions of whale optimization algorithm. Arabian Journal for Science and Engineering 44, 9653–9691 (2019).

    Article 

    Google Scholar
     

  • Mohamed, A. W., Hadi, A. A., Fattouh, A. M. & Jambi, K. M. Lshade with semi-parameter adaptation hybrid with cma-es for solving cec 2017 benchmark problems. In 2017 IEEE Congress on evolutionary computation (CEC), 145–152 (IEEE, 2017).

  • Yousri, D. & Mirjalili, S. Fractional-order cuckoo search algorithm for parameter identification of the fractional-order chaotic, chaotic with noise and hyper-chaotic financial systems. Engineering Applications of Artificial Intelligence 92, 103662 (2020).

    Article 

    Google Scholar
     

  • Baluja, S. Population-based incremental learning. a method for integrating genetic search based function optimization and competitive learning. Tech. Rep., Carnegie-Mellon Univ Pittsburgh Pa Dept Of Computer Science (1994).

  • Wang, G.-G., Deb, S., Gandomi, A. H., Zhang, Z. & Alavi, A. H. Chaotic cuckoo search. Soft Computing 20, 3349–3362 (2016).

    Article 

    Google Scholar
     

  • Tejani, G. G., Savsani, V. J., Patel, V. K. & Mirjalili, S. Truss optimization with natural frequency bounds using improved symbiotic organisms search. Knowledge-Based Systems 143, 162–178 (2018).

    Article 

    Google Scholar
     

  • Gupta, S. & Deep, K. A novel random walk grey wolf optimizer. Swarm and evolutionary computation 44, 101–112 (2019).

    Article 

    Google Scholar
     

  • Ma, H. & Simon, D. Blended biogeography-based optimization for constrained optimization. Engineering Applications of Artificial Intelligence 24, 517–525 (2011).

    Article 

    Google Scholar
     

  • Garg, V. & Deep, K. Performance of laplacian biogeography-based optimization algorithm on cec 2014 continuous optimization benchmarks and camera calibration problem. Swarm and Evolutionary Computation 27, 132–144 (2016).

    Article 

    Google Scholar
     

  • Wang, G.-G., Lu, M. & Zhao, X.-J. An improved bat algorithm with variable neighborhood search for global optimization. In 2016 IEEE Congress on Evolutionary Computation (CEC), 1773–1778 (IEEE, 2016).

  • Li, W., Wang, G.-G. & Alavi, A. H. Learning-based elephant herding optimization algorithm for solving numerical optimization problems. Knowledge-Based Systems 105675 (2020).

  • Rosner, B., Glynn, R. J. & Ting Lee, M.-L. Incorporation of clustering effects for the wilcoxon rank sum test: a large-sample approach. Biometrics 59, 1089–1098 (2003).

  • Pereira, D. G., Afonso, A. & Medeiros, F. M. Overview of friedman’s test and post-hoc analysis. Communications in Statistics-Simulation and Computation 44, 2636–2653 (2015).

    Article 
    MathSciNet 
    MATH 

    Google Scholar
     

  • Payne, R. B. & Sorensen, M. D. The cuckoos, vol. 15 (Oxford University Press, 2005).

  • Brown, C. T., Liebovitch, L. S. & Glendon, R. Lévy flights in dobe ju/’hoansi foraging patterns. Human Ecology 35, 129–138 (2007).

    Article 

    Google Scholar
     

  • Pavlyukevich, I. Lévy flights, non-local search and simulated annealing. Journal of Computational Physics 226, 1830–1844 (2007).

    Article 
    ADS 
    MathSciNet 
    CAS 
    MATH 

    Google Scholar
     

  • Shlesinger, M. F. Search research. Nature 443, 281–282 (2006).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Gherboudj, A., Layeb, A. & Chikhi, S. Solving 0–1 knapsack problems by a discrete binary version of cuckoo search algorithm. International Journal of Bio-Inspired Computation 4, 229–236 (2012).

    Article 

    Google Scholar
     

  • Ouyang, X., Zhou, Y., Luo, Q. & Chen, H. A novel discrete cuckoo search algorithm for spherical traveling salesman problem. Applied mathematics & information sciences 7, 777 (2013).

    Article 
    MathSciNet 

    Google Scholar
     

  • Ouaarab, A., Ahiod, B. & Yang, X.-S. Discrete cuckoo search algorithm for the travelling salesman problem. Neural Computing and Applications 24, 1659–1669 (2014).

    Article 

    Google Scholar
     

  • Shi, X. H., Liang, Y. C., Lee, H. P., Lu, C. & Wang, Q. Particle swarm optimization-based algorithms for tsp and generalized tsp. Information processing letters 103, 169–176 (2007).

    Article 
    MathSciNet 
    MATH 

    Google Scholar
     

  • Tuba, M., Subotic, M. & Stanarevic, N. Modified cuckoo search algorithm for unconstrained optimization problems. In Proceedings of the 5th European conference on European computing conference, 263–268 (World Scientific and Engineering Academy and Society (WSEAS), 2011).

  • El Aziz, M. A. & Hassanien, A. E. Modified cuckoo search algorithm with rough sets for feature selection. Neural Computing and Applications 29, 925–934 (2018).

    Article 

    Google Scholar
     

  • Rani, K. N. A., MALEK, M., Fareq, A. & Siew-Chin, N. Nature-inspired cuckoo search algorithm for side lobe suppression in a symmetric linear antenna array. Radioengineering 21 (2012).

  • Giridhar, M. S., Sivanagaraju, S., Suresh, C. V. & Umapathi Reddy, P. Analyzing the multi objective analytical aspects of distribution systems with multiple multi-type compensators using modified cuckoo search algorithm. International Journal of Parallel, Emergent and Distributed Systems 32, 549–571 (2017).

  • Tawfik, A. S., Badr, A. A. & Abdel-Rahman, I. F. One rank cuckoo search algorithm with application to algorithmic trading systems optimization. International journal of computer applications 64 (2013).

  • Rao, M. S. & Venkaiah, N. A modified cuckoo search algorithm to optimize wire-edm process while machining inconel-690. Journal of the Brazilian Society of Mechanical Sciences and Engineering 39, 1647–1661 (2017).

    Article 

    Google Scholar
     

  • Yang, X.-S. & Deb, S. Engineering optimisation by cuckoo search. arXiv preprint arXiv:1005.2908 (2010).

  • Zhou, Y., Zheng, H., Luo, Q. & Wu, J. An improved cuckoo search algorithm for solving planar graph coloring problem. Applied Mathematics & Information Sciences 7, 785 (2013).

    Article 
    MathSciNet 

    Google Scholar
     

  • Lin, J.-H., Lee, I.-H. et al. Emotional chaotic cuckoo search for the reconstruction of chaotic dynamics. In source: 11th WSEAS Int. Conf. on COmputational Intelligence, Man-Machine Systems and Cybernetics (CIMMACS’12), 123–128 (2012).

  • Kamoona, A. M. & Patra, J. C. A novel enhanced cuckoo search algorithm for contrast enhancement of gray scale images. Applied Soft Computing 85, 105749 (2019).

    Article 

    Google Scholar
     

  • Zhou, Y. & Zheng, H. A novel complex valued cuckoo search algorithm. The Scientific World Journal 2013 (2013).

  • Zheng, H. & Zhou, Y. A novel cuckoo search optimization algorithm based on gauss distribution. Journal of Computational Information Systems 8, 4193–4200 (2012).


    Google Scholar
     

  • Boushaki, S. I., Kamel, N. & Bendjeghaba, O. A new quantum chaotic cuckoo search algorithm for data clustering. Expert Systems with Applications 96, 358–372 (2018).

    Article 
    MATH 

    Google Scholar
     

  • Chandrasekaran, K. & Simon, S. P. Multi-objective scheduling problem: hybrid approach using fuzzy assisted cuckoo search algorithm. Swarm and Evolutionary Computation 5, 1–16 (2012).

    Article 

    Google Scholar
     

  • Zhang, M., Wang, H., Cui, Z. & Chen, J. Hybrid multi-objective cuckoo search with dynamical local search. Memetic Computing 10, 199–208 (2018).

    Article 

    Google Scholar
     

  • Binh, H. T. T. et al. Improved cuckoo search and chaotic flower pollination optimization algorithm for maximizing area coverage in wireless sensor networks. Neural computing and applications 30, 2305–2317 (2018).

    Article 

    Google Scholar
     

  • Daniel, E., Anitha, J. & Gnanaraj, J. Optimum laplacian wavelet mask based medical image using hybrid cuckoo search-grey wolf optimization algorithm. Knowledge-Based Systems 131, 58–69 (2017).

    Article 

    Google Scholar
     

  • Shehab, M., Khader, A. T., Laouchedi, M. & Alomari, O. A. Hybridizing cuckoo search algorithm with bat algorithm for global numerical optimization. The Journal of Supercomputing 75, 2395–2422 (2019).

    Article 

    Google Scholar
     

  • Abdel-Basset, M., Wang, G.-G., Sangaiah, A. K. & Rushdy, E. Krill herd algorithm based on cuckoo search for solving engineering optimization problems. Multimedia Tools and Applications 78, 3861–3884 (2019).

    Article 

    Google Scholar
     

  • Nawi, N. M., Khan, A. spsampsps Rehman, M. Z. A new cuckoo search based levenberg-marquardt (cslm) algorithm. In international conference on computational science and its applications, 438–451 (Springer, 2013).

  • Kanagaraj, G., Ponnambalam, S. & Jawahar, N. A hybrid cuckoo search and genetic algorithm for reliability-redundancy allocation problems. Computers & Industrial Engineering 66, 1115–1124 (2013).

    Article 

    Google Scholar
     

  • Kanagaraj, G., Ponnambalam, S. & Lim, W. C. E. Application of a hybridized cuckoo search-genetic algorithm to path optimization for pcb holes drilling process. In 2014 IEEE International Conference on Automation Science and Engineering (CASE), 373–378 (IEEE, 2014).

  • Lim, W., Kanagaraj, G. & Ponnambalam, S. A hybrid cuckoo search-genetic algorithm for hole-making sequence optimization. Journal of Intelligent Manufacturing 27, 417–429 (2016).

    Article 

    Google Scholar
     

  • Wang, G. et al. A hybrid meta-heuristic de/cs algorithm for ucav path planning. Journal of Information and Computational Science 9, 4811–4818 (2012).

    ADS 

    Google Scholar
     

  • Zhang, Z., Ding, S. & Jia, W. A hybrid optimization algorithm based on cuckoo search and differential evolution for solving constrained engineering problems. Engineering Applications of Artificial Intelligence 85, 254–268 (2019).

    Article 

    Google Scholar
     

  • Wang, G. et al. A hybrid metaheuristic de/cs algorithm for ucav three-dimension path planning. The Scientific World Journal 2012 (2012).

  • Nancharaiah, B. & Mohan, B. C. Hybrid optimization using ant colony optimization and cuckoo search in manet routing. In 2014 International Conference on Communication and Signal Processing, 1729–1734 (IEEE, 2014).

  • Babukartik, R. & Dhavachelvan, P. Hybrid algorithm using the advantage of aco and cuckoo search for job scheduling. International Journal of Information Technology Convergence and Services 2, 25 (2012).

    Article 

    Google Scholar
     

  • Sheikholeslami, R., Zecchin, A. C., Zheng, F. & Talatahari, S. A hybrid cuckoo-harmony search algorithm for optimal design of water distribution systems. Journal of Hydroinformatics 18, 544–563 (2016).

    Article 

    Google Scholar
     

  • Wang, G.-G., Gandomi, A. H., Zhao, X. & Chu, H. C. E. Hybridizing harmony search algorithm with cuckoo search for global numerical optimization. Soft Computing 20, 273–285 (2016).

    Article 
    CAS 

    Google Scholar
     

  • Dejam, S., Sadeghzadeh, M. & Mirabedini, S. J. Combining cuckoo and tabu algorithms for solving quadratic assignment problems. Journal of Academic and Applied Studies 2, 1–8 (2012).


    Google Scholar
     

  • Layeb, A. A novel quantum inspired cuckoo search for knapsack problems. International Journal of bio-inspired Computation 3, 297–305 (2011).

    Article 

    Google Scholar
     

  • Jovanovic, R., Kais, S. & Alharbi, F. H. Cuckoo search inspired hybridization of the nelder-mead simplex algorithm applied to optimization of photovoltaic cells. arXiv preprint arXiv:1411.0217 (2014).

  • Abdel-Baset, M. & Hezam, I. M. Solving linear least squares problems based on improved cuckoo search algorithm. Mathematical Sciences Letter 5, 199–202 (2016).

    Article 

    Google Scholar
     

  • Long, W., Cai, S., Jiao, J., Xu, M. & Wu, T. A new hybrid algorithm based on grey wolf optimizer and cuckoo search for parameter extraction of solar photovoltaic models. Energy Conversion and Management 203, 112243 (2020).

    Article 

    Google Scholar
     

  • Giveki, D., Salimi, H., Bahmanyar, G. & Khademian, Y. Automatic detection of diabetes diagnosis using feature weighted support vector machines based on mutual information and modified cuckoo search. arXiv preprint arXiv:1201.2173 (2012).

  • Zaw, M. M. & Mon, E. E. Web document clustering using cuckoo search clustering algorithm based on levy flight. International Journal of Innovation and Applied Studies 4, 182–188 (2013).


    Google Scholar
     

  • Tiwari, V. Face recognition based on cuckoo search algorithm. image 7, 9 (2012).

  • Bhandari, A., Soni, V., Kumar, A. & Singh, G. Cuckoo search algorithm based satellite image contrast and brightness enhancement using dwt-svd. ISA transactions 53, 1286–1296 (2014).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Gandomi, A. H., Yang, X.-S. & Alavi, A. H. Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Engineering with computers 29, 17–35 (2013).

    Article 

    Google Scholar
     

  • Kumar, A. & Chakarverty, S. Design optimization for reliable embedded system using cuckoo search. In 2011 3rd International Conference on Electronics Computer Technology, vol. 1, 264–268 (IEEE, 2011).

  • Madic, M. & Radovanovic, M. Application of cuckoo search algorithm for surface roughness optimization in co2 laser cutting. Annals of the Faculty of Engineering Hunedoara 11, 39 (2013).


    Google Scholar
     

  • Sudabattula, S. & Kowsalya, M. Optimal allocation of wind based distributed generators in distribution system using cuckoo search algorithm. Procedia Computer Science 92, 298–304 (2016).

    Article 

    Google Scholar
     

  • Piechocki, J., Ambroziak, D., Palkowski, A. & Redlarski, G. Use of modified cuckoo search algorithm in the design process of integrated power systems for modern and energy self-sufficient farms. Applied Energy 114, 901–908 (2014).

    Article 
    ADS 

    Google Scholar
     

  • Tran, C. D., Dao, T. T., Vo, V. S. & Nguyen, T. T. Economic load dispatch with multiple fuel options and valve point effect using cuckoo search algorithm with different distributions. International Journal of Hybrid Information Technology 8, 305–316 (2015).

    Article 

    Google Scholar
     

  • Pandey, A. C., Rajpoot, D. S. & Saraswat, M. Twitter sentiment analysis using hybrid cuckoo search method. Information Processing & Management 53, 764–779 (2017).

    Article 

    Google Scholar
     

  • Katarya, R. & Verma, O. P. An effective collaborative movie recommender system with cuckoo search. Egyptian Informatics Journal 18, 105–112 (2017).

    Article 

    Google Scholar
     

  • Dhabal, S. & Venkateswaran, P. An efficient gbest-guided cuckoo search algorithm for higher order two channel filter bank design. Swarm and Evolutionary Computation 33, 68–84 (2017).

    Article 

    Google Scholar
     

  • Chitara, D., Niazi, K. R., Swarnkar, A. & Gupta, N. Cuckoo search optimization algorithm for designing of a multimachine power system stabilizer. IEEE Transactions on Industry Applications 54, 3056–3065 (2018).

    Article 

    Google Scholar
     

  • Dong, Y., Zhang, Z. & Hong, W.-C. A hybrid seasonal mechanism with a chaotic cuckoo search algorithm with a support vector regression model for electric load forecasting. Energies 11, 1009 (2018).

    Article 

    Google Scholar
     

  • Sun, G. et al. Coverage optimization of vlc in smart homes based on improved cuckoo search algorithm. Computer Networks 116, 63–78 (2017).

    Article 

    Google Scholar
     

  • Nguyen, T. T. & Vo, D. N. Modified cuckoo search algorithm for multiobjective short-term hydrothermal scheduling. Swarm and evolutionary computation 37, 73–89 (2017).

    Article 

    Google Scholar
     

  • Sun, G. et al. Thinning of concentric circular antenna arrays using improved discrete cuckoo search algorithm. In 2017 IEEE Wireless Communications and Networking Conference (WCNC), 1–6 (IEEE, 2017).

  • Aslam, S. et al. An efficient home energy management scheme using cuckoo search. In International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 167–178 (Springer, 2017).

  • Cheng, J., Wang, L. & Xiong, Y. An improved cuckoo search algorithm and its application in vibration fault diagnosis for a hydroelectric generating unit. Engineering Optimization 50, 1593–1608 (2018).

    Article 
    ADS 

    Google Scholar
     

  • Zhou, X., Liu, Y., Li, B. & Li, H. A multiobjective discrete cuckoo search algorithm for community detection in dynamic networks. Soft Computing 21, 6641–6652 (2017).

    Article 

    Google Scholar
     

  • Zhang, X., Wan, Q. & Fan, Y. Applying modified cuckoo search algorithm for solving systems of nonlinear equations. Neural Computing and Applications 31, 553–576 (2019).

    Article 

    Google Scholar
     

  • Nguyen, T. T., Nguyen, T. T. & Le, B. Optimization of electric distribution network configuration for power loss reduction based on enhanced binary cuckoo search algorithm. Computers & Electrical Engineering 90, 106893 (2021).

    Article 

    Google Scholar
     

  • Alqahtani, F., Al-Makhadmeh, Z., Tolba, A. & Said, W. Internet of things-based urban waste management system for smart cities using a cuckoo search algorithm. Cluster Computing 23, 1769–1780 (2020).

    Article 

    Google Scholar
     

  • Mohanty, P. K. An intelligent navigational strategy for mobile robots in uncertain environments using smart cuckoo search algorithm. Journal of Ambient Intelligence and Humanized Computing 11, 6387–6402 (2020).

    Article 

    Google Scholar
     

  • Sadeghi, F. & Avokh, A. Load-balanced data gathering in internet of things using an energy-aware cuckoo-search algorithm. International Journal of Communication Systems 33, e4385 (2020).

    Article 

    Google Scholar
     

  • Cai, X. et al. An under-sampled software defect prediction method based on hybrid multi-objective cuckoo search. Concurrency and Computation: Practice and Experience 32, e5478 (2020).

    Article 

    Google Scholar
     

  • Ghobaei-Arani, M., Rahmanian, A. A., Aslanpour, M. S. & Dashti, S. E. Csa-wsc: cuckoo search algorithm for web service composition in cloud environments. Soft Computing 22, 8353–8378 (2018).

    Article 

    Google Scholar
     

  • Zhao, J., Wong, P. K., Xie, Z., Ma, X. & Hua, X. Design and control of an automotive variable hydraulic damper using cuckoo search optimized pid method. International Journal of Automotive Technology 20, 51–63 (2019).

    Article 

    Google Scholar
     

  • Pankaj, B. S., Naidu, M. N., Vasan, A. & Varma, M. R. Self-adaptive cuckoo search algorithm for optimal design of water distribution systems. Water Resources Management 34, 3129–3146 (2020).

    Article 

    Google Scholar
     

  • Shehab, M., Khader, A. T. & Al-Betar, M. A. A survey on applications and variants of the cuckoo search algorithm. Applied Soft Computing 61, 1041–1059 (2017).

    Article 

    Google Scholar
     

  • Salgotra, R., Singh, U., Saha, S. & Gandomi, A. H. Self adaptive cuckoo search: Analysis and experimentation. Swarm and Evolutionary Computation 60, 100751 (2021).

    Article 

    Google Scholar
     

  • Wolpert, D. H. & Macready, W. G. No free lunch theorems for optimization. IEEE transactions on evolutionary computation 1, 67–82 (1997).

    Article 

    Google Scholar
     

  • Salgotra, R., Singh, U., Saha, S. & Gandomi, A. H. Improving cuckoo search: Incorporating changes for cec 2017 and cec 2020 benchmark problems. In 2020 IEEE Congress on Evolutionary Computation (CEC), 1–7 (IEEE, 2020).

  • Salgotra, R., Singh, U., Singh, S. & Mittal, N. A hybridized multi-algorithm strategy for engineering optimization problems. Knowledge-Based Systems 217, 106790 (2021).

    Article 

    Google Scholar
     

  • Al-Hassan, W., Fayek, M. & Shaheen, S. Psosa: An optimized particle swarm technique for solving the urban planning problem. In 2006 International Conference on Computer Engineering and Systems, 401–405 (IEEE, 2006).

  • Chen, G., Huang, X., Jia, J. & Min, Z. Natural exponential inertia weight strategy in particle swarm optimization. In 2006 6th World Congress on Intelligent Control and Automation, vol. 1, 3672–3675 (IEEE, 2006).

  • Xin, J., Chen, G. & Hai, Y. A particle swarm optimizer with multi-stage linearly-decreasing inertia weight. In 2009 International Joint Conference on Computational Sciences and Optimization, vol. 1, 505–508 (IEEE, 2009).

  • Feng, Y., Teng, G.-F., Wang, A.-X. & Yao, Y.-M. Chaotic inertia weight in particle swarm optimization. In Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007), 475–475 (IEEE, 2007).

  • Gao, Y.-l., An, X.-h. & Liu, J.-m. A particle swarm optimization algorithm with logarithm decreasing inertia weight and chaos mutation. In 2008 International Conference on Computational Intelligence and Security, vol. 1, 61–65 (IEEE, 2008).

  • Yousri, D., Abd Elaziz, M. & Mirjalili, S. Fractional-order calculus-based flower pollination algorithm with local search for global optimization and image segmentation. Knowledge-Based Systems 105889 (2020).

  • Derrac, J., García, S., Molina, D. & Herrera, F. A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation 1, 3–18 (2011).

    Article 

    Google Scholar
     

  • Liang, J., Qu, B. & Suganthan, P. Problem definitions and evaluation criteria for the cec 2014 special session and competition on single objective real-parameter numerical optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore 635 (2013).

  • Kaveh, A., Vaez, S. R. H. & Hosseini, P. Simplified dolphin echolocation algorithm for optimum design of frame. Smart Struct Syst 21, 321–333 (2018).


    Google Scholar
     

  • Salgotra, R. & Gandomi, A. H. A novel multi-hybrid differential evolution algorithm for optimization of frame structures. Scientific Reports 14, 4877 (2024).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Talatahari, S., Gandomi, A. H., Yang, X.-S. & Deb, S. Optimum design of frame structures using the eagle strategy with differential evolution. Engineering Structures 91, 16–25 (2015).

    Article 

    Google Scholar
     

  • Degertekin, S. O. Optimum design of steel frames using harmony search algorithm. Structural and multidisciplinary optimization 36, 393–401 (2008).

    Article 

    Google Scholar
     

  • Gandomi, A. H. spsampsps Yang, X.-S. Benchmark problems in structural optimization. In Computational optimization, methods and algorithms, 259–281 (Springer, 2011).

  • Camp, C., Pezeshk, S. & Cao, G. Optimized design of two-dimensional structures using a genetic algorithm. Journal of structural engineering 124, 551–559 (1998).

    Article 

    Google Scholar
     

  • Kaveh, A. & Talatahari, S. An improved ant colony optimization for the design of planar steel frames. Engineering Structures 32, 864–873 (2010).

    Article 
    MATH 

    Google Scholar
     

  • Kaveh, A. & Talatahari, S. A discrete particle swarm ant colony optimization for design of steel frames. ASIAN JOURNAL OF CIVIL ENGINEERING (BUILDING AND HOUSING) (2008).

  • Kaveh, A. & Malakoutirad, S. Hybrid genetic algorithm and particle swarm optimization for the force method-based simultaneous analysis and design. IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY TRANSACTION B-ENGINEERING (2010).

  • Kaveh, A., Talatahari, S. & Khodadadi, N. The hybrid invasive weed optimization-shuffled frog-leaping algorithm applied to optimal design of frame structures. Periodica Polytechnica Civil Engineering 63, 882–897 (2019).


    Google Scholar
     

  • Kaveh, A. spsampsps Talatahari, S. Hybrid algorithm of harmony search, particle swarm and ant colony for structural design optimization. In Harmony search algorithms for structural design optimization, 159–198 (Springer, 2009).

  • Kaveh, A. & Talatahari, S. A discrete big bang-big crunchalgorithm for optimaldesign of skeletal structures. ASIAN JOURNAL OF CIVIL ENGINEERING (BUILDING AND HOUSING) (2010).

  • Kaveh, A. & Talatahari, S. Optimum design of skeletal structures using imperialist competitive algorithm. Computers & structures 88, 1220–1229 (2010).

    Article 
    MATH 

    Google Scholar
     

  • Kaveh, A., Ghazaan, M. I. & Saadatmand, F. Colliding bodies optimization with morlet wavelet mutation and quadratic interpolation for global optimization problems. Engineering with Computers 1–25 (2021).

  • Kaveh, A., Kamalinejad, M. & Hamedani, K. B. Enhanced versions of the shuffled shepherd optimization algorithm for the optimal design of skeletal structures. In Structures, vol. 29, 1463–1495 (Elsevier, 2021).

  • Talatahari, S. & Azizi, M. Optimum design of building structures using tribe-interior search algorithm. In Structures, vol. 28, 1616–1633 (Elsevier, 2020).

  • Davison, J. H. & Adams, P. F. Stability of braced and unbraced frames. Journal of the Structural Division 100, 319–334 (1974).

    Article 

    Google Scholar
     

  • Camp, C. V., Bichon, B. J. & Stovall, S. P. Design of steel frames using ant colony optimization. Journal of Structural Engineering 131, 369–379 (2005).

    Article 

    Google Scholar
     

  • Kaveh, A., Talatahari, S. & Alami, M. A new hybrid meta-heuristic for optimum design of frame structures. ASIAN JOURNAL OF CIVIL ENGINEERING (BUILDING AND HOUSING) (2012).

  • Bigham, A. & Gholizadeh, S. Topology optimization of nonlinear single-layer domes by an improved electro-search algorithm and its performance analysis using statistical tests. Structural and multidisciplinary optimization 62, 1821–1848 (2020).

    Article 
    MathSciNet 

    Google Scholar
     



  • Source link