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Dimensional decision covariance colony predation algorithm: global optimization and high−dimensional feature selection

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Abstract

The colony Predation Algorithm (CPA) has been proven to be one of the heuristic algorithms that can efficiently solve global optimization problems. Balancing the paradox between exploration and exploitation capabilities while mitigating premature convergence are two key subjects that need to be addressed in CPA research. To effectively alleviate these problems, this study proposes a CPA variant named Covariance Gaussian cuckoo Colony Predation Algorithm (CGCPA). Specifically, the designed gaussian cuckoo variable dimensional strategy is used to decentralize the agent population in CPA to enhance the search agents' population diversity and global search ability. The covariance matrix adaptation evolution strategy is used to enhance the convergence speed of the evolutionary agents and the ability to capture the global optimal solution at a later stage. This study subjects CGCPA to competitive experiments with ten basic metaheuristics and ten state−of−the−art algorithms on the IEEE CEC 2017 function test suite. Experimental results confirm that CGCPA outperforms several state−of−the−art DE variants and the latest proposed algorithms in terms of convergence speed and accuracy. In addition, this study proposes a discrete binary feature selection method to better select features in medical data classification named BCGCPA. Its feature selection capability is evaluated in detail on 12 high−dimensional biomedical datasets in the UCI machine learning repository. BCGCPA achieves the lowest classification error rate on all 12 high−dimensional datasets, realizing the best feature selection classification accuracy. BCGCPA can be an efficient pre−processing tool for the dimensionality reduction of high−dimensional biomedical data. It has crucial applications in search space optimization and feature selection of medical datasets.

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References

  • Abd Elaziz M et al (2020) A competitive chain−based Harris Hawks Optimizer for global optimization and multi−level image thresholding problems. Appl Soft Comput 95:106347

    Google Scholar 

  • Abdollahzadeh B, Soleimanian Gharehchopogh F, Mirjalili S (2021) Artificial gorilla troops optimizer: a new nature-inspired metaheuristic algorithm for global optimization problems. Int J Intell Syst 36(10):5887–5958

    Google Scholar 

  • Abdollahzadeh B, Gharehchopogh FS, Mirjalili S (2021b) African vultures optimization algorithm: A new nature−inspired metaheuristic algorithm for global optimization problems. Comput Ind Eng 158:107408

    Google Scholar 

  • Ahmadianfar I et al (2021) RUN beyond the metaphor: an efficient optimization algorithm based on Runge Kutta method. Expert Syst Appl 181:115079

    Google Scholar 

  • Ahmadianfar I et al (2022) INFO: An efficient optimization algorithm based on weighted mean of vectors. Expert Syst Appl 195:116516

    Google Scholar 

  • Anter AM, Ali M (2020) Feature selection strategy based on hybrid crow search optimization algorithm integrated with chaos theory and fuzzy c−means algorithm for medical diagnosis problems. Soft Comput 24(3):1565–1584

    Google Scholar 

  • Awadallah MA et al (2022) An enhanced binary rat swarm optimizer based on local−best concepts of PSO and collaborative crossover operators for feature selection. Comput Biol Med 147:105675

    Google Scholar 

  • Awadallah MA et al (2022) Binary Horse herd optimization algorithm with crossover operators for feature selection. Comput Biol Med 141:105152

    Google Scholar 

  • Beyer H−G, Schwefel H−P (2002) Evolution strategies–a comprehensive introduction. Nat Comput 1(1):3–52

    MathSciNet  MATH  Google Scholar 

  • Cai S et al (2019) Towards faster local search for minimum weight vertex cover on massive graphs. Inf Sci 471:64–79

    MathSciNet  MATH  Google Scholar 

  • Camacho−Villalón CL, Dorigo M, Stützle T Exposing the grey wolf, moth−flame, whale, firefly, bat, and antlion algorithms: six misleading optimization techniques inspired by bestial metaphors. International Transactions in Operational Research

  • Cao B et al (2020a) Diversified personalized recommendation optimization based on mobile data. IEEE Trans Intell Transp Syst 22(4):2133–2139

    Google Scholar 

  • Cao B et al (2020b) RFID reader anticollision based on distributed parallel particle swarm optimization. IEEE Internet Things J 8(5):3099–3107

    Google Scholar 

  • Cao B et al (2021a) Large−scale many−objective deployment optimization of edge servers. IEEE Trans Intell Transp Syst 22(6):3841–3849

    Google Scholar 

  • Cao B et al (2021b) Many−objective deployment optimization for a drone−assisted camera network. IEEE Transac Netw Sci Eng 8(4):2756–2764

    Google Scholar 

  • Cao B et al (2021c) A memetic algorithm based on two_Arch2 for multi−depot heterogeneous−vehicle capacitated Arc routing problem. Swarm Evol Comput 63:100864

    Google Scholar 

  • Cao B et al (2021d) Resource allocation in 5G IoV architecture based on SDN and fog−cloud computing. IEEE Trans Intell Transp Syst 22(6):3832–3840

    Google Scholar 

  • Cao B et al (2022) A multiobjective intelligent decision−making method for multistage placement of PMU in power grid enterprises. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/TII.2022.3215787

    Article  Google Scholar 

  • Chakraborty S et al (2021) COVID−19 X−ray image segmentation by modified whale optimization algorithm with population reduction. Comput Biol Med 139:104984

    Google Scholar 

  • Chandrashekar G, Sahin F (2014) A survey on feature selection methods. Comput Electr Eng 40(1):16–28

    Google Scholar 

  • Chang S, Shihong Y, Qi L, (2020) Clustering characteristics of UCI dataset. In 2020 39th Chinese Control Conference (CCC). IEEE

  • Chen W−N et al (2012) Particle swarm optimization with an aging leader and challengers. IEEE Trans Evol Comput 17(2):241–258

    Google Scholar 

  • Chen H et al (2020) Advanced orthogonal learning−driven multi−swarm sine cosine optimization: framework and case studies. Expert Syst Appl 144:113113

    Google Scholar 

  • Dang W et al (2022) A semi−supervised extreme learning machine algorithm based on the new weighted kernel for machine smell. Appl Sci 12(18):9213

    Google Scholar 

  • Derrac J et al (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1(1):3–18

    Google Scholar 

  • Ding Y, Zhou K, Bi W (2020) Feature selection based on hybridization of genetic algorithm and competitive swarm optimizer. Soft Comput 24(15):11663–11672

    Google Scholar 

  • Emam MM, Houssein EH, Ghoniem RM (2022) A modified reptile search algorithm for global optimization and image segmentation: Case study brain MRI images. Comput Biol Med 152:106404

    Google Scholar 

  • Emary E, Zawbaa HM, Hassanien AE (2016) Binary ant lion approaches for feature selection. Neurocomputing 213:54–65

    Google Scholar 

  • Fan Y et al (2021) A bioinformatic variant fruit fly optimizer for tackling optimization problems. Knowl−Based Syst 213:106704

    Google Scholar 

  • Faris H et al (2018) An efficient binary salp swarm algorithm with crossover scheme for feature selection problems. Knowl−Based Syst 154:43–67

    Google Scholar 

  • García S et al (2010) Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Inf Sci 180(10):2044–2064

    Google Scholar 

  • Ghafori S, Gharehchopogh FS (2021) Advances in spotted hyena optimizer: a comprehensive survey. Arch comput Methods Eng. https://doi.org/10.1007/s11831−021−09624−4

    Article  Google Scholar 

  • Gharehchopogh FS, Gholizadeh H (2019) A comprehensive survey: whale optimization algorithm and its applications. Swarm Evol Comput 48:1–24

    Google Scholar 

  • Gharehchopogh FS, Shayanfar H, Gholizadeh H (2020) A comprehensive survey on symbiotic organisms search algorithms. Artif Intell Rev 53(3):2265–2312

    Google Scholar 

  • Gharehchopogh FS (2022a) An improved tunicate swarm algorithm with best−random mutation strategy for global optimization problems. Journal of Bionic Engineering. p. 1–26

  • Gharehchopogh FS (2022b) Advances in tree seed algorithm: A comprehensive survey. Archives of Computational Methods in Engineering. p. 1–24

  • Gharehchopogh, F.S., et al., Advances in Sparrow Search Algorithm: A Comprehensive Survey. Archives of computational methods in engineering.

  • Hansen N, Ostermeier A (2001) Completely derandomized self−adaptation in evolution strategies. Evol Comput 9(2):159–195

    Google Scholar 

  • Hegazy AE, Makhlouf M, El−Tawel GS (2020) Improved salp swarm algorithm for feature selection. J King Saud Univ−Comput Inform Sci 32(3):335–344

    Google Scholar 

  • Heidari AA et al (2019) Harris hawks optimization: algorithm and applications. Futur Gener Comput Syst 97:849–872

    Google Scholar 

  • Houssein EH et al (2022) An efficient image segmentation method for skin cancer imaging using improved golden jackal optimization algorithm. Comput Biol Med 149:106075

    Google Scholar 

  • Hu B et al (2016) Feature selection for optimized high−dimensional biomedical data using an improved shuffled frog leaping algorithm. IEEE/ACM Trans Comput Biol Bioinf 15(6):1765–1773

    Google Scholar 

  • Hu G et al (2022) Multi−strategy assisted chaotic coot−inspired optimization algorithm for medical feature selection: A cervical cancer behavior risk study. Comput Biol Med 151:106239

    Google Scholar 

  • Jadhav S, He H, Jenkins K (2018) Information gain directed genetic algorithm wrapper feature selection for credit rating. Appl Soft Comput 69:541–553

    Google Scholar 

  • Jeyasingh S, Veluchamy M (2017) Modified bat algorithm for feature selection with the wisconsin diagnosis breast cancer (WDBC) dataset. Asian Pacific J Cancer Prev 18(5):1257

    Google Scholar 

  • Jia D, Zheng G, Khan MK (2011) An effective memetic differential evolution algorithm based on chaotic local search. Inf Sci 181(15):3175–3187

    Google Scholar 

  • Jin K et al (2022) Fives: A fundus image dataset for artificial Intelligence based vessel segmentation. Scientific Data 9(1):1–8

    MathSciNet  Google Scholar 

  • Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Global Optim 39(3):459–471

    MathSciNet  MATH  Google Scholar 

  • Kaur S et al (2020) Tunicate Swarm Algorithm: A new bio−inspired based metaheuristic paradigm for global optimization. Eng Appl Artif Intell 90:103541

    Google Scholar 

  • Kwak N, Choi C−H (2002) Input feature selection for classification problems. IEEE Trans Neural Netw 13(1):143–159

    Google Scholar 

  • Lazar C et al (2012) A survey on filter techniques for feature selection in gene expression microarray analysis. IEEE/ACM Trans Comput Biol Bioinf 9(4):1106–1119

    Google Scholar 

  • Li S, Liu Z (2022) Scheduling uniform machines with restricted assignment. Math Biosci Eng 19(9):9697–9708

    MathSciNet  MATH  Google Scholar 

  • Li Q et al (2017) An enhanced grey wolf optimization based feature selection wrapped kernel extreme learning machine for medical diagnosis. Comput Math Methods Med. https://doi.org/10.1155/2017/9512741

    Article  Google Scholar 

  • Li S et al (2020) Slime mould algorithm: A new method for stochastic optimization. Futur Gener Comput Syst 111:300–323

    Google Scholar 

  • Liang JJ et al (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10(3):281–295

    Google Scholar 

  • Liu T et al (2015) A fast approach for detection of erythemato−squamous diseases based on extreme learning machine with maximum relevance minimum redundancy feature selection. Int J Syst Sci 46(5):919–931

    MATH  Google Scholar 

  • Liu Y et al (2021a) Boosting slime mould algorithm for parameter identification of photovoltaic models. Energy 234:121164

    Google Scholar 

  • Liu R et al (2021b) SCCGAN: style and characters inpainting based on CGAN. Mob Netw Appl 26(1):3–12

    Google Scholar 

  • Liu Y et al (2022) Hierarchical particle optimization for cortical shape correspondence in temporal lobe resection. Comput Biol Med 152:106414

    Google Scholar 

  • Liu J et al (2022a) Dynamic multi−swarm differential learning Harris Hawks Optimizer and its application to optimal dispatch problem of cascade hydropower stations. Knowl−Based Syst 242:108281

    Google Scholar 

  • Liu Z et al (2022b) Instant diagnosis of gastroscopic biopsy via deep−learned single−shot femtosecond stimulated Raman histology. Nat Commun 13(1):1–12

    Google Scholar 

  • Liu Z, Wang Y, Feng J (2022) Vehicle−type strategies for manufacturer's car sharing. Kybernetes, 2022(ahead−of−print)

  • Lu H et al (2022) Multimodal fusion convolutional neural network with cross−attention mechanism for internal defect detection of magnetic tile. IEEE Access. https://doi.org/10.1109/ACCESS.2022.3180725

    Article  Google Scholar 

  • Luan D et al (2022) Robust two−stage location allocation for emergency temporary blood supply in postdisaster. Discret Dynam Nat Soc. https://doi.org/10.1155/2022/6184170

    Article  MATH  Google Scholar 

  • Mafarja M, Mirjalili S (2018) Whale optimization approaches for wrapper feature selection. Appl Soft Comput 62:441–453

    Google Scholar 

  • Mafarja M et al (2018) Binary dragonfly optimization for feature selection using time−varying transfer functions. Knowl−Based Syst 161:185–204

    Google Scholar 

  • Miramontes I et al (2017) A hybrid intelligent system model for hypertension diagnosis. Nature−inspired design of hybrid intelligent systems. Springer, Cham, pp 541–550

    Google Scholar 

  • Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl−Based Syst 96:120–133

    Google Scholar 

  • Mirjalili S, Lewis A (2013) S−shaped versus V−shaped transfer functions for binary particle swarm optimization. Swarm Evol Comput 9:1–14

    Google Scholar 

  • Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Google Scholar 

  • Mirjalili S, Mirjalili SM, Lewis A (2014a) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Google Scholar 

  • Mirjalili S, Mirjalili SM, Yang X−S (2014b) Binary bat algorithm. Neural Comput Appl 25(3–4):663–681

    Google Scholar 

  • Mohamed AW, Hadi AA, Jambi KM (2019) Novel mutation strategy for enhancing SHADE and LSHADE algorithms for global numerical optimization. Swarm Evol Comput 50:100455

    Google Scholar 

  • Mohamed AW et al. (2017) LSHADE with semi−parameter adaptation hybrid with CMA−ES for solving CEC 2017 benchmark problems. In 2017 IEEE Congress on evolutionary computation (CEC). IEEE

  • Ni Q et al (2022) Influence−based community partition with sandwich method for social networks. IEEE Transac Comput Soc Syst. https://doi.org/10.1109/TCSS.2022.3148411

    Article  Google Scholar 

  • Ozturk C, Hancer E, Karaboga D (2015) Dynamic clustering with improved binary artificial bee colony algorithm. Appl Soft Comput 28:69–80

    Google Scholar 

  • Piri J, Mohapatra P (2021) An analytical study of modified multi−objective Harris Hawk Optimizer towards medical data feature selection. Comput Biol Med 135:104558

    Google Scholar 

  • Qin AK, Huang VL, Suganthan PN (2008) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput 13(2):398–417

    Google Scholar 

  • Rashedi E, Nezamabadi−Pour H, Saryazdi S (2010) BGSA: Binary gravitational search algorithm. Nat Comput 9(3):727–745

    MathSciNet  MATH  Google Scholar 

  • Sayed GI, Soliman MM, Hassanien AE (2021) A novel melanoma prediction model for imbalanced data using optimized SqueezeNet by bald eagle search optimization. Comput Biol Med 136:104712

    Google Scholar 

  • Shayanfar H, Gharehchopogh FS (2018) Farmland fertility: a new metaheuristic algorithm for solving continuous optimization problems. Appl Soft Comput 71:728–746

    Google Scholar 

  • Sheng H, UrbanLF: A, et al (2022) Comprehensive light field dataset for semantic segmentation of urban scenes. IEEE Transac Circuits Syst Video Technol. https://doi.org/10.1109/TCSVT.2022.3187664

    Article  Google Scholar 

  • Shi B et al (2021) Evolutionary warning system for COVID−19 severity: colony predation algorithm enhanced extreme learning machine. Comput Biol Med 136:104698

    Google Scholar 

  • Storn R, Price K (1997) Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–359

    MathSciNet  MATH  Google Scholar 

  • Sun B et al (2022) Optimization planning method of distributed generation based on steady−state security region of distribution network. Energy Rep 8:4209–4222

    Google Scholar 

  • Thawkar S et al (2021) Breast cancer prediction using a hybrid method based on butterfly optimization algorithm and ant lion optimizer. Comput Biol Med 139:104968

    Google Scholar 

  • Tu J et al (2021) The colony predation algorithm. J Bionic Eng 18(3):674–710

    Google Scholar 

  • Tumar I et al (2020) Enhanced binary moth flame optimization as a feature selection algorithm to predict software fault prediction. IEEE Access 8:8041–8055

    Google Scholar 

  • Verleysen M, François D (2005) The curse of dimensionality in data mining and time series prediction. In International work−conference on artificial neural networks. Springer

  • Vieira SM et al (2013) Modified binary PSO for feature selection using SVM applied to mortality prediction of septic patients. Appl Soft Comput 13(8):3494–3504

    Google Scholar 

  • Wang G−G (2018) Moth search algorithm: a bio−inspired metaheuristic algorithm for global optimization problems. Memetic Computing 10(2):151–164

    Google Scholar 

  • Wang Y et al (2018) A restart local search algorithm for solving maximum set k−covering problem. Neural Comput Appl 29(10):755–765

    Google Scholar 

  • Wang G et al (2022a) Research on multi−modal autonomous diagnosis algorithm of COVID−19 based on whale optimized support vector machine and improved DS evidence fusion. Comput Biol Med 150:106181

    Google Scholar 

  • Wang M, Chen L, Chen H (2022b) Multi−strategy learning boosted colony predation algorithm for photovoltaic model parameter identification. Sensors 22(21):8281

    Google Scholar 

  • Wang S et al (2022c) Extendable multiple nodes recurrent tracking framework with RTU++. IEEE Trans Image Process 31:5257–5271

    Google Scholar 

  • Wei B et al (2020) Multiple adaptive strategies based particle swarm optimization algorithm. Swarm Evol Comput 57:100731

    Google Scholar 

  • Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82

    Google Scholar 

  • Wu Z et al (2018) hPSD: a hybrid PU−learning−based spammer detection model for product reviews. IEEE Transac Cybern 50(4):1595–1606

    Google Scholar 

  • Wu J et al (2020) An improved firefly algorithm for global continuous optimization problems. Expert Syst Appl 149:113340

    Google Scholar 

  • Wu Y et al (2022) Hybrid motion model for multiple object tracking in mobile devices. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2022.3219627

    Article  Google Scholar 

  • Xia X, Gui L, Zhan Z−H (2018) A multi−swarm particle swarm optimization algorithm based on dynamical topology and purposeful detecting. Appl Soft Comput 67:126–140

    Google Scholar 

  • Xiao Y et al (2020) The continuous pollution routing problem. Appl Math Comput 387:125072

    MATH  Google Scholar 

  • Xu G et al (2019) Particle swarm optimization based on dimensional learning strategy. Swarm Evol Comput 45:33–51

    Google Scholar 

  • Xu X et al (2022) Multi−objective robust optimisation model for MDVRPLS in refined oil distribution. Int J Prod Res 60(22):6772–6792

    Google Scholar 

  • Xu B et al (2022) Extremal Nelder-Mead colony predation algorithm for parameter estimation of solar photovoltaic models. Energy Sci Eng. https://doi.org/10.1002/ese3.1273

    Article  Google Scholar 

  • Yang X−S (2010) A new metaheuristic bat−inspired algorithm. Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, Berlin, pp 65–74

    Google Scholar 

  • Yang X-S, He X-S (2019) Mathematical foundations of nature−inspired algorithms. Springer, Cham

    MATH  Google Scholar 

  • Yang Y et al (2021) Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts. Expert Syst Appl 177:114864

    Google Scholar 

  • Yang X et al (2022) An optimized machine learning framework for predicting intradialytic hypotension using indexes of chronic kidney disease−mineral and bone disorders. Comput Biol Med 145:105510

    Google Scholar 

  • Yang D et al (2022) LFRSNet: a robust light field semantic segmentation network combining contextual and geometric features. Front Environ Sci. https://doi.org/10.3389/fenvs.2022.996513

    Article  Google Scholar 

  • Yang P et al. (2013) Ensemble based wrapper methods for feature selection and class imbalance learning. In Pacific Asia conference on knowledge discovery and data mining. Springer

  • Yu C et al (2021) Boosting quantum rotation gate embedded slime mould algorithm. Expert Syst Appl 181:115082

    Google Scholar 

  • Zhang J, Sanderson AC (2009) JADE: adaptive differential evolution with optional external archive. IEEE Trans Evol Comput 13(5):945–958

    Google Scholar 

  • Zhang Y et al (2019) Cost−sensitive feature selection using two−archive multi−objective artificial bee colony algorithm. Expert Syst Appl 137:46–58

    Google Scholar 

  • Zhang M, Chen Y, Lin J (2021) A privacy−preserving optimization of neighborhood−based recommendation for medical−aided diagnosis and treatment. IEEE Internet Things J 8(13):10830–10842

    Google Scholar 

  • Zhang H et al (2022) C2FDA: coarse−to−fine domain adaptation for traffic object detection. IEEE Trans Intell Transp Syst 23(8):12633–12647

    Google Scholar 

  • Zhao F et al (2019) A two−stage differential biogeography−based optimization algorithm and its performance analysis. Expert Syst Appl 115:329–345

    Google Scholar 

  • Zhou H et al (2018) A modified particle swarm optimization algorithm for a batch−processing machine scheduling problem with arbitrary release times and non−identical job sizes. Comput Ind Eng 123:67–81

    Google Scholar 

  • Zhuang Y et al (2022) An Effective WSSENet−based similarity retrieval method of large lung CT image databases. KSII Transac Internet Inform Syst. https://doi.org/10.3837/tiis.2022.07.013

    Article  Google Scholar 

  • Zhuang Y, Jiang N, Xu Y (2022) Progressive distributed and parallel similarity retrieval of large CT image sequences in mobile telemedicine networks. Wirel Commun Mob Comput. https://doi.org/10.1155/2022/6458350

    Article  Google Scholar 

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Acknowledgements

This work was supported in part by the Natural Science Foundation of Zhejiang Province (LZ22F020005), National Natural Science Foundation of China (Grant Nos. 62076185, U1809209).

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  1. Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China

    Boyang Xu, Zhennao Cai & Huiling Chen

  2. School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Ali Asghar Heidari

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BX: Contributions: Writing – Original Draft, Writing – Review & Editing, Software, Visualization, Investigation. AA: Contributions: Writing – Original Draft, Writing – Review & Editing, Software, Visualization, Investigation. ZC: Contributions: Writing – Original Draft, Writing – Review & Editing, Software, Visualization, Investigation. HC: Contributions: Conceptualization, Methodology, Formal Analysis, Investigation, Writing – Review & Editing, Funding Acquisition, Supervision.

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Xu, B., Heidari, A.A., Cai, Z. et al. Dimensional decision covariance colony predation algorithm: global optimization and high−dimensional feature selection. Artif Intell Rev 56, 11415–11471 (2023). https://doi.org/10.1007/s10462-023-10412-8

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