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Orchard algorithm (OA): a new meta-heuristic algorithm for solving discrete and continuous optimization problems. (English) Zbl 1540.90305

Summary: Meta-heuristic algorithms have been widely used to solve different optimization problems. There have always been ongoing efforts to develop new and efficient algorithms. In this paper, the Orchard Algorithm (OA) is designed and introduced, inspired by fruit gardening. In this process, various actions such as irrigation, fertilization, trimming, and grafting lead to a fruit orchard where most trees grow and produce fruit adequately. In OA, both explorations of the search space and exploitation of the best solutions are achieved using personal and social behavior. By introducing various operators such as annual growth, screening, and grafting, the algorithm can efficiently search and explore the search space. The performance of the proposed OA algorithm was evaluated on CEC2005, IEEE CEC06 2019, test functions, and five real-world engineering problems compared with 13 widely used and competitive algorithms. Thirty benchmark functions were used to compare the capabilities of the OA algorithm with other research. The OA yields far better results in many aspects than the other algorithms. The results show the OA’s superiority and this algorithm’s capability in solving optimization problems.

MSC:

90C59 Approximation methods and heuristics in mathematical programming
Full Text: DOI

References:

[1] Abdollahzadeh, B.; Gharehchopogh, F. S.; Mirjalili, S., African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems, Comput. Ind. Eng., 158, Article 107408 pp. (2021)
[2] Adrian, A. M.; Utamima, A.; Wang, K. J., A comparative study of GA, PSO and ACO for solving construction site layout optimization, KSCE J. Civ. Eng., 19, 3, 520-527 (2015)
[3] Aghababa, M. P.; Amrollahi, M. H.; Borjkhani, M., Application of GA, PSO, and ACO algorithms to path planning of autonomous underwater vehicles, J. Mar. Sci. Appl., 11, 3, 378-386 (2012)
[4] Alberdi, R.; Khandelwal, K., Comparison of robustness of metaheuristic algorithms for steel frame optimization, Eng. Struct., 102, 40-60 (2015)
[5] Ali, M. M.; Khompatraporn, C.; Zabinsky, Z. B., A numerical evaluation of several stochastic algorithms on selected continuous global optimization test problems, J. Global Optim., 31, 4, 635-672 (2005) · Zbl 1093.90028
[6] Alimoradi, M.; Azgomi, H.; Asghari, A., Trees social relations optimization algorithm: A new Swarm-Based metaheuristic technique to solve continuous and discrete optimization problems, Math. Comput. Simulation, 194, 629-664 (2022) · Zbl 1540.90284
[7] Alsheddy, A., Empowerment Scheduling: A Multi-Objective Optimization Approach using Guided Local Search (2011), University of Essex, (Doctoral dissertation)
[8] Atashpaz-Gargari, E.; Lucas, C., Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition, (2007 IEEE Congress on Evolutionary Computation (2007)), 4661-4667
[9] Azizi, M.; Talatahari, S.; Gandomi, A. H., Fire Hawk Optimizer: A novel metaheuristic algorithm, Artif. Intell. Rev., 1-77 (2022)
[10] Baniasadi, S.; Rostami, O.; Martín, D.; Kaveh, M., A novel deep supervised learning-based approach for intrusion detection in IoT systems, Sensors, 22, 12, 4459 (2022)
[11] Battiti, R.; Brunato, M.; Mariello, A., Reactive search optimization: learning while optimizing, (Handbook of Metaheuristics (2019)), 479-511
[12] Baykasoğlu, A.; Akpinar, Ş., Weighted Superposition Attraction (WSA): A swarm intelligence algorithm for optimization problems-Part 1: Unconstrained optimization, Appl. Soft Comput., 56, 520-540 (2017)
[13] Bejinariu, S. I.; Costin, H., A comparison of some nature-inspired optimization metaheuristics applied in biomedical image registration, Methods Inf. Med., 57, 05/06, 280-286 (2018)
[14] Braik, M.; Sheta, A.; Al-Hiary, H., A novel meta-heuristic search algorithm for solving optimization problems: capuchin search algorithm, Neural Comput. Appl., 33, 7, 2515-2547 (2021)
[15] Cai, W.; Yang, W.; Chen, X., A global optimization algorithm based on plant growth theory: plant growth optimization, (2008 International Conference on Intelligent Computation Technology and Automation (ICICTA), Vol. 1 (2008)), 1194-1199
[16] Carberry, A., How to graft a tree (2019), Available from: https://www.wikihow.com/Graft-a-Tree
[17] Carberry, A., How to plant fruit trees (2019), Available from: https://www.wikihow.com/Plant-Fruit-Trees
[18] Carson, J.; Shimizu, G.; Ingels, C.; Geisel, P. M.; Unruh, C. L., Fruit trees: Planting and care of young trees (2002)
[19] Cheng, M. Y.; Prayogo, D., Symbiotic organisms search: a new metaheuristic optimization algorithm, Comput. Struct., 139, 98-112 (2014)
[20] Cheraghalipour, A.; Hajiaghaei-Keshteli, M.; Paydar, M. M., Tree Growth Algorithm (TGA): A novel approach for solving optimization problems, Eng. Appl. Artif. Intell., 72, 393-414 (2018)
[21] Chetty, S.; Adewumi, A. O., Three new stochastic local search algorithms for continuous optimization problems, Comput. Optim. Appl., 56, 3, 675-721 (2013) · Zbl 1287.90065
[22] Civicioglu, P., Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm, Comput. Geosci., 46, 229-247 (2012)
[23] Civicioglu, P., Backtracking search optimization algorithm for numerical optimization problems, Appl. Math. Comput., 219, 15, 8121-8144 (2013) · Zbl 1288.65092
[24] Dorigo, M.; Maniezzo, V.; Colorni, A., Ant system: optimization by a colony of cooperating agents, IEEE Trans. Syst. Man Cybern. B, 26, 1, 29-41 (1996)
[25] Doğan, B.; Ölmez, T., A new metaheuristic for numerical function optimization: Vortex Search algorithm, Inform. Sci., 293, 125-145 (2015)
[26] Eberhart, R.; Kennedy, J., A new optimizer using particle swarm theory, (MHS’95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science (1995)), 39-43
[27] Ebrahimi, A.; Khamehchi, E., Sperm whale algorithm: an effective metaheuristic algorithm for production optimization problems, J. Nat. Gas Sci. Eng., 29, 211-222 (2016)
[28] Elbeltagi, E.; Hegazy, T.; Grierson, D., Comparison among five evolutionary-based optimization algorithms, Adv. Eng. Inform., 19, 1, 43-53 (2005)
[29] Eslami, N.; Yazdani, S.; Mirzaei, M.; Hadavandi, E., Aphid-Ant Mutualism: A novel nature-inspired metaheuristic algorithm for solving optimization problems, Math. Comput. Simulation (2022) · Zbl 1540.90294
[30] Ezugwu, A. E.; Adeleke, O. J.; Akinyelu, A. A.; Viriri, S., A conceptual comparison of several metaheuristic algorithms on continuous optimisation problems, Neural Comput. Appl., 32, 10, 6207-6251 (2020)
[31] Farmer, J. D.; Packard, N. H.; Perelson, A. S., The immune system, adaptation, and machine learning, Physica D, 22, 1-3, 187-204 (1986)
[32] Fogel, L. J.; Owens, A. J.; Walsh, M. J., Intelligent decision making through a simulation of evolution, Behav. Sci., 11, 4, 253-272 (1966) · Zbl 0148.40701
[33] Gandomi, A. H., Interior search algorithm (ISA): a novel approach for global optimization, ISA Trans., 53, 4, 1168-1183 (2014)
[34] Gandomi, A. H.; Alavi, A. H., Krill herd: a new bio-inspired optimization algorithm, Commun. Nonlinear Sci. Numer. Simul., 17, 12, 4831-4845 (2012) · Zbl 1266.65092
[35] Garner, G. R.J., The Grafter’s Handbook (2013), Chelsea Green Publishing
[36] Geem, Z. W.; Kim, J. H.; Loganathan, G. V., A new heuristic optimization algorithm: harmony search, Simulation, 76, 2, 60-68 (2001)
[37] Gharehchopogh, F. S.; Abdollahzadeh, B., An efficient harris hawk optimization algorithm for solving the travelling salesman problem, Cluster Comput., 25, 3, 1981-2005 (2022)
[38] Gharehchopogh, F. S.; Farnad, B.; Alizadeh, A., A modified farmland fertility algorithm for solving constrained engineering problems, Concurr. Comput.: Pract. Exper., 33, 17, Article e6310 pp. (2021)
[39] Glover, F., Heuristics for integer programming using surrogate constraints, Decis. Sci., 8, 1, 156-166 (1977)
[40] Glover, F., Future paths for integer programming and links to artificial intelligence, Comput. Oper. Res., 13, 5, 533-549 (1986) · Zbl 0615.90083
[41] Goldanloo, M. J.; Gharehchopogh, F. S., A hybrid OBL-based firefly algorithm with symbiotic organisms search algorithm for solving continuous optimization problems, J. Supercomput., 78, 3, 3998-4031 (2022)
[42] Goldschmidt, E. E., Plant grafting: new mechanisms, evolutionary implications, Front. Plant Sci., 5, 727 (2014)
[43] Haddad, O. B.; Afshar, A.; Mariño, M. A., Honey-bees mating optimization (HBMO) algorithm: a new heuristic approach for water resources optimization, Water Resour. Manag., 20, 5, 661-680 (2006)
[44] Hansen, N.; Ostermeier, A., Completely derandomized self-adaptation in evolution strategies, Evol. Comput., 9, 2, 159-195 (2001)
[45] Hanseth, O.; Aanestad, M., Bootstrapping networks, communities and infrastructures. On the evolution of ICT solutions in heath care, (Proceedings of the 1st International Conference on Information Technology in Health Care (ITHC’01) (2001))
[46] Hashim, F. A.; Houssein, E. H.; Hussain, K.; Mabrouk, M. S.; Al-Atabany, W., Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems, Math. Comput. Simulation, 192, 84-110 (2022) · Zbl 1540.90296
[47] Hastings, W. K., Monte Carlo sampling methods using Markov chains and their applications (1970) · Zbl 0219.65008
[48] Hatamlou, A., Black hole: A new heuristic optimization approach for data clustering, Inform. Sci., 222, 175-184 (2013)
[49] Hayyolalam, V.; Kazem, A. A.P., Black widow optimization algorithm: A novel meta-heuristic approach for solving engineering optimization problems, Eng. Appl. Artif. Intell., 87, Article 103249 pp. (2020)
[50] He, S.; Wu, Q. H.; Saunders, J. R., Group search optimizer: an optimization algorithm inspired by animal searching behavior, IEEE Trans. Evol. Comput., 13, 5, 973-990 (2009)
[51] Heidari, A. A.; Mirjalili, S.; Faris, H.; Aljarah, I.; Mafarja, M.; Chen, H., Harris hawks optimization: Algorithm and applications, Future Gener. Comput. Syst., 97, 849-872 (2019)
[52] Holland John, H., Adaptation in Natural and Artificial Systems (1975), University of Michigan Press: University of Michigan Press Ann Arbor · Zbl 0317.68006
[53] Hooke, R.; Jeeves, T. A., Direct SearchSolution of numerical and statistical problems, J. ACM, 8, 2, 212-229 (1961) · Zbl 0111.12501
[54] Humphrey, B. E., The Bench Grafter’s Handbook: Principles & Practice (2019), CRC Press
[55] Kadioglu, S.; Sellmann, M., Dialectic search, (International Conference on Principles and Practice of Constraint Programming (2009)), 486-500
[56] Kaidi, W.; Khishe, M.; Mohammadi, M., Dynamic levy flight chimp optimization, Knowl.-Based Syst., 235, Article 107625 pp. (2022)
[57] Karaboga, D., An Idea Based on Honey Bee Swarm for Numerical Optimization, Vol. 200Technical report-tr06, 1-10 (2005), Erciyes university, engineering faculty, computer engineering department
[58] Karci, A., Human being properties of saplings growing up algorithm, (2007 IEEE International Conference on Computational Cybernetics (2007)), 227-232
[59] Kashan, A. H., League championship algorithm: a new algorithm for numerical function optimization, (2009 International Conference of Soft Computing and Pattern Recognition (2009)), 43-48
[60] Kashan, A. H., A new metaheuristic for optimization: optics inspired optimization (OIO), Comput. Oper. Res., 55, 99-125 (2015) · Zbl 1348.90637
[61] Kashan, A. H.; Akbari, A. A.; Ostadi, B., Grouping evolution strategies: An effective approach for grouping problems, Appl. Math. Model., 39, 9, 2703-2720 (2015) · Zbl 1443.90242
[62] Kaur, S.; Awasthi, L. K.; Sangal, A. L.; Dhiman, G., Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization, Eng. Appl. Artif. Intell., 90, Article 103541 pp. (2020)
[63] Kaveh, M.; Kaveh, M.; Mesgari, M. S.; Paland, R. S., Multiple criteria decision-making for hospital location-allocation based on improved genetic algorithm, Appl. Geomat., 12, 3, 291-306 (2020)
[64] Kaveh, M.; Khishe, M.; Mosavi, M. R., Design and implementation of a neighborhood search biogeography-based optimization trainer for classifying sonar dataset using multi-layer perceptron neural network, Analog Integr. Circuits Signal Process., 100, 2, 405-428 (2019)
[65] Kaveh, A.; Mahdavi, V. R., Colliding Bodies Optimization: Extensions and Applications (2015) · Zbl 1316.90002
[66] Kaveh, M.; Mesgari, M. S., Hospital site selection using hybrid PSO algorithm-Case study: District 2 of Tehran, Sci.-Res. Q. Geogr. Data (SEPEHR), 28, 111, 7-22 (2019)
[67] Kaveh, M.; Mesgari, M. S., Improved biogeography-based optimization using migration process adjustment: An approach for location-allocation of ambulances, Comput. Ind. Eng., 135, 800-813 (2019)
[68] Kaveh, M.; Mesgari, M. S., Application of meta-heuristic algorithms for training neural networks and deep learning architectures: A comprehensive review, Neural Process. Lett., 1-104 (2022)
[69] M. Kaveh, M.S. Mesgari, A. Khosravi, Solving the local positioning problem using a four-layer artificial neural network, 7 (4) (2020) 21-40.
[70] Kernighan, B. W.; Lin, S., An efficient heuristic procedure for partitioning graphs, Bell Syst. Tech. J., 49, 2, 291-307 (1970) · Zbl 0333.05001
[71] Khishe, M.; Mosavi, M. R., Chimp optimization algorithm, Expert Syst. Appl., 149, Article 113338 pp. (2020)
[72] Kianfar, N.; Mesgari, M. S.; Mollalo, A.; Kaveh, M., Spatio-temporal modeling of COVID-19 prevalence and mortality using artificial neural network algorithms, Spat. Spatio-Temporal Epidemiol., 40, Article 100471 pp. (2022)
[73] Kirkpatrick, S.; Gelatt, C. D.; Vecchi, M. P., Optimization by simulated annealing, Science, 220, 4598, 671-680 (1983) · Zbl 1225.90162
[74] Koza, J. R., Genetic programming as a means for programming computers by natural selection, Stat. Comput., 4, 2, 87-112 (1994)
[75] Krishnanand, K. N.; Ghose, D., Glowworm swarm based optimization algorithm for multimodal functions with collective robotics applications, Multiagent Grid Syst., 2, 3, 209-222 (2006) · Zbl 1116.65068
[76] Labbi, Y.; Attous, D. B.; Gabbar, H. A.; Mahdad, B.; Zidan, A., A new rooted tree optimization algorithm for economic dispatch with valve-point effect, Int. J. Electr. Power Energy Syst., 79, 298-311 (2016)
[77] Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation, Vol. 2 (2001), Springer Science & Business Media
[78] Li, Q. Q.; Song, K.; He, Z. C.; Li, E.; Cheng, A. G.; Chen, T., The artificial tree (AT) algorithm, Eng. Appl. Artif. Intell., 65, 99-110 (2017)
[79] Liang, Y. C.; Cuevas Juarez, J. R., A novel metaheuristic for continuous optimization problems: Virus optimization algorithm, Eng. Optim., 48, 1, 73-93 (2016)
[80] Matyas, J., Random optimization, Autom. Remote Control, 26, 2, 246-253 (1965) · Zbl 0151.22802
[81] Mehrabian, A. R.; Lucas, C., A novel numerical optimization algorithm inspired from weed colonization, Ecol. Inform., 1, 4, 355-366 (2006)
[82] Merrikh-Bayat, F., The runner-root algorithm: a metaheuristic for solving unimodal and multimodal optimization problems inspired by runners and roots of plants in nature, Appl. Soft Comput., 33, 292-303 (2015)
[83] Mirhajianmoghadam, H.; Akbarzadeh-T, M. R.; Lotfi, E., A harmonic emotional neural network for non-linear system identification, (2016 24th Iranian Conference on Electrical Engineering. 2016 24th Iranian Conference on Electrical Engineering, ICEE (2016)), 1260-1265
[84] Mirhajianmoghadam, H.; Ghasemi, S. M., Efficient parameter selection for scaled trust-region Newton algorithm in solving bound-constrained nonlinear systems (2020), arXiv preprint arXiv:2009.04354
[85] Mirjalili, S., Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems, Neural Comput. Appl., 27, 4, 1053-1073 (2016)
[86] Mirjalili, S., SCA: a sine cosine algorithm for solving optimization problems, Knowl.-Based Syst., 96, 120-133 (2016)
[87] Mirjalili, S.; Gandomi, A. H.; Mirjalili, S. Z.; Saremi, S.; Faris, H.; Mirjalili, S. M., Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems, Adv. Eng. Softw., 114, 163-191 (2017)
[88] Mladenović, N.; Hansen, P., Variable neighborhood search, Comput. Oper. Res., 24, 11, 1097-1100 (1997) · Zbl 0889.90119
[89] Mortazavi, A.; Toğan, V.; Nuhoğlu, A., Interactive search algorithm: a new hybrid metaheuristic optimization algorithm, Eng. Appl. Artif. Intell., 71, 275-292 (2018)
[90] Moscato, P., On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts: Towards Memetic Algorithms (1989), Caltech concurrent computation program, C3P Report, 826
[91] Mucherino, A.; Seref, O., Monkey search: a novel metaheuristic search for global optimization, (AIP Conference Proceedings, Vol. 953 (2007), American Institute of Physics), 162-173, (1)
[92] K. Mudge, J. Janick, S. Scofield, E.E. Goldschmidt, A history of grafting, 35 (9) (2009).
[93] Murase, H., Finite element inverse analysis using a photosynthetic algorithm, Comput. Electron. Agric., 29, 1-2, 115-123 (2000)
[94] Najarian, M.; Sarmast, Z.; Ghasemi, S. M.; Sarmadi, S., Evolutionary vertical size reduction: A novel approach for big data computing, Int. J. Math. Appl., 6, 3, 215-225 (2018)
[95] Nakrani, S.; Tovey, C., On honey bees and dynamic server allocation in internet hosting centers, Adapt. Behav., 12, 3-4, 223-240 (2004)
[96] Olsen, J.; Valley, W.; Nina Azarenko, A., Growing Tree Fruits (2012), Master Gardener Publications
[97] Pan, J. S.; Zhang, L. G.; Wang, R. B.; Snášel, V.; Chu, S. C., Gannet optimization algorithm: A new metaheuristic algorithm for solving engineering optimization problems, Math. Comput. Simulation (2022) · Zbl 1540.90317
[98] Parker, M. L., Producing Tree Fruit for Home Use (1993), AG (USA)
[99] Pham, D. T.; Ghanbarzadeh, A.; Koç, E.; Otri, S.; Rahim, S.; Zaidi, M., The bees algorithm—a novel tool for complex optimisation problems, (Intelligent Production Machines and Systems (2006)), 454-459
[100] Premaratne, U.; Samarabandu, J.; Sidhu, T., A new biologically inspired optimization algorithm, (2009 International Conference on Industrial and Information Systems. 2009 International Conference on Industrial and Information Systems, ICIIS (2009)), 279-284
[101] Puchinger, J.; Raidl, G. R., Combining metaheuristics and exact algorithms in combinatorial optimization: A survey and classification, (International Work-Conference on the Interplay Between Natural and Artificial Computation (2005)), 41-53
[102] Qi, X.; Zhu, Y.; Chen, H.; Zhang, D.; Niu, B., An idea based on plant root growth for numerical optimization, (International Conference on Intelligent Computing (2013)), 571-578
[103] Rajabioun, R., Cuckoo optimization algorithm, Appl. Soft Comput., 11, 8, 5508-5518 (2011)
[104] Rashedi, E.; Nezamabadi-Pour, H.; Saryazdi, S., GSA: a gravitational search algorithm, Inform. Sci., 179, 13, 2232-2248 (2009) · Zbl 1177.90378
[105] Rastrigin, L. A., The convergence of the random search method in the extremal control of a many parameter system, Autom. Remote Control, 24, 1337-1342 (1963)
[106] Rodríguez, N.; Gupta, A.; Zabala, P. L.; Cabrera-Guerrero, G., Optimization algorithms combining (meta) heuristics and mathematical programming and its application in engineering, Math. Probl. Eng. (2018)
[107] Rostami, O.; Kaveh, M., Optimal feature selection for SAR image classification using biogeography-based optimization (BBO), artificial bee colony (ABC) and support vector machine (SVM): a combined approach of optimization and machine learning, Comput. Geosci., 25, 3, 911-930 (2021) · Zbl 1460.86004
[108] Rubinstein, R. Y., Optimization of computer simulation models with rare events, European J. Oper. Res., 99, 1, 89-112 (1997) · Zbl 0923.90051
[109] Sadeghi, F.; Rostami, O.; Yi, M. K.; Hwang, S. O., A deep learning approach for detecting Covid-19 using the chest X-ray images, Cmc-Comput. Mater. Contin., 74, 1, 751-768 (2023)
[110] Sadollah, A.; Bahreininejad, A.; Eskandar, H.; Hamdi, M., Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems, Appl. Soft Comput., 13, 5, 2592-2612 (2013)
[111] Saeidian, B.; Mesgari, M. S.; Ghodousi, M., Optimum allocation of water to the cultivation farms using Genetic Algorithm, Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., 40, 1, 631 (2015)
[112] Saeidian, B.; Mesgari, M. S.; Ghodousi, M., Evaluation and comparison of Genetic Algorithm and Bees Algorithm for location-allocation of earthquake relief centers, Int. J. Disaster Risk Reduct., 15, 94-107 (2016)
[113] Saeidian, B.; Mesgari, M. S.; Pradhan, B.; Alamri, A. M., Irrigation water allocation at farm level based on temporal cultivation-related data using meta-heuristic optimisation algorithms, Water, 11, 12, 2611 (2019)
[114] Saeidian, B.; Mesgari, M. S.; Pradhan, B.; Ghodousi, M., Optimized location-allocation of earthquake relief centers using PSO and ACO, complemented by GIS, clustering, and TOPSIS, ISPRS Int. J. Geo-Inf., 7, 8, 292 (2018)
[115] Salhi, A.; Fraga, E. S., Nature-inspired optimisation approaches and the new plant propagation algorithm (2011)
[116] Salimi, H., Stochastic fractal search: a powerful metaheuristic algorithm, Knowl.-Based Syst., 75, 1-18 (2015)
[117] Shah-Hosseini, H., The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm, Int. J. Bio-Inspired Comput., 1, 1-2, 71-79 (2009)
[118] Shah-Hosseini, H., Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation, Int. J. Comput. Sci. Eng., 6, 1-2, 132-140 (2011)
[119] Shayanfar, H.; Gharehchopogh, F. S., Farmland fertility: A new metaheuristic algorithm for solving continuous optimization problems, Appl. Soft Comput., 71, 728-746 (2018)
[120] Simon, D., Biogeography-based optimization, IEEE Trans. Evol. Comput., 12, 6, 702-713 (2008)
[121] Srinivas, N.; Deb, K., Muiltiobjective optimization using nondominated sorting in genetic algorithms, Evol. Comput., 2, 3, 221-248 (1994)
[122] Storn, R. S.; Price, K., Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces, J. Global Optim., 11, 4, 341-359 (1997) · Zbl 0888.90135
[123] P.N. Suganthan, N. Hansen, J.J. Liang, K. Deb, Y.P. Chen, A. Auger, S. Tiwari, Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization, KanGAL report, 2005, 2005.
[124] Talatahari, S.; Azizi, M.; Tolouei, M.; Talatahari, B.; Sareh, P., Crystal Structure Algorithm (CryStAl): A metaheuristic optimization method, IEEE Access, 9, 71244-71261 (2021)
[125] Tamura, K.; Yasuda, K., Spiral dynamics inspired optimization, J. Adv. Comput. Intell. Intell. Inform., 15, 8, 1116-1122 (2011)
[126] Teodorović, D., Bee colony optimization (BCO), (Innovations in Swarm Intelligence (2009)), 39-60
[127] Wang, C.; Cheng, H. Z.; Hu, Z. C.; Wang, Y., Distribution system optimization planning based on plant growth simulation algorithm, J. Shanghai Jiaotong Univ. (Science), 13, 4, 462-467 (2008) · Zbl 1229.90288
[128] Wang, G. G.; Deb, S.; Coelho, L. D.S., Elephant herding optimization, (2015 3rd International Symposium on Computational and Business Intelligence. 2015 3rd International Symposium on Computational and Business Intelligence, ISCBI (2015)), 1-5
[129] Wang, J.; Khishe, M.; Kaveh, M.; Mohammadi, H., Binary chimp optimization algorithm (BChOA): A new binary meta-heuristic for solving optimization problems, Cogn. Comput., 13, 5, 1297-1316 (2021)
[130] Wierstra, D.; Schaul, T.; Glasmachers, T.; Sun, Y.; Peters, J.; Schmidhuber, J., Natural evolution strategies, J. Mach. Learn. Res., 15, 1, 949-980 (2014) · Zbl 1318.68159
[131] Yang, X. S., Firefly algorithms for multimodal optimization, (International Symposium on Stochastic Algorithms (2009)), 169-178 · Zbl 1260.90164
[132] Yang, X. S., A new metaheuristic bat-inspired algorithm, (Nature Inspired Cooperative Strategies for Optimization (NICSO 2010) (2010)), 65-74 · Zbl 1197.90348
[133] Yang, X. S., Flower pollination algorithm for global optimization, (International Conference on Unconventional Computing and Natural Computation (2012)), 240-249 · Zbl 1374.68527
[134] Yang, X. S.; Deb, S., Cuckoo search via Lévy flights, (2009 World Congress on Nature & Biologically Inspired Computing (NaBIC) (2009)), 210-214
[135] Yapici, H.; Cetinkaya, N., A new meta-heuristic optimizer: pathfinder algorithm, Appl. Soft Comput., 78, 545-568 (2019)
[136] Zhang, H.; Zhu, Y.; Chen, H., Root growth model: a novel approach to numerical function optimization and simulation of plant root system, Soft Comput., 18, 3, 521-537 (2014)
[137] Zhao, Z.; Cui, Z.; Zeng, J.; Yue, X., Artificial plant optimization algorithm for constrained optimization problems, (2011 S International Conference on Innovations in Bio-Inspired Computing and Applications (2011)), 120-123
[138] Zheng, Y. J., Water wave optimization: a new nature-inspired metaheuristic, Comput. Oper. Res., 55, 1-11 (2015) · Zbl 1348.90652
[139] Zheng, Y. J.; Chen, S. Y.; Ling, H. F., Evolutionary optimization for disaster relief operations: A survey, Appl. Soft Comput., 27, 553-566 (2015)
[140] Zhou, Y. Z.; Wang, Y.; Chen, X.; Zhang, L.; Wu, K., A novel path planning algorithm based on plant growth mechanism, Soft Comput., 21, 2, 435-445 (2017)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.