Abstract
In this article, a novel population selection method, fitness distance balance (FDB), and predictive candidate (PC) solution generation hybridization with starling murmuration optimizer (SMO), FDBPC-SMO are proposed. In FDBPC-SMO algorithm, FDB selects subpopulations instead of the separating search strategy (SSS) in the original SMO. The separating size determined in SMO is given as input to the FDB, and the FDB generates the subpopulation based on the distances among the populations. The least squares strategy is applied to the population obtained at the end of the SMO, and the estimated population candidates are found and replaced with the worst solution candidates from the original population. By adding qualitative analysis, the effectiveness of the FDBPC-SMO has been examined based on the dimension and iteration. The success of FDBPC-SMO is the selection of more efficient candidate solutions from the previous population at each iteration, thus minimizing the possibility of getting stuck in the local optimum. The performance of FDBPC-SMO has been investigated on CEC2017 and CEC2019 test sets and seven engineering application problems. In addition, Wilcoxon and Friedman statistical tests confirm the convergence and fitness results of the proposed method. Accordingly, comparing to conventional and improved methods, it is clear that the convergence ability of FDBPC-SMO is superior.
Similar content being viewed by others
Data Availability Statement
Source codes used in analyzing the datasets are available from the corresponding author upon reasonable request.
References
Chong EK, Żak SH (2013) An introduction to optimization, vol 75. Wiley, New York
Ruder S (2016) An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747
Dantzig GB (2002) Linear programming. Oper Res 50(1):42–47
Nocedal J, Wright SJ (2006) Quadratic programming. Numer Optim, pp 448–492
Bellman R (1966) Dynamic programming. Science 153(3731):34–37
Lydia A, Francis S (2019) Adagrad-an optimizer for stochastic gradient descent. Int J Inf Comput Sci 6(5):566–568
Zeiler MD (2012) Adadelta: an adaptive learning rate method. arXiv preprint arXiv:1212.5701
Mirjalili S (2016) Sca: a sine cosine algorithm for solving optimization problems. Knowl-Based Syst 96:120–133
Hashim FA, Houssein EH, Mabrouk MS, Al-Atabany W, Mirjalili S (2019) Henry gas solubility optimization: a novel physics-based algorithm. Futur Gener Comput Syst 101:646–667
Sörensen K, Glover F (2013) Metaheuristics. Encyclopedia Oper Res Manage Sci 62:960–970
Chong HY, Yap HJ, Tan SC, Yap KS, Wong SY (2021) Advances of metaheuristic algorithms in training neural networks for industrial applications. Soft Comput 25(16):11209–11233
Zhang H, Nguyen H, Bui X-N, Pradhan B, Mai N-L, Vu D-A (2021) Proposing two novel hybrid intelligence models for forecasting copper price based on extreme learning machine and meta-heuristic algorithms. Resour Policy 73:102195
Karim AM (2022) A new sparse auto-encoder based framework using grey wolf optimizer for data classification problem. arXiv preprint arXiv:2201.12493
Abd Elaziz M, Dahou A, Abualigah L, Yu L, Alshinwan M, Khasawneh AM, Lu S (2021) Advanced metaheuristic optimization techniques in applications of deep neural networks: a review. Neural Comput Appl 33(21):14079–14099
Lin L, Gen M (2009) Auto-tuning strategy for evolutionary algorithms: balancing between exploration and exploitation. Soft Comput 13(2):157–168
Örnek BN, Aydemir SB, Düzenli T, Özak B (2022) A novel version of slime mould algorithm for global optimization and real world engineering problems: enhanced slime mould algorithm. Math Comput Simul 198:253–288
Ho Y-C, Pepyne DL (2002) Simple explanation of the no-free-lunch theorem and its implications. J Optim Theory Appl 115(3):549–570
Piotrowski AP, Napiorkowski JJ (2018) Step-by-step improvement of jade and shade-based algorithms: Success or failure? Swarm Evol Comput 43:88–108
Cui L, Li G, Zhu Z, Lin Q, Wong K-C, Chen J, Lu N, Lu J (2018) Adaptive multiple-elites-guided composite differential evolution algorithm with a shift mechanism. Inf Sci 422:122–143
Torabi S, Safi-Esfahani F (2018) Improved raven roosting optimization algorithm (irro). Swarm Evol Comput 40:144–154
Jana B, Mitra S, Acharyya S (2019) Repository and mutation based particle swarm optimization (rmpso): A new pso variant applied to reconstruction of gene regulatory network. Appl Soft Comput 74:330–355
Ali MZ, Awad NH, Reynolds RG, Suganthan PN (2018) A balanced fuzzy cultural algorithm with a modified levy flight search for real parameter optimization. Inf Sci 447:12–35
Gao W-f, Liu S-y, Huang L-l (2013) A novel artificial bee colony algorithm based on modified search equation and orthogonal learning. IEEE Trans Cybern 43(3):1011–1024
Huang Q, Zhang K, Song J, Zhang Y, Shi J (2019) Adaptive differential evolution with a lagrange interpolation argument algorithm. Inf Sci 472:180–202
Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization. Swarm Intell 1(1):33–57
Abualigah L, Yousri D, Abd Elaziz M, Ewees AA, Al-Qaness MA, Gandomi AH (2021) Aquila optimizer: a novel meta-heuristic optimization algorithm. Comput Ind Eng 157:107250
Al-Khateeb B, Ahmed K, Mahmood M, Le D-N (2021) Rock hyraxes swarm optimization: a new nature-inspired metaheuristic optimization algorithm. Comput Mater Continua 68(1):643–654
Yuan Y, Ren J, Wang S, Wang Z, Mu X, Zhao W (2022) Alpine skiing optimization: a new bio-inspired optimization algorithm. Adv Eng Softw 170:103158
Zhong C, Li G, Meng Z (2022) Beluga whale optimization: a novel nature-inspired metaheuristic algorithm. Knowl-Based Syst 109215
Chopra N, Ansari MM (2022) Golden jackal optimization: a novel nature-inspired optimizer for engineering applications. Expert Syst Appl 198:116924
Hashim FA, Hussien AG (2022) Snake optimizer: a novel meta-heuristic optimization algorithm. Knowl-Based Syst 242:108320
Zamani H, Nadimi-Shahraki MH, Gandomi AH (2022) Starling murmuration optimizer: a novel bio-inspired algorithm for global and engineering optimization. Comput Methods Appl Mech Eng 392:114616
Whitley D (1994) A genetic algorithm tutorial. Stat Comput 4(2):65–85
Storn R (1996) On the usage of differential evolution for function optimization. Proceedings of North American Fuzzy Information Processing, pp 519–523. IEEE
De Castro LN, Von Zuben FJ (2000) The clonal selection algorithm with engineering applications. In: Proceedings of GECCO, vol 2000, pp 36–39
Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713
Sulaiman MH, Mustaffa Z, Saari MM, Daniyal H (2020) Barnacles mating optimizer: a new bio-inspired algorithm for solving engineering optimization problems. Eng Appl Artif Intell 87:103330
Hu Z, Gao C, Su Q (2021) A novel evolutionary algorithm based on even difference grey model. Expert Syst Appl 176:114898
Feng Z-K, Niu W-J, Liu S (2021) Cooperation search algorithm: a novel metaheuristic evolutionary intelligence algorithm for numerical optimization and engineering optimization problems. Appl Soft Comput 98:106734
Shi Y (2011) Brain storm optimization algorithm. In: International Conference in Swarm Intelligence, pp 303–309. Springer
Rao RV, Savsani VJ, Vakharia D (2011) Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303–315
Askari Q, Younas I, Saeed M (2020) Political optimizer: a novel socio-inspired meta-heuristic for global optimization. Knowl-Based Syst 195:105709
Emami H (2022) Stock exchange trading optimization algorithm: a human-inspired method for global optimization. J Supercomput 78(2):2125–2174
Jahangiri M, Hadianfard MA, Najafgholipour MA, Jahangiri M, Gerami MR (2020) Interactive autodidactic school: a new metaheuristic optimization algorithm for solving mathematical and structural design optimization problems. Comput Struct 235:106268
Bouchekara H (2020) Most valuable player algorithm: a novel optimization algorithm inspired from sport. Oper Res Int J 20(1):139–195
Kashan AH (2009) League championship algorithm: a new algorithm for numerical function optimization. In: 2009 International Conference of Soft Computing and Pattern Recognition, pp 43–48. IEEE
Salih SQ, Alsewari AA (2020) A new algorithm for normal and large-scale optimization problems: nomadic people optimizer. Neural Comput Appl 32(14):10359–10386
Kahraman HT, Aras S, Gedikli E (2020) Fitness-distance balance (fdb): a new selection method for meta-heuristic search algorithms. Knowl-Based Syst 190:105169
Ahmadianfar I, Bozorg-Haddad O, Chu X (2020) Gradient-based optimizer: a new metaheuristic optimization algorithm. Inf Sci 540:131–159
Abualigah L, Diabat A, Mirjalili S, Abd Elaziz M, Gandomi AH (2021) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng 376:113609
Ahmadianfar I, Heidari AA, Gandomi AH, Chu X, Chen H (2021) Run beyond the metaphor: an efficient optimization algorithm based on Runge-Kutta method. Expert Syst Appl 181:115079
Ahmadianfar I, Heidari AA, Noshadian S, Chen H, Gandomi AH (2022) Info: an efficient optimization algorithm based on weighted mean of vectors. Expert Syst Appl 195:116516
Tayarani NM-H, Akbarzadeh-TM (2008) Magnetic optimization algorithms a new synthesis. In: 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp 2659–2664. IEEE
Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) Gsa: a gravitational search algorithm. Inf Sci 179(13):2232–2248
Abualigah L (2020) Multi-verse optimizer algorithm: a comprehensive survey of its results, variants, and applications. Neural Comput Appl 32(16):12381–12401
Kaveh A, Bakhshpoori T (2016) Water evaporation optimization: a novel physically inspired optimization algorithm. Comput Struct 167:69–85
Kaveh A, Akbari H, Hosseini SM (2020) Plasma generation optimization: a new physically-based metaheuristic algorithm for solving constrained optimization problems. Eng Comput
Zitouni F, Harous S, Maamri R (2020) The solar system algorithm: a novel metaheuristic method for global optimization. IEEE Access 9:4542–4565
Hashim FA, Hussain K, Houssein EH, Mabrouk MS, Al-Atabany W (2021) Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems. Appl Intell 51(3):1531–1551
Lam A, Li VO (2012) Chemical reaction optimization: a tutorial. Memetic Comput 4(1):3–17
Zhao W, Wang L, Zhang Z (2019) Atom search optimization and its application to solve a hydrogeologic parameter estimation problem. Knowl-Based Syst 163:283–304
Wei Z, Huang C, Wang X, Han T, Li Y (2019) Nuclear reaction optimization: a novel and powerful physics-based algorithm for global optimization. IEEE Access 7:66084–66109
Rahnamayan S, Tizhoosh HR, Salama MM (2006) Opposition-based differential evolution algorithms. In: 2006 IEEE International Conference on Evolutionary Computation, pp 2010–2017. IEEE
Ewees AA, Abd Elaziz M, Houssein EH (2018) Improved grasshopper optimization algorithm using opposition-based learning. Expert Syst Appl 112:156–172
Shekhawat S, Saxena A (2020) Development and applications of an intelligent crow search algorithm based on opposition based learning. ISA Trans 99:210–230
Gupta S, Deep K (2019) A hybrid self-adaptive sine cosine algorithm with opposition based learning. Expert Syst Appl 119:210–230
Jiang H, Yang Y, Ping W, Dong Y (2020) A novel hybrid classification method based on the opposition-based seagull optimization algorithm. IEEE Access 8:100778–100790
Yu X, Xu W, Li C (2021) Opposition-based learning grey wolf optimizer for global optimization. Knowl-Based Syst 226:107139
Hussien AG (2022) An enhanced opposition-based salp swarm algorithm for global optimization and engineering problems. J Ambient Intell Humaniz Comput 13(1):129–150
Kohli M, Arora S (2018) Chaotic grey wolf optimization algorithm for constrained optimization problems. J Comput Design Eng 5(4):458–472
Kaur G, Arora S (2018) Chaotic whale optimization algorithm. J Comput Design Eng 5(3):275–284
Sayed GI, Tharwat A, Hassanien AE (2019) Chaotic dragonfly algorithm: an improved metaheuristic algorithm for feature selection. Appl Intell 49(1):188–205
Qiao W, Yang Z (2019) Modified dolphin swarm algorithm based on chaotic maps for solving high-dimensional function optimization problems. IEEE Access 7:110472–110486
Ibrahim A, Ali HA, Eid MM, El-kenawy E-SM (2020) Chaotic harris hawks optimization for unconstrained function optimization. In: 2020 16th International Computer Engineering Conference (ICENCO), pp 153–158. IEEE
Ouertani MW, Manita G, Korbaa O (2021) Chaotic lightning search algorithm. Soft Comput 25(3):2039–2055
Yıldız BS, Pholdee N, Panagant N, Bureerat S, Yildiz AR, Sait SM (2022) A novel chaotic henry gas solubility optimization algorithm for solving real-world engineering problems. Eng Comput 38(2):871–883
Onay FK, Aydemır SB (2022) Chaotic hunger games search optimization algorithm for global optimization and engineering problems. Math Comput Simul 192:514–536
Aydemır SB (2022) A novel arithmetic optimization algorithm based on chaotic maps for global optimization. Evolut Intell, pp 1–16
Zamani H, Nadimi-Shahraki MH, Gandomi AH (2021) Qana: quantum-based avian navigation optimizer algorithm. Eng Appl Artif Intell 104:104314
Chiang H-P, Chou Y-H, Chiu C-H, Kuo S-Y, Huang Y-M (2014) A quantum-inspired tabu search algorithm for solving combinatorial optimization problems. Soft Comput 18(9):1771–1781
Ganesan V, Sobhana M, Anuradha G, Yellamma P, Devi OR, Prakash KB, Naren J (2021) Quantum inspired meta-heuristic approach for optimization of genetic algorithm. Comput Electric Eng 94:107356
Wang D, Chen H, Li T, Wan J, Huang Y (2020) A novel quantum grasshopper optimization algorithm for feature selection. Int J Approx Reason 127:33–53
Agrawal R, Kaur B, Sharma S (2020) Quantum based whale optimization algorithm for wrapper feature selection. Appl Soft Comput 89:106092
Sayed GI, Darwish A, Hassanien AE (2019) Quantum multiverse optimization algorithm for optimization problems. Neural Comput Appl 31(7):2763–2780
Gao Z-M, Zhao J, et al. (2019) An improved grey wolf optimization algorithm with variable weights. Comput Intell Neurosci
Zhang Y-J, Wang Y-F, Yan Y-X, Zhao J, Gao Z-M (2022) Lmraoa: An improved arithmetic optimization algorithm with multi-leader and high-speed jumping based on opposition-based learning solving engineering and numerical problems. Alex Eng J 61(12):12367–12403
Zhao J, Gao Z-M (2022) The heterogeneous aquila optimization algorithm. Math Biosci Eng 19:5867–5904
Zhao J, Gao Z-M, Chen H-F (2022) The simplified aquila optimization algorithm. IEEE Access 10:22487–22515
AYDEMİR SB (2022) Küresel optimizasyon için gauss kaotik haritası ile kartal optimizasyonu. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 34(1), 85–104
Pant M, Thangaraj R, Abraham A (2011) De-pso: a new hybrid meta-heuristic for solving global optimization problems. New Math Natural Comput 7(03):363–381
Wang F, Luo L, He X-s, Wang Y (2011) Hybrid optimization algorithm of pso and cuckoo search. In: 2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC), pp. 1172–1175. IEEE
Mafarja MM, Mirjalili S (2017) Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing 260:302–312
Pasandideh SHR, Khalilpourazari S (2018) Sine cosine crow search algorithm: a powerful hybrid meta heuristic for global optimization. arXiv preprint arXiv:1801.08485
Gaidhane PJ, Nigam MJ (2018) A hybrid grey wolf optimizer and artificial bee colony algorithm for enhancing the performance of complex systems. J Comput Sci 27:284–302
Nenavath H, Jatoth RK (2019) Hybrid sca-tlbo: a novel optimization algorithm for global optimization and visual tracking. Neural Comput Appl 31(9):5497–5526
Zhang Z, Ding S, Jia W (2019) A hybrid optimization algorithm based on cuckoo search and differential evolution for solving constrained engineering problems. Eng Appl Artif Intell 85:254–268
Şenel FA, Gökçe F, Yüksel AS, Yiğit T (2019) A novel hybrid pso-gwo algorithm for optimization problems. Eng Comput 35(4):1359–1373
Dhiman G (2021) Ssc: a hybrid nature-inspired meta-heuristic optimization algorithm for engineering applications. Knowl-Based Syst 222:106926
Dhiman G (2021) Esa: a hybrid bio-inspired metaheuristic optimization approach for engineering problems. Eng Comput 37(1):323–353
Akyol S (2022) A new hybrid method based on aquila optimizer and tangent search algorithm for global optimization. J Ambient Intell Human Comput, pp 1–21
Sahoo SK, Saha AK (2022) A hybrid moth flame optimization algorithm for global optimization. J Bionic Eng, pp 1–22
Mahajan S, Abualigah L, Pandit AK, Altalhi M (2022) Hybrid aquila optimizer with arithmetic optimization algorithm for global optimization tasks. Soft Comput 26(10):4863–4881
Gao S, Yu Y, Wang Y, Wang J, Cheng J, Zhou M (2019) Chaotic local search-based differential evolution algorithms for optimization. IEEE Trans Syst Man Cybern Syst 51(6):3954–3967
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
Li Y, Han T, Tang S, Huang C, Zhou H, Wang Y (2023) An improved differential evolution by hybridizing with estimation-of-distribution algorithm. Inf Sci 619:439–456
Piotrowski AP (2018) L-shade optimization algorithms with population-wide inertia. Inf Sci 468:117–141
Mohamed AW, Hadi AA, Fattouh AM, Jambi KM (2017) Lshade with semi-parameter adaptation hybrid with cma-es for solving cec 2017 benchmark problems. In: 2017 IEEE Congress on Evolutionary Computation (CEC), pp 145–152. IEEE
Meng Z, Pan J-S, Tseng K-K (2019) Pade: an enhanced differential evolution algorithm with novel control parameter adaptation schemes for numerical optimization. Knowl-Based Syst 168:80–99
Layeb A (2022) Tangent search algorithm for solving optimization problems. Neural Comput Appl 34(11):8853–8884
Mehmet K, KAHRAMAN H (2020) Arz-talep tabanli optimizasyon algoritmasinin fdb yöntemi ile iyileştirilmesi: Mühendislik tasarim problemleri üzerine kapsamli bir araştirma. Mühendislik Bilimleri ve Tasarım Dergisi 8(5), 156–172(2020)
Madadi MR, Akbarifard S, Qaderi K (2020) Performance evaluation of improved symbiotic organism search algorithm for estimation of solute transport in rivers. Water Resour Manage 34(4):1453–1464
Aras S, Gedikli E, Kahraman HT (2021) A novel stochastic fractal search algorithm with fitness-distance balance for global numerical optimization. Swarm Evol Comput 61:100821
Guvenc U, Duman S, Kahraman HT, Aras S, Katı M (2021) Fitness-distance balance based adaptive guided differential evolution algorithm for security-constrained optimal power flow problem incorporating renewable energy sources. Appl Soft Comput 108:107421
SUİÇMEZ Ç, KAHRAMAN H, YILMAZ C, IŞIK MF, CENGİZ E (2021) Improved slime-mould-algorithm with fitness distance balance-based guiding mechanism for global optimization problems. Düzce Üniversitesi Bilim ve Teknoloji Dergisi 9(6), 40–54
CENGİZ E, YILMAZ C, KAHRAMAN H, SUİÇMEZ Ç Improved runge kutta optimizer with fitness distance balance-based guiding mechanism for global optimization of high-dimensional problems. Düzce Üniversitesi Bilim ve Teknoloji Dergisi 9(6), 135–149
Bakir H, Guvenc U, Kahraman HT, Duman S (2022) Improved lévy flight distribution algorithm with fdb-based guiding mechanism for avr system optimal design. Comput Ind Eng 168:108032
Tang Z, Tao S, Wang K, Lu B, Todo Y, Gao S (2022) Chaotic wind driven optimization with fitness distance balance strategy. Int J Comput Intell Syst 15(1):1–28
Oszust M, Sroka G, Cymerys K (2021) A hybridization approach with predicted solution candidates for improving population-based optimization algorithms. Inf Sci 574:133–161
Hamza F, Ferhat D, Abderazek H, Dahane M (2020) A new efficient hybrid approach for reliability-based design optimization problems. Eng Comput, pp 1–24
Rao RV, Waghmare G (2017) A new optimization algorithm for solving complex constrained design optimization problems. Eng Optim 49(1):60–83
Qais MH, Hasanien HM, Alghuwainem S (2020) Transient search optimization: a new meta-heuristic optimization algorithm. Appl Intell 50(11):3926–3941
Houssein EH, Saad MR, Hashim FA, Shaban H, Hassaballah M (2020) Lévy flight distribution: a new metaheuristic algorithm for solving engineering optimization problems. Eng Appl Artif Intell 94:103731
Gandomi AH, Yang X-S, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29(1):17–35
Çimen ME, Garip Z, Boz AF (2021) Comparison of metaheuristic optimization algorithms with a new modifieddeb feasibility constraint handling technique. Turk J Electr Eng Comput Sci 29(7):3270–3289
Aras S, Kahraman HT, Gedkli E (2018) Determination of the effects of penalty coefficient on the meta-heuristic optimization process. In: 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), IEEE
Abualigah L, Elaziz MA, Khasawneh AM, Alshinwan M, Ibrahim RA, Al-qaness MA, Mirjalili S, Sumari P, Gandomi AH (2022) Meta-heuristic optimization algorithms for solving real-world mechanical engineering design problems: a comprehensive survey, applications, comparative analysis, and results. Neural Comput Appl, pp 1–30
Pan J-S, Zhang L-G, Wang R-B, Snášel V, Chu S-C (2022) Gannet optimization algorithm: a new metaheuristic algorithm for solving engineering optimization problems. Math Comput Simul 202:343–373
Dhawale D, Kamboj VK, Anand P (2021) An effective solution to numerical and multi-disciplinary design optimization problems using chaotic slime mold algorithm. Eng Comput, pp 1–39
Zhao S, Zhang T, Ma S, Chen M (2022) Dandelion optimizer: a nature-inspired metaheuristic algorithm for engineering applications. Eng Appl Artif Intell 114:105075
Dehghani M, Trojovská E, Trojovskỳ P (2022) A new human-based metaheuristic algorithm for solving optimization problems on the base of simulation of driving training process. Sci Rep 12(1):1–21
Ma J, Xia D, Guo H, Wang Y, Niu X, Liu Z, Jiang S (2022) Metaheuristic-based support vector regression for landslide displacement prediction: a comparative study. Landslides 19(10):2489–2511
Aydemir SB (2023) Enhanced marine predator algorithm for global optimization and engineering design problems. Adv Eng Softw 184:103517
Cheng G, Lang C, Han J (2022) Holistic prototype activation for few-shot segmentation. IEEE Trans Pattern Anal Mach Intell 45(4):4650–4666
Lang C, Cheng G, Tu B, Li C, Han J (2023) Base and meta: a new perspective on few-shot segmentation. IEEE Trans Pattern Anal Mach Intell
Lang C, Cheng G, Tu B, Han J (2022) Learning what not to segment: A new perspective on few-shot segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8057–8067
Lang C, Wang J, Cheng G, Tu B, Han J (2023) Progressive parsing and commonality distillation for few-shot remote sensing segmentation. IEEE Trans Geosci Remote Sens
Funding
Not applicable.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
Author declares that they have no conflict of interest.
Ethical approval
This article does not contain any studies with human participants or animals performed by any of the authors.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Aydemir, S.B. Ideal solution candidate search for starling murmuration optimizer and its applications on global optimization and engineering problems. J Supercomput 80, 4083–4156 (2024). https://doi.org/10.1007/s11227-023-05618-0
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11227-023-05618-0