Crayfish optimization algorithm
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Abedinpourshotorban H, Shamsuddin SM, Beheshti Z, Jawawi DN (2016) Electromagnetic field optimization: a physics - inspired metaheuristic optimization algorithm. Swarm Evol Comput 26:8–22. https://doi.org/10.1016/j.swevo.2015.07.002
Abualigah L, Diabat A, Mirjalili S, Abd Elaziz M, Gandomi AH (2021a) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng 376:113609. https://doi.org/10.1016/j.cma.2020.113609
Abualigah L, Yousri D, Abd Elaziz M, Ewees AA, Al - Qaness MA, Gandomi AH (2021b) Aquila optimizer: a novel meta - heuristic optimization algorithm. Comput Ind Eng 157:107250. https://doi.org/10.1016/j.cie.2021.107250
Abualigah L, Abd Elaziz M, Sumari P, Geem ZW, Gandomi AH (2022) Reptile search algorithm (RSA): a nature - inspired meta - heuristic optimizer. Expert Syst Appl 191:116158. https://doi.org/10.1016/j.eswa.2021.116158
Allan EL, Froneman PW, Hodgson AN (2006) Effects of temperature and salinity on the standard metabolic rate (SMR) of the caridean shrimp Palaemon peringueyi. J Exp Mar Biol Ecol 337(1):103–108. https://doi.org/10.1016/j.jembe.2006.06.006
Alsattar HA, Zaidan AA, Zaidan BB (2020) Novel meta - heuristic bald eagle search optimisation algorithm. Artif Intell Rev 53:2237–2264. https://doi.org/10.1007/s10462-019-09732-5
Babalik A, Cinar AC, Kiran MS (2018) A modification of tree - seed algorithm using Deb’s rules for constrained optimization. Appl Soft Comput 63:289–305. https://doi.org/10.1016/j.asoc.2017.10.013
Banzhaf W, Koza JR, Ryan C, Spector L, Jacob C (2000) Genetic programming. IEEE Intell Syst their Appl 15(3):74–84. https://ieeexplore.ieee.org/abstract/document/846288
Baykasoğlu A, Akpinar Ş (2015) Weighted superposition attraction (WSA): a swarm intelligence algorithm for optimization problems–Part 2: constrained optimization. Appl Soft Comput 37:396–415. https://doi.org/10.1016/j.asoc.2015.08.052
Baykasoğlu A, Ozsoydan FB (2015) Adaptive firefly algorithm with chaos for mechanical design optimization problems. Appl Soft Comput 36:152–164. https://doi.org/10.1016/j.asoc.2015.06
Bellman KL, Krasne FB (1983) Adaptive complexity of interactions between feeding and escape in crayfish. Science 221(4612):779–781
Berrill M, Chenoweth B (1982) The burrowing ability of nonburrowing crayfish. Am Midl Nat. https://doi.org/10.2307/2425310
Beyer HG, Schwefel HP (2002) Evolution strategies–a comprehensive introduction. Nat Comput 1:3–52. https://doi.org/10.1023/A:1015059928466
Braik M, Hammouri A, Atwan J, Al - Betar MA, Awadallah MA (2022) White shark optimizer: a novel bio - inspired meta - heuristic algorithm for global optimization problems. Knowl Based Syst 243:108457. https://doi.org/10.1016/j.knosys.2022.108457
Chen H, Chen L, Zhang G (2022) Block - structured integer programming: can we parameterize without the largest coefficient? Discrete Optim 46:100743. https://doi.org/10.1016/j.disopt.2022.100743
Cheng S, Qin Q, Chen J, Shi Y (2016) Brain storm optimization algorithm: a review. Artif Intell Rev 46:445–458. https://doi.org/10.1007/s10462-016-9471-0
Chickermane HE, M. I. A. N. T, Gea HC (1996) Structural optimization using a new local approximation method. Int J Numer Methods Eng 39(5):829–846.
Crandall KA, De Grave S (2017) An updated classification of the freshwater crayfishes (Decapoda: Astacidea) of the world, with a complete species list. J Crustac Biol 37(5):615–653. https://doi.org/10.1093/jcbiol/rux070
Dantzig GB (2002) Linear programming. Oper Res 50(1):42–47. https://doi.org/10.1287/opre.50.1.42.17798
Daryalal M, Bodur M, Luedtke JR (2022) Lagrangian dual decision rules for multistage stochastic mixed-integer programming. Operations Res. https://doi.org/10.1287/opre.2022.2366
Das M, Roy A, Maity S, Kar S, Sengupta S (2022) Solving fuzzy dynamic ship routing and scheduling problem through new genetic algorithm. Decis Making: Appl Manage Eng 5(2):329–361. https://doi.org/10.31181/dmame181221030d
Dhiman G, Kumar V (2017) Spotted hyena optimizer: a novel bio - inspired based metaheuristic technique for engineering applications. Adv Eng Softw 114:48–70. https://doi.org/10.1016/j.advengsoft.2017.05.014
Dhiman G, Kaur A (2019) STOA: a bio - inspired based optimization algorithm for industrial engineering problems. Eng Appl Artif Intell 82:148–174. https://doi.org/10.1016/j.engappai.2019.03.021
Dhiman G, Kumar V (2019) Seagull optimization algorithm: theory and its applications for large - scale industrial engineering problems. Knowl Based Syst 165:169–196. https://doi.org/10.1016/j.knosys.2018.11.024
Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1(4):28–39. https://ieeexplore.ieee.org/abstract/document/4129846
Ezugwu AE, Shukla AK, Nath R, Akinyelu AA, Agushaka JO, Chiroma H, Muhuri PK (2021) Metaheuristics: a comprehensive overview and classification along with bibliometric analysis. Artif Intell Rev 54:4237–4316. https://doi.org/10.1007/s10462-020-09952-0
Ezugwu AE, Agushaka JO, Abualigah L, Mirjalili S, Gandomi AH (2022) Prairie dog optimization algorithm. Neural Comput Appl 34(22):20017–20065. https://doi.org/10.1007/s00521-022-07530-9
Florey CL, Moore PA (2019) Analysis and description of burrow structure in four species of freshwater crayfishes (Decapoda: Astacoidea: Cambaridae) using photogrammetry to recreate casts as 3D models. J Crustacean Biology 39(6):711–719. https://doi.org/10.1093/jcbiol/ruz075
García - Guerrero M, Hernández - Sandoval P, Orduña - Rojas J, Cortés - Jacinto E (2013) Effect of temperature on weight increase, survival, and thermal preference of juvenile redclaw crayfish Cherax quadricarinatus. Hidrobiológica 23(1):73–81
Gandomi AH, Yang XS, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng With Comput 29:17–35. https://doi.org/10.1007/s00366-011-0241-y
Gautier A, Granot F (1994) On the equivalence of constrained and unconstrained flows. Discrete Appl Math 55(2):113–132. https://doi.org/10.1016/0166-218X(94)90003-5
Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68. https://doi.org/10.1177/003754970107600201
Graham ZA, Stubbs MB, Loughman ZJ (2022) Digging ability and digging performance in a hyporheic gravel - dwelling crayfish, the hairy crayfish Cambarus friaufi (Hobbs 1953)(Decapoda: Astacidae: Cambaridae). J Crustac Biol 42(1):ruac002. https://doi.org/10.1093/jcbiol/ruac002
Hatamlou A (2013) Black hole: a new heuristic optimization approach for data clustering. Inf Sci 222:175–184. https://doi.org/10.1016/j.ins.2012.08.023
Hashim FA, Hussien AG (2022) Snake optimizer: a novel meta - heuristic optimization algorithm. Knowl Based Syst 242:108320. https://doi.org/10.1016/j.knosys.2022.108320
Hashim FA, Houssein EH, Mabrouk MS, Al - Atabany W, Mirjalili S (2019) Henry gas solubility optimization: a novel physics - based algorithm. Future Gener Computer Syst 101:646–667. https://doi.org/10.1016/j.future.2019.07.015
Hashim FA, Houssein EH, Hussain K, Mabrouk MS, Al - Atabany W (2022) Honey badger algorithm: new metaheuristic algorithm for solving optimization problems. Math Comput Simul 192:84–110. https://doi.org/10.1016/j.matcom.2021.08.013
Hayyolalam V, Kazem AAP (2020) Black widow optimization algorithm: a novel meta - heuristic approach for solving engineering optimization problems. Eng Appl Artif Intell 87:103249. https://doi.org/10.1016/j.engappai.2019.103249
He Q, Wang L (2007) An effective co - evolutionary particle swarm optimization for constrained engineering design problems. Eng Appl Artif Intell 20(1):89–99. https://doi.org/10.1016/j.engappai.2006.03.003
Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Future Gener Computer Syst 97:849–872. https://doi.org/10.1016/j.future.2019.02.028
Jaderyan M, Khotanlou H (2016) Virulence optimization algorithm. Appl Soft Comput 43:596–618. https://doi.org/10.1016/j.asoc.2016.02.038
Jia H, Peng X, Lang C (2021) Remora optimization algorithm. Expert Syst Appl 185:115665. https://doi.org/10.1016/j.eswa.2021.115665
Jia H, Sun K, Li Y, Cao N (2022a) Improved marine predators algorithm for feature selection and SVM optimization. KSII Trans Internet Inform Syst (TIIS) 16(4):1128–1145. https://doi.org/10.3837/tiis.2022.04.003
Jia H, Zhang W, Zheng R, Wang S, Leng X, Cao N (2022b) Ensemble mutation slime mould algorithm with restart mechanism for feature selection. Int J Intell Syst 37(3):2335–2370. https://doi.org/10.1002/int.22776
Jones CM, Ruscoe IM (2001) Assessment of five shelter types in the production of redclaw crayfish Cherax quadricarinatus (Decapoda: Parastacidae) under earthen pond conditions. J World Aquaculture Soc 32(1):41–52. https://doi.org/10.1111/j.1749-7345.2001.tb00920.x
Kamboj VK, Nandi A, Bhadoria A, Sehgal S (2020) An intensify Harris Hawks optimizer for numerical and engineering optimization problems. Appl Soft Comput 89:106018. https://doi.org/10.1016/j.asoc.2019.106018
Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Global Optim 39:459–471. https://doi.org/10.1007/s10898-007-9149-x
Kaveh A, Khayatazad M (2012) A new meta - heuristic method: ray optimization. Comput Struct 112:283–294. https://doi.org/10.1016/j.compstruc.2012.09.003
Kaveh A, Dadras A (2017) A novel meta - heuristic optimization algorithm: thermal exchange optimization. Adv Eng Softw 110:69–84. https://doi.org/10.1016/j.advengsoft.2017.03.014
Kennedy J, Eberhart R (1995) Particle swarm optimization. In Proceedings of ICNN'95 - international conference on neural networks (vol 4, pp 1942–1948). IEEE. https://ieeexplore.ieee.org/abstract/document/488968
Khishe M, Mosavi MR (2020) Chimp optimization algorithm. Expert Syst Appl 149:113338. https://doi.org/10.1016/j.eswa.2020.113338
Kirkpatrick S, Gelatt CD Jr, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680
Kouba A, Petrusek A, Kozák P (2014) Continental - wide distribution of crayfish species in Europe: update and maps. Knowl Manage Aquat Ecosyst. https://doi.org/10.1051/kmae/2014007
Larson ER, Olden JD (2011) The state of crayfish in the Pacific Northwest. Fisheries 36(2):60–73. https://doi.org/10.1577/03632415.2011.10389069
Liu Q, Li N, Jia H, Qi Q, Abualigah L (2022) Modified remora optimization algorithm for global optimization and multilevel thresholding image segmentation. Mathematics 10(7):1014. https://doi.org/10.3390/math10071014
Ma C, Huang H, Fan Q, Wei J, Du Y, Gao W (2022) Grey wolf optimizer based on aquila exploration method. Expert Syst Appl 205:117629. https://doi.org/10.1016/j.eswa.2022.117629
Ma B, Hu Y, Lu P, Liu Y (2023) Running City game optimizer: a game - based metaheuristic optimization algorithm for global optimization. J Comput Des Eng 10(1):65–107. https://doi.org/10.1093/jcde/qwac131
Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98. https://doi.org/10.1016/j.advengsoft.2015.01.010
Mirjalili S (2016a) Dragonfly algorithm: a new meta - heuristic optimization technique for solving single - objective, discrete, and multi - objective problems. Neural Comput Appl 27:1053–1073. https://doi.org/10.1007/s00521-015-1920-1
Mirjalili S (2016b) SCA: a sine cosine algorithm for solving optimization problems. Knowl Based Syst 96:120–133. https://doi.org/10.1016/j.knosys.2015.12.022
Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008
Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi - verse optimizer: a nature - inspired algorithm for global optimization. Neural Comput Appl 27:495–513. https://doi.org/10.1007/s00521-015-1870-7
Mzili T, Riffi ME, Mzili I, Dhiman G (2022) A novel discrete rat swarm optimization (DRSO) algorithm for solving the traveling salesman problem. Decis making: Appl Manage Eng 5(2):287–299. https://doi.org/10.31181/dmame0318062022m
Mzili I, Mzili T, Riffi ME (2023) Efficient routing optimization with discrete penguins search algorithm for MTSP. Decis Making: Appl Manage Eng 6(1):730–743. https://doi.org/10.31181/dmame04092023m
Payette AL, McGaw IJ (2003) Thermoregulatory behavior of the crayfish Procambarus clarki in a burrow environment. Comp Biochem Physiol A: Mol Integr Physiol 136(3):539–556. https://doi.org/10.1016/S1095-6433(03)00203-4
Precup RE, David RC, Roman RC, Petriu EM, Szedlak - Stinean AI (2021) Slime mould algorithm - based tuning of cost - effective fuzzy controllers for servo systems. Int J Comput Intell Syst 14(1):1042–1052. https://www.atlantis-press.com/journals/ijcis/125954163
Qi H, Zhang G, Jia H, Xing Z (2021) A hybrid equilibrium optimizer algorithm for multi - level image segmentation. Math Biosci Eng 18:4648–4678
Rao RV, Savsani VJ, Vakharia DP (2012) Teaching–learning - based optimization: an optimization method for continuous non - linear large scale problems. Inf Sci 183(1):1–15. https://doi.org/10.1016/j.ins.2011.08.006
Rao H, Jia H, Wu D, Wen C, Li S, Liu Q, Abualigah L (2022) A modified group teaching optimization algorithm for solving constrained engineering optimization problems. Mathematics 10(20):3765. https://doi.org/10.3390/math10203765
Rashedi E, Nezamabadi - Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248. https://doi.org/10.1016/j.ins.2009.03.004
Satapathy S, Naik A (2016) Social group optimization (SGO): a new population evolutionary optimization technique. Complex & Intell Syst 2(3):173–203. https://doi.org/10.1007/s40747-016-0022-8
Seyyedabbasi A, Kiani F (2022) Sand cat swarm optimization: a nature - inspired algorithm to solve global optimization problems. Eng With Comput. https://doi.org/10.1007/s00366-022-01604-x
Sinha N, Chakrabarti R, Chattopadhyay PK (2003) Evolutionary programming techniques for economic load dispatch. IEEE Trans Evol Comput 7(1):83–94. https://ieeexplore.ieee.org/abstract/document/1179910
Song M, Jia H, Abualigah L, Liu Q, Lin Z, Wu D, Altalhi M (2022) Modified harris hawks optimization algorithm with exploration factor and random walk strategy. Comput Intell Neurosci. https://doi.org/10.1155/2022/4673665
Storn R, Price K (1997) Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341. https://doi.org/10.1023/A:1008202821328
Wang S, Hussien AG, Jia H, Abualigah L, Zheng R (2022) Enhanced remora optimization algorithm for solving constrained engineering optimization problems. Mathematics 10(10):1696. https://doi.org/10.3390/math10101696
Wen C, Jia H, Wu D, Rao H, Li S, Liu Q, Abualigah L (2022) Modified remora optimization algorithm with multistrategies for global optimization problem. Mathematics 10(19):3604. https://doi.org/10.3390/math10193604
Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82. https://ieeexplore.ieee.org/abstract/document/585893
Wu D, Rao H, Wen C, Jia H, Liu Q, Abualigah L (2022) Modified sand cat swarm optimization algorithm for solving constrained engineering optimization problems. Mathematics 10(22):4350. https://doi.org/10.3390/math10224350
Xie L, Han T, Zhou H, Zhang ZR, Han B, Tang A (2021) Tuna swarm optimization: a novel swarm - based metaheuristic algorithm for global optimization. Comput Intell Neurosci 2021:1–22. https://doi.org/10.1155/2021/9210050
Xing B, Gao WJ, Xing B, Gao WJ (2014) Imperialist competitive algorithm. In: Kacprzyk J, Jain LC (eds) Innovative computational intelligence: a rough guide to 134 clever algorithms. Springer, Berlin. https://doi.org/10.1007/978-3-319-03404-1_15
Zhang Y, Jin Z (2020) Group teaching optimization algorithm: a novel metaheuristic method for solving global optimization problems. Expert Syst Appl 148:113246. https://doi.org/10.1016/j.eswa.2020.113246