Crayfish optimization algorithm

Artificial Intelligence Review - Tập 56 Số S2 - Trang 1919-1979 - 2023
Heming Jia1, Honghua Rao1, Changsheng Wen1, Seyedali Mirjalili2,3
1School of Information Engineering, Sanming University, Sanming, China
2Centre for Artificial Intelligence Research and Optimisation, Torrens University, Adelaide, Australia
3University Research and Innovation Center, Obuda University, Budapest, Hungary

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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

Holland JH (1992) Genetic algorithms. Sci Am 267(1):66–73. https://www.jstor.org/stable/24939139

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

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. https://doi.org/10.1016/j.engappai.2022.105075