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Engineering with Computers
Erschienen in: Engineering with Computers 4/2021

28.02.2020 | Original Article

A new hybrid chaotic atom search optimization based on tree-seed algorithm and Levy flight for solving optimization problems

verfasst von: Saeid Barshandeh, Maryam Haghzadeh

Erschienen in: Engineering with Computers | Ausgabe 4/2021

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Abstract

Optimizing the high computational real-world problems is a challenging task that has taken a great deal of efforts in the last decade. The meta-heuristic algorithms have brought countless benefits. As a result, numerous meta-heuristic algorithms have been developed by getting inspired from natural phenomena. The atom search optimization (ASO) is a physics-based meta-heuristic, which has been developed little while ago. Although ASO is capable of solving various problems, due to low convergence speed and lack of proper balance between exploration and exploitation, it is not efficient enough in sorting out real-world convoluted problems. In the present paper, the convergence speed of ASO is improved using chaotic maps and Levy flight random walk. In addition, ASO is hybridized with the tree-seed algorithm (TSA) to improve exploration and exploitation capabilities and make a proper balance between them. TSA is an innovative intelligent meta-heuristic algorithm that has been inspired by the growth of trees and spreading their seeds and has a decent exploration ability. Furthermore, a novel technique has been applied on the proposed hybrid algorithm as a solution for departure of local optimums. Besides, the effectiveness of our contributions is validated by testing the proposed hybrid algorithm on a vast set of benchmark functions comprising unimodal, multimodal, fixed dimension, shifted–rotated and composite. The obtained results have been compared with several other new and powerful meta-heuristic algorithms in terms of descriptive and inferential statistics. In addition, the algorithms are tested on seven real-life engineering problems. The results of the experiments indicated the effectiveness of contributions and the superiority of the proposed hybrid algorithm over its akin counterparts.
Vorheriger Artikel Genetic algorithm hybridized with multilayer perceptron to have an economical slope stability design
Nächster Artikel Sine cosine grey wolf optimizer to solve engineering design problems

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Literatur
1.
Tang K, Yáo X, Suganthan PN, MacNish C, Chen Y-P, Chen C-M, Yang Z (2007) Benchmark functions for the CEC’2008 special session and competition on large scale global optimization. Nature Inspired Computation and Applications Laboratory, USTC, p 24
2.
3.
4.
Patin EC, Thompson A, Orr SJ (2018) Pattern recognition receptors in fungal immunity. In: Seminars in cell & developmental biology. Elsevier
5.
Chaudhary L, Singh B (2019) Community detection using an enhanced Louvain method in complex networks. In: International conference on distributed computing and internet technology. Springer, pp 243–250
6.
Ashourvan A, Telesford QK, Verstynen T, Vettel JM, Bassett DS (2019) Multi-scale detection of hierarchical community architecture in structural and functional brain networks. PLoS ONE 14(5):e0215520
7.
Dey B, Bhattacharyya B, Sharma S (2019) Robust economic dispatch of microgrid with highly penetrated renewables and energy storage system. Int J Energy Optim Eng IJEOE 8(1):67–87
8.
McLarty D, Panossian N, Jabbari F, Traverso A (2019) Dynamic economic dispatch using complementary quadratic programming. Energy 166:755–764
9.
Bertsimas D, Jaillet P, Martin S (2019) Online vehicle routing: the edge of optimization in large-scale applications. Oper Res 67(1):143–162MathSciNet
10.
Zhang S, Zhang W, Gajpal Y, Appadoo S (2019) Ant colony algorithm for routing alternate fuel vehicles in multi-depot vehicle routing problem. In: Decision science in action. Springer, pp 251–260
11.
Chopard B, Tomassini M (2018) Problems, algorithms, and computational complexity. In: An introduction to metaheuristics for optimization. Springer, pp 1–14
12.
Nabil E (2016) A modified flower pollination algorithm for global optimization. Expert Syst Appl 57:192–203
13.
Nenavath H, Jatoth RK (2018) Hybridizing sine cosine algorithm with differential evolution for global optimization and object tracking. Appl Soft Comput 62:1019–1043
14.
Farnad B, Jafarian A, Baleanu D (2018) A new hybrid algorithm for continuous optimization problem. Appl Math Model 55:652–673MathSciNetMATH
15.
Khalilpourazari S, Khalilpourazary S (2019) An efficient hybrid algorithm based on water cycle and moth-flame optimization algorithms for solving numerical and constrained engineering optimization problems. Soft Comput 23(5):1699–1722
16.
Mafarja MM, Mirjalili S (2017) Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing 260:302–312
17.
18.
Ali AF, Tawhid MA (2017) A hybrid particle swarm optimization and genetic algorithm with population partitioning for large scale optimization problems. Ain Shams Eng J 8(2):191–206
19.
Mortazavi A, Toğan V, Nuhoğlu A (2018) Interactive search algorithm: a new hybrid metaheuristic optimization algorithm. Eng Appl Artif Intell 71:275–292
20.
Aydilek IB (2018) A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems. Appl Soft Comput 66:232–249
21.
Ibrahim RA, Elaziz MA, Lu S (2018) Chaotic opposition-based grey-wolf optimization algorithm based on differential evolution and disruption operator for global optimization. Expert Syst Appl 108:1–27
22.
Zhang X, Kang Q, Cheng J, Wang X (2018) A novel hybrid algorithm based on biogeography-based optimization and grey wolf optimizer. Appl Soft Comput 67:197–214
23.
24.
Arora S, Singh H, Sharma M, Sharma S, Anand P (2019) A new hybrid algorithm based on grey wolf optimization and crow search algorithm for unconstrained function optimization and feature selection. IEEE Access 7:26343–26361
25.
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
26.
Zhang X, Kang Q, Wang X (2019) Hybrid biogeography-based optimization with shuffled frog leaping algorithm and its application to minimum spanning tree problems. Swarm and Evolut Comput 49:245–265
27.
Jia H, Lang C, Oliva D, Song W, Peng X (2019) Hybrid grasshopper optimization algorithm and differential evolution for multilevel satellite image segmentation. Remote Sens 11(9):1134
28.
Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Future Generat Comput Syst 97:849–872
29.
Dhiman G, Kumar V (2017) Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Adv Eng Softw 114:48–70
30.
Dhiman G, Kumar V (2018) Emperor penguin optimizer: a bio-inspired algorithm for engineering problems. Knowl Based Syst 159:20–50
31.
Dhiman G, Kaur A (2019) STOA: a bio-inspired based optimization algorithm for industrial engineering problems. Eng Appl Artif Intell 82:148–174
32.
Dhiman G, Kumar V (2019) Seagull optimization algorithm: theory and its applications for large-scale industrial engineering problems. Knowl Based Syst 165:169–196
33.
Fausto F, Cuevas E, Valdivia A, González A (2017) A global optimization algorithm inspired in the behavior of selfish herds. Biosystems 160:39–55
34.
Jain M, Singh V, Rani A (2019) A novel nature-inspired algorithm for optimization: squirrel search algorithm. Swarm Evolut Comput 44:148–175
35.
Yang X-S (2009) Firefly algorithms for multimodal optimization. In: International symposium on stochastic algorithms. Springer, pp 169–178
36.
Gomes GF, da Cunha SS, Ancelotti AC (2019) A sunflower optimization (SFO) algorithm applied to damage identification on laminated composite plates. Eng Comput 35(2):619–626
37.
Arora S, Singh S (2019) Butterfly optimization algorithm: a novel approach for global optimization. Soft Comput 23(3):715–734
38.
Eesa AS, Brifcani AMA, Orman Z (2013) Cuttlefish algorithm-a novel bio-inspired optimization algorithm. Int J Sci Eng Res 4(9):1978–1986
39.
Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47
40.
Biyanto TR, Irawan S, Febrianto HY, Afdanny N, Rahman AH, Gunawan KS, Pratama JA, Bethiana TN (2017) Killer whale algorithm: an algorithm inspired by the life of killer whale. Proc Comput Sci 124:151–157
41.
Shadravan S, Naji H, Bardsiri VK (2019) The Sailfish optimizer: a novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems. Eng Appl Artif Intell 80:20–34
42.
Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98
43.
Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248MATH
44.
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
45.
Abedinpourshotorban H, Shamsuddin SM, Beheshti Z, Jawawi DN (2016) Electromagnetic field optimization: a physics-inspired metaheuristic optimization algorithm. Swarm Evolut Comput 26:8–22
46.
Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680MathSciNetMATH
47.
Hashim FA, Houssein EH, Mabrouk MS, Al-Atabany W, Mirjalili S (2019) Henry gas solubility optimization: a novel physics-based algorithm. Future Gener Comput Syst 101:646–667
48.
Patel VK, Savsani VJ (2015) Heat transfer search (HTS): a novel optimization algorithm. Inf Sci 324:217–246
49.
Doğan B, Ölmez T (2015) A new metaheuristic for numerical function optimization: vortex Search algorithm. Inf Sci 293:125–145
50.
Hatamlou A (2013) Black hole: a new heuristic optimization approach for data clustering. Inf Sci 222:175–184MathSciNet
51.
Javidy B, Hatamlou A, Mirjalili S (2015) Ions motion algorithm for solving optimization problems. Appl Soft Comput 32:72–79
52.
Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67
53.
Kaveh A, Khayatazad M (2012) A new meta-heuristic method: ray optimization. Comput Struct 112:283–294
54.
Ray T, Liew K-M (2003) Society and civilization: an optimization algorithm based on the simulation of social behavior. IEEE Trans Evol Comput 7(4):386–396
55.
Meng X-B, Gao XZ, Lu L, Liu Y, Zhang H (2016) A new bio-inspired optimisation algorithm: bird Swarm algorithm. J Exp Theor Artif Intell 28(4):673–687
56.
Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp Swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191
57.
Chu S-C, Tsai P-W, Pan J-S (2006) Cat swarm optimization. In: Pacific Rim international conference on artificial intelligence. Springer, pp 854–858
58.
Oyekan J, Hu H (2013) Ant robotic swarm for visualizing invisible hazardous substances. Robotics 2(1):1–18
59.
Oftadeh R, Mahjoob M, Shariatpanahi M (2010) A novel meta-heuristic optimization algorithm inspired by group hunting of animals: hunting search. Comput Math Appl 60(7):2087–2098MATH
60.
Eberhart R, Kennedy J (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks. Citeseer, pp 1942–1948
61.
Yapici H, Cetinkaya N (2019) A new meta-heuristic optimizer: pathfinder algorithm. Appl Soft Comput 78:545–568
62.
Ahmadi S-A (2017) Human behavior-based optimization: a novel metaheuristic approach to solve complex optimization problems. Neural Comput Appl 28(1):233–244
63.
Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68
64.
Mousavirad SJ, Ebrahimpour-Komleh H (2017) Human mental search: a new population-based metaheuristic optimization algorithm. Appl Intell 47(3):850–887
65.
Xu Y, Cui Z, Zeng J (2010) Social emotional optimization algorithm for nonlinear constrained optimization problems. In: International conference on swarm, evolutionary, and memetic computing. Springer, pp 583–590
66.
Kumar M, Kulkarni AJ, Satapathy SC (2018) Socio evolution & learning optimization algorithm: a socio-inspired optimization methodology. Future Gener Comput Syst 81:252–272
67.
Raouf OA, Hezam IM (2017) Sperm motility algorithm: a novel metaheuristic approach for global optimisation. Int J Oper Res 28(2):143–163MathSciNetMATH
68.
Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: 2007 IEEE congress on evolutionary computation. IEEE, pp 4661–4667
69.
Jaddi NS, Alvankarian J, Abdullah S (2017) Kidney-inspired algorithm for optimization problems. Commun Nonlinear Sci Numer Simul 42:358–369MATH
70.
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
71.
Gonçalves MS, Lopez RH, Miguel LFF (2015) Search group algorithm: a new metaheuristic method for the optimization of truss structures. Comput Struct 153:165–184
72.
Moosavian N (2015) Soccer league competition algorithm for solving knapsack problems. Swarm Evolut Comput 20:14–22
73.
Olorunda O, Engelbrecht AP (2008) Measuring exploration/exploitation in particle swarms using swarm diversity. In: 2008 IEEE congress on evolutionary computation (IEEE world congress on computational intelligence). IEEE, pp 1128–1134
74.
Elaziz MA, Mirjalili S (2019) A hyper-heuristic for improving the initial population of whale optimization algorithm. Knowl Based Syst 172:42–63
75.
Kiran MS (2015) TSA: tree-seed algorithm for continuous optimization. Expert Syst Appl 42(19):6686–6698
76.
Saha S, Mukherjee V (2018) A novel chaos-integrated symbiotic organisms search algorithm for global optimization. Soft Comput 22(11):3797–3816
77.
Farah A, Belazi A (2018) A novel chaotic Jaya algorithm for unconstrained numerical optimization. Nonlinear Dyn 93(3):1451–1480
78.
Sayed GI, Khoriba G, Haggag MH (2018) A novel chaotic salp swarm algorithm for global optimization and feature selection. Appl Intell 48(10):3462–3481
79.
Arora S, Anand P (2019) Chaotic grasshopper optimization algorithm for global optimization. Neural Comput Appl 31(8):4385–4405
80.
Masdari M, Barshande S, Ozdemir S (2019) CDABC: chaotic discrete artificial bee colony algorithm for multi-level clustering in large-scale WSNs. J Supercomput 75(11):7174–7208
81.
Ali AF (2015) A hybrid gravitational search with levy flight for global numerical optimization. Inf Sci Lett 4:71–83
82.
Jensi R, Jiji GW (2016) An enhanced particle swarm optimization with levy flight for global optimization. Appl Soft Comput 43:248–261
83.
Li N, Li G, Deng Z (2017) An improved sine cosine algorithm based on levy flight. In: 9th international conference on digital image processing (ICDIP 2017). International Society for Optics and Photonics, p 104204R
84.
Aydoğdu İ, Akın A, Saka MP (2016) Design optimization of real world steel space frames using artificial bee colony algorithm with Levy flight distribution. Adv Eng Softw 92:1–14
85.
Shayanfar H, Gharehchopogh FS (2018) Farmland fertility: a new metaheuristic algorithm for solving continuous optimization problems. Appl Soft Comput 71:728–746
86.
87.
Liang J, Qu B, Suganthan P, Hernández-Díaz AG (2013) Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and Nanyang Technological University, Singapore. Tech Rep 201212(34):281–295
88.
Liang J, Qu B, Suganthan P (2013) Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore, Computational Intelligence Laboratory, p 635
89.
Woolson R (2007) Wilcoxon signed-rank test. Wiley encyclopedia of clinical trials. Wiley, New York, pp 1–3
90.
Parouha RP (2018) An efficient differential evolution for engineering design problems. Int J Appl Eng Res 13(12):10845–10854
91.
El Dor A, Clerc M, Siarry P (2012) Hybridization of differential evolution and particle swarm optimization in a new algorithm: DEPSO-2S. In: Swarm and evolutionary computation. Springer, pp 57–65
92.
Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl Based Syst 96:120–133
93.
Zhao W, Zhang Z, Wang L (2020) Manta ray foraging optimization: an effective bio-inspired optimizer for engineering applications. Eng Appl Artif Intell 87:103300
94.
Liu H, Xu S, Wang X, Yang S, Meng J (2018) A multi-response adaptive sampling approach for global metamodeling. Proc Inst Mech Eng C J Mech Eng Sci 232(1):3–16
95.
Prasad B, Kumar A, Singh K (2015) Optimization of thermo hydraulic performance in three sides artificially roughened solar air heaters. Sol Energy 111:313–319
96.
Prasad B, Saini J (1991) Optimal thermohydraulic performance of artificially roughened solar air heaters. Sol Energy 47(2):91–96
Metadaten
Titel
A new hybrid chaotic atom search optimization based on tree-seed algorithm and Levy flight for solving optimization problems
verfasst von
Saeid Barshandeh
Maryam Haghzadeh
Publikationsdatum
28.02.2020
Verlag
Springer London
Erschienen in
Engineering with Computers / Ausgabe 4/2021
Print ISSN: 0177-0667
Elektronische ISSN: 1435-5663
DOI
https://doi.org/10.1007/s00366-020-00994-0

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