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MOAAA/D: a decomposition-based novel algorithm and a structural design application

Published: 14 June 2024 Publication History

Abstract

When real-world engineering challenges are examined adequately, it becomes clear that multi-objective need to be optimized. Many engineering problems have been handled utilizing the decomposition-based optimization approach according to the literature. The performance of multi-objective evolutionary algorithms is highly dependent on the balance of convergence and diversity. Diversity and convergence are not appropriately balanced in the decomposition technique, as they are in many approaches, for real-world problems. A novel Multi-Objective Artificial Algae Algorithm based on Decomposition (MOAAA/D) is proposed in the paper to solve multi-objective structural problems. MOAAA/D is the first multi-objective algorithm that uses the decomposition-based method with the artificial algae algorithm. MOAAA/D, which successfully draws a graph on 24 benchmark functions within the area of two common metrics, also produced promising results in the structural design problem to which it was applied. To facilitate the design of the "rectangular reinforced concrete column" using MOAAA/D, a solution space was derived by optimizing the rebar ratio and the concrete quantity to be employed.

References

[1]
Babalik A, Ozkis A, Uymaz SA, and Kiran MS A multi-objective artificial algae algorithm Appl Soft Comput 2018 68 377-395
[2]
Özkış A and Babalık A A novel metaheuristic for multi-objective optimization problems: The multi-objective vortex search algorithm Inf Sci 2017 402 124-148
[3]
Chi Y and Liu J Learning of fuzzy cognitive maps with varying densities using a multiobjective evolutionary algorithm IEEE Trans Fuzzy Syst 2015 24 1 71-81
[4]
Fan Z, Fang Y, Li W, Cai X, Wei C, and Goodman E MOEA/D with angle-based constrained dominance principle for constrained multi-objective optimization problems Appl Soft Comput 2019 74 621-633
[5]
İnik Ö, Altıok M, Ülker E, and Koçer B MODE-CNN: A fast converging multi-objective optimization algorithm for CNN-based models Appl Soft Comput 2021 109 107582
[6]
Altiok M, Alakara EH, Gündüz M, and Ağaoğlu MN A multi-objective genetic algorithm for the hot mix asphalt problem Neural Comput Appl 2023 35 11 8197-8225
[7]
Fonseca CM and Fleming PJ Genetic algorithms for multiobjective optimization: formulationdiscussion and generalization Icga 1993 93 July 416-423
[8]
Horn J, Nafpliotis N, Goldberg DE (1994) A niched Pareto genetic algorithm for multiobjective optimization, In: Proceedings of the first IEEE conference on evolutionary computation. IEEE world congress on computational intelligence, IEEE, pp. 82–87.
[9]
Srinivas N and Deb K Muiltiobjective optimization using nondominated sorting in genetic algorithms Evol Comput 1994 2 3 221-248
[10]
Coello CC, Lechuga MS, MOPSO: A proposal for multiple objective particle swarm optimization, In: Proceedings of the 2002 congress on evolutionary computation. CEC'02 (Cat. No. 02TH8600), vol. 2, pp. 1051–1056: IEEE.
[11]
Cao J, Zhang J, Zhao F, and Chen Z A two-stage evolutionary strategy based MOEA/D to multi-objective problems Expert Syst Appl 2021 185 115654
[12]
Deb K, Pratap A, Agarwal S, and Meyarivan T A fast and elitist multiobjective genetic algorithm: NSGA-II IEEE Trans Evol Comput 2002 6 2 182-197
[13]
Chaudhari P, Thakur AK, Kumar R, Banerjee N, Kumar A (2022) Comparison of NSGA-III with NSGA-II for multi objective optimization of adiabatic styrene reactor, Materials Today: Proceedings 57:1509–1514
[14]
Wang X, Chen G, and Xu S Bi-objective green supply chain network design under disruption risk through an extended NSGA-II algorithm Cleaner Logistics and Supply Chain 2022 3 100025
[15]
Karakoyun M, Ozkis A, and Kodaz H A new algorithm based on gray wolf optimizer and shuffled frog leaping algorithm to solve the multi-objective optimization problems Appl Soft Comput 2020 96 106560
[16]
Phan DH, Suzuki J (2013) R2-IBEA: R2 indicator based evolutionary algorithm for multiobjective optimization, In: 2013 IEEE congress on evolutionary computation, pp. 1836–1845: IEEE.
[17]
Chabane B, Basseur M, and Hao J-K R2-IBMOLS applied to a practical case of the multiobjective knapsack problem Expert Syst Appl 2017 71 457-468
[18]
Zhang Q and Li H MOEA/D: A multiobjective evolutionary algorithm based on decomposition IEEE Trans Evol Comput 2007 11 6 712-731
[19]
Wang W, Dai S, Zhao W, and Wang C Multi-objective optimization of hexahedral pyramid crash box using MOEA/D-DAE algorithm Appl Soft Comput 2022 118 108481
[20]
Yang M, Gan Y, Gao L, and Zhu X A structural optimization model of a biochemical detection micromixer based on RSM and MOEA/D Chem Eng Process Process Intensif 2022 173 108832
[21]
Jiao R, Zeng S, Li C, and Ong Y-S Two-type weight adjustments in MOEA/D for highly constrained many-objective optimization Inf Sci 2021 578 592-614
[22]
Fan Q and Yan X Multi-objective modified differential evolution algorithm with archive-base mutation for solving multi-objectivep-xylene oxidation process J Intell Manuf 2018 29 1 35-49
[23]
Li H and Zhang Q Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II IEEE Trans Evol Comput 2008 13 2 284-302
[24]
Zhang Q, Liu W, Li H (2009) The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances, In: 2009 IEEE congress on evolutionary computation, pp. 203–208: IEEE.
[25]
Ke L, Zhang Q, and Battiti R MOEA/D-ACO: A multiobjective evolutionary algorithm using decomposition and antcolony IEEE Trans Cybern 2013 43 6 1845-1859
[26]
Zhang Q, Liu W, Tsang E, and Virginas B Expensive multiobjective optimization by MOEA/D with Gaussian process model IEEE Trans Evol Comput 2009 14 3 456-474
[27]
Li H, Deb K, Zhang Q, Suganthan PN, and Chen L Comparison between MOEA/D and NSGA-III on a set of novel many and multi-objective benchmark problems with challenging difficulties Swarm Evol Comput 2019 46 104-117
[28]
Zhang Y, Wang G-G, Li K, Yeh W-C, Jian M, and Dong J Enhancing MOEA/D with information feedback models for large-scale many-objective optimization Inf Sci 2020 522 1-16
[29]
Peng W, Zhang Q (2008) A decomposition-based multi-objective particle swarm optimization algorithm for continuous optimization problems, In: 2008 IEEE international conference on granular computing, pp. 534–537: IEEE.
[30]
Nasiraghdam H and Jadid S Optimal hybrid PV/WT/FC sizing and distribution system reconfiguration using multi-objective artificial bee colony (MOABC) algorithm Sol Energy 2012 86 10 3057-3071
[31]
Zitzler E, Laumanns M, Thiele L (2001) SPEA2: Improving the strength Pareto evolutionary algorithm. TIK Report 103
[32]
Uymaz SA, Tezel G, and Yel E Artificial algae algorithm (AAA) for nonlinear global optimization Appl Soft Comput 2015 31 153-171
[33]
Clerc M (2011) Standard particle swarm optimisation from 2006 to 2011, Particle Swarm Central, 253
[34]
Karaboga D Artificial bee colony algorithm Scholarpedia 2010 5 3 6915
[35]
Yang X-S and He X Bat algorithm: literature review and applications Int J Bio-inspired Comput 2013 5 3 141-149
[36]
Storn R and Price K Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces J Global Optim 1997 11 341-359
[37]
Wolpert DH and Macready WG No free lunch theorems for optimization IEEE Trans Evol Comput 1997 1 1 67-82
[38]
Vargas DE, Lemonge AC, Barbosa HJ, and Bernardino HS Solving multi-objective structural optimization problems using GDE3 and NSGA-II with reference points Eng Struct 2021 239 112187
[39]
Premkumar M, Jangir P, and Sowmya R MOGBO: A new multiobjective gradient-based optimizer for real-world structural optimization problems Knowl-Based Syst 2021 218 106856
[40]
Chou J-S and Truong D-N Multiobjective optimization inspired by behavior of jellyfish for solving structural design problems Chaos, Solitons Fractals 2020 135 109738
[41]
Ho-Huu V, Hartjes S, Visser HG, and Curran R An improved MOEA/D algorithm for bi-objective optimization problems with complex Pareto fronts and its application to structural optimization Expert Syst Appl 2018 92 430-446
[42]
Zhao L et al. Multi-objective optimization analysis of structural design for large cooling towers Heat Transfer Eng 2017 38 11–12 1135-1145
[43]
Hughes O, Ma M, Paik JK (2014) Applications of vector evaluated genetic algorithms (VEGA) in ultimate limit state based ship structural design, In: International conference on offshore mechanics and arctic engineering, vol. 45493, p. V007T12A006: American Society of Mechanical Engineers.
[44]
Liao X et al. A framework of optimal design of thermal management system for lithium-ion battery pack using multi-objectives optimization J Electrochem Energy Conv Storage 2021 18 2 021005
[45]
Bekdaş G, Nigdeli M, Yücel M, Kayabekir A (2021) Yapay Zeka Optimizasyon Algoritmaları ve Mühendislik Uygulamaları, Seçkin Yayıncılık, Ankara
[46]
Yücel M, Nigdeli SM, and Bekdaş G Generation of sustainable models with multi-objective optimum design of reinforced concrete (RC) structures Structures 2022 40 223-236
[47]
Afshari H, Hare W, and Tesfamariam S Constrained multi-objective optimization algorithms: Review and comparison with application in reinforced concrete structures Appl Soft Comput 2019 83 105631
[48]
Jelušič P and Žula T Sustainable design of circular reinforced concrete column sections via multi-objective optimization Sustainability 2023 15 15 11689
[49]
Martins AM, Simões LM, Negrão JH, and Lopes AV Sensitivity analysis and optimum design of reinforced concrete frames according to Eurocode 2 Eng Optim 2020 52 12 2011-2032
[50]
Pareto V (1964) Cours d'économie politique. Librairie Droz
[51]
Khettabi I, Benyoucef L, and Amine Boutiche M Sustainable multi-objective process planning in reconfigurable manufacturing environment: adapted new dynamic NSGA-II vs New NSGA-III Int J Prod Res 2022 60 20 6329-6349
[52]
Zheng Z, Lin J, Hu Y, Zhou Q, and Yi C Dynamic unbalance identification and quantitative diagnosis of cardan shaft in high-speed train based on improved TQWT-RBFNN-NSGA-II method Eng Fail Anal 2022 136 106226
[53]
Bao L, Zheng M, Zhou Q, Gao P, Xu Y, and Jiang H Multi-objective optimization of partition temperature of steel sheet by NSGA-II using response surface methodology Case Stud Therm Eng 2022 31 101818
[54]
Tombak GI, Güzelhan ŞN, Afacan E, Dündar G (2022) Simulated annealing assisted NSGA-III-based multi-objective analog IC sizing tool. Integration
[55]
Xu J, Tang H, Wang X, Qin G, Jin X, and Li D NSGA-II algorithm-based LQG controller design for nuclear reactor power control Ann Nucl Energy 2022 169 108931
[56]
Trivedi A, Srinivasan D, Sanyal K, and Ghosh A A survey of multiobjective evolutionary algorithms based on decomposition IEEE Trans Evol Comput 2016 21 3 440-462
[57]
Canter-Lund H, Lund JW (1995) Freshwater algae: their microscopic world explored. Bristol: Biopress 582
[58]
Zitzler E, Deb K, and Thiele L Comparison of multiobjective evolutionary algorithms: Empirical results Evol Comput 2000 8 2 173-195
[59]
Deb K, Thiele L, Laumanns M, Zitzler E (2005) Scalable test problems for evolutionary multiobjective optimization, In: Evolutionary Multiobjective Optimization: teoretical advances and applications. London Springer London, pp. 105–145
[60]
Huband S, Hingston P, Barone L, and While L A review of multiobjective test problems and a scalable test problem toolkit IEEE Trans Evol Comput 2006 10 5 477-506
[61]
Fonseca CM and Fleming PJ Multiobjective optimization and multiple constraint handling with evolutionary algorithms. I. A unified formulation IEEE Trans Syst Man Cybern-Part A Syst Humans 1998 28 1 26-37
[62]
Kursawe F (1990) A variant of evolution strategies for vector optimization, In: International conference on parallel problem solving from nature, pp. 193–197: Springer.
[63]
Schaffer JD (1985) Multiple objective optimization with vector evaluated genetic algorithms, In: Proceedings of the first international conference on genetic algorithms and their applications, Lawrence Erlbaum Associates. Inc., Publishers.
[64]
Vlennet R, Fonteix C, and Marc I Multicriteria optimization using a genetic algorithm for determining a Pareto set Int J Syst Sci 1996 27 2 255-260
[65]
Durillo JJ and Nebro AJ jMetal: A Java framework for multi-objective optimization Adv Eng Softw 2011 42 10 760-771
[66]
Biswas S, Das S, Suganthan PN, Coello CAC (2014) Evolutionary multiobjective optimization in dynamic environments: A set of novel benchmark functions, In: 2014 IEEE congress on evolutionary computation (CEC), pp. 3192–3199: IEEE.
[67]
Nigdeli SM, Bekdaş G, Yang X-S (2016) Application of the flower pollination algorithm in structural engineering, In: Metaheuristics and optimization in civil engineering. Springer, pp. 25–42.
[68]
Committee A (2008), Building code requirements for structural concrete (ACI 318–08) and commentary, American Concrete Institute.
[69]
Anonymous (2023, 5.18.2023) Karakod: The price of one cubic meter of concrete. Available: https://www.karekod.org/blog/hazir-beton-fiyatlari-2023/
[71]
Anonymous (2023, 5.18.2023) Demirfiyatlari.com:1 ton steel price May, 2023 Available:https://www.demirfiyatlari.com/

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Published In

cover image Neural Computing and Applications
Neural Computing and Applications  Volume 36, Issue 28
Oct 2024
499 pages

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 14 June 2024
Accepted: 25 March 2024
Received: 21 May 2023

Author Tags

  1. Multi-objective
  2. Decomposition-based
  3. Hybrid
  4. Structural problems
  5. Design optimization

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  • Research-article

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  • Tokat Gaziosmanpasa University

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