A Comparative Study of Recent Multi-objective Metaheuristics for Solving Constrained Truss Optimisation Problems
Multi-objective truss optimisation is a research topic that has been less investigated in the literature compared to the single-objective cases. This paper investigates the comparative performance of fourteen new and established multi-objective metaheuristics when solving truss optimisation problems...
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| Published in | Archives of computational methods in engineering Vol. 28; no. 5; pp. 4031 - 4047 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Dordrecht
Springer Netherlands
01.08.2021
Springer Nature B.V |
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| Online Access | Get full text |
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| Abstract | Multi-objective truss optimisation is a research topic that has been less investigated in the literature compared to the single-objective cases. This paper investigates the comparative performance of fourteen new and established multi-objective metaheuristics when solving truss optimisation problems. The optimisers include multi-objective ant lion optimiser, multi-objective dragonfly algorithm, multi-objective grasshopper optimisation algorithm, multi-objective grey wolf optimiser, multi-objective multi-verse optimisation, multi-objective water cycle algorithm, multi-objective Salp swarm algorithm, success history-based adaptive multi-objective differential evolution, success history–based adaptive multi-objective differential evolution with whale optimisation, non-dominated sorting genetic algorithm II, hybridisation of real-code population-based incremental learning and differential evolution, differential evolution for multi-objective optimisation, multi-objective evolutionary algorithm based on decomposition, and unrestricted population size evolutionary multi-objective optimisation algorithm. The design problem is assigned to minimise structural mass and compliance subject to stress constraints. Eight classical trusses found in the literature are used for setting up the design test problems. Various optimisers are then implemented to tackle the problems. A comprehensive comparative study is given to critically analyse the performance of all algorithms in this problem area. The results provide new insights to the pros and cons of evolutionary multi-objective optimisation algorithms when addressing multiple, often conflicting objective in truss optimisation. The results and findings of this work assist with not only solving truss optimisation problem better but also designing customised algorithms for such problems. |
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| AbstractList | Multi-objective truss optimisation is a research topic that has been less investigated in the literature compared to the single-objective cases. This paper investigates the comparative performance of fourteen new and established multi-objective metaheuristics when solving truss optimisation problems. The optimisers include multi-objective ant lion optimiser, multi-objective dragonfly algorithm, multi-objective grasshopper optimisation algorithm, multi-objective grey wolf optimiser, multi-objective multi-verse optimisation, multi-objective water cycle algorithm, multi-objective Salp swarm algorithm, success history-based adaptive multi-objective differential evolution, success history–based adaptive multi-objective differential evolution with whale optimisation, non-dominated sorting genetic algorithm II, hybridisation of real-code population-based incremental learning and differential evolution, differential evolution for multi-objective optimisation, multi-objective evolutionary algorithm based on decomposition, and unrestricted population size evolutionary multi-objective optimisation algorithm. The design problem is assigned to minimise structural mass and compliance subject to stress constraints. Eight classical trusses found in the literature are used for setting up the design test problems. Various optimisers are then implemented to tackle the problems. A comprehensive comparative study is given to critically analyse the performance of all algorithms in this problem area. The results provide new insights to the pros and cons of evolutionary multi-objective optimisation algorithms when addressing multiple, often conflicting objective in truss optimisation. The results and findings of this work assist with not only solving truss optimisation problem better but also designing customised algorithms for such problems. Multi-objective truss optimisation is a research topic that has been less investigated in the literature compared to the single-objective cases. This paper investigates the comparative performance of fourteen new and established multi-objective metaheuristics when solving truss optimisation problems. The optimisers include multi-objective ant lion optimiser, multi-objective dragonfly algorithm, multi-objective grasshopper optimisation algorithm, multi-objective grey wolf optimiser, multi-objective multi-verse optimisation, multi-objective water cycle algorithm, multi-objective Salp swarm algorithm, success history-based adaptive multi-objective differential evolution, success history–based adaptive multi-objective differential evolution with whale optimisation, non-dominated sorting genetic algorithm II, hybridisation of real-code population-based incremental learning and differential evolution, differential evolution for multi-objective optimisation, multi-objective evolutionary algorithm based on decomposition, and unrestricted population size evolutionary multi-objective optimisation algorithm. The design problem is assigned to minimise structural mass and compliance subject to stress constraints. Eight classical trusses found in the literature are used for setting up the design test problems. Various optimisers are then implemented to tackle the problems. A comprehensive comparative study is given to critically analyse the performance of all algorithms in this problem area. The results provide new insights to the pros and cons of evolutionary multi-objective optimisation algorithms when addressing multiple, often conflicting objective in truss optimisation. The results and findings of this work assist with not only solving truss optimisation problem better but also designing customised algorithms for such problems. |
| Author | Yildiz, Ali Riza Bureerat, Sujin Pholdee, Nantiwat Panagant, Natee Mirjalili, Seyedali |
| Author_xml | – sequence: 1 givenname: Natee surname: Panagant fullname: Panagant, Natee organization: Sustainable Infrastructure Research and Development Centre, Department of Mechanical Engineering, Faculty of Engineering, Khon Kaen University – sequence: 2 givenname: Nantiwat surname: Pholdee fullname: Pholdee, Nantiwat organization: Sustainable Infrastructure Research and Development Centre, Department of Mechanical Engineering, Faculty of Engineering, Khon Kaen University – sequence: 3 givenname: Sujin surname: Bureerat fullname: Bureerat, Sujin email: sujbur@kku.ac.th organization: Sustainable Infrastructure Research and Development Centre, Department of Mechanical Engineering, Faculty of Engineering, Khon Kaen University – sequence: 4 givenname: Ali Riza surname: Yildiz fullname: Yildiz, Ali Riza organization: Department of Automotive Engineering, Bursa Uludağ University – sequence: 5 givenname: Seyedali surname: Mirjalili fullname: Mirjalili, Seyedali organization: Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, YFL (Yonsei Frontier Lab), Yonsei University |
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| Cites_doi | 10.1080/0305215X.2013.823194 10.12989/sem.2014.50.3.365 10.1007/s12205-019-2119-2 10.1162/106365600568158 10.1007/s00366-018-0629-z 10.1016/j.eswa.2015.10.039 10.1007/s00366-020-01010-1 10.1007/s11831-020-09429-x 10.1007/s00158-019-02302-x 10.1007/s11831-016-9203-2 10.1007/s00158-003-0316-5 10.1016/j.knosys.2018.08.005 10.1016/j.eswa.2019.01.068 10.1016/j.ins.2012.10.008 10.1007/s10489-016-0825-8 10.1016/j.asoc.2014.10.042 10.1016/j.knosys.2017.07.018 10.1016/j.compstruc.2011.08.010 10.1007/978-3-540-31880-4_35 10.1007/s00707-018-2149-8 10.1016/0020-7683(94)00306-H 10.1007/s11831-020-09420-6 10.1016/j.advengsoft.2017.07.002 10.1007/978-3-540-31880-4_36 10.1016/j.compstruc.2012.12.010 10.1007/978-3-319-59072-1_18 10.2514/3.10739 10.1016/j.asoc.2010.09.003 10.1016/j.compstruc.2012.04.015 10.1007/s11831-019-09343-x 10.1007/s00366-018-0612-8 10.1080/10556780903548265 10.1007/s11831-019-09318-y 10.1007/s00158-010-0507-9 10.1007/s00521-015-1920-1 10.1007/s10489-017-1019-8 10.1109/4235.996017 10.1007/s11831-016-9187-y 10.1109/TEVC.2007.892759 10.1007/s12205-017-0095-y 10.1007/s00158-011-0709-9 10.1016/j.compstruc.2017.06.016 10.1007/s11831-019-09336-w 10.1504/IJBIC.2013.057191 10.1016/0045-7949(94)00617-C |
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| References | GandomiAHTalatahariSTadbiriFAlaviAHKrill herd algorithm for optimum design of truss structuresInt J Bio-Inspired Comput20135528128810.1504/IJBIC.2013.057191 NoilublaoNBureeratSSimultaneous topology, shape and sizing optimisation of a three-dimensional slender truss tower using multiobjective evolutionary algorithmsComput Struct201189232531253810.1016/j.compstruc.2011.08.010 PholdeeNBureeratSHybridisation of real-code population-based incremental learning and differential evolution for multiobjective design of trussesInf Sci2013223136152299855710.1016/j.ins.2012.10.008 TejaniGGSavsaniVJPatelVKBureeratSTopology, shape, and size optimization of truss structures using modified teaching-learning based optimizationAdv Comput Des201724313331 WuC-YTsengK-YTruss structure optimization using adaptive multi-population differential evolutionStruct Multidisc Optim201042457559010.1007/s00158-010-0507-9 TejaniGGPholdeeNBureeratSPrayogoDMultiobjective adaptive symbiotic organisms search for truss optimization problemsKnowl-Based Syst201816139841410.1016/j.knosys.2018.08.005 KavehADadrasAMontazeranAHChaotic enhanced colliding bodies algorithms for size optimization of truss structuresActa Mech2018229728832907381653410.1007/s00707-018-2149-81397.70021 PholdeeNBureeratSPerformance enhancement of multiobjective evolutionary optimisers for truss design using an approximate gradientComput Struct2012106–10711512410.1016/j.compstruc.2012.04.015 MirjaliliSJangirPMirjaliliSZSaremiSTrivediINOptimization of problems with multiple objectives using the multi-verse optimization algorithmKnowl-Based Syst2017134507110.1016/j.knosys.2017.07.018 MirjaliliSJangirPSaremiSMulti-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problemsAppl Intell2017461799510.1007/s10489-016-0825-8 PatelVKRajaBDComparative performance of recent advanced optimization algorithms for minimum energy requirement solutions in water pump switching networkArch Comput Methods Eng202010.1007/s11831-020-09429-x Robič T, Filipič B (2005) DEMO: differential evolution for multiobjective optimization. In: Evolutionary multi-criterion optimization, pp 520–533 AittokoskiTMiettinenKEfficient evolutionary approach to approximate the Pareto-optimal set in multiobjective optimization, UPS-EMOAOptim Methods Softw2010256841858272417110.1080/10556780903548265 KumarSTejaniGGPholdeeNBureeratSMulti-objective modified heat transfer search for truss optimizationEng Comput202010.1007/s00366-020-01010-1 ZhangQLiHMOEA/D: a multiobjective evolutionary algorithm based on decompositionIEEE Trans Evol Comput200711671273110.1109/TEVC.2007.892759 SchutteJFGroenwoldAASizing design of truss structures using particle swarmsStruct Multidisc Optim200325426126910.1007/s00158-003-0316-5 PanagantNBureeratSTaiKA novel self-adaptive hybrid multi-objective meta-heuristic for reliability design of trusses with simultaneous topology, shape and sizing optimisation design variablesStruct Multidisc Optim201960519371955403476110.1007/s00158-019-02302-x Kaveh A, Ahmadi B (2014) Sizing, geometry and topology optimization of trusses using force method and supervised charged system search. Struct Eng Mech 50(3): 365–382 XiaLXiaQHuangXXieYMBi-directional evolutionary structural optimization on advanced structures and materials: a comprehensive reviewArch Comput Methods Eng2018252437478377647710.1007/s11831-016-9203-21392.74074 PholdeeNBureeratSA comparative study of eighteen self-adaptive metaheuristic algorithms for truss sizing optimisationKSCE J Civ Eng20182282982299310.1007/s12205-017-0095-y TechasenTWansasuebKPanagantNPholdeeNBureeratSSimultaneous topology, shape, and size optimization of trusses, taking account of uncertainties using multi-objective evolutionary algorithmsEng Compu201935272174010.1007/s00366-018-0629-z AbualigahLShehabMAlshinwanMMirjaliliSElazizMAAnt lion optimizer: a comprehensive survey of its variants and applicationsArch Comput Methods Eng202010.1007/s11831-020-09420-6 RaoRVSarojAOclońPTalerJDesign optimization of heat exchangers with advanced optimization techniques: a reviewArch Comput Methods Eng202027251754810.1007/s11831-019-09318-y LieuQXDoDTTLeeJAn adaptive hybrid evolutionary firefly algorithm for shape and size optimization of truss structures with frequency constraintsComput Struct20181959911210.1016/j.compstruc.2017.06.016 TejaniGGPholdeeNBureeratSPrayogoDGandomiAHStructural optimization using multi-objective modified adaptive symbiotic organisms searchExpert Syst Appl201912542544110.1016/j.eswa.2019.01.068 HajelaPLeeEGenetic algorithms in truss topological optimizationInt J Solids Struct1995322233413357134995210.1016/0020-7683(94)00306-H0919.73113 GreinerDHajelaPTruss topology optimization for mass and reliability considerations—co-evolutionary multiobjective formulationsStruct Multidisc Optim2012454589613290911710.1007/s00158-011-0709-91274.74228 ChenG-SBrunoRJSalamaMOptimal placement of active/passive members in truss structures using simulated annealingAIAA J19912981327133410.2514/3.10739 MirjaliliSDragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problemsNeural Comput Appl201627410531073413844710.1007/s00521-015-1920-1 YildizARAbderazekHMirjaliliSA comparative study of recent non-traditional methods for mechanical design optimizationArch Computat Methods Eng201910.1007/s11831-019-09343-x VeldhuizenDAVLamontGBMultiobjective evolutionary algorithms: analyzing the state-of-the-artEvol Comput20008212514710.1162/106365600568158 KavehAKhayatazadMRay optimization for size and shape optimization of truss structuresComput Struct2013117829410.1016/j.compstruc.2012.12.010 YancangLZhenYApplication of improved bat algorithm in truss optimizationKSCE J Civ Eng20192362636264310.1007/s12205-019-2119-2 TangZHuXPériauxJMulti-level hybridized optimization methods coupling local search deterministic and global search evolutionary algorithmsArch Comput Methods Eng201910.1007/s11831-019-09336-w PholdeeNBureeratSHybrid real-code population-based incremental learning and approximate gradients for multi-objective truss designEng Optim201446810321051319704410.1080/0305215X.2013.823194 SadollahAEskandarHKimJHWater cycle algorithm for solving constrained multi-objective optimization problemsAppl Soft Comput20152727929810.1016/j.asoc.2014.10.042 DebKPratapAAgarwalSMeyarivanTA fast and elitist multiobjective genetic algorithm: NSGA-IIIEEE Trans Evol Comput20026218219710.1109/4235.996017 Sierra MR, Coello Coello CA (2005) Improving PSO-based multi-objective optimization using crowding, mutation and ∈-dominance. In: Evolutionary multi-criterion optimization, Berlin, pp 505–519. https://doi.org/10.1007/978-3-540-31880-4_35 MirjaliliSSaremiSMirjaliliSMdos CoelhoLSMulti-objective grey wolf optimizer: a novel algorithm for multi-criterion optimizationExpert Syst Appl20164710611910.1016/j.eswa.2015.10.039 GreinerDPeriauxJEmperadorJMGalvánBWinterGGame theory based evolutionary algorithms: a review with Nash applications in structural engineering optimization problemsArch Comput Methods Eng2017244703750371245110.1007/s11831-016-9187-y1394.90566 OhsakiMGenetic algorithm for topology optimization of trussesComput Struct1995572219225134333610.1016/0045-7949(94)00617-C0900.73501 Pholdee N, Bureerat S, Jaroenapibal P, Radpukdee T (2017) Many-objective optimisation of trusses through meta-heuristics. In: Advances in neural networks—ISNN 2017, Cham, pp 143–152. https://doi.org/10.1007/978-3-319-59072-1_18 TejaniGGSavsaniVJBureeratSPatelVKSavsaniPTopology optimization of truss subjected to static and dynamic constraints by integrating simulated annealing into passing vehicle search algorithmsEng Comput201935249951710.1007/s00366-018-0612-8 MirjaliliSZMirjaliliSSaremiSFarisHAljarahIGrasshopper optimization algorithm for multi-objective optimization problemsAppl Intell201848480582010.1007/s10489-017-1019-8 MirjaliliSGandomiAHMirjaliliSZSaremiSFarisHMirjaliliSMSalp Swarm Algorithm: a bio-inspired optimizer for engineering design problemsAdv Eng Softw201711416319110.1016/j.advengsoft.2017.07.002 SonmezMArtificial Bee Colony algorithm for optimization of truss structuresApplied Soft Computing20111122406241810.1016/j.asoc.2010.09.003 L Xia (9531_CR3) 2018; 25 QX Lieu (9531_CR18) 2018; 195 P Hajela (9531_CR11) 1995; 32 S Mirjalili (9531_CR35) 2016; 27 Q Zhang (9531_CR43) 2007; 11 M Sonmez (9531_CR16) 2011; 11 9531_CR17 GG Tejani (9531_CR32) 2018; 161 N Panagant (9531_CR28) 2019; 60 S Mirjalili (9531_CR38) 2017; 134 T Techasen (9531_CR27) 2019; 35 GG Tejani (9531_CR15) 2017; 2 DAV Veldhuizen (9531_CR45) 2000; 8 AR Yildiz (9531_CR2) 2019 VK Patel (9531_CR4) 2020 D Greiner (9531_CR6) 2017; 24 S Mirjalili (9531_CR34) 2017; 46 G-S Chen (9531_CR12) 1991; 29 N Pholdee (9531_CR30) 2013; 223 N Pholdee (9531_CR29) 2012; 106–107 N Noilublao (9531_CR9) 2011; 89 Z Tang (9531_CR7) 2019 9531_CR42 GG Tejani (9531_CR23) 2019; 125 K Deb (9531_CR41) 2002; 6 SZ Mirjalili (9531_CR36) 2018; 48 9531_CR46 N Pholdee (9531_CR25) 2018; 22 JF Schutte (9531_CR13) 2003; 25 L Yancang (9531_CR20) 2019; 23 S Mirjalili (9531_CR40) 2017; 114 A Kaveh (9531_CR22) 2013; 117 T Aittokoski (9531_CR44) 2010; 25 9531_CR1 AH Gandomi (9531_CR21) 2013; 5 A Kaveh (9531_CR19) 2018; 229 S Mirjalili (9531_CR37) 2016; 47 RV Rao (9531_CR5) 2020; 27 GG Tejani (9531_CR24) 2019; 35 S Kumar (9531_CR33) 2020 N Pholdee (9531_CR31) 2014; 46 A Sadollah (9531_CR39) 2015; 27 L Abualigah (9531_CR8) 2020 M Ohsaki (9531_CR10) 1995; 57 D Greiner (9531_CR26) 2012; 45 C-Y Wu (9531_CR14) 2010; 42 |
| References_xml | – reference: PholdeeNBureeratSPerformance enhancement of multiobjective evolutionary optimisers for truss design using an approximate gradientComput Struct2012106–10711512410.1016/j.compstruc.2012.04.015 – reference: XiaLXiaQHuangXXieYMBi-directional evolutionary structural optimization on advanced structures and materials: a comprehensive reviewArch Comput Methods Eng2018252437478377647710.1007/s11831-016-9203-21392.74074 – reference: YancangLZhenYApplication of improved bat algorithm in truss optimizationKSCE J Civ Eng20192362636264310.1007/s12205-019-2119-2 – reference: PholdeeNBureeratSHybrid real-code population-based incremental learning and approximate gradients for multi-objective truss designEng Optim201446810321051319704410.1080/0305215X.2013.823194 – reference: KavehADadrasAMontazeranAHChaotic enhanced colliding bodies algorithms for size optimization of truss structuresActa Mech2018229728832907381653410.1007/s00707-018-2149-81397.70021 – reference: MirjaliliSZMirjaliliSSaremiSFarisHAljarahIGrasshopper optimization algorithm for multi-objective optimization problemsAppl Intell201848480582010.1007/s10489-017-1019-8 – reference: PanagantNBureeratSTaiKA novel self-adaptive hybrid multi-objective meta-heuristic for reliability design of trusses with simultaneous topology, shape and sizing optimisation design variablesStruct Multidisc Optim201960519371955403476110.1007/s00158-019-02302-x – reference: YildizARAbderazekHMirjaliliSA comparative study of recent non-traditional methods for mechanical design optimizationArch Computat Methods Eng201910.1007/s11831-019-09343-x – reference: TejaniGGSavsaniVJBureeratSPatelVKSavsaniPTopology optimization of truss subjected to static and dynamic constraints by integrating simulated annealing into passing vehicle search algorithmsEng Comput201935249951710.1007/s00366-018-0612-8 – reference: MirjaliliSDragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problemsNeural Comput Appl201627410531073413844710.1007/s00521-015-1920-1 – reference: Sierra MR, Coello Coello CA (2005) Improving PSO-based multi-objective optimization using crowding, mutation and ∈-dominance. In: Evolutionary multi-criterion optimization, Berlin, pp 505–519. https://doi.org/10.1007/978-3-540-31880-4_35 – reference: Kaveh A, Ahmadi B (2014) Sizing, geometry and topology optimization of trusses using force method and supervised charged system search. Struct Eng Mech 50(3): 365–382 – reference: GreinerDHajelaPTruss topology optimization for mass and reliability considerations—co-evolutionary multiobjective formulationsStruct Multidisc Optim2012454589613290911710.1007/s00158-011-0709-91274.74228 – reference: WuC-YTsengK-YTruss structure optimization using adaptive multi-population differential evolutionStruct Multidisc Optim201042457559010.1007/s00158-010-0507-9 – reference: TejaniGGPholdeeNBureeratSPrayogoDGandomiAHStructural optimization using multi-objective modified adaptive symbiotic organisms searchExpert Syst Appl201912542544110.1016/j.eswa.2019.01.068 – reference: HajelaPLeeEGenetic algorithms in truss topological optimizationInt J Solids Struct1995322233413357134995210.1016/0020-7683(94)00306-H0919.73113 – reference: PholdeeNBureeratSHybridisation of real-code population-based incremental learning and differential evolution for multiobjective design of trussesInf Sci2013223136152299855710.1016/j.ins.2012.10.008 – reference: SchutteJFGroenwoldAASizing design of truss structures using particle swarmsStruct Multidisc Optim200325426126910.1007/s00158-003-0316-5 – reference: KavehAKhayatazadMRay optimization for size and shape optimization of truss structuresComput Struct2013117829410.1016/j.compstruc.2012.12.010 – reference: DebKPratapAAgarwalSMeyarivanTA fast and elitist multiobjective genetic algorithm: NSGA-IIIEEE Trans Evol Comput20026218219710.1109/4235.996017 – reference: GreinerDPeriauxJEmperadorJMGalvánBWinterGGame theory based evolutionary algorithms: a review with Nash applications in structural engineering optimization problemsArch Comput Methods Eng2017244703750371245110.1007/s11831-016-9187-y1394.90566 – reference: AittokoskiTMiettinenKEfficient evolutionary approach to approximate the Pareto-optimal set in multiobjective optimization, UPS-EMOAOptim Methods Softw2010256841858272417110.1080/10556780903548265 – reference: AbualigahLShehabMAlshinwanMMirjaliliSElazizMAAnt lion optimizer: a comprehensive survey of its variants and applicationsArch Comput Methods Eng202010.1007/s11831-020-09420-6 – reference: MirjaliliSSaremiSMirjaliliSMdos CoelhoLSMulti-objective grey wolf optimizer: a novel algorithm for multi-criterion optimizationExpert Syst Appl20164710611910.1016/j.eswa.2015.10.039 – reference: TechasenTWansasuebKPanagantNPholdeeNBureeratSSimultaneous topology, shape, and size optimization of trusses, taking account of uncertainties using multi-objective evolutionary algorithmsEng Compu201935272174010.1007/s00366-018-0629-z – reference: GandomiAHTalatahariSTadbiriFAlaviAHKrill herd algorithm for optimum design of truss structuresInt J Bio-Inspired Comput20135528128810.1504/IJBIC.2013.057191 – reference: SadollahAEskandarHKimJHWater cycle algorithm for solving constrained multi-objective optimization problemsAppl Soft Comput20152727929810.1016/j.asoc.2014.10.042 – reference: ZhangQLiHMOEA/D: a multiobjective evolutionary algorithm based on decompositionIEEE Trans Evol Comput200711671273110.1109/TEVC.2007.892759 – reference: PholdeeNBureeratSA comparative study of eighteen self-adaptive metaheuristic algorithms for truss sizing optimisationKSCE J Civ Eng20182282982299310.1007/s12205-017-0095-y – reference: TangZHuXPériauxJMulti-level hybridized optimization methods coupling local search deterministic and global search evolutionary algorithmsArch Comput Methods Eng201910.1007/s11831-019-09336-w – reference: LieuQXDoDTTLeeJAn adaptive hybrid evolutionary firefly algorithm for shape and size optimization of truss structures with frequency constraintsComput Struct20181959911210.1016/j.compstruc.2017.06.016 – reference: ChenG-SBrunoRJSalamaMOptimal placement of active/passive members in truss structures using simulated annealingAIAA J19912981327133410.2514/3.10739 – reference: RaoRVSarojAOclońPTalerJDesign optimization of heat exchangers with advanced optimization techniques: a reviewArch Comput Methods Eng202027251754810.1007/s11831-019-09318-y – reference: MirjaliliSGandomiAHMirjaliliSZSaremiSFarisHMirjaliliSMSalp Swarm Algorithm: a bio-inspired optimizer for engineering design problemsAdv Eng Softw201711416319110.1016/j.advengsoft.2017.07.002 – reference: Robič T, Filipič B (2005) DEMO: differential evolution for multiobjective optimization. In: Evolutionary multi-criterion optimization, pp 520–533 – reference: VeldhuizenDAVLamontGBMultiobjective evolutionary algorithms: analyzing the state-of-the-artEvol Comput20008212514710.1162/106365600568158 – reference: PatelVKRajaBDComparative performance of recent advanced optimization algorithms for minimum energy requirement solutions in water pump switching networkArch Comput Methods Eng202010.1007/s11831-020-09429-x – reference: TejaniGGSavsaniVJPatelVKBureeratSTopology, shape, and size optimization of truss structures using modified teaching-learning based optimizationAdv Comput Des201724313331 – reference: MirjaliliSJangirPSaremiSMulti-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problemsAppl Intell2017461799510.1007/s10489-016-0825-8 – reference: NoilublaoNBureeratSSimultaneous topology, shape and sizing optimisation of a three-dimensional slender truss tower using multiobjective evolutionary algorithmsComput Struct201189232531253810.1016/j.compstruc.2011.08.010 – reference: KumarSTejaniGGPholdeeNBureeratSMulti-objective modified heat transfer search for truss optimizationEng Comput202010.1007/s00366-020-01010-1 – reference: SonmezMArtificial Bee Colony algorithm for optimization of truss structuresApplied Soft Computing20111122406241810.1016/j.asoc.2010.09.003 – reference: MirjaliliSJangirPMirjaliliSZSaremiSTrivediINOptimization of problems with multiple objectives using the multi-verse optimization algorithmKnowl-Based Syst2017134507110.1016/j.knosys.2017.07.018 – reference: Pholdee N, Bureerat S, Jaroenapibal P, Radpukdee T (2017) Many-objective optimisation of trusses through meta-heuristics. In: Advances in neural networks—ISNN 2017, Cham, pp 143–152. https://doi.org/10.1007/978-3-319-59072-1_18 – reference: TejaniGGPholdeeNBureeratSPrayogoDMultiobjective adaptive symbiotic organisms search for truss optimization problemsKnowl-Based Syst201816139841410.1016/j.knosys.2018.08.005 – reference: OhsakiMGenetic algorithm for topology optimization of trussesComput Struct1995572219225134333610.1016/0045-7949(94)00617-C0900.73501 – volume: 46 start-page: 1032 issue: 8 year: 2014 ident: 9531_CR31 publication-title: Eng Optim doi: 10.1080/0305215X.2013.823194 – ident: 9531_CR17 doi: 10.12989/sem.2014.50.3.365 – volume: 23 start-page: 2636 issue: 6 year: 2019 ident: 9531_CR20 publication-title: KSCE J Civ Eng doi: 10.1007/s12205-019-2119-2 – volume: 8 start-page: 125 issue: 2 year: 2000 ident: 9531_CR45 publication-title: Evol Comput doi: 10.1162/106365600568158 – volume: 35 start-page: 721 issue: 2 year: 2019 ident: 9531_CR27 publication-title: Eng Compu doi: 10.1007/s00366-018-0629-z – volume: 47 start-page: 106 year: 2016 ident: 9531_CR37 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2015.10.039 – year: 2020 ident: 9531_CR33 publication-title: Eng Comput doi: 10.1007/s00366-020-01010-1 – year: 2020 ident: 9531_CR4 publication-title: Arch Comput Methods Eng doi: 10.1007/s11831-020-09429-x – volume: 60 start-page: 1937 issue: 5 year: 2019 ident: 9531_CR28 publication-title: Struct Multidisc Optim doi: 10.1007/s00158-019-02302-x – volume: 25 start-page: 437 issue: 2 year: 2018 ident: 9531_CR3 publication-title: Arch Comput Methods Eng doi: 10.1007/s11831-016-9203-2 – volume: 25 start-page: 261 issue: 4 year: 2003 ident: 9531_CR13 publication-title: Struct Multidisc Optim doi: 10.1007/s00158-003-0316-5 – volume: 161 start-page: 398 year: 2018 ident: 9531_CR32 publication-title: Knowl-Based Syst doi: 10.1016/j.knosys.2018.08.005 – volume: 125 start-page: 425 year: 2019 ident: 9531_CR23 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2019.01.068 – volume: 223 start-page: 136 year: 2013 ident: 9531_CR30 publication-title: Inf Sci doi: 10.1016/j.ins.2012.10.008 – volume: 46 start-page: 79 issue: 1 year: 2017 ident: 9531_CR34 publication-title: Appl Intell doi: 10.1007/s10489-016-0825-8 – volume: 27 start-page: 279 year: 2015 ident: 9531_CR39 publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2014.10.042 – volume: 2 start-page: 313 issue: 4 year: 2017 ident: 9531_CR15 publication-title: Adv Comput Des – volume: 134 start-page: 50 year: 2017 ident: 9531_CR38 publication-title: Knowl-Based Syst doi: 10.1016/j.knosys.2017.07.018 – volume: 89 start-page: 2531 issue: 23 year: 2011 ident: 9531_CR9 publication-title: Comput Struct doi: 10.1016/j.compstruc.2011.08.010 – ident: 9531_CR46 doi: 10.1007/978-3-540-31880-4_35 – volume: 229 start-page: 2883 issue: 7 year: 2018 ident: 9531_CR19 publication-title: Acta Mech doi: 10.1007/s00707-018-2149-8 – volume: 32 start-page: 3341 issue: 22 year: 1995 ident: 9531_CR11 publication-title: Int J Solids Struct doi: 10.1016/0020-7683(94)00306-H – year: 2020 ident: 9531_CR8 publication-title: Arch Comput Methods Eng doi: 10.1007/s11831-020-09420-6 – volume: 114 start-page: 163 year: 2017 ident: 9531_CR40 publication-title: Adv Eng Softw doi: 10.1016/j.advengsoft.2017.07.002 – ident: 9531_CR42 doi: 10.1007/978-3-540-31880-4_36 – volume: 117 start-page: 82 year: 2013 ident: 9531_CR22 publication-title: Comput Struct doi: 10.1016/j.compstruc.2012.12.010 – ident: 9531_CR1 doi: 10.1007/978-3-319-59072-1_18 – volume: 29 start-page: 1327 issue: 8 year: 1991 ident: 9531_CR12 publication-title: AIAA J doi: 10.2514/3.10739 – volume: 11 start-page: 2406 issue: 2 year: 2011 ident: 9531_CR16 publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2010.09.003 – volume: 106–107 start-page: 115 year: 2012 ident: 9531_CR29 publication-title: Comput Struct doi: 10.1016/j.compstruc.2012.04.015 – year: 2019 ident: 9531_CR2 publication-title: Arch Computat Methods Eng doi: 10.1007/s11831-019-09343-x – volume: 35 start-page: 499 issue: 2 year: 2019 ident: 9531_CR24 publication-title: Eng Comput doi: 10.1007/s00366-018-0612-8 – volume: 25 start-page: 841 issue: 6 year: 2010 ident: 9531_CR44 publication-title: Optim Methods Softw doi: 10.1080/10556780903548265 – volume: 27 start-page: 517 issue: 2 year: 2020 ident: 9531_CR5 publication-title: Arch Comput Methods Eng doi: 10.1007/s11831-019-09318-y – volume: 42 start-page: 575 issue: 4 year: 2010 ident: 9531_CR14 publication-title: Struct Multidisc Optim doi: 10.1007/s00158-010-0507-9 – volume: 27 start-page: 1053 issue: 4 year: 2016 ident: 9531_CR35 publication-title: Neural Comput Appl doi: 10.1007/s00521-015-1920-1 – volume: 48 start-page: 805 issue: 4 year: 2018 ident: 9531_CR36 publication-title: Appl Intell doi: 10.1007/s10489-017-1019-8 – volume: 6 start-page: 182 issue: 2 year: 2002 ident: 9531_CR41 publication-title: IEEE Trans Evol Comput doi: 10.1109/4235.996017 – volume: 24 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| Snippet | Multi-objective truss optimisation is a research topic that has been less investigated in the literature compared to the single-objective cases. This... Multi-objective truss optimisation is a research topic that has been less investigated in the literature compared to the single-objective cases. This paper... |
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| SubjectTerms | Adaptive algorithms Comparative studies Constraints Design optimization Engineering Evolutionary algorithms Evolutionary computation Genetic algorithms Heuristic methods Machine learning Mathematical and Computational Engineering Multiple objective analysis Original Paper Sorting algorithms Trusses |
| Title | A Comparative Study of Recent Multi-objective Metaheuristics for Solving Constrained Truss Optimisation Problems |
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