Particle swarm optimization (PSO), a heuristic optimization method, has been successfully applied in solving many optimization problems in real-value search space. The original binary particle swarm optimization (BPSO) uses the concept of bit flipping of the binary string to convert the velocity from a real code into a binary code. However, the conversion process cannot be reversed, and it is difficult to extend this framework to solve certain discrete optimization problems. An enhanced binary particle swarm algorithm is proposed in this study based on pure binary bit-string frameworks to deal with structural topology optimization problems. Further, two enhancement strategies, stress-based strategy and pair-switched strategy, were developed to improve the performance of the proposed algorithm for topology optimization of structure. The results of experimental cases demonstrated in this study show that the proposed enhanced binary particle swarm optimization (EBPSO) with two developed strategies is an efficient population-based approach for finding the optimal design for structural topology optimization problems of minimum compliance design and minimum weight design.
RozvanyG. I. N.Aims, scope, methods, history and unified terminology of computer-aided topology optimization in structural mechanics. Struct. Multidiscip. Optim.2001, 21(2), 90–108.
2.
YangK. W.LiuA. G.ChengC. C.LoY. L.Topology and shape optimizations of substrates for chirp fiber bragg grating spectrum tuningJ. Lightw. Tech.2002, 20(7), 1182–1187.
3.
Gersborg-HansenA.BendsøeM. P.SigmundO.Topology optimization of heat conduction problems using the finite volume method. Struct. Multidiscip. Optim.2006, 31, 251–259.
4.
DuY.FangZ.WuZ.TianQ.Thermo mechanical compliant actuator design using meshless topology optimization. In Proceedings of the Seventh International Conference on Asia simulation conference system simulation and scientific computing, Beijing, China, 2008, pp. 1018–1025.
5.
CanfieldS.FreckerM.Topology optimization of compliant mechanical amplifiers for piezoelectric actuators. Struct. Multidiscip. Optim.2000, 20, 269–279.
6.
MauteK.FrangopolD. M.Reliability-based design of MEMS mechanisms by topology optimization. Comput. Struct.2003, 81, 813–824.
7.
BendsøeM. P.KikuchiN.Generating optimal topologies in structural design using a homogenization method. Comput. Meth. Appl. Mech. Eng.1988, 71, 197–224.
8.
SigmundO.A 99 line topology optimization code written in MATLABStruct. Multidiscip. Optim.2001, 21(2), 120–127.
9.
BendøeM. P.Optimal shape design as a material distribution problem. Struct. Optim.1989, 1, 193–202.
10.
BendøeM. P.SigmundO.Topology optimization: theory, methods and applications2003 (Springer, Berlin).
11.
WangS. Y.TaiK.WangM. Y.An enhanced genetic algorithm for structural topology optimization. Int. J. Numer. Meth. Eng.2006, 65(1), 18–44.
ZhouM.RozvanyG. I. N.On the validity of ESO type methods in topology optimization’Struct. Multidiscip. Optim.2001, 21, 80–83.
14.
WangS. Y.TaiK.Structural topology design optimization using genetic algorithm with a bit-array representation. Comput. Meth. Appl. Mech. Eng.2005, 194, 3749–3770.
15.
BalamuruganR.RamakrishnanC. V.SinghN.Performance evaluation of a two stage adaptive genetic algorithm (TSAGA) in structural topology optimizationAppl. Soft Comput.2008, 8, 1607–1624.
16.
GoldbergD.SamtaniM.Engineering optimization via genetic algorithm. In Proceedings of the Ninth Conference on Electronic computation, Alabama at Birmingham, 1986, pp. 471–482.(American Society of Civil Engineers).
17.
ChapmanC. D.SaitouK.JakielaM. J.Genetic algorithms as an approach to configuration and topology design. ASME J. Mech. Des.1994, 116, 1005–1012.
18.
ChapmanC. D.JakielaM. J.Genetic algorithm-based structural topology design with compliance and topology simplification considerations. ASME J. Mech. Des.1996, 118, 89–98.
WuC. Y.LuhG. C.Multi-model topological optimization of structure using subpopulation-based niche genetic algorithms. In Proceedings of the Second China-Japan-Korea Joint Symposium on Optimization of structure and mechanical systems, November 2002, pp. 63–68.
21.
WuC. Y.TsengK. Y.ZhangC. B.KuC. C.Engineering optimization using distributed artificial immune algorithm. In Proceedings of the Tenth World Multi-Conference on Systemics, cybernetics and informatics, Orlando, Florida, USA, 16–19 July 2006, vol. 3, pp. 105–110.
22.
WuC. Y.TsengK. Y.Topology optimization of structures using differential evolution’J. Syst. Cybern. Inform.2008, 6(1), 46–51.
23.
KavehA.HassaniB.ShojaeeS.TavakkoliS. M.Structural topology optimization using ant colony methodology. Eng. Struct.2008, 30, 2559–2565.
24.
WuC. Y.ZhangC. B.WangC. J.Topology optimization of structures using ant colony optimization. In Proceedings of the First ACM/SIGEVO Summit on Genetic and evolutionary computation, 2009, pp. 601–607.
25.
KennedyJ.EberhartR.Particle swarm optimization. In Proceedings of the IEEE International Conference on Neural, 1995, vol. 4, pp. 1942–1948.
26.
MeissnerM.SchmukerM.SchneiderG.Optimized particle swarm optimization (OPSO) and its application to artificial neural network training. BMC Bioinform.2006, 7(125), 1–11.
27.
AbidoM. A.Optimal power flow using particle swarm optimization. Int. J. Electr. Power Energy Syst.2002, 24, 563–571.
28.
LeeW. S.ChenY. T.WuT. H.Optimization for ice-storage air-conditioning system using particle swarm algorithm. Appl. Energy2009, 86, 1589–1595.
29.
KennedyJ.EberhartR.A discrete binary version of the particle swarm algorithm. In Proceedings of the 1997 Conference on Systems, man, cybernetics, 1997, pp. 4104—4108.
30.
AfshinmaneshF.MarandiA.Rahimi-KianA., ‘A novel binary particle swarm optimization method using artificial immune system. In Proceedings of IEEE International Conference on Computer as a tool, EUROCON 2005, 2005, pp. 217–220.
YuanX.NieH.SuA.WangaL.YuanY.An improved binary particle swarm optimization for unit commitment problem. Expert Syst. Appl.2009, 36, 8049–8055.
33.
KennedyJ.EberhartR.Swarm intelligence2001(Morgan Kaufmann Publishers, Inc., San Francisco, CA).
34.
ClercM.KennedyJ.The particle swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput.2002, 6(1), 58–73.
35.
ZhengY. L.MaL. H.ZhangL. Y.QianJ. X.On the convergence analysis and parameter selection in particle swarm optimization. In Proceedings of the International Conference on Machine learning and cybernetics, 2003, vol. 3, pp. 1802–1807.
36.
MitsuiK.Optimal shape and topology of structure searched by ants' foraging behavior’ Report of the Research Institute of Industrial Technology, Nihon University, 2006, vol. 83, pp. 1–10.
37.
WangM. Y.WangS. Y.Bilaterial filtering for structural topology optimizationInt. J. Numer. Meth. Eng.2005, 63(13), 1911–1938.
