Optimum design of steel structures with the particle swarm optimization method based on EC3

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dc.contributor.author Plevris, V en
dc.contributor.author Batavanis, A en
dc.contributor.author Papadrakakis, M en
dc.date.accessioned 2014-03-01T02:53:25Z
dc.date.available 2014-03-01T02:53:25Z
dc.date.issued 2011 en
dc.identifier.uri http://hdl.handle.net/123456789/36301
dc.relation.uri http://www.scopus.com/inward/record.url?eid=2-s2.0-80054825054&partnerID=40&md5=37e21be27266eb6dd302c2246ad2e623 en
dc.subject EC3 en
dc.subject Optimum design en
dc.subject Particle swarm optimization en
dc.subject Steel structures en
dc.subject.other Analysis results en
dc.subject.other Applied loads en
dc.subject.other Axial forces en
dc.subject.other Biaxial bending en
dc.subject.other Constraint reactions en
dc.subject.other Design spaces en
dc.subject.other EC3 en
dc.subject.other Eurocode 3 en
dc.subject.other Evolution strategies en
dc.subject.other Fine tuning en
dc.subject.other Finite element software en
dc.subject.other Gradient based en
dc.subject.other Linear analysis en
dc.subject.other Mathematical algorithms en
dc.subject.other Natural phenomena en
dc.subject.other Nodal displacement en
dc.subject.other Nonconvex optimization en
dc.subject.other Optimization algorithms en
dc.subject.other Optimization method en
dc.subject.other Optimum designs en
dc.subject.other Particle swarm en
dc.subject.other Particle swarm optimization method en
dc.subject.other Physical movements en
dc.subject.other Potential solutions en
dc.subject.other PSO algorithms en
dc.subject.other Search Algorithms en
dc.subject.other Setting parameters en
dc.subject.other Shear force en
dc.subject.other Social interactions en
dc.subject.other Software tool en
dc.subject.other Speed of convergence en
dc.subject.other Steel frame en
dc.subject.other Structural design optimization en
dc.subject.other Test examples en
dc.subject.other Velocity vectors en
dc.subject.other Biology en
dc.subject.other Civil engineering en
dc.subject.other Computational methods en
dc.subject.other Computer programming en
dc.subject.other Computer software en
dc.subject.other Constrained optimization en
dc.subject.other Convex optimization en
dc.subject.other Cooling systems en
dc.subject.other Design en
dc.subject.other Earthquakes en
dc.subject.other Engineering geology en
dc.subject.other Finite element method en
dc.subject.other Genetic algorithms en
dc.subject.other Shape optimization en
dc.subject.other Steel structures en
dc.subject.other Structural analysis en
dc.subject.other Structural dynamics en
dc.subject.other Structural optimization en
dc.subject.other Three dimensional en
dc.subject.other Vector spaces en
dc.subject.other Particle swarm optimization (PSO) en
dc.title Optimum design of steel structures with the particle swarm optimization method based on EC3 en
heal.type conferenceItem en
heal.publicationDate 2011 en
heal.abstract A number of optimization algorithms have been used in structural design optimization in the past, ranging from gradient-based mathematical algorithms to probabilistic-based search algorithms, for addressing global non-convex optimization problems. Many probabilistic-based algorithms have been inspired by natural phenomena, such as Evolutionary Programming (EP), Genetic Algorithms (GA), Evolution Strategies (ES), among others. Recently, a family of optimization methods has been developed based on the simulation of social interactions among members of a specific species. One of these methods is the Particle Swarm Optimization (PSO) method that is based on the behavior reflected in flocks of birds, bees and fish that adjust their physical movements to avoid predators and seek for food. In PSO, as in GA, a population of potential solutions is considered and utilized to search within the design space. However, its members do not reproduce but rather communicate with each other their knowledge of solutions in order to reach the optimum. Each ""particle"", ""flies"" through the multi-dimensional design space, with a certain velocity vector for each iteration. In this study, a discrete PSO algorithm is employed for the optimization of 2D and 3D steel frames and the results are compared to the ones obtained with a discrete GA. Both methods are applied in single-objective, discrete, constrained structural engineering optimization problems where the aim is to minimize the weight of the steel structure under various constraints on displacements and forces (biaxial bending with axial force and shear force) which are based on Eurocode 3. The constraints are checked by performing a Finite Element analysis for every candidate optimum design. A new linear analysis software tool for three-dimensional frames has been developed, featuring some distinct characteristics. The applied loads can be nodal or elemental (uniform, triangular or trapezoidal in any direction within an element), while any release (translational or rotational) can be implemented at an end of any element, in any of the 6 Degrees Of Freedom (DOFs). The output of the analysis program includes the constraint reactions, nodal displacements, forces at the ends of the elements, plus the displacements of the released DOFs of all elements with releases, and any displacement or any force at any given point within an element. The accuracy of the analysis results is verified by a direct comparison to the corresponding results of a reliable commercial finite element software program. For each method, the performance, functionality and effect of different setting parameters are studied. After a fine tuning of the parameters, the results are compared to each other. The comparison is done with regard to the speed of convergence, in terms of number of objective function evaluations, and accuracy of the solution. Various 2D and 3D steel structures are considered as test examples. en
heal.journalName ECCOMAS Thematic Conference - COMPDYN 2011: 3rd International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering: An IACM Special Interest Conference, Programme en

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