Optimization of concrete I-beams using a new hybrid glowworm swarm algorithm

In this paper a new hybrid glowworm swarm algorithm (SAGSO) for solving structural optimization problems is presented. The structure proposed to be optimized here is a simply-supported concrete I-beam defined by 20 variables. Eight different concrete mixtures are studied, varying the compressive strength grade and compacting system. The solutions are evaluated following the Spanish Code for structural concrete. The algorithm is applied to two objective functions, namely the embedded CO2 emissions and the economic cost of the structure. The ability of glowworm swarm optimization (GSO) to search in the entire solution space is combined with the local search by Simulated Annealing (SA) to obtain better results than using the GSO and SA independently. Finally, the hybrid algorithm can solve structural optimization problems applied to discrete variables. The study showed that large sections with a highly exposed surface area and the use of conventional vibrated concrete (CVC) with the lower strength grade minimize the CO2 emissionsGarcía Segura, T.; Yepes Piqueras, V.; Martí Albiñana, JV.; Alcalá González, J. (2014). Optimization of concrete I-beams using a new hybrid glowworm swarm algorithm. Latin American Journal of Solids and Structures. 11(7):1190-1205. doi:10.1590/S1679-78252014000700007S11901205117Alinia Ahandani, M., Vakil Baghmisheh, M. T., Badamchi Zadeh, M. A., & Ghaemi, S. (2012). Hybrid particle swarm optimization transplanted into a hyper-heuristic structure for solving examination timetabling problem. Swarm and Evolutionary Computation, 7, 21-34. doi:10.1016/j.swevo.2012.06.004Chen, S.-M., Sarosh, A., & Dong, Y.-F. (2012). Simulated annealing based artificial bee colony algorithm for global numerical optimization. Applied Mathematics and Computation, 219(8), 3575-3589. doi:10.1016/j.amc.2012.09.052Collins, F. (2010). 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Layout Optimization of Planar Braced Frames Using Modified Dolphin Monitoring Operator

Determining the optimum placement of braces in steel frames has always been one of the most challenging issues in structural engineering. In this paper, the size and placement of the X-braces in planar frame structures is determined in a way that the total weight of the braced frames becomes minimum, while satisfying the design requirements and constraints. Variables of the optimization contain the cross sections for beams, columns, and X-braces as well as the placement of these braces in the frames. Attempt has also been made to consider all the constraints of an actual design problem. One of the other objectives of this study is to investigate the effect of including or excluding some of the constraints affecting the optimization of the planar frame design. For this purpose, the Colliding Bodies Optimization (CBO) and CBO-MDM algorithms have been utilized. Modified Dolphin Monitoring (MDM) operator is recently developed for improving the performance of the metaheuristic algorithms. Here, this operator is utilized to enhance the performance of the CBO algorithm to optimize the weight of the frames. For additional comparison of the results, the particle swarm optimization (PSO) algorithm and imperialist competitive algorithm (ICA) are used

Improved dynamical particle swarm optimization method for structural dynamics

A methodology to the multiobjective structural design of buildings based on an improved particle swarm optimization algorithm is presented, which has proved to be very efficient and robust in nonlinear problems and when the optimization objectives are in conflict. In particular, the behaviour of the particle swarm optimization (PSO) classical algorithm is improved by dynamically adding autoadaptive mechanisms that enhance the exploration/exploitation trade-off and diversity of the proposed algorithm, avoiding getting trapped in local minima. A novel integrated optimization system was developed, called DI-PSO, to solve this problem which is able to control and even improve the structural behaviour under seismic excitations. In order to demonstrate the effectiveness of the proposed approach, the methodology is tested against some benchmark problems. Then a 3-story-building model is optimized under different objective cases, concluding that the improved multiobjective optimization methodology using DI-PSO is more efficient as compared with those designs obtained using single optimization.Peer ReviewedPostprint (published version

Particle swarm optimization with sequential niche technique for dynamic finite element model updating

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State-of-the-art in aerodynamic shape optimisation methods

Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners

A comparison of two global optimization algorithms with sequential niche technique for structural model updating

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