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). Inclusion of carbonation during the life cycle of built and recycled concrete: influence on their carbon footprint. The International Journal of Life Cycle Assessment, 15(6), 549-556. doi:10.1007/s11367-010-0191-4Dutta, R., Ganguli, R., & Mani, V. (2011). Swarm intelligence algorithms for integrated optimization of piezoelectric actuator and sensor placement and feedback gains. Smart Materials and Structures, 20(10), 105018. doi:10.1088/0964-1726/20/10/105018Fan, S.-K. S., & Zahara, E. (2007). A hybrid simplex search and particle swarm optimization for unconstrained optimization. European Journal of Operational Research, 181(2), 527-548. doi:10.1016/j.ejor.2006.06.034García-Segura, T., Yepes, V., & Alcalá, J. (2013). Life cycle greenhouse gas emissions of blended cement concrete including carbonation and durability. The International Journal of Life Cycle Assessment, 19(1), 3-12. doi:10.1007/s11367-013-0614-0Gong, Q. Q., Zhou, Y. Q., & Yang, Y. (2010). Artificial Glowworm Swarm Optimization Algorithm for Solving 0-1 Knapsack Problem. Advanced Materials Research, 143-144, 166-171. doi:10.4028/www.scientific.net/amr.143-144.166Hare, W., Nutini, J., & Tesfamariam, S. (2013). A survey of non-gradient optimization methods in structural engineering. Advances in Engineering Software, 59, 19-28. doi:10.1016/j.advengsoft.2013.03.001He, S., Prempain, E., & Wu, Q. H. (2004). An improved particle swarm optimizer for mechanical design optimization problems. Engineering Optimization, 36(5), 585-605. doi:10.1080/03052150410001704854Karaboga, D., & Basturk, B. (2008). On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing, 8(1), 687-697. doi:10.1016/j.asoc.2007.05.007Khan, K., & Sahai, A. (2012). A Glowworm Optimization Method for the Design of Web Services. International Journal of Intelligent Systems and Applications, 4(10), 89-102. doi:10.5815/ijisa.2012.10.10Kicinger, R., Arciszewski, T., & Jong, K. D. (2005). Evolutionary computation and structural design: A survey of the state-of-the-art. Computers & Structures, 83(23-24), 1943-1978. doi:10.1016/j.compstruc.2005.03.002Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671-680. doi:10.1126/science.220.4598.671Koide, R. M., França, G. von Z. de, & Luersen, M. A. (2013). An ant colony algorithm applied to lay-up optimization of laminated composite plates. Latin American Journal of Solids and Structures, 10(3), 491-504. doi:10.1590/s1679-78252013000300003Krishnanand, K. N., & Ghose, D. (2009). Glowworm swarm optimisation: a new method for optimising multi-modal functions. International Journal of Computational Intelligence Studies, 1(1), 93. doi:10.1504/ijcistudies.2009.025340Li, L. J., Huang, Z. B., & Liu, F. (2009). A heuristic particle swarm optimization method for truss structures with discrete variables. Computers & Structures, 87(7-8), 435-443. doi:10.1016/j.compstruc.2009.01.004Liao, W.-H., Kao, Y., & Li, Y.-S. (2011). A sensor deployment approach using glowworm swarm optimization algorithm in wireless sensor networks. Expert Systems with Applications, 38(10), 12180-12188. doi:10.1016/j.eswa.2011.03.053Luo, Q. F., & Zhang, J. L. (2011). Hybrid Artificial Glowworm Swarm Optimization Algorithm for Solving Constrained Engineering Problem. Advanced Materials Research, 204-210, 823-827. doi:10.4028/www.scientific.net/amr.204-210.823Martí, J. V., Gonzalez-Vidosa, F., Yepes, V., & Alcalá, J. (2013). Design of prestressed concrete precast road bridges with hybrid simulated annealing. Engineering Structures, 48, 342-352. doi:10.1016/j.engstruct.2012.09.014Martinez-Martin, F. J., Gonzalez-Vidosa, F., Hospitaler, A., & Yepes, V. (2013). A parametric study of optimum tall piers for railway bridge viaducts. Structural Engineering and Mechanics, 45(6), 723-740. doi:10.12989/sem.2013.45.6.723Medina, J. R. (2001). Estimation of Incident and Reflected Waves Using Simulated Annealing. Journal of Waterway, Port, Coastal, and Ocean Engineering, 127(4), 213-221. doi:10.1061/(asce)0733-950x(2001)127:4(213)Parsopoulos, K. E., & Vrahatis, M. N. (2002). Natural Computing, 1(2/3), 235-306. doi:10.1023/a:1016568309421Paya-Zaforteza, I., Yepes, V., González-Vidosa, F., & Hospitaler, A. (2010). On the Weibull cost estimation of building frames designed by simulated annealing. Meccanica, 45(5), 693-704. doi:10.1007/s11012-010-9285-0Sarma, K. C., & Adeli, H. (1998). Cost Optimization of Concrete Structures. Journal of Structural Engineering, 124(5), 570-578. doi:10.1061/(asce)0733-9445(1998)124:5(570)Shieh, H.-L., Kuo, C.-C., & Chiang, C.-M. (2011). Modified particle swarm optimization algorithm with simulated annealing behavior and its numerical verification. Applied Mathematics and Computation, 218(8), 4365-4383. doi:10.1016/j.amc.2011.10.012Sideris, K. K., & Anagnostopoulos, N. S. (2013). Durability of normal strength self-compacting concretes and their impact on service life of reinforced concrete structures. Construction and Building Materials, 41, 491-497. doi:10.1016/j.conbuildmat.2012.12.042Valdez, F., Melin, P., & Castillo, O. (2011). An improved evolutionary method with fuzzy logic for combining Particle Swarm Optimization and Genetic Algorithms. Applied Soft Computing, 11(2), 2625-2632. doi:10.1016/j.asoc.2010.10.010Wang, H., Sun, H., Li, C., Rahnamayan, S., & Pan, J. (2013). Diversity enhanced particle swarm optimization with neighborhood search. Information Sciences, 223, 119-135. doi:10.1016/j.ins.2012.10.012Yepes, V., Gonzalez-Vidosa, F., Alcala, J., & Villalba, P. (2012). CO2-Optimization Design of Reinforced Concrete Retaining Walls Based on a VNS-Threshold Acceptance Strategy. Journal of Computing in Civil Engineering, 26(3), 378-386. doi:10.1061/(asce)cp.1943-5487.000014

Performance of Some Metaheuristic Algorithms for Multiuser Detection in TTCM-Assisted Rank-Deficient SDMA-OFDM System

We propose two novel and computationally efficient metaheuristic algorithms based on Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) principles for Multiuser Detection (MUD) in Turbo Trellis Coded modulation- (TTCM-) based Space Division Multiple Access (SDMA) Orthogonal Frequency Division Multiplexing (OFDM) system. Unlike gradient descent methods, both ABC and PSO methods ensure minimization of the objective function without the solution being trapped into local optima. These techniques are capable of achieving excellent performance in the so-called overloaded system, where the number of transmit antennas is higher than the number of receiver antennas, in which the known classic MUDs fail. The performance of the proposed algorithm is compared with each other and also against Genetic Algorithm- (GA-) based MUD. Simulation results establish better performance, computational efficiency, and convergence characteristics for ABC and PSO methods. It is seen that the proposed detectors achieve similar performance to that of well-known optimum Maximum Likelihood Detector (MLD) at a significantly lower computational complexity and outperform the traditional MMSE MUD.</p