Abstract
In this article, multi-objective optimization of braced frames is investigated using a novel hybrid algorithm. Initially, the applied evolutionary algorithms, ant colony optimization (ACO) and genetic algorithm (GA) are reviewed, followed by developing the hybrid method. A dynamic hybridization of GA and ACO is proposed as a novel hybrid method which does not appear in the literature for optimal design of steel braced frames. Not only the cross section of the beams, columns and braces are considered to be the design variables, but also the topologies of the braces are taken into account as additional design variables. The hybrid algorithm explores the whole design space for optimum solutions. Weight and maximum displacement of the structure are employed as the objective functions for multi-objective optimal design. Subsequently, using the weighted sum method (WSM), the two objective problem are converted to a single objective optimization problem and the proposed hybrid genetic ant colony algorithm (HGAC) is developed for optimal design. Assuming different combination for weight coefficients, a trade-off between the two objectives are obtained in the numerical example section. To make the final decision easier for designers, related constraint is applied to obtain practical topologies. The achieved results show the capability of HGAC to find optimal topologies and sections for the elements.
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References
Holland J H. Adaptation in Natural and Artificial Systems. Ann Arbor: University of Michigan Press, 1975
Goldberg D E. Genetic Slgorithms in Search, Optimization, and Machine Learning. Boston, MA: Addison-Wesley Longman Publication Co, 1989
Fogel G L, Owens A J, Walsh M J. Artificial Intelligence through Simulated Evolution. New York: John Wiley, 1996
Kirkpatrick S, Gelatt C D, Vecchi M P. Optimization by Simulated Annealing. Science, 1983, 220(4598): 671–680
Rechenberg I. Evolution Strategy: Optimization of Technical Systems according to the Principle of Biological Evolution. Stuttgart: Frommann-Holzboog, 1973
Dorigo M. Optimization, Learning and Natural Algorithms. Pissertation for the Doctoral Degree. Politecnico di Milano, Italy, 1992
Dorigo M, Maniezzo V, Colorni A. Ant system: Optimization by a colony of cooperating agents. IEEE Transactions on System, Man, and Cybernetics-Part B, 1996, 26(1): 29–41
Kennedy J, Eberhart R. Particle swarm optimization. Proceedings of the International Conference on Neural Networks. 1995, 1942–1948
Glover F. Tabu search-Part I. ORSA Journal on Computing, 1989, 1 (3): 190–206
Sanaei E, Babaei M. Topology optimization of structures using cellular automata with constant strain triangles. International Journal of Civil Engineering, 2012, 10(3): 179–188
Elbeltagi E, Hegazy T, Grierson D E. Comparison among five evolutionary-based optimization algorithms. Advanced Engineering Informatics, 2005, 19(1): 43–53
Krishnamoorthy C S. Structural optimization in practice: potential applications of genetic algorithms. Structural Engineering and Mechanics, 2001, 11(2): 151–170
Pezeshk S, Camp C V. State of the Art on the Use of Genetic Algorithms in Design of Steel Structures. In Recent Advanced in Optimal Structural Design, ASCE (edited by S. Burns), 2002, 1–31
Espinoza F P, Minsker B S, Goldberg D E. Adaptive hybrid genetic algorithm for groundwater remediation design. Journal of Water Resources Planning and Management, 2005, 131(1): 14–24
Takahashi R. A hybrid Method of Genetic Algorithms and Ant Colony Optimization to Solve the Traveling Salesman Problem. IEEE International Conference on Machine Learning and Applications, Miami Beach, FL, 2009
Zukhri Z, Paputungan I V. A hybrid optimization algorithm based on genetic algorithm and ant colony optimization. International Journal of Artificial Intelligence & Applications, 2013, 4(5): 63–75 (IJAIA)
Chan C M, Wong K M. Structural topology and element sizing design optimization of tall steel frameworks using a hybrid OC–GA method. Structural and Multidisciplinary Optimization, 2008, 35(5): 473–488
Chen M, Lu Q. A co-evolutionary model based on dynamic combination of genetic algorithm and ant colony algorithm. In: Proceedings of the 6th International Conference on Parallel and Distributed Computing. Applications and Technologies, 2005, 941–944
Kicinger R, Arciszewski T. Empirical analysis of memetic algorithms for conceptual design of steel structural systems in tall building. In: Advances in Engineering Structures, Mechanics & Construction, (edited by Pandey M, Wie W C, and Xu L). Netherlands: Springer, 2006, 277–288
Kareem A, Spence S M J, Bernardini E, Bobby S, Wei D. Using computational fluid dynamics to optimize tall building design. CTBUH Journal Issue III, 2013, 3: 38–43
Spence S M J, Kareem A. Data-enabled design and optimization (DEDOpt): Tall steel building frameworks. Computers & Structures, 2013, 129: 134–147
Bobby S, Spence S M J, Bernardini E, Kareem A. Performancebased topology optimization for wind-excited tall buildings: A framework. Engineering Structures, 2014, 74: 242–255
Babaei M. Exploring practical optimal topology for reinforced concrete moment resisting frame structures. American Journal of Civil Engineering, 2015, 3(4): 102–106
Babaei M, Dadash Amiri J. Determining the optimal topology for intermediate steel moment resisting frames with eccentric braces in hybrid system. International Journal of Structural Engineering (in press)
Babaei M, Jabbar M. Optimal intermediate steel moment resisting frames with different spans and story numbers. Journal of Engineering and Applied Sciences (Asian Research Publishing Network), 2016, 10(8): 223–226
Kaveh A, Jahanshahi M, Khanzadi M. Plastic analysis of frames using genetic algorithm and ant colony algorithm. Asian Journal of Civil Engineering, 2008, 3: 9227–9246
Babaei M, Mollayi M. Multi-objective optimization of reinforced concrete frames using NSGA-II algorithm, under review
Xu Y L, LimMH, Ong Y S, Tang J. A GA-ACO local search hybrid algorithm for solving quadratic assignment problem. Genetic and Evolutionary Computation Conference, 2006, 599–605
Babaei M, Asemani R, Kazemi F. Exploring for optimal number and location of trusses in core and outrigger belt truss system In proceedings of the 1st International & 5th National Conference of Steel and Structure, Iranian Association of Steel Structures, Iran, 2015
Athan T W, Papalambros P Y. A note on weighted criteria methods for compromise solution multi-objective optimization. Engineering Optimization, 1996, 27(2): 155–176
Koski J, Silvennoinen R. Norm methods and partial weighting in multi-criterion optimization of structures. International Journal for Numerical Methods in Engineering, 1987, 24(6): 1101–1121
Marler R T, Arora J S. Survey of multi-objective optimization methods for engineering. Structural and Multidisciplinary Optimization, 2004, 26(6): 369–395
Bullnheimer B, Hartl R F, Strauss C. An improved ant system algorithm for the vehicle routing problem. European Journal of Operational Research, 1999, 89: 319–328
Babaei M. Multi-Objective Optimization of Mid and High Rise Steel Structures using an Efficient Hybrid Evolutionary Algorithm. Dissertation for the Doctoral Degree. Iran University of Science and Technology, 2012
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Babaei, M., Sanaei, E. Multi-objective optimal design of braced frames using hybrid genetic and ant colony optimization. Front. Struct. Civ. Eng. 10, 472–480 (2016). https://doi.org/10.1007/s11709-016-0368-4
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DOI: https://doi.org/10.1007/s11709-016-0368-4