Accelerated Particle Swarm Optimizer for Optimizing Problems of Structural Engineering
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Abstract
The aim of the present work is to find a solution to non-linear constrained optimization problems of structure. Constrained optimization difficulties are practical shortcomings. The loopholes of traditional numerical methods are being removed by heuristic methods as no requirement of the functional derivatives is desired and approaches to the global way out. This article presents a “penalty guided Accelerated Particle Swarm Optimization (APSO) algorithm” to search the problem’s optimal solution in the feasible region of whole search domain. There is numerical result and comparison of the structural design optimization problems. The way out by the current perspective proves to be the better than other techniques and it can be said that our findings show better solutions to engineering problems than those earlier obtained using current algorithms.
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Sharma (Dwivedi), M., Alam, S., & Khan, M. (2018). Accelerated Particle Swarm Optimizer for Optimizing Problems of Structural Engineering. SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology, 10(01), 47-54. https://doi.org/10.18090/samriddhi.v10i01.7
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Research Article
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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[2] J. S. Arora, Introduction to Optimum Design, McGraw- Hill, New York, 1989.
[3] K. S. Lee and Z. W. Geem, A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice, Computer Methods in Applied Mechanics and Engineering, 194 (2005), 3902– 3933.
[4] L. C. Cagnina, S. C. Esquivel and C. A. C. Coello, Solving engineering optimization problems with the simple constrained particle swarm optimizer, Informatica, 32 (2008), 319–326.
[5] C. Guo, J. Hu, B. Ye and Y. Cao. Swarm intelligence for mixed variable design optimization. Journal of Zheijiang University SCIENCE, 5(7):851–860, 1994.
[6] Yang X. S., Nature-Inspired Metaheuristic Algorithms, Luniver Press, (2008).
[7] Yang X. S., Engineering Optimization: An Introduction with Metaheuristic Applications, John Wiley and Sons, (2010).
[8] X. Hu, R. Eberhart and Y. Shi. Engineering optimization with particle swarm. 2003.
[9] E. Mezura and C. Coello. Useful Infeasible Solutions in Engineering Optimization with Evolutionary Algorithms. In Proceedings of the 4th Mexican International Conference on Artificial Intelligence, MICAI 2005. Lecture Notes on Artificial Intelligence No. 3789, pages 652–662. 2005.
[10] J. Kennedy and R. C. Eberhart, Particle swarm optimization, in: Proc. of IEEE International Conference on Neural Networks, Piscataway, NJ. pp. 1942-1948 (1995).
[11] Nalluri, S. K., & Parasaram, V. K. B. (2015). Automating Software Builds with Jenkins: Design Patterns and Failure Handling. International Journal of Technology, Management and Humanities, 1(01), 16-33. https://doi.org/10.21590/ijtmh.01.02.03
[12] J. Kennedy, R. C. Eberhart, Swarm intelligence, Academic Press, 2001.
[13] J. Golinski. An adaptive optimization system applied to machine synthesis. Mechanism and Machine Synthesis. 8(1973), pages 419–436, 1973.
[14] Z. Michalewicz, GeneticAlgorithms + Data Structures
= Evolution Programs, Springer - Verlag, Berlin, 1994.
[15] K. M. Ragsdell and D. T. Phillips, Optimal design of a class of welded structures using geometric programming, ASME Journal of Engineering for Industries, 98 (1976), 1021–1025.
[16] K. Ragsdell, and D. Phillips. Optimal design of a class of welded structures using geometric programming. ASME Journal of Engineering for Industries, 98(3):1021–1025, 1976.
[17] L. C. Cagnina, S. C. Esquivel and C. A. C. Coello, Solving engineering optimization problems with the simple constrained particle swarm optimizer, Informatica, 32 (2008), 319–326.
[18] A. H. Gandomi, X. S. Yang, and A. H. Alavi, Mixed variable structural optimization using firefly algorithm, Computers and Structures, 89 (2011a), 2325–2336.
[19] Nalluri, S. K., & Parasaram, V. K. B. (2015). Automating Software Builds with Jenkins: Design Patterns and Failure Handling. International Journal of Technology, Management and Humanities, 1(01), 16-33.