Multi-mutation scheme adaptive differential evolution for solving truss sizing optimization
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Published:
May 18, 2018
Keywords:
Meta-heuristics Adaptive algorithms Truss sizing optimization Differential evolution Multi-mutation schemes
Main Article Content
Abstract
A novel adaptive differential evolution algorithm called Multi-Mutation Scheme Adaptive Differential Evolution (MMADE) is developed in this article. Several truss sizing optimization problems have been posed for performance test. The proposed adaptive algorithm is integrated with adaptive scaling factor, crossover ratio and mutation schemes selection. Results obtained from the proposed algorithm are compared to recent adaptive algorithms from literature. The MMADE show very competitive performance compared to those state-of-the-art adaptive algorithms.
Article Details
How to Cite
Panagant, N., & Bureerat, S. (2018). Multi-mutation scheme adaptive differential evolution for solving truss sizing optimization. Asia-Pacific Journal of Science and Technology, 23(3), APST–23. https://doi.org/10.14456/apst.2018.5
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Research Articles
References
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[2] Hassajan S, Lamom A, Cheerarot R.,2017. Effect of materials selection on the minimum cost for optimal design of reinforced concrete beams using hill climbing algorithm. Asia-Pacific Journal of Science and Technology 17, 385-400.
[3] Sriworamas K, Bureerat S, Vangpaisal T.,2017. Multi objective evolutionary algorithms for pipe network design and rehabilitation: comparative study on large and small scale problems. Asia-Pacific Journal of Science and Technology 17, 366-74.
[4] Kunakote T, Bureerat S.,2017. Multiobjective two-stage optimization of a plate structure using a population-based incremental learning method. Asia-Pacific Journal of Science and Technology 19, 233-44.
[5] Kaewploy S.,2015. Optimal parameters in precipitation hardening of 6061 aluminium alloy using box-behnken design. Asia-Pacific Journal of Science and Technology 20, 369-80.
[6] Rahami H, Kaveh A, Gholipour Y.,2008. Sizing, geometry and topology optimization of trusses via force method and genetic algorithm. Engineering Structures 30, 2360-9.
[7] Ahrari A, Atai AA, Deb K.,2015. Simultaneous topology, shape and size optimization of truss structures by fully stressed design based on evolution strategy. Engineering Optimization 47, 1063-84.
[8] Bureerat S, Pholdee N.,2015. Optimal truss sizing using an adaptive differential evolution algorithm. Journal of Computing in Civil Engineering 30, 04015019.
[9] Pholdee N, Bureerat S.,2017. A comparative study of eighteen self-adaptive metaheuristic algorithms for truss sizing optimisation. KSCE Journal of Civil Engineering, 1-2.
[10] Panagant N, Bureerat S.,2018. Truss topology, shape and sizing optimization by fully stressed design based on hybrid grey wolf optimization and adaptive differential evolution. Engineering Optimization 10, 1-7.
[11] Tejani GG, Savsani VJ, Patel VK.,2016. Adaptive symbiotic organisms search (SOS) algorithm for structural design optimization. Journal of Computational Design and Engineering 3, 226-49.
[12] Tejani GG, Savsani VJ, Patel VK., 2016. Modified sub-population teaching-learning-based optimization for design of truss structures with natural frequency constraints. Mechanics Based Design of Structures and Machines 44, 495-513.
[13] Savsani VJ, Tejani GG, Patel VK.,2016. Truss topology optimization with static and dynamic constraints using modified subpopulation teaching–learning-based optimization. Engineering Optimization 48, 1990-2006.
[14] Tejani GG, Savsani VJ, Patel VK, Mirjalili S.,2017 Truss optimization with natural frequency bounds using improved symbiotic organisms search. Knowledge-Based Systems, 11.
[15] Tejani GG, Savsani VJ, Bureerat S, Patel VK.,2017. Topology and Size Optimization of Trusses with Static and Dynamic Bounds by Modified Symbiotic Organisms Search. Journal of Computing in Civil Engineering 32, 04017085.
[16] Tejani GG, Saysani VJ, Patel VK, Bureerat S.,2017. Topology, shape, and size optimization of truss structures using modified teaching-learning based optimization. Advances In Computational Design 2, 313-31.
[17] Noilublao N, Bureerat S.,2011. Simultaneous topology, shape and sizing optimisation of a three-dimensional slender truss tower using multiobjective evolutionary algorithms. Computers & Structures 89, 2531-8.
[18] Pholdee N, Bureerat S.,2014. Hybrid real-code population-based incremental learning and approximate gradients for multi-objective truss design. Engineering Optimization 46, 1032-51.
[19] Storn R, Price K.,1997. Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization 11, 341-59.
[20] Storn R, Price K.,1996. Minimizing the real functions of the ICEC'96 contest by differential evolution. In Evolutionary Computation, 1996., Proceedings of IEEE International Conference on May 20, 842-844.
[21] Liu J, Lampinen J.,2005. A fuzzy adaptive differential evolution algorithm. Soft Computing 9, 448-62.
[22] Qin AK, Suganthan PN. 2005. Self-adaptive differential evolution algorithm for numerical optimization. InEvolutionary Computation. The 2005 IEEE Congress on 2005 Sep 2 2, 1785-1791.
[23] Teo J.,2006. Exploring dynamic self-adaptive populations in differential evolution. Soft Computing 10, 673-86.
[24] Brest J, Greiner S, Boskovic B, Mernik M, Zumer V.,2006. Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE transactions on evolutionary computation 10, 646-57.
[25] Zhang J, Sanderson AC.,2009. JADE: adaptive differential evolution with optional external archive. IEEE Transactions on evolutionary computation 13, 945-58.
[26] Tanabe R, Fukunaga A.,2013. Success-history based parameter adaptation for differential evolution. InEvolutionary Computation (CEC), IEEE Congress on 2013 Jun 20, 71-78.
[27] Tanabe R, Fukunaga AS.,2014. Improving the search performance of SHADE using linear population size reduction. InEvolutionary Computation (CEC), IEEE Congress on 2014 Jul 6, 1658-1665.