RESEARCH ON THE SEQUENTIAL QUADRATIC PROGRAMMING DIFFERENTIAL EVOLUTION, JAYA, AND JAYA'S EVOLUTIONARY ALGORITHM
Article Sidebar
Main Article Content
Abstract
This study focuses on researching and comparing optimization algorithms such as Sequential Quadratic Programming, Differential Evolution, Jaya, and Jaya's evolutionary algorithm. Sequential Quadratic Programming is an optimization method based on mathematical programming, where constraints and objective functions are represented by convex and differentiable functions. Differential Evolution is a combinatorial evolutionary algorithm that utilizes genetic operators such as crossover and mutation to generate new generations of individuals. Jaya is an optimization algorithm based on continuous improvement of the population, where individuals are updated based on the current best solution. This study focuses on the specific application of the Jaya evolutionary algorithm (iJaya) compared to other algorithms and compares the performance of these algorithms in solving optimization problems through real-world examples of fiber orientation optimization in stiffened composite plates. Experiments on popular optimization problems and measure factors such as runtime, accuracy, and the ability to search for optimal solutions were conducted. The research results will provide an overview of the performance and advantages of each algorithm, thereby providing recommendations for selecting the appropriate algorithm for corresponding problems in the construction field.
Downloads
Article Details
References
numerical efficiency of different sequential linear programming-based algorithms for structural
optimisation problems. Computers & Structures.
2000;76(6): 713–728. https://doi.org/10.1016/S0045-
7949(99)00185-6.
[2] Lamberti L, Pappalettere C. Improved sequential linear programming formulation for structural weight
minimization. Computer Methods in Applied Mechanics and Engineering. 2004;193(33–35): 3493–3521.
https://doi.org/10.1016/j.cma.2003.12.040.
[3] Sedaghati R. Benchmark case studies in
structural design optimization using the force
method. International Journal of Solids
and Structures. 2005;42(21–22): 5848–5871.
https://doi.org/10.1016/j.ijsolstr.2005.03.030.
[4] Nguyen TT, Ho HV, Dang TH, Bui XT, Lam PT.
Optimization analysis of stiffened composite plate by
sequential quadratic programming. Journal of Science
and Technology. 2013;51(1B): 156–165.
[5] Koza JR. Genetic programming: on the programming
of computers by means of natural selection. Cambridge, MA: MIT Press; 1992.
[6] Kennedy J, Eberhart R. Particle swarm optimization. In: Proceedings of ICNN’95 – International Conference on Neural Networks, Perth,
WA, Australia. USA: IEEE; 1995. p.1942–1948.
https://doi.org/10.1109/ICNN.1995.488968.
[7] Storn R, Price K. Differential evolutionA simple and efficient heuristic for global
optimization over continuous spaces. Journal
of Global Optimization. 1997;11: 341–359.
https://doi.org/10.1023/A:1008202821328.
[8] Basturk B, Karaboga D. An artificial bee colony
(ABC) algorithm for numeric function optimization.
In: Proceedings of the IEEE Swarm Intelligence Symposium. USA: IEEE; 2006. p.127– 135.
[9] Rao R. Jaya: A simple and new optimization algorithm for solving constrained and unconstrained
optimization problems. International Journal of Industrial Engineering Computations. 2016;7(1): 19–
34. https://doi.org/10.5267/j.ijiec.2015.8.004.
[10] Dinh CD, Ho HV, Vo DT, Ngo THQ, Nguyen
TT. Efficiency of Jaya algorithm for solving the
optimization-based structural damage identification
problem based on a hybrid objective function.
Engineering Optimization. 2018;50(8): 1233–1251.
https://doi.org/10.1080/0305215X.2017.1367392.
[11] Dinh CD, Vo DT, Ho HV, Nguyen TT. Damage assessment in plate-like structures using a
two-stage method based on modal strain energy
change and Jaya algorithm. Inverse Problems in
Science and Engineering. 2019;27(2): 166–189.
https://doi.org/10.1080/17415977.2018.1454445.
[12] Rao RV, More KC. Design optimization
and analysis of selected thermal devices
using self-adaptive Jaya algorithm. Energy
Conversion and Management. 2017;140: 24–35.
https://doi.org/10.1016/j.enconman.2017.02.068.
[13] Rao RV, More KC. Optimal design and
analysis of mechanical draft cooling tower
using improved Jaya algorithm. International Journal of Refrigeration. 2017;82: 312–324.
https://doi.org/10.1016/j.ijrefrig.2017.06.024.
[14] Rao RV, Rai DP. Optimisation of welding
processes using quasi-oppositional-based Jaya
algorithm. Journal of Experimental & Theoretical
Artificial Intelligence. 2017;29(5): 1099–1117.
https://doi.org/10.1080/0952813X.2017.1309692.
[15] Kolli M, Chandrashekhara K. Finite element analysis
of stiffened laminated plates under transverse loading.
Composites science and technology. 1996;56(12):
1355–1361. https://doi.org/10.1016/S0266-
3538(96)00086-3.
[16] Padhye N, Bhardawaj P, Deb K. Improving differential evolution through a unified approach. Journal of Global Optimization. 2013;55: 771–799.
https://doi.org/10.1007/s10898-012-9897-0.
[17] Lam PT, Nguyen HS, Ho HV, Nguyen TT. Optimization of stiffened composite plate using adjusted different evolution algorithm. In: Proceeding of the 7th
International Conference on Computational Methods
(ICCM2016). Berkeley, CA, USA; 2016. p.76–85.