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Journals / An International Journal of Optimization and Control: Theories & Applications (IJOCTA) / 2020 / Cilt: 10 - Sayı: 1

An improved differential evolution algorithm with a restart technique to solve systems of nonlinear equations

Jeerayut WETWEERAPONG
Pikul PUPHASUK
Pages
118–136
Publication Date
2022-10-11
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Experiment results show thatDE-R can successfully solve all the test problems with fast convergence speedand give high quality solutions.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this research, an improved differential evolution algorithm with a restarttechnique (DE-R) is designed for solutions of systems of nonlinear equationswhich often occurs in solving complex computational problems involving variablesof nonlinear models.
📊
Key Findings & Data:DE-R adds a new strategy for mutation operationand a restart technique to prevent premature convergence and stagnation duringthe evolutionary search to the basic DE algorithm.
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Conclusion & Impact:Experiment results show thatDE-R can successfully solve all the test problems with fast convergence speedand give high quality solutions.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

In this research, an improved differential evolution algorithm with a restarttechnique (DE-R) is designed for solutions of systems of nonlinear equationswhich often occurs in solving complex computational problems involving variablesof nonlinear models. DE-R adds a new strategy for mutation operationand a restart technique to prevent premature convergence and stagnation duringthe evolutionary search to the basic DE algorithm. The proposed methodis evaluated on various real world and synthetic problems and compared withthe recently developed methods in the literature. Experiment results show thatDE-R can successfully solve all the test problems with fast convergence speedand give high quality solutions. It also outperforms the compared methods.

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Cite This Article

WETWEERAPONG, J., PUPHASUK, P. (2022). An improved differential evolution algorithm with a restart technique to solve systems of nonlinear equations. An International Journal of Optimization and Control: Theories & Applications (IJOCTA), Cilt: 10 - Sayı: 1, 118–136. https://ajindex.com/articles/440574

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References (14)

  1. Boussaid, I., Lepagnot, J., & Siarry, P. (2013). A survey on optimization metaheuristics. Information sciences, 237, 82–117
  2. Siddique, N., & Adeli, H. (2015). Nature inspired computing: An overview and some future directions. Cogn Comput, 7, 706–714
  3. Nanda, S. J., & Panda, G. (2014). A survey on nature inspired metaheuristic algorithms for partitional clustering. Swarm and Evolutionary Computation, 16, 1—18
  4. Jos´e-Garc´ıa, A., & G´omez-Flores, W. (2016). Automatic clustering using nature-inspired metaheuristics: A survey. Applied Soft Computing, 41, 192—213
  5. Hamm, L., Brorsen, B. W., & Hagan, M. T. (2007). Comparison of Stochastic Global Optimization Methods to Estimate Neural Network Weights. Neural Process Lett, 26, 145— 158
  6. Piotrowski, A. P. (2014). Differential evolution algorithms applied to neural network training suffer from stagnation. Applied Soft Computing, 21, 382—406
  7. Raja, M. A. Z., Umar, M., Sabir, Z., Khan, J. A., & Baleanu, D. (2018). A new stochastic computing paradigm for the dynamics of nonlinear singular heat conduction model of the human head. Eur. Phys. J. Plus, 133(364), DOI 10.1140/epjp/i2018-12153-4
  8. Sabir, Z., Manzar, M. A., Raja, M. A. Z., Sheraz, M., & Wazwaz, A. M. (2018). Neuroheuristics for nonlinear singular Thomas- Fermi systems. Applied Soft Computing, 65, 152-169
  9. Raja, M. A. Z., Shah, Z., Manzar, M. A., Ahmad, I., Awais, M.,& Baleanu, D. (2018). A new stochastic computing paradigm for nonlinear Painlev´e II systems in applications of random matrix theory. Eur. Phys. J. Plus, 133(254), DOI 10.1140/epjp/i2018-12080-4
  10. Raja, M. A. Z., Zameer, A., Kiani, A. K., Shehzad, A., & Khan, A. R. (2018). Natureinspired computational intelligence integration with Nelder-Mead method to solve nonlinear benchmark models. Neural Comput & Applic, 29, 1169-1193
  11. Ahmad, I., Zahid, H., Ahmad, F., Raja, M. A. Z., & Baleanu, D. (2019). Design of computational intelligent procedure for thermal analysis of porous fin model. Chinese Journal of Physics, 59, 641-655
  12. Broyden, C. G. (1965). A class of methods for solving nonlinear simultaneous equations. Mathematics of computation, 19(92), 577–593
  13. Martinez, J. M. (1994). Algorithms for solving nonlinear systems of equations. In: E. Spedicato, ed. Algorithms for Continuous Optimization-The state of the art. Kluwer Academic Publishers, London, 81–108
  14. Kelley, C. T. (1995). Iterative methods for solving linear and nonlinear equations. SIAM