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Operational framework for recent advances in backtracking search optimisation algorithm: A systematic review and performance evaluation

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  • Hassan, Bryar A.
  • Rashid, Tarik A.

Abstract

Backtracking search optimisation algorithm (BSA) is a commonly used meta-heuristic optimisation algorithm and was proposed by Civicioglu in 2013. When it was first used, it exhibited its strong potential for solving numerical optimisation problems. Additionally, the experiments conducted in previous studies demonstrated the successful performance of BSA and its non-sensitivity toward the several types of optimisation problems. This success of BSA motivated researchers to work on expanding it, e.g., developing its improved versions or employing it for different applications and problem domains. However, there is a lack of literature review on BSA; therefore, reviewing the aforementioned modifications and applications systematically will aid further development of the algorithm. This paper provides a systematic review and meta-analysis that emphasise on reviewing the related studies and recent developments on BSA. Hence, the objectives of this work are two-fold: (i) First, two frameworks for depicting the main extensions and the uses of BSA are proposed. The first framework is a general framework to depict the main extensions of BSA, whereas the second is an operational framework to present the expansion procedures of BSA to guide the researchers who are working on improving it. (ii) Second, the experiments conducted in this study fairly compare the analytical performance of BSA with four other competitive algorithms: differential evolution (DE), particle swarm optimisation (PSO), artificial bee colony (ABC), and firefly (FF) on 16 different hardness scores of the benchmark functions with different initial control parameters such as problem dimensions and search space. The experimental results indicate that BSA is statistically superior than the aforementioned algorithms in solving different cohorts of numerical optimisation problems such as problems with different levels of hardness score, problem dimensions, and search spaces. This study can act as a systematic and meta-analysis guide for the scholars who are working on improving BSA.

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  • Hassan, Bryar A. & Rashid, Tarik A., 2020. "Operational framework for recent advances in backtracking search optimisation algorithm: A systematic review and performance evaluation," Applied Mathematics and Computation, Elsevier, vol. 370(C).
    • Handle: RePEc:eee:apmaco:v:370:y:2020:i:c:s0096300319309117
    DOI: 10.1016/j.amc.2019.124919
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    1. Naz Niamul Islam & M A Hannan & Azah Mohamed & Hussain Shareef, 2016. "Improved Power System Stability Using Backtracking Search Algorithm for Coordination Design of PSS and TCSC Damping Controller," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-17, January.
    2. Md Shafiullah & M. A. Abido & Md Ismail Hossain & A. H. Mantawy, 2018. "An Improved OPP Problem Formulation for Distribution Grid Observability," Energies, MDPI, vol. 11(11), pages 1-16, November.
    3. Yu, Kunjie & Liang, J.J. & Qu, B.Y. & Cheng, Zhiping & Wang, Heshan, 2018. "Multiple learning backtracking search algorithm for estimating parameters of photovoltaic models," Applied Energy, Elsevier, vol. 226(C), pages 408-422.
    4. Kuntal Bhattacharjee, 2018. "Economic Dispatch Problems Using Backtracking Search Optimization," International Journal of Energy Optimization and Engineering (IJEOE), IGI Global, vol. 7(2), pages 39-60, April.
    5. Hong Zhao & Fan Min & William Zhu, 2013. "Cost-Sensitive Feature Selection of Numeric Data with Measurement Errors," Journal of Applied Mathematics, Hindawi, vol. 2013, pages 1-13, April.
    6. Jianzhong Zhou & Na Sun & Benjun Jia & Tian Peng, 2018. "A Novel Decomposition-Optimization Model for Short-Term Wind Speed Forecasting," Energies, MDPI, vol. 11(7), pages 1-27, July.
    7. Jianzhong Zhou & Chu Zhang & Tian Peng & Yanhe Xu, 2018. "Parameter Identification of Pump Turbine Governing System Using an Improved Backtracking Search Algorithm," Energies, MDPI, vol. 11(7), pages 1-18, June.
    8. Modiri-Delshad, Mostafa & Aghay Kaboli, S. Hr. & Taslimi-Renani, Ehsan & Rahim, Nasrudin Abd, 2016. "Backtracking search algorithm for solving economic dispatch problems with valve-point effects and multiple fuel options," Energy, Elsevier, vol. 116(P1), pages 637-649.
    9. Chu Zhang & Chaoshun Li & Tian Peng & Xin Xia & Xiaoming Xue & Wenlong Fu & Jianzhong Zhou, 2018. "Modeling and Synchronous Optimization of Pump Turbine Governing System Using Sparse Robust Least Squares Support Vector Machine and Hybrid Backtracking Search Algorithm," Energies, MDPI, vol. 11(11), pages 1-21, November.
    10. Liqun Qi, 1993. "Convergence Analysis of Some Algorithms for Solving Nonsmooth Equations," Mathematics of Operations Research, INFORMS, vol. 18(1), pages 227-244, February.
    11. Xiaohui Yuan & Xiaotao Wu & Hao Tian & Yanbin Yuan & Rana Muhammad Adnan, 2016. "Parameter Identification of Nonlinear Muskingum Model with Backtracking Search Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(8), pages 2767-2783, June.
    12. Sizhou Sun & Lisheng Wei & Jie Xu & Zhenni Jin, 2019. "A New Wind Speed Forecasting Modeling Strategy Using Two-Stage Decomposition, Feature Selection and DAWNN," Energies, MDPI, vol. 12(3), pages 1-24, January.
    13. Vitayasak, Srisatja & Pongcharoen, Pupong & Hicks, Chris, 2017. "A tool for solving stochastic dynamic facility layout problems with stochastic demand using either a Genetic Algorithm or modified Backtracking Search Algorithm," International Journal of Production Economics, Elsevier, vol. 190(C), pages 146-157.
    14. Tarik A Rashid & Dosti K Abbas & Yalin K Turel, 2019. "A multi hidden recurrent neural network with a modified grey wolf optimizer," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-23, March.
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    3. Lv, Sheng-Xiang & Wang, Lin, 2022. "Deep learning combined wind speed forecasting with hybrid time series decomposition and multi-objective parameter optimization," Applied Energy, Elsevier, vol. 311(C).

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