Operational Framework for Recent Advances in Backtracking Search Optimisation Algorithm: A Systematic Review and Performance Evaluation
Bryar A. Hassan, Tarik A. Rashid

TL;DR
This paper systematically reviews and evaluates the Backtracking Search Algorithm (BSA), proposing frameworks for its extensions and demonstrating its superior performance over other algorithms across various benchmark problems.
Contribution
It introduces two frameworks for understanding BSA's extensions and applications, and provides a comprehensive performance comparison with other optimization algorithms.
Findings
BSA outperforms DE, PSO, ABC, and FF in various benchmark tests.
BSA shows robustness across different problem hardness levels and dimensions.
The study offers structured guidance for future BSA improvements.
Abstract
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…
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