# Operational Framework for Recent Advances in Backtracking Search Optimisation Algorithm: A Systematic Review and Performance Evaluation

arXiv: https://arxiv.org/abs/1911.13011 · HTML: https://mlsanity.com/paper/1911.13011/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2019.

## Built on by (most cited first, 6 of 6)

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- [Performance evaluation results of evolutionary clustering algorithm star for clustering heterogeneous datasets](https://mlsanity.com/paper/2105.02810/) (2021)
- [Formal context reduction in deriving concept hierarchies from corpora using adaptive evolutionary clustering algorithm star](https://mlsanity.com/paper/2107.04781/) (2021)
- [An Improved Deep Convolutional Neural Network by Using Hybrid Optimization Algorithms to Detect and Classify Brain Tumor Using Augmented MRI Images](https://mlsanity.com/paper/2206.04056/) (2022)
- [Comparative Analysis of AES, Blowfish, Twofish, Salsa20, and ChaCha20 for Image Encryption](https://mlsanity.com/paper/2407.16274/) (2024)

## Abstract (arXiv)

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.