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

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 twofold: (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.In artificial intelligence (AI), besides the existing traditional symbolic and statistical methods [1], computational intelligence (CI) has emerged as a recent research area [2]. According to [3], CI deals with collections of nature-inspired algorithms and approaches that in turn have been used to deal with complex practical problems that cannot be feasibly or effectively solved by traditional methods. CI comprises three subfields: fuzzy logic, evolutionary computation, and neural network.The concept of swarm intelligence (SI) is a part of evolutionary computation. It deals with artificial and natural systems that comprise several individuals and possess the ability of self-organisation and decentralised control.This concept, which was primarily initiated by Gerardo Beni and Jing Wang in 1989 in the context of cellular robotic systems, is used in AI [4]. Typically, the SI system comprises a collection of agents. The agents interact with their own environment or with each other. SI syst...

Improved Fitness-Dependent Optimizer Algorithm

“…Finally, adaptation and hybridization of the IFDO with other algorithms will be the main focus of future work. Also, the performance of IFDO can be further evaluated against other popular algorithms, such as WOA-BAT [65], Donkey and Smuggler Optimisation [66], and Modified Grey Wolf Optimiser [67], Modifications of Dragonfly Algorithm [68], Modifications of Backtracking Algorithm [69]. [31].…”

Improved Fitness-Dependent Optimizer Algorithm

“…Finally, adaptation and hybridization of the IFDO with other algorithms will be the main focus of future work. Also, the performance of IFDO can be further evaluated against other popular algorithms, such as WOA-BAT [65], Donkey and Smuggler Optimisation [66], and Modified Grey Wolf Optimiser [67], Modifications of Dragonfly Algorithm [68], Modifications of Backtracking Algorithm [69].…”

Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation

“…In more than 85% of the results, P value is less than 0.05%, which proves that the results are significant and we can reject the null hypothesis that there is no difference between the means. It is worth mentioning that the performance of CSO can be further evaluated by comparing it against other new algorithms such as donkey and smuggler optimization algorithm [109], modified grey wolf optimizer [110], BSA and its variants [111], WOA and its variants [112], and other modified versions of DA [113].…”