Backtracking Search Optimization Algorithm
Bryar A. Hassan, Alla Ahmad Hassan, Aram M. Ahmed, Tarik Ahmed Rashid, Abdul Ameer Abdulla · 2025
Evolutionary computing algorithms are one of the families of global optimization algorithms. These algorithms are problem solvers with a metaheuristic or stochastic optimization character and members of the population-based trial family. One of the recent and common population-based approaches used for mathematical problems is the Backtracking Search Optimization Algorithm (BSA). In the current chapter, a practical tutorial on BSA is provided. At first glance, evolutionary algorithms and their artificial implementation are introduced. After that, the basic steps of BSA are explained. Next, the current source code programs used to implement BSA are presented. An example explains how BSA works on a numerical optimization problem. Lastly, based on the potential of BSA for minimizing different optimization problems, a wide overview (extensive description) of the algorithm is illustrated via a general framework of BSA variants. It is intended that this chapter will offer helpful references and advice to scholars seeking to work on BSA and enhance it accordingly.