•  
  •  
 

Abstract

A new human-based heuristic optimization method, named the Snooker-Based Optimization Algorithm (SBOA), is introduced in this study. The inspiration for this method is drawn from the traits of sales elites—those qualities every salesperson aspires to possess. Typically, salespersons strive to enhance their skills through autonomous learning or by seeking guidance from others. Furthermore, they engage in regular communication with customers to gain approval for their products or services. Building upon this concept, SBOA aims to find the optimal solution within a given search space, traversing all positions to obtain all possible values. To assesses the feasibility and effectiveness of SBOA in comparison to other algorithms, we conducted tests on ten single-objective functions from the 2019 benchmark functions of the Evolutionary Computation (CEC), as well as twenty-four single-objective functions from the 2022 CEC benchmark functions, in addition to four engineering problems. Seven comparative algorithms were utilized: the Differential Evolution Algorithm (DE), Sparrow Search Algorithm (SSA), Sine Cosine Algorithm (SCA), Whale Optimization Algorithm (WOA), Butterfly Optimization Algorithm (BOA), Lion Swarm Optimization (LSO), and Golden Jackal Optimization (GJO). The results of these diverse experiments were compared in terms of accuracy and convergence curve speed. The findings suggest that SBOA is a straightforward and viable approach that, overall, outperforms the aforementioned algorithms.

Keywords

autonomous learning, Evolutionary Computation, Heuristic optimization method, Single objective function, search space

Article Type

Special Issue Article

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Download

Share

COinS