African Buffalo Optimization Algorithm Based T-Way Test Suite Generation Strategy for Electronic-Payment Transactions

Abstract

The use of meta-heuristics in Combinatorial Interaction Testing (CIT) is becoming more and more popular due to their effectiveness and efficiency over the traditional methods especially in authenticating electronic payment (epayment) transactions. Concomitantly, over the past two decades, there has been a rise both in the development of metaheuristics and their application to diverse theoretical and practical areas including CIT in e-payments. In the implementation of t-way strategies (the t is used to represent the interaction strength), mixed results have been reported; some very exciting but, in other cases, the performance of metaheuristics has been, to say the least, below par. This mixed trend has led many researchers to explore alternate ways of improving the effectiveness and efficiency of metaheuristics in CIT, hence this study. It must be emphasized, however, that available literature indicates that no particular metaheuristic testing strategy has had consistent superior performance over the others in diverse testing environments and configurations. The need for effectiveness, therefore, necessitates the need for algorithm hybridization to deploy only the component parts of algorithms that have been proven to enhance overall search capabilities while at the same time eliminating the demerits of particular algorithms in the hybridization procedure. In this paper, therefore, a hybrid variant of the African Buffalo Optimization (ABO) algorithm is proposed for CIT. Four hybrid variants of the ABO are proposed through a deliberate improvement of the ABO with four algorithmic components. Experimental procedures indicate that the hybridization of the ABO with these algorithmic components led to faster convergence and greater effectiveness superior to the outcomes of existing techniques, thereby placing the algorithm among the best when compared with other methods/techniques.

Minimizing the total waste in the one-dimensional cutting stock problem with the African buffalo optimization algorithm

“…For all of the instances and after preliminary experiments, the chosen population size of buffaloes was 90, and the chosen number of iterations to search for the best solution is set to 440, where both quantities are in the range suggested in Odili et al (2022) . The values of the learning factors lp1 and lp2 were set to 0.3 and 0.6, respectively, based on Gherboudj (2019) .…”

Construction of Prioritized T-Way Test Suite Using Bi-Objective Dragonfly Algorithm

“…The performance of a GSA based t-way strategy was benchmarked against the currently established t-way strategies, and the GSA based t-way strategy produced competitive results against other strategies [22]. However, another t-way strategy based on the African Buffalo Optimization (ABO) algorithm ensured greater effectiveness and faster convergence in t-way testing [23].…”

Pairwise test case generation with harmony search, one-parameter-at-at-time, seeding, and constraint mechanism integration

“…In the context of search-based software engineering, which deals with optimizing processes in software engineering, many recent studies have turned to metaheuristics mechanisms to address the challenge of generating pairwise testing, commonly referred to as the combinatorial explosion problem [18]- [20].…”