Exhaustive testing is a very strenuous undertaking due to its numerous combinations. Consequently, searching for and sampling an optimal test suite from viable groups of test cases (TC) has turned out to be a central concern. To tackle this matter, the use of t-way testing (Where t represents the strength of the interaction) has become well known. In order to facilitate future development and summarize the realization so far, the major objective of this paper is to portray an important comparison of the introduced optimization algorithms (OA) as a base of the t-way test suite generation strategy, and to suggest a new strategy focusing on the Artificial Bee Colony (ABC) Algorithm, known as Artificial Bee Colony Strategy (ABCS). The experimental results showed that ABCS can compete against the existing (both AI-based and computational-based) strategies in terms of generating the optimum test case. © 2018 IEEE.