Elitism Based Migrating Birds Optimization Algorithm for Optimization Testing

Migrating Birds Optimization Algorithm (MBO) has gained popularity in solving various engineering problems because it yielded a good and consistent result. In this paper, we combined MBO and elitism to solve the Combinatorial Interaction Testing (CIT) problem i.e. to find a set of minimum test case which is an NP-Complete problem. This proposed strategy is the first to utilize population based metaheuristic algorithm i.e. MBO with elitism for solving CIT problem. Elitism is a preservation method that preserves the best population and introduces it back into the next population. Here, we used elitism to preserve the best test cases in order to improve the effectiveness of MBO in generating the minimum set of test cases. This strategy is named as MBO Testing Strategy with elitism (MTS-e). As a comparison with the original MBO we also developed a strategy without elitism, namely MBO Testing Strategy (MTS). MTS yielded a comparable result to the benchmark strategies while MTS-e outperformed most of the benchmarked strategies. The experimental result shows that elitism enhanced the performance of MBO as the mean of the best generated test cases for MTS-e is better than the mean generated by benchmarked strategies