Enhanced Optimizer Algorithm and its Application to Software Testing

Abstract

ABSTRACT Optimisation algorithm is currently one of the most applicable techniques to solve real-world problems by finding the best solution from all feasible solutions in the search space. This paper proposes enhanced multiverse optimiser algorithm that is inspired from the physics multiverse theory. The proposed algorithm suggests an enhancement of multiverse optimiser algorithm . It enhances the performance of multiverse optimiser to find the global minimal value among search space and solve the problems in the multiverse optimiser algorithm. In order to confirm the performance of the suggested algorithm, it has been benchmarked with benchmark functions challenging optimisation problems. The proposed algorithm is compared with state-of-the-art optimisation algorithm to confirm its performance; it is being compared with particle swarm optimisation, sine cosine algorithm, grey wolf optimiser, moth-flame optimisation and multiverse optimiser. Also, the algorithm is applied on software testing and test data generation, the results of the benchmarked functions and the test data generation proves that the proposed algorithm is able to provide very competitive results and outperforms other compared algorithms over the tested cases.