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.

中文翻译：

---

增强优化算法及其在软件测试中的应用

摘要 优化算法是目前通过从搜索空间中的所有可行解中寻找最佳解来解决现实世界问题的最适用技术之一。本文受物理多元宇宙理论的启发，提出了增强型多元宇宙优化器算法。所提出的算法提出了对多元宇宙优化器算法的改进。提高了多元宇宙优化器的性能，在搜索空间中寻找全局极小值，解决了多元宇宙优化器算法中的问题。为了确认建议算法的性能，它已经用具有挑战性的优化问题的基准函数进行了基准测试。将所提出的算法与最先进的优化算法进行比较以确认其性能；它正在与粒子群优化、正余弦算法、灰狼优化器、飞蛾火焰优化和多元宇宙优化器进行比较。此外，该算法应用于软件测试和测试数据生成，基准函数和测试数据生成的结果证明所提出的算法能够提供非常有竞争力的结果，并且在测试案例中优于其他比较算法。