Finding an optimal solution for emerging cyber physical systems (CPS) for better efficiency and robustness is one of the major issues. Meta-heuristic is emerging as a promising field of study for solving various optimization problems applicable to different CPS systems. In this paper, we propose a new meta-heuristic algorithm based on Multiverse Theory, named MVA, that can solve NP-hard optimization problems such as non-linear and multi-level programming problems as well as applied optimization problems for CPS systems. MVA algorithm inspires the creation of the next population to be very close to the solution of initial population, which mimics the nature of parallel worlds in multiverse theory. Additionally, MVA distributes the solutions in the feasible region similarly to the nature of big bangs. To illustrate the effectiveness of the proposed algorithm, a set of test problems is implemented and measured in terms of feasibility, efficiency of their solutions and the number of iterations taken in finding the optimum solution. Numerical results obtained from extensive simulations have shown that the proposed algorithm outperforms the state-of-the-art approaches while solving the optimization problems with large feasible regions.

为新兴网络物理系统 (CPS) 寻找最佳解决方案以提高效率和稳健性是主要问题之一。元启发式正在成为一个有前途的研究领域，用于解决适用于不同 CPS 系统的各种优化问题。在本文中，我们提出了一种新的基于多元宇宙理论的元启发式算法，称为 MVA，它可以解决 NP-hard 优化问题，如非线性和多级规划问题以及 CPS 系统的应用优化问题。MVA算法启发下一个种群的创建非常接近初始种群的解，这模仿了多元宇宙理论中平行世界的性质。此外，MVA 将解决方案分布在可行域中，类似于大爆炸的性质。为了说明所提出算法的有效性，实施了一组测试问题，并根据其解决方案的可行性、效率和寻找最佳解决方案的迭代次数进行了测量。从广泛的模拟中获得的数值结果表明，所提出的算法在解决具有大可行区域的优化问题时优于最先进的方法。