Hosseini Eghbal, Ghafoor Kayhan Zrar, Emrouznejad Ali, Sadiq Ali Safaa, Rawat Danda B
Mechanical and Energy Engineering Department, Erbil Technical Engineering College, Erbil Polytechnic University, Kurdistan Region, Iraq.
Department of Software, Informatics Engineering, Salahaddin University-Erbil, Erbil, 44001 Iraq.
Appl Intell (Dordr). 2021;51(6):3275-3292. doi: 10.1007/s10489-020-01920-z. Epub 2020 Nov 11.
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.
为新兴的信息物理系统（CPS）找到一个能提高效率和增强鲁棒性的最优解决方案是主要问题之一。元启发式算法正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，我们提出了一种基于多元宇宙理论的新元启发式算法，名为MVA，它可以解决NP难优化问题，如非线性和多级规划问题，以及CPS系统的应用优化问题。MVA算法促使下一代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本质。此外，MVA类似于大爆炸的本质，将解分布在可行域中。为了说明所提算法的有效性，实施了一组测试问题，并从可行性、解的效率以及找到最优解所需的迭代次数方面进行了衡量。从广泛模拟中获得的数值结果表明，在解决具有大可行域的优化问题时，所提算法优于现有方法。
