Novel metaheuristic based on multiverse theory for optimization problems in emerging systems

Eghbal Hosseini, Kayhan Zrar Ghafoor, Ali Emrouznejad, Ali Safaa Sadiq, Danda B Rawat

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)3275–3292
Number of pages18
JournalApplied Intelligence
Issue number6
Early online date11 Nov 2020
Publication statusPublished - Jun 2021


  • Bi-level optimization
  • Constrained optimization
  • Meta-heuristics
  • Multiverse algorithm (MVA)


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