Novel metaheuristic based on multiverse theory for optimization problems in emerging systems
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Novel metaheuristic based on multiverse theory for optimization problems in emerging systems Eghbal Hosseini1 · Kayhan Zrar Ghafoor2,3 · Ali Emrouznejad4 · Ali Safaa Sadiq5,6 · Danda B. Rawat7 Accepted: 30 August 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract 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. Keywords Meta-heuristics · Constrained optimization · Multiverse algorithm (MVA) · Bi-level optimization
1 Introduction Meta-heuristic algorithms can be applied for training neural networks in solving real-life problems, though each algorithm has its own limitations. For instance, there are recently some of the prominent meta-heuristic algorithms that been widely used in optimizing the neural network accuracy include Particle Swarm Optimisation (PSO) [5], Bat Algorithm (BA) [9], FireFly (FF) [12]. However, literature cannot identify a single algorithm to be the best for solving all optimization problems, this has also been proved by the well-known No Free Lunch (NFL) theorem [29]. In this theorem, there was a logical prove supporting the aforementioned claim that there is no such meta-heuristic best suited for solving all types of optimization problems. In another words, there is a group of meta-heuristic algorithms perform the best in solving a set of problems, while the same group might give poor performance in
Danda B. Rawat
[email protected]
Extended author information available on the last page of the article.
solving different set of optimization problems. Hence, this NFL theorem has opened the door to researchers on keep developing new algorithms trying to achieve the best solution for different kind of problems. Besides, the challenges of heavy computational cost, existence of hastily convergence, mutation rate, crossover rate, time taken in fitness evaluation chiefs to boost current
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