Hierarchical Model of Parallel Metaheuristic Optimization Algorithms
E. Yu. Seliverstov, Anatoly Karpenko · Procedia Computer Science · 2019
The paper introduces a novel model of parallel metaheuristic optimization algorithms. The hierarchical graph model of a parallel optimization algorithm is proposed. It consists of the model for a parallel optimization algorithm at the top level of the hierarchy and the model for a sequential optimization algorithm at the bottom level. The unified representation of a metaheuristic optimization algorithm, which allows representing a class of metaheuristic algorithms, is used. The extension of the proposed model to the parametric hierarchical model is proposed. Graph model transformations for a parallel algorithm analysis and synthesis are introduced. The representation of several metaheuristic algorithms with the proposed model is discussed.