This paper proposes a novel versatile genetic algorithm (GA) for solving the graph distribution problem. The new GA is based mainly on an inspirational idea that exploits the Newton’s universal gravitation law to introduce a novel GA fitness function. By this,the new GA preserves the workload balancing property between the different sites of the graph network and reduces the inter-processors communications overhead. Moreover, three main variants of the novel GA are developed. The two first; centralized and distributed variants, are developed to conduct a graph distribution over homogeneous architectures. The third variant is a distributed one devoted for heterogeneous architectures where the impact of the auto-adaptation features of the GA emerges. The results obtained and the comparative studies show the effectiveness of the proposed methods.

This paper proposes a new framework based on Binary Decision Diagrams (BDD) for the graph distribution problem in the context of explicit model checking. The BDD are yet used to represent the state space for a symbolic verification model checking. Thus, we took advantage of high compression ratio of BDD to encode not only the state space, but also the place where each state will be put. So, a fitness function that allows a good balance load of states over the nodes of an homogeneous network is used. Furthermore, a detailed explanation of how to calculate the inter-site edges between different nodes based on the adapted data structure is presented.

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