> In our computational study, two groups of instances were considered: Randomly generated graphs from [43]: For this set of instances the parameters for the generation were set as follows: ˈ S ˈ ∈ { 20, 50, 100 }, ˈ R ˈ ∈ { 20, 50, 100 }.
We present experimental results on randomly generated graphs with different statistical properties, showing the crucial effects of the implementation, the robustness of different techniques, and their empirical scalability.
Empirical results confirming the superior performance of the algorithm to others found in the literature for randomly generated graphs are reported.
The comparison studies, based on both randomly generated graphs and the graphs of some real applications, show that our scheduling algorithm outperforms the existing scheduling algorithms in terms of makespan, scheduling length ratio, and reliability.
In this paper, we have also conducted the simulation experiments to verify the effectiveness and efficiency of DMSCRO over a large set of randomly generated graphs and the graphs for real-world problems.
Our experiments, based on both randomly generated graphs and the graphs of some real applications, show that our hierarchical scheduling algorithm performs much better than the existing scheduling algorithms in terms of system reliability, schedule length, and speedup.
Dr Barabasi's latest paper on the matter, just published in the Proceedings of the National Academy of Sciences, presents a general framework for improving the accuracy of Internet models.Until 1999, the standard way of modelling the Internet was to use randomly generated graphs, in which routers were represented by points and the links between them by lines.
We test MGA on randomly generated graphs and find that the average ratio between the number of instances associated with the algorithm's output and the number of instances associated with an optimum solution is far better than the worst-case bound.
The experimental results for large-sized problems from a large set of randomly generated graphs as well as graphs of real-world problems with various characteristics show that the proposed MPQGA algorithm outperforms two non-evolutionary heuristics and a random search method in terms of schedule quality.
