Abstract
Robustness is one of the most important properties to consider when designing networked infrastructure systems such as the road network, airline network, and the electric power grid. A critical concern with such systems is that functionality must be maintained during the occurrence of natural disasters or deliberate attacks on their components. Multiple network robustness measures, each capturing different features of interest, have been formulated to evaluate the capability of a system to withstand such failures or attacks. These previously proposed robustness measures are sometimes uncorrelated or negatively correlated; hence, optimizing a single measure may not improve the overall robustness of the network. In this chapter, we propose a new approach addressing the budget-constrained multi-objective optimization problem of determining the set of new edges (of given size) that maximally improve multiple robustness measures. Experimental results show that adding the edges suggested by our approach significantly improves the network robustness, compared to previously proposed algorithms. The networks improved by our approach also maintain high robustness during random or targeted node attacks.
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Notes
- 1.
Similar results were obtained when the experiments were carried out for 100 generated scale-free networks by: (1) fixing the number of nodes and changing power law parameter in the aforementioned range, and (2) changing the number of nodes in the aforementioned range and fixing the power law parameter.
- 2.
We have experimented with other crossover operators such as two-point crossover, and the results were similar compared to the one-point crossover. Hence, the results with one-point crossover are reported here.
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Gunasekara, R.C., Mohan, C.K., Mehrotra, K. (2018). Multi-objective Optimization to Improve Robustness in Networks. In: Mandal, J., Mukhopadhyay, S., Dutta, P. (eds) Multi-Objective Optimization. Springer, Singapore. https://doi.org/10.1007/978-981-13-1471-1_5
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