Abstract
In this paper, we study relative performance of local search methods used for the Maximum Lifetime Coverage Problem (MLCP) solving. We consider nine algorithms obtained by swapping problem-specific major steps between three local search algorithms we proposed earlier: LS\(_{\mathrm {HMA}}\), LS\(_{\mathrm {CAIA}}\), and LS\(_{\mathrm {RFTA}}\). A large set of tests carried out with the benchmark data set SCP1 showed that the algorithm based on the hypergraph model approach (HMA) is the most effective. The remaining results of other algorithms divide them into two groups: effective ones, and weak ones. The findings expose the strengths and weaknesses of the problem-specific steps applied in the local search methods.
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Trojanowski, K., Mikitiuk, A. (2020). Local Search Approaches with Different Problem-Specific Steps for Sensor Network Coverage Optimization. In: Le Thi, H., Le, H., Pham Dinh, T. (eds) Optimization of Complex Systems: Theory, Models, Algorithms and Applications. WCGO 2019. Advances in Intelligent Systems and Computing, vol 991. Springer, Cham. https://doi.org/10.1007/978-3-030-21803-4_41
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