The last few years have seen an increasing demand for high-capacity Internet services, and this need has intensified in the years 2020 and 2021. In 2020 and 2021, Internet usage grew by 50% in several European countri...
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The last few years have seen an increasing demand for high-capacity Internet services, and this need has intensified in the years 2020 and 2021. In 2020 and 2021, Internet usage grew by 50% in several European countries, mainly due to home office, video streaming, hybrid teaching, and others. High-capacity optical networks usually meet this growing demand for Internet services. Thus, investigations that can improve the quality of optical networks are highly relevant in the current context. One of the research problems in this area is related to the physical topology design (PTD) of optical networks, which is classified as NP-hard. Several studies on PTD consider the application of meta-heuristics that obtain suboptimal solutions in a time compatible with engineering applications. However, meta-heuristics and local search techniques have been combined in several other optimization problems, which is not typical for the PTD problem. This paper proposes a solution to the PTD problem that combines a known multipurpose optimization algorithm, the NSGA-III, with operators considering the domain-specific knowledge of the problem to provide superior-quality networks. According to our results, the new proposal presents quality up to 8% higher than previous proposals concerning the hypervolume metrics (HV), maintaining a similar computational cost.
The computational complexity of the multiobjective optimization (MOO) increases drastically in the presence of the large number of objectives. It is desirable to lower the complexity of the existing MOO algorithms. In...
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ISBN:
(纸本)9788132210375
The computational complexity of the multiobjective optimization (MOO) increases drastically in the presence of the large number of objectives. It is desirable to lower the complexity of the existing MOO algorithms. In this work we present an algorithm which periodically rearranges the objectives in the objective set such that the conflicting objectives are evaluated and compared earlier than non-conflicting ones. Differential Evolution (DE) is used as the underlying search technique. DE is designed especially for the real optimization problems. We have studied the reduction in the number of function computations and timing requirement achieved with the proposed technique. Remarkably, it is found to be much reduced as compared to the traditional approach. The variation of the gain in the number of objective computations vis-a-vis the number of objectives is demonstrated for a large number of benchmark MOO problems. Additionally, the relationship between the frequency of reordering the objectives and the number of objective computations is also established experimentally.
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