Nature–inspired metaheuristic algorithms to find near–OGR sequences for WDM channel allocation and their performance comparison

• Autorzy: Shonak Bansal , Neena Gupta , Arun Kumar Singh
• Języki publikacji: EN

Abstrakty

EN

Nowadays, nature–inspired metaheuristic algorithms are most powerful optimizing algorithms for solving the NP–complete problems. This paper proposes three approaches to find near–optimal Golomb ruler sequences based on nature–inspired algorithms in a reasonable time. The optimal Golomb ruler (OGR) sequences found their application in channel–allocation method that allows suppression of the crosstalk due to four–wave mixing in optical wavelength division multiplexing systems. The simulation results conclude that the proposed nature–inspired metaheuristic optimization algorithms are superior to the existing conventional and nature–inspired algorithms to find near–OGRs in terms of ruler length, total optical channel bandwidth, computation time, and computational complexity. Based on the simulation results, the performance of proposed different nature–inspired metaheuristic algorithms are being compared by using statistical tests. The statistical test results conclude the superiority of the proposed nature–inspired optimization algorithms.

Słowa kluczowe

EN

Channel spacing; Conventional computing; Equally and unequally spaced channel allocation; Four–wave mixing; Metaheuristic; Nature–inspired algorithm; Near–optimal Golomb ruler; Optimization

Kategorie tematyczne

• 90C29: Multi-objective and goal programming
• 91-08: Computational methods
• 90C26: Nonconvex programming, global optimization

• Wydawca: De Gruyter Open
• Czasopismo: Open Mathematics
• Rocznik: 2017
• Tom: 15
• Numer: 1
• Strony: 520-547

Daty

wydano

2016-05-05

online

2016-05-05

otrzymano

2016-05-09

zaakceptowano

2017-02-21

Twórcy

autor

Shonak Bansal

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autor

Neena Gupta

• , , , ,

autor

Arun Kumar Singh

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Identyfikatory

DOI

10.1515/math-2017-0045