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

Abstract

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.