A Comparative Study of Nature-Inspired Metaheuristic Algorithms in Search of Near-to-optimal Golomb Rulers for the FWM Crosstalk Elimination in WDM Systems

Nowadays, nature-inspired metaheuristic algorithms are the most powerful optimizing algorithms for solving NP-complete problems. This paper proposes five recent approaches to find near-optimal Golomb ruler (OGR) sequences based on nature-inspired algorithms in a reasonable time. The optimal Golomb ruler sequences found their application in channel-allocation method that allows suppression of the crosstalk due to four-wave mixing (FWM) in optical wavelength division multiplexing (WDM) systems. The simulation results conclude that the proposed nature-inspired metaheuristic optimization algorithms are superior to the existing conventional computing algorithms, i.e., Extended Quadratic Congruence (EQC) and Search algorithm (SA) and nature-inspired algorithms, i.e., Genetic algorithms (GAs), Biogeography-based optimization (BBO) and simple Big bang–Big crunch (BB-BC) optimization algorithm to find near-OGRs in terms of ruler length, total optical channel bandwidth and computation time.