Nature-Inspired Hybrid Multi-objective Optimization Algorithms in Search of Near-OGRs to Eliminate FWM Noise Signals in Optical WDM Systems and their Performance Comparison

Abstract

Nowadays, the multi-objective optimizing algorithms which are inspired by nature are widely used in solving the many objectives of NP-complete engineering and industrial design problems. To eliminate one of the prevailing nonlinear optical noise mechanisms, i.e., four-wave mixing noise signals in the optical wavelength division multiplexing (WDM) systems, several unequally spaced channel-allocation algorithms were found that require increased bandwidth than the equally spaced channel-allocation. One of the bandwidths efficient, a USCA algorithm can be designed by considering the optimal Golomb rulers (OGRs) in optical WDM systems. Two nature-inspired multi-objective optimization algorithms (MOAs), namely multi-objective big bang–big crunch (MOBB-BC) optimization algorithm and multi-objective firefly optimization algorithm (MOFA), to solve NP-complete OGR problem in an optical WDM system are proposed here. Additionally, the algorithms MOBB-BC and MOFA are hybridized by introducing differential evolution (DE) based mutation and random walk features in their standard forms. These two features have the potential to explore new search space for MOAs to search for OGRs in a reasonable time. The algorithms presented here solve the two objectives in optical WDM systems, one is the occupied length by the ruler, and the other is total channel bandwidth obtained by OGRs. The obtained results suggest that the considered nature-inspired MOAs are computationally better than the other algorithms to search near-OGRs. To assert the improvement in the MOAs further, the non-parametric Friedman statistical analysis test is employed. The results conclude that for large mark values, the hybridization of MOFA with both DE mutation and Levy-flight strategies potentially performs better than the MOBB-BC and its hybrid forms to search OGR sequences. The hybrid algorithm MOBB-BC can explore OGRs up to 8-marks and near-OGRs for 9 to 20-marks with a maximum computation time of 27 h, whereas hybrid MOFA can examine up to 16-mark OGRs, but finds 17 to 20-mark near-OGRs with the maximum computation time of 20 h. The success rate of the proposed hybrid MOFA to search best OGRs up to 20-marks is 80%, whereas for hybrid algorithm MOBB-BC is 40%.