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Nature-Inspired-Based Multi-Objective Hybrid Algorithms to Find Near-OGRs for Optical WDM Systems and Their Comparison
Abstract
Nature-inspired-based approaches are powerful optimizing algorithms to solve the NP-complete problems having multiple objectives. In this chapter, two nature-inspired-based multi-objective optimization algorithms (MOAs) and their hybrid forms are proposed to find the optimal Golomb rulers (OGRs) in a reasonable time. The OGRs can be used as a channel-allocation algorithm that allows suppression of the four-wave mixing crosstalk in optical wavelength division multiplexing systems. The presented results conclude that the proposed MOAs outperforms the existing conventional classical and nature-inspired-based algorithms to find near-OGRs in terms of ruler length, total occupied optical bandwidth, bandwidth expansion factor, computation time, and computational complexity. In order to find the superiority of proposed MOAs, the performances of the proposed algorithms are also analyzed by using statistical tests.
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