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In Dense Wavelength Division Multiplexing (DWDM) technologies, the optimal packet scheduling issue is commonly encountered in multiple channel networks. NP-hard problems deal with finding a way to rearrange packets from multiple channels into a finite and rare channel. Genetic Algorithm (GA) is one of the most efficient ways to solve these issues. We hope to find a better solution to our task through the GA characteristics of multiprocessor searching and survival of the fittest. Therefore, a modified and achievable hardware architecture of GA is presented in this paper. This architecture can increase both the speed of packet scheduling and the efficiency of DWDM in Optical Communication Networks. way to solve this problem. It is concluded that crossover and mutation of chromosome and fitness function calculation can converge faster and therefore allow an efficient way to find the optimal solution [6]. In this paper, besides using the Matlab simulation software to prove the practicality and superiority of GA, we also presented an achievable hardware architecture in DWDM of optimal packet scheduling [1, 2, 3, 7, 8]. This paper is organized as follows: Section 2 introduces the ways in which GA solves the optimal packet scheduling and presents the simulation results. Section 3 describes a hardware architecture designed for GA. Section 4 contains our conclusion. |
