A Novel Hybrid Multi–objective BB–BC based Channel Allocation Algorithm to Reduce FWM Crosstalk and its Comparative Study

Abstract

Nature is a good source of inspirations for us. The algorithms developed from the nature are most powerful algorithms for optimizing many complex engineering design problems having multiple objectives (multi-objective). This paper presents an hybrid algorithm based on Multi-objective Big bang-Big Crunch (MOBB-BC) nature-inspired optimization algorithm with Genetic crossover and Differential evolution (DE) mutation operators for solving the minimum length ruler called Optimal Golomb ruler (OGR) as channel-allocation problem to reduce four-wave mixing crosstalk (FWM) effects in optical wavelength division multiplexing (WDM) systems. The comparative study of simulation results obtained by proposed hybrid Multi-objective BB-BC (HMOBB-BC) algorithm demonstrates better and efficient generation of OGRs in a reasonable computational time compared to simple BB-BC algorithm and one of the existing nature-inspired algorithms i.e. Genetic algorithm (GA). Also, the proposed hybrid algorithm outperforms the two existing conventional algorithms i.e. Extended quadratic congruence (EQC) and Search algorithm (SA), in terms of ruler length and total channel bandwidth.

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

“…Since the exact approaches for nding Golomb rulers is impractical in terms of computational resources, di erent nature-inspired based metaheuristic optimization algorithms [26][27][28][29][30][31][32][33][34][35][36][37][38][39] have been proposed to nd optimal and near-optimal Golomb rulers sequences at a reasonable time. Therefore, the nature-inspired based metaheuristic optimization algorithms appear to be the best solutions for such NP-complete OGR problem.…”

“…To date, no e cient algorithm is known for nding the shortest length ruler. The realization of nature-inspired metaheuristic optimization algorithms such as Memetic approach [26], Tabu search (TS) [26], Genetic algorithms (GAs) [27][28][29][30] and its hybridizations (HGA) [29], Biogeography based optimization (BBO) [30][31][32], Big bang-Big crunch (BB-BC) [33][34][35][36], Fire y algorithm (FA) [36][37][38], and hybrid evolutionary (HE) algorithms [38] in nding relatively good solutions to such NP-complete problems, provide a good starting point for algorithms of nding near-OGRs. Therefore, nature-inspired algorithms seem to be very e ective solutions for such NP-complete problems.…”

Optimal Golomb Ruler Sequences asWDM Channel-Allocation Algorithm Generation: Cuckoo Search Algorithm with Mutation

“…Golomb rules represent a class of NP-complete problems [19]. Some algorithms like Genetic algorithms (GAs) and its hybrid form [20]- [23], Biogeography based optimization (BBO) [24]- [26], Big bang crunch algorithm [27], [28] and its hybridization [29], Firefly algorithm (FA) [30], Cuckoo search algorithm (CSA) [31], Multi-objective flower pollination algorithm (MOFPA) and its hybridization form [32], and Bat inspired algorithm [33] had been applied in finding the better solution of such NPcomplete problems. In attempt to reduce the FWM crosstalk effect in WDM, unequally spaced channel allocation by using the concept of optimal Golomb ruler (OGR) sequences [34]- [38] has been used in this paper.…”