A Novel SoftComputing Algorithm for Channel Allocation in WDM Systems

Bansal, Shonak; Singh, Karamjit

doi:10.5120/14869-3244

Cited by 7 publications

(6 citation statements)

References 24 publications

(35 reference statements)

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“…The success of ORGs problem solve by nature-inspired algorithms such as Genetic Algorithm [24]- [28], Tabu Search (TS) [27], Biogeography Based Optimization. (BBO) [28]- [30], Big Bang-Big Crunch (BB-BC) evolution theory [32], [33], and Firefly Algorithm (FA) [34]. Hence, nature-inspired algorithms seem to be very effective solutions for the NPcomplete problems.…”

Section: Introductionmentioning

confidence: 99%

A Novel Bat Algorithm for Channel Allocation to Reduce FWM Crosstalk in WDM Systems

Ritu¹,

Bansal²,

Sharma³

2016

IJCA

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In this paper on which work done is nature inspired algorithm named Bat algorithm. nature is good source for inspiration in life in different way. Even in many search, nature gives good example for optimization many complex problems in engineering fields. Bat algorithm is metaheuristic algorithm like particle swarm, firefly. This paper formulates on echolocation behavior to reduce the crosstalk like FWM in optical wavelength division multiplexing (WDM) system for solving channel allocation problems by using concept of OGR (Optimal Golomb ruler). The comparative study of simulation results obtained by proposed metaheuristic Bat algorithm demonstrates better and efficient generation of OGRs without the requirement of increasing total bandwidth of channel, unlike the two existing conventional algorithms i.e. Extended quadratic congruence (EQC) and Search algorithm (SA), in terms of ruler length and total channel bandwidth.

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Section: Introductionmentioning

confidence: 99%

A Novel Bat Algorithm for Channel Allocation to Reduce FWM Crosstalk in WDM Systems

Ritu¹,

Bansal²,

Sharma³

2016

IJCA

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“…Several different algorithms are proposed to solve Golomb ruler problem such as exact methods [19], [20], constraint programming [21], local searches [22] and exhaustive parallel search [23]. There are various nature-inspired based algorithms such as Genetic algorithm (GA) [24]- [28], Biogeography Based Optimization (BBO) [28]- [30], Big Bang-Big Crunch (BB-BC) algorithm [31], [32], Firefly algorithm (FA) [33], Cuckoo search based algorithm (CSA) [34], and Multi-objective flower pollination algorithm (MOFPA) and its hybridization form [35] to solve the OGRs problem. This paper introduces two concepts i.e.…”

Section: Introductionmentioning

confidence: 99%

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

Bali¹,

Bansal²,

Kamboj³

2015

IJCA

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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.

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“…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.…”

Section: Exact Approachesmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

2017

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Nowadays, nature-inspired metaheuristic algorithms are most powerful optimizing algorithms for solving the NP-complete problems. This paper proposes three approaches to nd near-optimal Golomb ruler sequences based on nature-inspired algorithms in a reasonable time. The optimal Golomb ruler (OGR) sequences found their application in channel-allocation method that allows suppression of the crosstalk due to four-wave mixing in optical wavelength division multiplexing systems. The simulation results conclude that the proposed nature-inspired metaheuristic optimization algorithms are superior to the existing conventional and nature-inspired algorithms to nd near-OGRs in terms of ruler length, total optical channel bandwidth, computation time, and computational complexity. Based on the simulation results, the performance of proposed di erent nature-inspired metaheuristic algorithms are being compared by using statistical tests. The statistical test results conclude the superiority of the proposed nature-inspired optimization algorithms.

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A Novel SoftComputing Algorithm for Channel Allocation in WDM Systems

Cited by 7 publications

References 24 publications

A Novel Bat Algorithm for Channel Allocation to Reduce FWM Crosstalk in WDM Systems

A Novel Bat Algorithm for Channel Allocation to Reduce FWM Crosstalk in WDM Systems

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

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

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