Genetic evolutionary algorithm for optimal allocation of wavelength converters in WDM optical networks

Optical Networks are considered as the future of broadband communications. One of the popular solutions for wide area networks is Wavelength Division Multiplexing. Wavelength Converters are now a necessity for Wavelength Division Multiplexing Network, but at the same time their cost is a very important factor to consider in network design. It is required that allocated Wavelength Converters should be utilized as much as possible. In this work, Adaptive form of Genetic Algorithm has been applied to utilize maximally the allocation of Wavelength Converters in the optimal manner over the selective nodes available in network. To estimate the fitness, Utilization matrix has been employed. Adaptive in crossover probability and mutation probability have been applied, which are based on current fitness for selected members in crossover and mutation operation. Proposed algorithm has achieved the optimal solution with Maximum Utilization of Wavelength Converter in WDM Optical Networks.

The huge bandwidth of optical fibres is exploited through wavelength division multiplexing technology, which introduces new complexities in the routing problem. In this context, the wavelength converter allocation problem has become a key factor to minimize blocking. The wavelength converter allocation problem has been treated as a mono-objective problem minimizing the number of wavelength converters or minimizing blocking; however, both criteria are in conflict with each other. Therefore, the wavelength converter allocation problem is studied here in a pure multi-objective optimization context for more appropriate decision making. This work proposes a multi-objective optimization approach based on an evolutionary algorithm which simultaneously minimizes blocking and the number of wavelength converters. Extensive simulations on three real optical networks show promising results in the sense that our algorithm generates the trade-off curve between blocking and the number of converters needed, and outperforms a recently proposed approach.

This chapter reviews and expands our work on the relationship between linkage structure, that is how decision variables of a problem are linked with (dependent on) one another, and the performance of three basic types of genetic evolutionary algorithms (GEAs): hill climbing, genetic algorithm and bottom-up self-assembly (compositional). It explores how concepts and quantitative methods from the field of social/complex networks can be used to characterize or explain problem difficulty for GEAs. It also re-introduces two novel concepts – inter-level conflict and specificity – which view linkage structure from a level perspective. In general, the basic GEAs performed well on our test problems with linkage structures resembling those empirically observed in many real-world networks. This is a positive indication that the structure of real-world networks which evolved without any central organization such as biological networks is not only influenced by evolution and therefore exhibit non-random properties, but also influences its own evolution in the sense that certain structures are easier for evolutionary forces to adapt for survival. However, this necessarily implies the difficulty of certain other structures. Hence, the need to go beyond basic GEAs to what we call GEAs with “brains”, of which linkage-learning GEAs is one species.

Due to the conflict between reducing cost and improving water supply performance, how to select the appropriate pipe diameter is a current challenge. In this paper, the problem is transformed into a multi-objective optimization problem, and the evolutionary genetic optimization algorithm is used to solve the problem to determine the optimal selection of pipe diameter in the pipe network. To solve this problem, the evolutionary genetic algorithm was coupled with EPANET hydraulic simulation software in Python environment. The results show that NSGA-II and NSGA-III perform better in two typical case tests. Moreover, the increase of the objective functions will lead to an increase in the amount of data in the optimal solution set, and will affect the optimal value of each objective function. That shows that the balance between the economy and reliability of water supply can be successfully found by coupling the hydraulic model and the multi-objective optimization algorithm, which can provide an auxiliary decision for enterprises.

A novel dynamic survivable routing in WDM optical networks with/without sparse wavelength conversion

In this paper we deal with online broadcasting and multicasting in a WDM optical network with shared light splitter bank. Our objective is to maximize the network throughput. Since light splitting and wavelength conversion switching in WDM optical networks is cost expensive and fabrication difficult, we assume that only a fraction of network nodes are equipped with limited number of light splitting and/or wavelength conversion switches, and they are shared by all incoming and outgoing signals at each installed node. We first propose two cost models of realizing a broadcast or multicast request to model the consumption of network resources, particularly in modelling the light splitting and/or wavelength conversion resources consumption. We then show that under either of the two proposed cost models, finding a cost-optimal broadcast or multicast tree for a broadcast or multicast request is NP-complete, and instead devise approximation and heuristic algorithms for it. We finally conduct experiments to evaluate the performance of the proposed algorithms.

This paper describes the advantages introduced by wavelength conversion in the design, implementation and management of future WDM optical networks and nodes. We describe the routing and wavelength assignment process in a WDM network and then we will demonstrate the major advantages introduced by wavelength conversion, mainly regarding the dimensioning aspects but also including considerations on the physical and management issues.

We study the blocking probability and performance of single‐fiber and multifiber optical networks with wavelength division multiplexing (WDM). We extend the well‐known analytical blocking probability model by Barry and Humblet to the general model, which is proposed for both single‐fiber and multifiber network paths with any kind of wavelength conversion (no, limited, or full wavelength conversion) and for uniform and nonuniform link loads. We investigate the effect of the link load, wavelength conversion degree, and the number of wavelengths, fibers, and hops on blocking probability. We also extend the definition of the gain of wavelength conversion by Barry and Humblet to the gain of performance, which is fully general. Thanks to this definition and implementation of our model, we compare different WDM node architectures and present interesting results.

The crowd-creation space is a manifestation of the development of innovation theory to a certain stage. With the creation of the crowd-creation space, the problem of optimizing the resource allocation of the crowd-creation space has become a research hotspot. The emergence of cloud computing provides a new idea for solving the problem of resource allocation. Common cloud computing resource allocation algorithms include genetic algorithms, simulated annealing algorithms, and ant colony algorithms. These algorithms have their obvious shortcomings, which are not conducive to solving the problem of optimal resource allocation for crowd-creation space computing. Based on this, this paper proposes an In the cloud computing environment, the algorithm for optimizing resource allocation for crowd-creation space computing adopts a combination of genetic algorithm and ant colony algorithm and optimizes it by citing some mechanisms of simulated annealing algorithm. The algorithm in this paper is an improved genetic ant colony algorithm (HGAACO). In this paper, the feasibility of the algorithm is verified through experiments. The experimental results show that with 20 tasks, the ant colony algorithm task allocation time is 93ms, the genetic ant colony algorithm time is 90ms, and the improved algorithm task allocation time proposed in this paper is 74ms, obviously superior. The algorithm proposed in this paper has a certain reference value for solving the creative space computing optimization resource allocation.

At present, the study on the pathogenesis of Alzheimer's disease (AD) by multimodal data fusion analysis has been attracted wide attention. It often has the problems of small sample size and high dimension with the multimodal medical data. In view of the characteristics of multimodal medical data, the existing genetic evolution random neural network cluster (GERNNC) model combine genetic evolution algorithm and neural network for the classification of AD patients and the extraction of pathogenic factors. However, the model does not take into account the non-linear relationship between brain regions and genes and the problem that the genetic evolution algorithm can fall into local optimal solutions, which leads to the overall performance of the model is not satisfactory. In order to solve the above two problems, this paper made some improvements on the construction of fusion features and genetic evolution algorithm in GERNNC model, and proposed an improved genetic evolution random neural network cluster (IGERNNC) model. The IGERNNC model uses mutual information correlation analysis method to combine resting-state functional magnetic resonance imaging data with single nucleotide polymorphism data for the construction of fusion features. Based on the traditional genetic evolution algorithm, elite retention strategy and large variation genetic algorithm are added to avoid the model falling into the local optimal solution. Through multiple independent experimental comparisons, the IGERNNC model can more effectively identify AD patients and extract relevant pathogenic factors, which is expected to become an effective tool in the field of AD research.

In wavelength division multiplexed all optical networks; lightpath establishes a connection between sending and receiving nodes bypassing the electronic processing at intermediate nodes. One of the prime objectives of Routing and Wavelength Assignment (RWA) problem is to maximize the number of connections efficiently by choosing the best routes. Although there are several algorithms available, improving the blocking performance in optical networks and finding optimal solutions for RWA problem has still remained a challenging issue. Wavelength conversion can be helpful in restricting the problem of wavelength continuity constraint but it increases complexity in the network. In this paper, we propose new weight dependent routing and wavelength assignment strategy for all optical networks without use of wavelength converters. Proposed weight function reduces blocking probability significantly, improving the network performance at various load conditions. Further, due to absence of wavelength converters, the cost and complexity of network reduces. Results show that the proposed strategy performs better than earlier reported methods.

In this paper, temporal and spectral characteristics of uniform nonlinear fiber Bragg gratings (FBG) are investigated in detail using numerical solutions of nonlinear coupled mode equations. The acquired data are considered in terms of using nonlinear FBGs as wavelength converters and all-optical on-off switches in WDM optical networks. The numerical experiments are focused on transmission properties of FBGs based on nonlinear chalcogenide glasses which are suitable candidates due to their strong third-order optical nonlinearities.

The quadcopter is an unmanned aerial vehicle that has been used in civil and military applications because of its relatively low cost, simple design, and ease of maintenance in addition to its unique flying characteristics and vast potential. Six Proportional-Integral-Derivative (PID) controllers are usually built for the position and attitude control of the quadcopter. However, tuning the controllers’ parameters poses a real challenge, as the system is highly coupled and contains two cascaded control loops while being underactuated (only four control inputs for the six degrees of freedom). In this study, a quadcopter model has been implemented by using MATLAB / SIMULINK where the model has been constructed to be integrated with the evolutionary genetic algorithm in addition to a third-party toolbox that is used to simulate fractional order calculus. The gains of the traditional and the fractional-order proportional-integral-derivative controllers have been tuned by using the genetic algorithm against different path shapes. The performance of the manually and the genetic algorithm tuned proportional-integral-derivative controllers have been compared with the genetic algorithm tuned fractional-order proportional-integral-derivative controllers. The genetic algorithm tuned fractional-order proportional-integral-derivative controllers have been superior and more robust in all cases with a lower fitness function value, much smother responses, and higher trajectory tracking ability.

Heterogeneous network is a novel network architecture proposed in Long-Term-Evolution~(LTE), which highly increases the capacity and coverage compared with the conventional networks. However, in order to provide the best services, appropriate resource management must be applied. In this paper, we consider the joint optimization problem of user association, subchannel allocation, and power allocation for downlink transmission in Multi-cell Multi-association Orthogonal Frequency Division Multiple Access (OFDMA) heterogeneous networks. To solve the optimization problem, we first divide it into two subproblems: 1) user association and subchannel allocation for fixed power allocation; 2) power allocation for fixed user association and subchannel allocation. Subsequently, we obtain a locally optimal solution for the joint optimization problem by solving these two subproblems alternately. For the first subproblem, we derive the globally optimal solution based on graph theory. For the second subproblem, we obtain a Karush-Kuhn-Tucker (KKT) optimal solution by a low complexity algorithm based on the difference of two convex functions approximation (DCA) method. In addition, the multi-antenna receiver case and the proportional fairness case are also discussed. Simulation results demonstrate that the proposed algorithms can significantly enhance the overall network throughput.

This paper presents new analytical models for computing the blocking performance of translucent optical networks with sparsely located optical-electrical-optical regeneration nodes. Due to physical layer impairments such as noise and crosstalk, there is a maximum distance [called the transmission reach (TR)] that optical signals can travel before they have to be regenerated. Regeneration nodes that are allocated to a lightpath can also be used for wavelength conversion besides mitigating PLIs. The quality of transmission of a lightpath is satisfied if the length of each segment between two allocated regeneration nodes is less than the TR. We develop analytical models to estimate the call blocking probability for two typical regeneration node allocation policies. The first policy allocates regeneration nodes when the lightpath needs either regeneration or wavelength conversion, while the other allocation policy allocates regenerators only for signal regeneration. The analytical models strike a balance between computational complexity and prediction accuracy. The models are validated using extensive simulation results for a variety of network topologies and parameters.