Designing a fine‐grained parallel differential evolution with Pareto tournaments for solving an optical networking problem

In this paper a novel approach for modelling Routing and Wavelength Assignment (RWA) problem in wavelength-routed Dense Wavelength Division Multiplexing (DWDM) optical networks is proposed. A new idea based on Artificial Bee Colony (ABC) algorithm is introduced for solving RWA problem which is known to be an NP-hard problem. In the proposed ABC-RWA approach every food source represents a possible and feasible lightpath between each original and destination node pair in demand matrix. The positions of food sources are modified by some artificial bees in the population where the aim is to discover the places of food sources. The food source with the highest nectar value seems to be a solution which is evaluated by the fitness function. The simulation results demonstrate the ability and efficiency of proposed approach for solving RWA in real-world optical networks. The proposed approach could be extended for dynamic RWA schemes in real-time applications and employed by network resilience architectures.

In WDM networks, static Routing Wavelength Assignment (RWA) problem is NP-complete. The Integer Linear Programming (ILP) is too complicated to implement. In most existing algorithms, it is usually split into routing and wavelength assignment, which is not completely precise. Mainly motivated by that imprecision, the Layered-Graph model was introduced into static RWA problem, which can consider the routing and wavelength assignment jointly. Three proposed algorithms using this model are described in this paper. Their performances are also evaluated by series of exhaustive simulations.

This paper aims to study the lightpaths provisioning problem in optical WDM networks with scarce available wavelengths under the static (off-line) traffic demands such that network operator’s (NO’s) revenue is maximized. To achieve this goal, a NO has to be addressed with the issue how to solve the call admission control jointly with the lightpaths routing and wavelength assignment (RWA) problem in efficient manner. The improved bee colony optimization (BCOi) metaheuristic is applied to solve the considered revenue maximization (Max-Rev) problem. We evaluated the performances of the proposed BCOi Max-Rev framework by performing numerous simulation experiments in different realistic WDM optical network topologies. We observed that our BCOi Max-Rev algorithm is an efficient tool to produce high quality solutions within reasonable amount of CPU time. It has been proved that BCOi Max-Rev solutions just slightly deviate from optimal solutions (at most 1%) and considerably outperform some heuristic algorithms, such as the Max-Profit and FCFS. In addition, our Max-Rev BCOi algorithm is able to produce better solution quality compared to the constructive BCO approach (up to 3.5% in the case of NSFNet and 5% in the case of EON). Finally, we compared the BCOi to differential evolution (DE) approach in the case of more complex networks, such as the USA optical network topology. The results show that our BCOi always outperforms DE metaheuristic, whereby the profit improvement could reach up to 20 % in some instances.

The most promising technology for exploiting optical networks is the Wavelength Division Multiplexing (WDM). When it is necessary to establish a set of demands, a problem comes up, this problem is known as: Routing and Wavelength Assignment problem (RWA problem). In this paper, we present a Hybrid OpenMP+MPI version of the Differential Evolution (DE), but adapted to a multiobjective context (DEPT), for solving this problem. We have studied the behavior of the Hybrid DEPT algorithm by comparing it with an MPI version of the DEPT algorithm. For this comparison, we have used a real-world topology (NTT, Japan) and a homogeneous cluster composed of 16 multi-core nodes, each node with 8 cores (a total of 128 cores). We can conclude that using a Hybrid OpenMP+MPI version of the DEPT algorithm is very suitable to solve the RWA problem in a reasonable time.

This chapter is an introduction of the Wavelength-division multiplexing (WDM) technologies (such as Dense WDM and coarse WDM) and their recent applications in optical networks. WDM is used to multiplex multiple optical carrier signals on a single optical fibre by using different wavelengths of laser light to carry different signals. This allows for a multiplication in available bandwidth and, in addition, makes possible to perform bidirectional communications over one strand of fibre. We present the optical components used in WDM and review some of the most important applications of the technology.

In the past few decades, the architecture of optical networks has undergone significant evolution, from the earliest wavelength-division multiplexing (WDM) optical networks to elastic optical networks (EONs) and later to space-division multiplexing (SDM) EONs, to address the continuous growth of Internet traffic. By 2024, Pbps-level optical networks are expected, far exceeding the capacity limit of single-mode fibers. The massive SDM era is on the horizon. In this context, a newly designed architecture for optical networks called the spatial channel network (SCN) architecture, which achieves high cost efficiency by means of practical hierarchical optical cross-connects, has recently been proposed. However, the evolution of optical network architectures will simultaneously present challenges related to network optimization. For instance, with the evolution from WDM optical networks to EONs, the kernel network optimization problem was transformed from the routing and wavelength assignment (RWA) problem into the routing and spectrum assignment (RSA) problem due to the additionally introduced constraint of spectrum contiguity. Similarly, specially designed algorithms are also expected to be essential for addressing the network optimization problem in SCNs. In this paper, we define this new problem as the routing, spatial channel, and spectrum assignment (RSCSA) problem. We propose an integer linear programming (ILP) model and a heuristic algorithm to solve the RSCSA problem. We examine the performance of the proposed approaches via simulation experiments. The results show that both proposed approaches are effective in finding the optimal solutions or solutions close to the lower bounds. To the best of our knowledge, this is the first work to focus on the network optimization problem in SCNs.

Solving multi-objective routing and wavelength assignment in WDM network using hybrid evolutionary computation approach

A post-optimization method for the routing and wavelength assignment problem applied to scheduled lightpath demands

Different integer linear programming (ILP) formulations have been proposed for the routing and wavelength assignment problem in WDM optical networks, mainly for asymmetrical systems, more than for symmetrical systems, under different objectives. We propose a synthesis of the mathematical models for symmetrical systems, with a unified and simplified notation for four widely used objectives. As for asymmetrical traffic models (Jaumard, Meyer, and Thiongane, 2004), we show that all formulations, both link and path formulations, are equivalent in terms of the upper bound value provided by the optimal solution of their linear programming relaxation, although their number of variables and constraints differ. We propose an experimental comparison of the best linear relaxation bounds with the optimal ILP solutions whenever it is possible, for several network and traffic instances.

In large dynamic networks it is extremely difficult to maintain accurate routing information on all network nodes. We introduce a novel algorithm which can decrease the impact of the problem greatly. Simulations show that it improves the blocking performance significantly in optical networks with Conversion Capability.

In this paper we consider the routing and wavelength assignment problem in a wavelength routed all optical network. Inspired by techniques from artificial intelligence, in particular the Blocking Island (BI) abstraction, we propose a simple and intelligent routing and wavelength assignment (RWA) algorithm: BI_RWA. This algorithm can be used in arbitrarily connected optical networks. In addition, it is general enough such that with some simple modifications, it can be applied to different optical networking scenarios: static or dynamic traffic, single or multiple fiber links between node pairs, with or without wavelength converters. We have conducted simulation experiments to evaluate the performance of our algorithm. The simulation is carried out in two parts: static traffic and dynamic traffic. The results will demonstrate that our RWA algorithm outperforms state-of-the-art related algorithms.

A cis-regulatory element is a non-coding DNA sequence in or near a gene required for its expression, often containing transcription factors binding sites (TFBS). Identifying new TFBSs is fundamental to understand the regulation process of these genes. The optimization problem responsible for these findings is the Motif Discovery Problem (MDP), which is formulated as a short DNA pattern discovery problem. In addition, the MDP is an NP-hard optimization problem since it has to find short strings mixed among a large amount of biological information. In this paper, we propose and study a new hybrid multiobjective evolutionary algorithm based on Differential Evolution (DE) named Hybrid Differential Evolution with Pareto Tournaments (H-DEPT). To demonstrate the superiority of our proposed algorithm on a biological level, we compare the predictions made with those predicted by MEME, BioProspector and BioOptimizer when solving a set of four real sequence datasets. Finally, by using OpenMP, we also demonstrate that this algorithm is highly suitable to be parallelized. As we will see, we have performed several experiments with multi-core systems of 2, 4, and 8 cores; obtaining good parallel results.

This paper designs a Ka-Band satellite antenna using parallel differential evolution (DE). The antenna is of wide beam-width and left hand circular polarization (LHCP). The electromagnetic numerical value is calculated by the improved software NEC2 based on the method of moments. The parallel differential evolution computing platform consists of some computers and MPI is used as the messaging protocol among computers. The single-arm Ka-Band antenna with 8-segments designed in this method has obtained very good performance including gains, axial ratios and VSWRs in a shorter period.

In this paper, we deal with the static Routing and Wavelength Assignment (RWA) problem in networks with no wavelength converters, and where a given static set of connection demands is prearranged. Our objective is to maximize the number of optical connection-requests that can be established for a given number of wavelengths. A mathematical formulation for Max-RWA was presented. In this article, we implement and compare the performance of two random search algorithms namely: the genetic algorithm and the tabu search algorithm. Using these metaheuristics we solved approximately the wavelength assignment problem for Max-RWA while we computed its routing by a deterministic method which is the backtracking. Therefore we conducted many extensive experiments under different circumstances. Diagrams and representative numerical examples indicate the accuracy of our algorithms.

Various static resource allocation algorithms have been used in WDM networks to allocate resources such as wavelength channels, transmitters, receivers, and wavelength converters to a given set of static lightpath demands. However, although optimized resource allocations can be obtained, it remains an open issue how to determine which resources are the bottlenecks in achieving better performance. Existing static resource allocation algorithms do not explicitly measure the impact of changes of network resources or lightpath demands on the design objective. We propose such a measurement based on the Lagrangian relaxation framework. We use the optimized values of Lagrange multipliers as a direct measurement of the criticality of resources. Such a quantitative measurement can be naturally acquired along with the optimization process to obtain the optimal solution (or a near-optimal solution) to the static routing and wavelength assignment problem. We investigate three practical applications of the resource criticality (RC) analysis in WDM network planning. In the first application, we use our proposed measurement to identify critical resources and thus to decide the best way to add or reallocate resources. In the second application, we estimate the impact of the addition or removal of lightpath demands on the design objective. This kind of estimation helps to set a proper price for lightpath demands. In the third application, the results of the RC analysis are used to speed up the convergence of the optimization process for different network scenarios.