Wavelength Converter Allocation in Optical Networks: An Evolutionary Multi-objective Optimization Approach

In this article, a genetic evolutionary algorithm is proposed for efficient allocation of wavelength converters in WDM optical networks. Since wavelength converters are expensive, it is desirable that each node in WDM optical networks uses a minimum number of wavelength converters to achieve a near-ideal performance. The searching capability of genetic evolutionary algorithm has been exploited for this purpose. The distinguished feature of the proposed approach lies in handling the conflicting circumstances during allocation of wavelength converters considering various practical aspects (e.g., spatial problem, connectivity of a node with other nodes) rather than arbitrarily to possibly improve the overall blocking performance of WDM optical networks. The proposed algorithm is compared with a previous approach to establish its effectiveness and the results demonstrate the ability of the proposed algorithm to efficiently solve the problem of Optimal Wavelength Converters Allocation (OWCA) in practical WDM optical networks.

Controller design is performed to meet some performance criteria. Some of them are the gain and phase margins, control effort, settling time, overshoot, amongst other indexes. Obtaining a controller or compensator that fits all these criteria (or some of them) for a closed loop control is done in both empirical and analytical procedures. The most of these procedures assumes the plant to be controlled could be modelled as first or second-order system. This paper aims to show a way to find the controller parameters based on the multi-objective optimization with a evolutionary approach. The systems to be controlled must be the type SISO (Single Input Single Output) whose transfer functions may have order higher than second order.

In optical burst switching networks, wavelength converters (WCs) of core nodes are used to decrease the burst loss rate. The implementation of the WCs is difficult in the current technology and the cost of WCs is high. So some core nodes may be configured insufficient WCs to reduce the cost in OBS networks. However, many data channel scheduling algorithms do not count the number of WCs and the performance of burst loss rate is not good in the condition of insufficient WCs. To overcome the defect, two novel batch scheduling algorithm with insufficiency of WC are proposed in this paper. The former algorithm improves the WCs’ resource utilization probability to reduce the burst loss rate and the later algorithm saves the WCs’ resource for the incoming bursts to use to improve the burst loss performance. The later algorithm can reduce more burst loss rate with the same number of WCs, compared with the other scheduling algorithms. The simulation results show that the later algorithm is more effective in reducing the burst loss rate with insufficient WCs.

Precision component displacement, processing, and manipulation in an industrial environment require the high-precision positioning and orientation of industrial robots. However, industrial robots’ positioning includes uncertainties due to assembly and manufacturing tolerances. It is therefore required to use calibration techniques for industrial robot parameters. One of the major sources of uncertainty is the one associated with industrial robot geometrical parameter values. In this paper, using multi-objective meta-heuristic optimization approaches and optical metrology measurements, more accurate Denavit–Hartenberg (DH) geometrical parameters of an industrial robot are estimated. The sensor data used to perform this calibration are the absolute 3D position readings using a highly accurate laser tracker (LT) and industrial robot joint angle readings. Other than position accuracy, the mean absolute deviation of the DH parameters from the manufacturer’s given parameters is considered as the second objective function. Therefore, the optimization problem investigated in this paper is a multi-objective one. The solution to the multi-objective optimization problem is obtained using different evolutionary and swarm optimization approaches. The evolutionary optimization approaches are nondominated sorting genetic algorithms and a multi-objective evolutionary algorithm based on decomposition. The swarm optimization approach considered in this paper is multi-objective particle swarm optimization. It is observed that NSGAII outperforms the other two optimization algorithms in terms of a more diverse Pareto front and the function corresponding to the positional accuracy. It is further observed that through using NSGAII for calibration purposes, the root mean squared for positional error has been improved significantly compared with nominal values.

In this paper, we present a surrogate-based multi-objective evolutionary optimization approach to optimize airfoil aerodynamic designs. Our approach makes use of multiple surrogate models which operate in parallel with the aim of combining their features when solving a costly multi-objective optimization problem. The proposed approach is used to solve five multiobjective airfoil aerodynamic optimization problems. We compare the performance of a multi-objective evolutionary algorithm with surrogates with respect to the same approach without using surrogates. Our preliminary results indicate that our proposal can achieve a substantial reduction in the number of objective function evaluations, which has obvious advantages for dealing with expensive objective functions such as those involved in aeronautical optimization problems.

This paper describes the development and application of a new multi-objective evolutionary optimization approach for the design and upgrading of water distribution systems with multiple pumps and service reservoirs. The optimization model employs a pressure-driven analysis simulator that accounts for the minimum node pressure constraints and conservation of mass and energy. Pump scheduling, tank siting and tank design are integrated seamlessly in the optimization without introducing additional heuristic procedures. The computational solution of the optimization problem is entirely penalty-free, thanks to pressure-driven analysis and the inclusion of explicit criteria for tank depletion and replenishment. The model was applied to the Anytown network that is a benchmark optimization problem. Many new solutions were achieved that are cheaper and offer superior performance compared to previous solutions in the literature. Detailed and extensive simulations of the solutions achieved were carried out. Spatial and temporal variations in water quality were investigated by simulating the chlorine residual and disinfection by-products in addition to water age. The hydraulic requirements were satisfied; efficiency of pumps was consistently high; effective operation of the new and existing tanks was achieved; water quality was improved; and overall computational efficiency was high. The formulation is entirely generic.

The research, development, and deployment of wavelength division multiplexing (WDM) technology are now evolving at a rapid pace to fulfill the increasing bandwidth requirement and deploy new network services. Routing and wavelength assignment algorithms play a key role in improving the performance of wavelength-routed all-optical networks. We study networks with dynamic wavelength routing and develop accurate analytical models for evaluating the blocking performance under dynamic input traffic in different topologies. Two dynamic routing algorithms are first developed and the performances of the algorithms in single-fiber WDM networks are studied using both analytical models and simulation. We also develop efficient algorithms to optimally place a given number of wavelength converters on a path of a network. Finally we consider the effect of multiple fibers on WDM networks without wavelength conversion. We develop analytical models for evaluating the blocking performance of multifiber networks with fixed-path routing, alternate-path routing, and fixed-path least-congestion routing algorithms. The number of fibers required to provide high performance in multifiber networks with different routing algorithms are also studied.

Many Evolutionary Algorithms (EAs) have been proposed over the last decade aiming at solving multi- and many-objective optimization problems. Although EA literature is rich in performance metrics designed specifically to evaluate the convergence ability of these algorithms, most of these metrics require the knowledge of the true Pareto Optimal (PO) front. In this paper, we suggest a novel Karush-Kuhn-Tucker (KKT) based proximity measure using Benson’s method (we call it B-KKTPM). B-KKTPM can determine the relative closeness of any point from the true PO front, without prior knowledge of this front. Finally, we integrate the proposed metric with two recent algorithms and apply it on several multi and many-objective optimization problems. Results show that B-KKTPM can be used as a termination condition for an Evolutionary Multi-objective Optimization (EMO) approach.

This paper presents an application of a constrained multiobjective evolutionary algorithm for the design of active suspension controllers for light rail vehicles with the aim of providing superior ride comfort within the suspension's stroke limitation. A multibody dynamic model of a three‐car train is derived and the control parameters are optimized. Force cancellation, skyhook damper, and track‐following are used to synthesize the active controller. Selection of the active suspension parameters is aided by an evolutionary computation algorithm to get the best compromise between ride quality and suspension deflections due to irregular gradient tracks. An evolutionary multiobjective optimization approach accompanied with the Pareto set is proposed to deal with the complicated control design problem.

Most combinatorial problems are complex and very hard to solve. That is why, lots of methods focus on the optimization according to a single criterion. The combining of several criteria presents additional complexity and new problems.

A multi-objective evolutionary approach for the electric vehicle charging stations problem

Exposure to noise is a significant problem for communities that exist near airports. The distribution of noise exposure can be positively affected by changes in the procedures that aircraft follow in the vicinity of an airport (e.g. rate of ascent, ground track, etc.). When considering such changes, a decision maker often has to weigh the objective of lower noise impact against 'more practical' considerations such as fuel consumption and time-of-flight. This study presents a method of numerical optimization which seeks to find the optimal-tradeoff set (Pareto front) of flight procedures given information about an airport and the surrounding population and geography. This front will only include procedures such that an aggregate noise metric cannot be improved without detriment to a more practical objective. A contemporary multi-objective evolutionary algorithm is used as the basis of the optimization effort. Results from a simulated military airfield near Asheville, NC are shown. Ways in which decision makers are empowered by having access to a Pareto front are discussed.

Evolutionary algorithms have been widely used to tackle multi-objective optimization problems. Despite many successful applications there is a lack of theoretical knowledge on how and why this type of algorithm works particularly well for multi-objective problems. This special issue presents current advances to the theoretical understanding of evolutionary algorithms for multi-objective optimization. It comprises four very high quality papers which have been selected via a rigorous reviewing process. The first paper “On the Effect of Populations in Evolutionary Multi-Objective Optimisation” by Oliver Giel and Per Kristian Lehre studies the basic question whether a population is provably useful to compute the whole Pareto front of a multi-objective optimization problem. They compare restart strategies of several single-objective approaches based on one individual to a natural multi-objective approach using a population. Their analyses point out the benefits of the multi-objective evolutionary algorithm in a rigorous way. Using illustrative example functions they provide not only proven results but also a good intuition about why population-based approaches can outperform individual-based approaches in multi-objective optimization. The paper “Exploring the Runtime of an Evolutionary Algorithm for the Multi-Objective Shortest Path Problem” by Christian Horoba examines the behavior of evolutionary algorithms for the classical NP-hard multi-objective shortest path problem. The author considers an evolutionary approach based on -dominance and proves that this algorithm constitutes a fully polynomial-time randomized approximation scheme (FPRAS) for the problem. The algorithm achieves this by mimicking a problemspecific algorithm for that problem. This provides general insight into why evolutionary algorithms can perform well for problems that allow for efficient problem-specific algorithms. Hypervolume-based algorithms have become very popular in evolutionary multiobjective optimization. In the paper, “An Efficient Algorithm for Computing Hypervolume Contributions,” Karl Bringmann and Tobias Friedrich present a faster algorithm for determining the individual with the smallest hypervolume contribution in a given population. Furthermore, they study the problem of removing λ > 1 individuals with the smallest hypervolume contribution from a given population and present an elegant and fast method to carry out this task.

A multi-objective optimization-based computational approach to nondestructive evaluation of damage in structural components, and more generally in solid continua, is discussed and numerically evaluated. The multi-objective approach provides a substantial improvement in the capabilities to traverse the optimization search space to minimize the measurement error and produce accurate damage estimates. Through simulated test problems based on the characterization of damage in structural steel components, including internal pipe surface geometry as well as material loss within a plate structure utilizing steady-state dynamic measurements of outer surface displacement, a multi-objective genetic algorithm optimization approach is shown to provide substantial computational improvement over single-objective strategies. The multi-objective approach consistently and efficiently produces more accurate characterization results in contrast to equivalent single-objective strategies. More importantly, the multi-objective approach is shown to exhibit consistently better tolerance to test measurement noise and measurement sparsity. Moreover, the multi-objective strategy was found to provide improved diversity in the solution estimates for ill-posed problems, which is an important step leading to insight into the necessary changes to the testing or parameterization to subsequently produce more accurate and unique solutions to such inverse characterization problems.

In wavelength division multiplexed (WDM) optical networks, each wavelength conversion performed along the light path of the connection causes some signal-to-noise deterioration. There is therefore an upper bound (threshold) on the number of wavelength conversions that a signal can go through when it is switched optically from its source to its destination. Our test results have shown that the blocking performance of optical routing could deteriorate significantly under the impact of the conversion cascading constraint. In this paper, we review the negative impact of wavelength conversion cascading on the performance of all-optical routing and we examine the implications of the presence of the conversion cascading constraint on the validity of the performance results for two previously proposed routing protocols for optical networks.