Optimum Design and Characterization of Rare Earth-Doped Fibre Amplifiers by Means of Particle Swarm Optimization Approach

The rapid increasing of internet services requires communication capacity of optical fibre networks. Such a task can be carried out by Er3+-doped fibre amplifiers, which allow to overcome limits of unrelayed communication distances. The development of efficient numerical codes provides an accurate understanding of the optical amplifier behaviour and reliable qualitative and quantitative predictions of the amplifier performance in a large variety of configurations. Therefore, the design and optimization of the optical fibre can benefit of this important tool. This chapter proposes an approach based on the Particle Swarm Optimization (PSO) for the optimal design and the characterization of a photonic crystal fibre amplifier. Such approach is employed to find the optimal parameters maximizing the gain of the amplifier. The comparison with respect to a conventional algorithm shows that the proposed solution provides accurate results. Subsequently, the presented method is used to study the amplifier behaviour by evaluating the curves of optimal fibre length, erbium concentration, gain, and pumping configuration. Finally, the PSO based algorithm is exploited to determine the upconversion parameters corresponding to a desired value of gain. This application is particularly intriguing since it allows recovery of the values of parameters of the optical amplifier, which cannot be directly measured.

Particle Swarm Optimization Algorithms Applied to Antenna and Microwave Design Problems

Particle Swarm Optimization (PSO) is an evolutionary optimization algorithm inspired by the social behavior of birds flocking and fish schooling. Numerous PSO variants have been proposed in the literature for addressing different problem types. In this chapter, the authors apply different PSO variants to common antenna and microwave design problems. The Inertia Weight PSO (IWPSO), the Constriction Factor PSO (CFPSO), and the Comprehensive Learning Particle Swarm Optimization (CLPSO) algorithms are applied to real-valued optimization problems. Correspondingly, discrete PSO optimizers such as the binary PSO (binPSO) and the Boolean PSO with velocity mutation (BPSO-vm) are used to solve discrete-valued optimization problems. In case of a multi-objective optimization problem, the authors apply two multi-objective PSO variants. Namely, these are the Multi-Objective PSO (MOPSO) and the Multi-Objective PSO with Fitness Sharing (MOPSO-fs) algorithms. The design examples presented here include microwave absorber design, linear array synthesis, patch antenna design, and dual-band base station antenna optimization. The conclusion and a discussion on future trends complete the chapter.

Short-Term Generation Scheduling Solved with a Particle Swarm Optimizer

In this chapter, a particle swarm optimizer is applied to solve the problem of short-term Hydrothermal Generation Scheduling Problem – one day to one week in advance. The optimization problems have been formulated taking into account binary and real variables (water discharge rates and thermal states of the units). This proposal is based on a strategy to generate and keep the decision variables on feasible space through the correction operators, which were applied to each constraint. Such operators not only improve the quality of the final solutions, but also significantly improve the convergence of the search process due to the use of feasible solutions. The results and effectiveness of the proposed technique are compared to those previously discussed in the literature such as PSO, GA, and DP, among others.

Distributed Task Allocation in Swarms of Robots

This chapter introduces a swarm intelligence-inspired approach for target allocation in large teams of autonomous robots. For this purpose, the Distributed Bees Algorithm (DBA) was proposed and developed by the authors. The algorithm allows decentralized decision-making by the robots based on the locally available information, which is an inherent feature of animal swarms in nature. The algorithm’s performance was validated on physical robots. Moreover, a swarm simulator was developed to test the scalability of larger swarms in terms of number of robots and number of targets in the robot arena. Finally, improved target allocation in terms of deployment cost efficiency, measured as the average distance traveled by the robots, was achieved through optimization of the DBA’s control parameters by means of a genetic algorithm.

Microwave Circuit Design

Circuit models play an important role in the design and optimization of microwave circuits (circuits in the GHz frequency range). These circuit models contain many parameters, including parasitic elements, necessary to correctly model the behavior of transistors at high frequencies. These models are often designed based on a series of measurements. Because of its ability to efficiently locate the global optimum of an objective function, particle swarm optimization (PSO) can be a useful tool when matching a model to its measurements. In this chapter, PSO will be used to calculate a transistor’s small-signal model parameters, determine the noise parameters of the transistor, and design a microwave mixer. The mixer is designed at 39.25 GHz, and a comparison between measurements and simulation results shows good agreement.

Nondestructive Analysis of Dielectric Bodies by Means of an Ant Colony Optimization Method

Electromagnetic approaches based on inverse scattering are very important in the field of nondestructive analysis of dielectric targets. In most cases, the inverse scattering problem related to the reconstruction of the dielectric properties of unknown targets starting from measured field values can be recast as an optimization problem. Due to the ill-posedness of this inverse problem, the application of global optimization techniques seems to be a very suitable choice. In this chapter, the authors review the use of the Ant Colony Optimization method, which is a stochastic optimization algorithm that has been found to provide very good results in a plethora of applications in the area of electromagnetics as well as in other fields of electrical engineering.

Using Swarm Intelligence for Optimization of Parameters in Approximations of Fractional-Order Operators

This chapter applies Particle Swarm Optimization (PSO) to rational approximation of fractional order differential or integral operators. These operators are the building blocks of Fractional Order Controllers, that often can improve performance and robustness of control loops. However, the implementation of fractional order operators requires a rational approximation specified by a transfer function, i.e. by a set of zeros and poles. Since the quality of the approximation in the frequency domain can be measured by the linearity of the Bode magnitude plot and by the “flatness” of the Bode phase plot in a given frequency range, the zeros and poles must be properly set. Namely, they must guarantee stability and minimum-phase properties, while enforcing zero-pole interlacing. Hence, the PSO must satisfy these requirements in optimizing the zero-pole location. Finally, to enlighten the crucial role of the zero-pole distribution, the outputs of the PSO optimization are compared with the results of classical schemes. The comparison shows that the PSO algorithm improves the quality of the approximation, especially in the Bode phase plot.

Optimum Design of Hybrid EDFA/FRA by Particle Swarm Optimization

In this chapter, the authors introduce the hybrid erbium-doped fiber amplifier (EDFA)/fiber Raman amplifier (FRA) and its optimization procedure by particle swarm optimization (PSO). EDFAs, FRAs, and their combinations, which have the advantages of both, are the most important optical fiber amplifiers that overcome the signal power attenuations in the long-haul communication. After choosing a proper configuration for a hybrid EDFA/FRA, users have to choose its numerous parameters such as the lengths, pump powers, number and wavelengths of pumps, number of signal channels and their wavelengths, the signal input powers, the kind of the fibers and their characteristics such as the radius of the core, numerical apertures, and the density of Er3+ ions in the EDFA. As can be seen, there are many parameters that need to be chosen properly. Here, efficient heuristic optimization method of PSO is used to solve this problem.

Design Automation, Modeling, Optimization, and Testing of Analog/RF Circuits and Systems by Particle Swarm Optimization

This chapter is an overview of the applications of particle swarm optimization for circuits and systems. The chapter is targeted for the Analog/RF circuits and systems designers. Design automation, modeling, optimization and testing of analog/RF circuits using particle swarm optimization is presented. Various applications of particle swarm optimization for circuits and systems are explained by examples.

MO-TRIBES for the Optimal Design of Analog Filters

The authors present the use of swarm intelligence in the analog design field. MO-TRIBES, which is an adaptive user-parameter less version of the multi-objective particle swarm optimization technique, is applied for the optimal design of a versatile building block, namely a CMOS current conveyor transconductance amplifier (CCTA). The optimized CCTA is used for the design of a universal filter. Good reached results are highlighted via SPICE simulations, and are compared to the theoretical ones. The use of such adaptive optimization algorithm is of great interest in the analog circuit design, as it is highlighted in this chapter.