Fractional Order Filter Discretization by Particle Swarm Optimization Method

This chapter presents a voltage-mode technique for realizing fractional-order filters. Low-voltage OTAs are employed as active elements. Fractional-order filters are designed with the additional benefit that different types of filter functions are realized using the same topology. As a result, the resulted filter functions are generalized. Firstly, fractional order lowpass, highpass, allpass, and bandpass filters of order α are realized the efficiency of which is verified through simulations results. In addition, fractional order lowpass, highpass, bandpass, and bandstop filters of order α + β are also given. Both designs employed MOS transistors biased in subthreshold region, offering also the benefit of low voltage operation. The design of these topologies is realized using the AMS 0.35 μm CMOS technology.

This paper investigates the use of nonresonant LC loads in the design of multi-band antennas. In this approach, the loads are made to have inductive reactances at some operating frequencies and capacitive reactances at other frequencies; the loads are never resonant. This has the effect of increasing or decreasing the effective electrical length of the antenna. Also, the entire structure supports significant radiating currents at all operating frequencies. Although this approach is not new, the design process has historically been relatively complicated or has relied on trial-and-error. A much more straightforward and automatic approach based on the particle swarm optimization (PSO) method is described here. The PSO method was chosen because of its speed and relatively simple implementation. The focus is on achieving an input impedance with zero reactance and not on obtaining a specific radiation pattern shape, since the patterns of short monopoles and dipoles are essentially invariant with respect to length

Lotka-Volterra competition model has been applied in life sciences including competition between species, predicting the Aeromonas hydrophila growth on fish surface. The competition model also has been used in social sciences, including competition in the Korean stock market and competition between two types of bank, namely commercial bank and rural bank in Indonesia. It is well-known that the analytical solution of the Lotka-Volterra is unknown. Here gradient-base methods such as Newton method and Levenberg-Marquardt are difficult to be implemented to estimate paramaters of the model. In order to estimate parameters in the model, one need to use heuristic method such as genetic algorithm, particle swarm optimization, firefly algorithm or other heuristic methods. In this paper, we compared performance of particle swarm optimization and firefly algorithm in parameter estimation of Lotka-Volterra type competition model. Here we used the profit data of commercial bank and rural bank, where the data cited from literature. We found the mean absolute percentage error (MAPE) of firefly algorithm is a little bit smaller than the error of particle swarm optimization method. We also found variance of the error of firefly algorithm is lower than the particle swarm optimization method. Hence, for parameter estimation of Lotka-Volterra competition model, firefly algorithm is more beneficial than the particle swarm optimization method.

The design optimization of two-stage cylindrical helical gear reducer is discussed in this paper. The volume and the center gear distance are adopted as objective functions separately, and the particle swarm optimization(PSO) method is used to improve the design quality. The design variables contain discrete variables, which are converted to discrete variables by the nearby principle in order to improve the running efficiency. Inertia weight coefficient is used in the PSO algorithm to adjust the coverage speed. The optimization result shows that our method is better than other optimization method, and it proves that PSO method is meaningful to the reducer’s design.

In this paper, the mathematical models of two water distribution network systems, namely a Serial Network and a Branched Network have been considered as nonlinear optimization problems and Particle Swarm Optimization (PSO) method has been used to obtain the global optimal solution in each case. The PSO is a heuristic optimization method. Herein, the PSO has been used to determine the minimum cost of the water distribution network system for a Serial Network and Branched Network. The numerical results indicate the robustness of the PSO method. It suggests that PSO method can be effectively used for any generalized water distribution network system.

Due to its complexity and non-deterministic polynomial-time hard characteristic, multirobot task allocation problem remains a challenging issue in the field of cooperative robotics. Thanks to its easy implementation and promising convergence speed, the particle swarm optimization method has recently aroused increasing research interest in the area of multirobot task allocation problem. However, the efficiency of the standard particle swarm optimization is hindered by several deficiencies such as the inefficient capabilities in balancing exploration and exploitation, as well as the high likelihood of plunging into stagnation. Aiming at enhancing the performance of particle swarm optimization via remedying these two drawbacks, this paper proposes an improved particle swarm optimization method, which integrates standard particle swarm optimization 2011 with evolutionary game theory. To prevent particles being locked into stagnation, particles in the proposed particle swarm optimization first adopt the updating rules of standard particle swarm optimization 2011 to undertake their movements. Subsequently, attempting to well trade off the exploration and exploitation capabilities of particles, a novel self-adaptive strategy, which is determined by the evolutionary stable strategies of evolutionary game theory and the iteration number of particle swarm optimization, is presented to adaptively adjust the main control parameters of particles in the proposed particle swarm optimization. Since the convergence of particle swarm optimization remains paramount and dramatically affects the performance of particle swarm optimization, this paper also analytically investigates the convergence of the proposed particle swarm optimization and provides a convergence-guaranteed parameter selection principle for the proposed method. Finally, leveraging the development of the proposed particle swarm optimization, this paper completes the design of a new particle swarm optimization–based multirobot task allocation method. The performance of the new particle swarm optimization–based multirobot task allocation method is tested through three different allocation cases against four well-known evolutionary methods. Experimental results confirm that the proposed method generally outperforms its contenders in terms of the solution quality. Moreover, the proposed method performs slightly better than the majority of its peers as far as the computation time is concerned.

In this study, adaptive artificial intelligence control algorithms were designed using the particle swarm optimisation (PSO) method for safe take-off and landing of aircraft. The performance of the designed control algorithms, which were applied to the control of the aircraft’s pitch angle, was compared with each other and with studies in the literature. Integrated squared error (ISE) was used as a fitness function in the design of PSO-based proportional–integral–derivative (PSO-PID), PSO-based fuzzy logic controller (PSO-FLC) and PSO-based self-adaptive fuzzy-PID (PSO-SAF-PID) controllers. Performance comparisons between the designed controllers were made based on the transient behaviours such as overshoot, rise time and settling time, and statistical error analyses including mean squared error (MSE), mean absolute error (MAE), ISE and integrated absolute error (IAE). It was observed that the performance of the PID controller, FLC and SAF-PID controllers improved significantly with the application of the PSO method. First, the mathematical model of the system was derived. Second, traditional control methods (PID control algorithm) and artificial intelligence control methods (FLC and SAF-PID type control algorithms) were designed to control the modelled system. Then, to improve the performance of these designed control algorithms, the optimal control parameters of the PID algorithm were determined using the PSO method. At the same time, membership function weight coefficients of the FLC algorithm and SAF-PID type control algorithm were determined using the PSO algorithm. Determining the control parameters and membership function coefficients at optimum values significantly affects the performance of control algorithms. Optimal control parameters and membership coefficients have significantly improved control performance. Furthermore, tests performed on transfer functions with different system parameters have shown that the PSO-SAF-PID controller is robust.

Selecting appropriate weighting matrices for desired linear quadratic regulator (LQR) controller design using evolutionary algorithms is presented in this paper. Obviously, it is not easy to determine the appropriate weighting matrices for an optimal control system and a suitable systematic method is not presented for this goal. In other words, there is no direct relationship between weighting matrices and control system characteristics, and selecting these matrices is done by using trial and error based on designer’s experience. In this paper, we use the particle swarm optimization (PSO) method which is inspired by the social behavior of fish and birds in finding food sources to determine these matrices. Stable convergence characteristics and high calculation speed are the advantages of the proposed method. Simulation results demonstrate that in comparison with genetic algorithms (GAs), the PSO method is very efficient and robust in designing of optimal LQR controller. Key words: Linear quadratic regulator (LQR), weighting matrices, particle swarm optimization, genetic algorithm.

In this work, a new design of continuous time current differencing buffered amplifier based low-pass, high-pass, band-pass, all-pass and notch fractional-order filters is reported. The design of proposed filters is based on the approximation of fractional-order filters by using an appropriate integer order transfer function. Signal flow graph approach is used for the realization of fractional-order filters of order (1 + α). The frequency responses of the proposed circuits are verified using MATLAB in conjunction with SPICE. The evaluation of the realized fractional-order filters are also performed through the AC analysis and corner analysis. Furthermore, stability and sensitivity investigations are also investigated. It is observed from the simulation results that the fractional-order filters are appropriate for IC implementation.

A deep-learning artificial neural network (NN) combined with the particle swarm optimization (PSO) method has been proposed to inversely design the semiconductor laser with high accuracy and computational speed. This method is exempt from the single-solution problem of tandem NN and can be highly useful to extract the possible problematic parameters in the failure analysis of a device. The light-current curves and small signal responses have been tested against the benchmarks calculated by the traveling-wave model to demonstrate the NN's robustness and efficiency in simulating the laser behavior for further use in the inverse design by PSO.

In rural regions with limited access to the power grid, self-reliance for electricity generation is paramount. This study focuses on enhancing the design of stand-alone photovoltaic installations (SAPV) to replace conventional fuel generators thanks to the decreasing costs of PV modules and batteries. This study presents a particle swarm optimization (PSO) method for the reliable and cost-effective sizing of SAPV systems. The proposed method considers the variability of PV generation and domestic demand and optimizes the system design to minimize the total cost of ownership while ensuring a high level of reliability. The results show that for the PSO method with 500 iterations, the error is around 2%, and the simulation time is approximately 2.25 s. Moreover, the PSO method allows a much lower number of iterations to be used in the Monte Carlo simulation, with a total of 100 iterations used to obtain the averaged results. The optimization results, encompassing installed power, battery capacity, reliability, and annual costs, reveal the effectiveness of our approach. Notably, our discretized PSO algorithm converges, yielding specific parameters like 9900 W of installed power and a battery configuration of five 3550 Wh units for the case study under consideration. In summary, our work presents an efficient SAPV system design methodology supported by concrete numerical outcomes, considering supply reliability and installation and operational costs.

To determine propagation characteristics in buildings for fifth-generation (5G) wireless communication, it is necessary to obtain the complex permittivity of the construction materials at millimeter-wave band. Backward reconstruction of complex permittivity of construction materials in free space at millimeter-waves remains a challenging and up to date complex problem. A procedure for the estimation of the complex permittivity of construction materials based on Particle Swarm Optimization (PSO) method is presented in this paper. The complex permittivity is extracted from the transmission and reflection coefficients for perpendicular polarization over 40-50 GHz, and verified by reflection coefficients for parallel polarization. This method is relatively precise and fast which gives a good estimation of complex permittivity.

This study presents fractional order filters to enhance the performance of the conventional linear quadratic regulator (LQR) method for optimal robust control of a simple civil structure. The introduced filters modify the state variables fed back to the constant gain controller. Four combinations of fractional order filter and LQR are considered and optimized based on a new performance criterion defined in the paper. Introducing fractional order filters is shown to considerably improve the results for both the artificially generated ground motions and previously recorded earthquake data.

The method of Particle Swarm Optimization (PSO) is an epitome in optimization process for solving classification, clustering, and many other multi-dimensional problems. PSO is found to be stronger than few state of the art population-based coup such as Ant colony optimization (ACO), genetic algorithms (GAs), Optimization of the herd of elephants known as Elephant herd optimization (EHO) together with other algorithms under certain real-life scenarios like path finding, feature selection, etc. Originally PSOs were implemented by means of optimizing the velocity/speed and position update equations so that it could locate the best global position of the particles. Many researchers have designed different approaches by combining human behavioral aspects to these equations in order to further optimize the PSOs performance. In this text we have proposed the use of a 4-staged teacher-learning behavior (TLB) inspired PSO for optimizing the system performance. Under different test functions, the proposed algorithm was tested and evaluated and its performance was compared relative with the modern state of the art PSO systems. It is observed that the proposed PSO reduces the convergence time by more than 20%, and also reduces the overall computational complexity to half. We conclude this text by making some acute observations about the implemented system, and recommend methods which can be used for further optimizing the system performance.

This paper presents optimization of fractional order Butterworth filter using a meta-heuristic algorithm. Interior Search Algorithm is used for the algorithm, and performance is compared with the existing optimization technique which is simulated annealing. Global best is calculated using the algorithm for calculating the coefficient for the transfer function, optimizing it using the fitness function. The results are verified with Tow Thomas fractional order filter using Pspice, using an approximated fractional capacitor built using Carlson’s method to obtain the Fractional Order Filter. MATLAB and Pspice results were utilized to calculate the coefficients of the transfer function of the fractional order low pass filter.