Design of recursive digital integrators and differentiators using particle swarm optimization

This paper presents an efficient approach to determine the optimal set of coefficients for the design of wideband infinite impulse response (IIR) digital integrators (DIs) and digital differentiators (DDs) of first, second, third, and fourth order, meeting the accurate magnitude response specification, using a recently proposed evolutionary optimisation algorithm called enhanced colliding bodies optimisation (ECBO). To demonstrate the effectiveness of the proposed approach, the results of the ECBO-based designs have been compared with those of eight other nature-inspired metaheuristic optimisation algorithms. Parametric and non-parametric statistical hypothesis tests are conducted to validate the consistency of the performance of the ECBO-based DIs and DDs. Simulation results demonstrate that ECBO-based designs achieve the least absolute magnitude error and demonstrate a competitive group delay response of the designed DIs and DDs of different orders as compared with the designs based on the competing algorithms. The proposed DIs and DDs also outperform those of the design approaches published in literature and achieve the best responses in terms of the maximum absolute magnitude error over a wide frequency range.

This article investigates the optimal results of new improved fractional order integrators (FOIs) of different orders. Mathematical models of FOIs have been first developed by a single-step procedure of direct linear interpolation of fractional integrators based on Al-Alaoui operator in fractional domain itself, instead of using three steps of the well-known conventional method, namely, digital interpolation, series expansion and truncation. Later, these transfer functions (TFs) are optimised for their coefficient values for finding a minimum error function by particle swarm optimisation (PSO) algorithm. Simulation results of magnitude responses, phase responses and relative magnitude errors (dB) for all the proposed half integrators have validated the effectiveness of this new technique of interpolation of fractional order operators, mixed with PSO algorithm. A parallel comparison has been also drawn between the proposed optimised half integrators and those obtained by discretisation of PSO optimised integer order digital integrators (DIs) to properly support the proposed novel combination of interpolation and PSO, both applied together in fractional domain.

Accurate estimation of lithium-ion battery life is essential to assure the reliable operation of the energy supply system. This study develops regression models for battery prognostics using statistical methods. The resultant regression models can not only monitor a battery’s degradation trend but also accurately predict its remaining useful life (RUL) at an early stage. Three sets of test data are employed in the training stage for regression models. Another set of data is then applied to the regression models for validation. The fully discharged voltage (Vdis) and internal resistance (R) are adopted as aging parameters in two different mathematical models, with polynomial and exponential functions. A particle swarm optimization (PSO) process is applied to search for optimal coefficients of the regression models. Simulations indicate that the regression models using Vdis and R as aging parameters can build a real state of health profile more accurately than those using cycle number, N. The Monte Carlo method is further employed to make the models adaptive. The subsequent results, however, show that this results in an insignificant improvement of the battery life prediction. A reasonable speculation is that the PSO process already yields the major model coefficients.

This brief presents a general theory of the Newton-Cotes digital integrators which is derived by applying the z-transform technique to the closed-form Newton-Cotes integration formula. Based on this developed theory, a new wideband third-order trapezoidal digital integrator is found to be a class of trapezoidal digital integrators. The novel wideband third-order trapezoidal integrator accurately approximates the ideal integrator over the whole Nyquist frequency range and compares favourably with existing integrators. Based on the designed wideband third-order trapezoidal integrator, a new wideband digital differentiator is designed, which approximates the ideal differentiator reasonably well over the whole Nyquist frequency range and compares favourably with existing differentiators. The low orders and high accuracies of the novel wideband trapezoidal integrator and the new wideband differentiator make them attractive for real-time applications

The purpose of this study is to develop a wireless networked lighting system for office buildings, which can reduce the energy consumption while meeting users' lighting preferences. By using particle swarm optimization, the system is able to optimize the dimming ratio of luminaires according to the real time natural illumination and occupancy condition.In this paper we make a prototype system and test the feasibility and efficiency of the system. The prototype consists of one wireless control module, three illumination sensors and four fluorescent lamps with dimming capacity. The illumination sensors collect and send the data to the control module. After the process of PSO (Particle Swarm Optimization), the module finally sets the power of the lamps according to the PSO result.After real experiments in a certain designed office, it was proved that the system can successfully control the illuminations, and can save considerable energy.

In this study, a novel easy‐to‐use meta‐heuristic method for simultaneous identification of model structure and the associated parameters for linear systems is developed. This is achieved via a constrained multidimensional particle swarm optimization (PSO) mechanism developed by hybridizing two main methodologies: one for negating the limit for fixing the particle’s dimensions within the PSO process and another for enhancing the exploration ability of the particles by adopting a cyclic neighborhood topology of the swarm. This optimizer consecutively searches the dimensional optimum of particles and then the positional optimum in the search space, whose dimension is specified by the explored optimal dimension. The dimensional optimum provides the optimal model structure, while the positional optimum provides the optimal model parameters. Typical numerical examples are considered for evaluation purposes, which clearly demonstrate that the proposed PSO scheme provides novel and powerful impetus with remarkable reliability toward simultaneous identification of model structure and unknown model parameters. Furthermore, identification experiments are conducted on a magnetic levitation system and a robotic manipulator with joint flexibility to demonstrate the effectiveness of the proposed strategy in practical applications.

Design of wideband infinite impulse response (IIR) digital fractional order integrators (DFOIs) based on a bio-inspired metaheuristic optimization approach called the cat swarm optimization (CSO) algorithm is presented in this paper. To investigate the efficiency of the proposed approach, the CSO-based DFOIs are evaluated against those of the approximations designed using real-coded genetic algorithm (RGA), standard particle swarm optimization (PSO), and differential evolution (DE) by different magnitude and phase response error metrics. Simulation results reveal the better frequency response of the CSO-based DFOIs in comparison with the competing designs. Both parametric and nonparametric statistical hypothesis tests validate the performance consistency of CSO. Comparisons with the cited literature confirm the efficacy of the proposed models.

Band selection is a common technique to reduce the dimensionality of hyperspectral imagery. When the desired object information is known, the reduction process can be achieved by selecting the bands that contain the most object information. It is expected that these selected bands can offer an overall satisfactory detection and classification performance. In this paper, we propose a new particle swarm optimization (PSO) based supervised band-selection algorithm that uses the known class signatures only without examining the original bands or the need of class training samples. Thus, this method requires much less computing time than other traditional methods. However, the PSO process itself may introduce additional computation cost. To tackle this problem, we propose parallel implementations via emerging general-purpose graphics processing units that can provide satisfactory results in speedup when compared to the cluster-based parallel implementation.

Particle swarm optimisation (PSO) was developed by Kennedy and Eberhart in 1995 (Kennedy & Eberhart, 1995) inspired by the collective behaviour of natural birds or fish. PSO is a stochastic optimisation technique that uses a behaviour of population composed by many search points called particle. In spite of easy implementation in computer algorithms, it is well known as a powerful numerical optimizer. In the typical PSO algorithms, a set of particles searches the optimal solution in the problem space efficiently, by sharing the common attractor called global best. There are many modified versions of PSO by improving convergence property to a certain problem. While, a standard PSO is defined by Bratton and Kennedy (Bratton & Kennedy, 2007) to give a real standard for PSO studies. PSO seems as one of the evolutionary computations (ECs), and it has been shown that PSO is comparable to a genetic algorithm (Angeline, 1998). Thus, a lot of studies have demonstrated the effectiveness of PSO family in optimizing various continuous and discrete optimization problems. And a plenty of applications of PSO, such as the neural network training, PID controller tuning, electric system optimisation have been studied and achieved well results (Kennedy, 1997). However, PSO is often failed in searching the global optimal solution in the case of the objective function has a large number of dimensions. The reason of this phenomenon is not only existence of the local optimal solutions, the velocities of the particles sometimes lapsed into the degeneracy, so that the successive range is restricted in the sub-plain of the whole search hyper-plain. The sub-plane that is defined by finite number of particle velocities is a partial space in the whole search space. The issue of local optima in PSO has been studied and proposed several modifications on the basic particle driven equation (Parsopoulos et al., 2001; Hendtlass, 2005; Liang et al., 2006). There used a kind of adaptation technique or randomized method (e.g. mutation in evolutionary computations) to keep particles velocities or to accelerate them. Although such improvements work well and have ability to avoid fall in the local optima, the problem of early convergence by the degeneracy of some dimensions is still remaining, even if there are no local optima. Hence the PSO algorithm does not always work well for the high-dimensional function.

The optimal power flow (OPF) problem was introduced by Carpentier in 1962 as a network constrained economic dispatch problem. Since then, it has been intensively studied and widely used in power system operation and planning. In the past few decades, many stochastic optimization methods such as Genetic Algorithm (GA), Evolutionary Programming (EP), and Particle Swarm Optimization (PSO) have been applied to solve the OPF problem. In particular, PSO is a newly proposed population based stochastic optimization algorithm. The main idea behind it is based on the food-searching behavior of birds and fish. Compared with other stochastic optimization methods, PSO has comparable or even superior search performance for some hard optimization problems in real power systems. Nowadays, some modifications such as breeding and selection operators are considered to make the PSO superior and robust. In this paper, we propose the Modified PSO (MPSO), in which the mutation operator of GA is incorporated into the conventional PSO to improve the search performance. To verify the optimal solution searching ability, the proposed approach has been evaluated on an IEEE 30-bus test system. The results showed that performance of the proposed approach is better than that of the standard PSO.

The particle swarm optimization (PSO) is an alternative for global optimization. A standard for PSO (SPSO) was defined which took into the latest developments, and was used as a baseline for performance testing of improvements. In SPSO, however, particles need to search the optimal solution in the constraint space, and the item velocity in PSO makes particles difficult to adjust themselves to meet those complicated constraints. A new PSO without the item velocity based on SPSO is proposed in this article. The new algorithm inherits the capability of SPSO with fast convergence and high accuracy. This research proves that the convergence process of PSO has nothing to do with the velocity and the proposed method modified simple PSO (msPSO) can converge. The experiments show that msPSO is able to achieve a good result.

Short-term scheduling of cascade reservoirs using an immune algorithm-based particle swarm optimization

Nonlinear optimization is becoming challengeable in information sciences and various industrial applications, but nonlinear problems solved by the classical particle swarm-based methods are usually characterized with low efficiency, accuracy, and convergence speed in specific issues. To solve these problems and enhance the nonlinear optimization performance, an improved metaheuristic particle swarm optimization (PSO) model is proposed here. First, the optimization principles and model of the new method are introduced, and algorithms of the improved PSO are presented. Then, influence of the model parameters, input dimensions, and different nonlinear problems on the PSO optimization characterizations are studied by Pareto set solving and optimization performance comparison. The analysis regarding diverse nonlinear problems and optimization methods demonstrates the feasibility of the proposed method. Finally, the performance evaluation is exhibited by the case study of nonlinear parameter optimization, CEC benchmark problems, and rank sum test, which all verify its effectiveness and reliability, as well as the significance and great application promise. Additionally, the future work is discussed.

A novel Cooperative Motion Estimation Algorithm based on Particle Swarm Optimization and its multicore implementation

The implementation of stable, accurate, and wideband second-order microwave integrators (SOMIs) is presented in this paper. These designs of SOMIs are obtained by using different combinations of transmission line sections and shunt stubs in cascading. Particle swarm optimization (PSO), cuckoo search algorithm (CSA), and gravitational search algorithm (GSA) are applied to obtain the optimal values of the characteristic impedances of these line elements to approximate the magnitude response of ideal second-order integrator (SOI). The performance measure criteria for the proposed SOMIs are carried out based on magnitude response, absolute magnitude error, phase response, convergence rate, pole-zero plot, and improvement graph. The simulation results and statistical analysis demonstrate that GSA surpasses the PSO and CSA to approximate the ideal SOI in all state-of-the-art, that is eligible for wide-band microwave integrator. The designed SOMI is compact in size and suitable to cover microwave applications. The magnitude errors for the proposed SOMIs GSA based are as low as 4.9954 and 3.6573, respectively. The structure of the designed SOMI is implemented in the form of microstrip line on RT/Duroid substrate with dielectric constant 2.2 and having height 0.762 mm. The simulated and measured magnitude result agrees well with the ideal one in the frequency range of 3–15 GHz.