A Novel MPPT Method Based on PSO and ABC Algorithms for Solar Cell

Mixed-mode integrated circuits become increasingly popular in modern VLSI Chip design. However, designing these circuits in CMOS technology is no small feat given the need to balance a range of conflicting performance criteria and design constraints. In this work, optimized two-stage operational amplifier designed using meta heuristics ABC (Artificial Bee Colony) and PSO (Particle Swarm Optimization) algorithms. By leveraging ABC and PSO algorithms, the design process can efficiently achieve the desired performance goals by optimally sizing of the circuit components. In the proposed automated optimization environment, implementation of the ABC and PSO algorithms are implemented in Python language and integrated with the Ngspice simulation tool using BSIM4 MOSFET models in TSMC’s 130nm technology node. The entire optimization environment is set up on the Ubuntu operating system, and the results achieved with the ABC and PSO are compared with earlier reported work in which proposed optimized operational amplifier achieved higher unity gain bandwidth and higher CMRR. Proposed automated design environment interface between Ngspice circuit simulator and evolutionary algorithm to obtain the optimum results for the mentioned multidimensional design problem.

A novel performance criterion approach to optimum design of PID controller using cuckoo search algorithm for AVR system

In this study, an ABC-Local Search (ABC-Ls) method was proposed by including a new local search procedure into the standard artificial bee colony (ABC) algorithm to perform the parameter estimation of photovoltaic systems (PV). The aim of the proposed ABC-Ls method was to improve the exploration capability of the standard ABC with a new local search procedure in addition to the exploitation and exploration balance of the standard ABC algorithm. The proposed ABC-Ls method was first tested on 15 well-known benchmark functions in the literature. In the results of the Friedman Mean Rank test used for statistical analysis, ABC-Ls method successfully ranked first with a value of 1.300 in benchmark functions. After obtaining successful results on the benchmark tests, the proposed ABC-Ls method was applied to the single diode, double diode and Photowatt-PWP-201 PV modules of PV systems for parameter estimations. In addition, the proposed ABC-Ls method has been applied to the KC200GT PV module for parameter estimation under different temperature and irradiance conditions of the PV modules. The success of ABC-Ls method was compared with genetic algorithm (GA), particle swarm optimization (PSO) algorithm, ABC algorithm, tree seed algorithm (TSA), Jaya, Atom search optimization (ASO). The comparison results were presented in tables and graphics in detail. The RMSE values for the parameter estimation of single diode, double diode and Photowatt-PWP-201 PV module of the proposed ABC-LS method were found as 9.8602E−04, 9.8257E−04 and 2.4251E−03, respectively. In this context, the proposed ABC-LS method has been compared with the literature for parameter estimation of single diode, double diode and Photowatt-PWP-201 PV module and it has been found that it provides a parameter estimation similar or better than other studies. The proposed ABC-Ls method for parameter estimation of the KC200GT PV module under different conditions is shown in convergence graphs and box plots, where it achieves more successful, effective and stable results than GA, PSO, ABC, TSA, Jaya and ASO algorithms.

This study addresses the modeling of high-order FIR (Finite Impulse Response) systems using lower-order FIR filters, which is a significant challenge in digital filter design and system identification. The primary objective is to approximate the amplitude frequency response of a fifth-order FIR system with a fourth-order FIR model while minimizing the modeling error. To achieve this, four metaheuristic optimization algorithms—Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Quick ABC (qABC), and Memetic Algorithm (MA)—are employed. Each algorithm is executed in 30 independent trials to assess its robustness and performance.The modeling task is formulated as an optimization problem where the mean squared error (MSE) between the reference system’s frequency response and the approximated model’s response is minimized. The performance of each algorithm is evaluated not only by their convergence behavior over iterations but also by detailed frequency-domain analyses, including both amplitude response and power spectrum comparisons. Furthermore, the statistical properties of each algorithm’s output—such as mean, standard deviation, minimum and maximum MSE values—are used to evaluate consistency and accuracy.The results show that the ABC algorithm achieves the best overall balance, providing the lowest average MSE and smallest variance, indicating strong stability and accuracy. The MA algorithm, while achieving the lowest minimum MSE in some runs, exhibits higher variability, suggesting less consistency. The qABC algorithm demonstrates fast convergence and high potential for accurate modeling, but its relatively high standard deviation points to a lack of solution stability. The PSO algorithm, although able to reach low error values in some runs, generally produces more dispersed and inconsistent solutions due to its broader error range.In conclusion, this work presents a comprehensive comparative evaluation of metaheuristic algorithms for low-order FIR filter modeling. The findings highlight the superiority of ABC and MA algorithms in terms of accuracy and stability, and point toward the potential of hybrid or adaptive versions of these algorithms for future filter design and system identification problems.Keywords: FIR filter, system modeling, PSO, ABC, Quick ABC, MA.

In this paper, Artificial Bee Colony (ABC) algorithm which inspired from the behavior of honey bees swarm is presented. ABC is a stochastic population-based evolutionary algorithm for problem solving. ABC algorithm, which is considered one of the most recently swarm intelligent techniques, is proposed to optimize least square support vector machine (LSSVM) to predict the daily stock prices. The proposed model is based on the study of stocks historical data, technical indicators and optimizing LSSVM with ABC algorithm. ABC selects best free parameters combination for LSSVM to avoid over-fitting and local minima problems and improve prediction accuracy. LSSVM optimized by Particle swarm optimization (PSO) algorithm, LSSVM, and ANN techniques are used for comparison with proposed model. Proposed model tested with twenty datasets representing different sectors in S&P 500 stock market. Results presented in this paper show that the proposed model has fast convergence speed, and it also achieves better accuracy than compared techniques in most cases.

Considering the slow convergence speed and unstable transmission capacity of photovoltaic systems in maximum power point tracking (MPPT), this paper proposes a MPPT control strategy based on the fine-layered population algorithm. This strategy improves the search efficiency of the algorithm by fine-layered sequence reconstruction of the sparrow search algorithm (SSA) and particle swarm optimization algorithm (PSO) populations. Firstly, a PSO population pause mechanism is introduced to avoid the premature convergence problem of PSO. Secondly, a fine-layered sequence is used to generate a uniformly distributed initial population, and the cooperative optimization of the sparrow group and the particle swarm is designed to improve the compatibility of the algorithm. The two main populations are decomposed, the number of individuals in the population is optimized, and the individual optimal fitness value of the population is updated. At the same time, the population position is reorganized in each layer to enable each layer to optimize independently, making the entire population easier to collaborate, increasing population diversity, and thereby improving search efficiency. Finally, strategies such as filling functions and sign matrices are introduced into the speed update position of the improved hierarchical population algorithm (PSSSA) to reduce the adverse effects of power fluctuations caused by boundary effects, improve the convergence and speed of the algorithm, and ensure that the optimal solution is obtained after each update. To verify the effectiveness of the PSSSA algorithm, a simulation model under the weather conditions of a certain laboratory in Northeast China was constructed using Matlab. The simulation results show that when it is applied to the maximum power tracking problem, in the case of rapid changes in external environmental illumination, it can accurately track the maximum power point within 0.2 seconds. Its tracking stability is significantly better than the Particle Swarm Optimization (PSO) algorithm, the Seagull Search Algorithm (SSA), and the Perturbation Observation (P&O) algorithm. The size of the maximum power point is approximately 64% larger than that of the PSO algorithm. At the same time, it can also ensure the stability and rapidity of the tracking effect.

In the present research, four meta-heuristic algorithms named Genetic Algorithm (GA), Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC) have been used for optimal design of cascade spillway to minimize the construction cost.Usually, the traditional design methods such as Vittal and Porey (VP) method or experimental modeling are used to solve this problem leading to infeasible or near optimal solution.The main novelty of this paper is to use effective methods to solve this complex highly constrained problem.Therefore, due to unique features of meta-heuristic algorithms, these algorithms are used here to minimize the construction cost of cascade spillway as the energy dissipater structure.As the case study, cascade spillway of Tehri dam in India had been chosen.The algorithms results have been compared together and with the VP results.Comparison of the results show the effectiveness and affectivity of these algorithms to solve this optimization problem.In other words, when three-stepped spillway are considered, the results are improved with 16.16%, 16.4%, 17.73% and 17.63% respectively using GA, GSA, PSO and ABC algorithms and in the same manner, for four-stepped spillway, the results are improved 14.5%, 16.1%, 16.45% and 16.05% respectively using GA, GSA, PSO and ABC algorithms in comparison with the VP results.


 
 
 
 Fossil energy sources experience a decrease each year when the demand increases significantly. In the case of environmental issues, renewable energy sources (RES) can be energy alternatives. The photovoltaic module is RES with unique characteristics, especially partial shading conditions. This condition leads to the PV characteristic curve experiencing multiple peaks. The paper conducted the simulation of the PV solar panel module using MATLAB Simulink. The Maximum Power Point Tracking (MPPT) PV is also described based on a particle swarm optimization (PSO) algorithm. The proposed algorithm can address multiple peak curve problems due to partial shading conditions. For comparison, the conventional algorithm, perturb & observe, is presented. The PV module is divided into three group cells with irradiance differences for each group to illustrate the partial shading condition. The result shows that the PSO algorithm guarantees optimal and fast response for the operating PowerPoint. It needs about 0.04 seconds to maintain at the optimal power point, 129 Watt, compared with the perturb and observe algorithm performance that only kept at the lower operating power point, 67 Watt at 0.06 second. Thus, the PSO algorithm can tackle the partial shading condition with a fast response to maintain the maximum PowerPoint. Therefore, the PSO algorithm is the proper solution for tracking the optimum operating power point under partial shading conditions.
 
 
 

The partial shading of a photovoltaic array repeatedly occurs in the natural environment, which can cause a failure of a conventional maximum power point tracking (MPPT) algorithm. In this paper, the convergence conditions of the standard particle swarm optimization (PSO) algorithm are deduced by the functional analysis, and then the influence of the random variables and inertia factor of the algorithm on the trajectory in the particle swarm optimization is analyzed. Based on the analysis results, an improved particle swarm optimization (IPSO) algorithm, which adopts both global and local modes to locate the maximum power point, is proposed. Compared to the standard PSO algorithm, in the improved PSO algorithm, many random and interfered variables are removed, and the structure is optimized significantly. The proposed algorithm is first simulated in MATLAB to ensure its capability. The feasibility of the approach is validated through physical implementation and experimentation. Results demonstrate that the proposed algorithm has the capability to track the global maximum power point within 3.3 s with an accuracy of 99%. Compared with five recently developed Global MPPT algorithms, the proposed IPSO algorithm achieved better performance in the maximum power tracking in the partial shading conditions.

This paper addresses the usefulness of Artificial Bee Colony (ABC) optimization algorithm for assessment of distribution system reliability. The penalty cost functions are formulated which is related to the failure rate and repair time of each distribution segment. And also, satisfy the reliability constraint such as SAIFI, SAIDI, CAIDI and AENS. The finest values of failure rate and repair time cost function are evaluated using ABC algorithm for reliability enhancement. The performance comparison between ABC and Particle Swarm Optimization (PSO) algorithm also has been done in this paper. In addition, for showing the effectiveness of proposed methodology the numerical results have been compared to the different intelligent techniques that are available in the published literature.

The analysis of commodity demand predication in supply chain network based on particle swarm optimization algorithm

In order to use photovoltaic cell effectively and improve its photoelectric conversion efficiency, the maximum power point of photovoltaic generation system should be tracked rapidly and stably. In this paper after comparison and analysis common methods used in controller of photovoltaic systems such as Fuzzy and P&O, proposed an approach combined from FLC and particle swarm optimization algorithm (PSO) as an appropriate method to achieve maximum power point tracking (MPPT). Indeed Fuzzy logic control can cope with photovoltaic system using heuristic knowledge rules, but tuning the control parameters is not straightforward. PSO performs an on-line haphazard global search for input and output scaling factors of a PD-type fuzzy controller. The objective function of the PSO algorithm has been defined to minimize slope of P-V curve. The simulation results in SIMULINK of MATLAB indicate that proposed method can effectively eliminate the power oscillation around MPP and raise stability and reach steady state of the system.

Maximum power point tracking (MPPT) control is one of the essential requirements in harnessing wind power of wind energy conversion systems (WECS). The more precise the maximum power point (MPP) is determined, the more optimal the WECS is operated. Amongst MPPT algorithms, a hill-climb search (HCS) algorithm is preferred because of its simplicity however it also has few drawbacks such as the difficulty of selecting an appropriate step size, the premature convergence phenomenon and the speed-efficiency trade-off. A cuckoo search (CS) algorithm is proposed in this paper for finding out a MPP of a WECS driven by a doubly-fed induction generator (DFIG) under various wind speeds which mostly overcomes the above disadvantages. Then, the DFIG-WECS is controlled to track the MPP. This ensures that the DFIG-WECS is always operated at MPPs regardless of various wind speeds. It is realized that the proposed CS algorithm is also a population-based algorithm inspired from the breeding behavior of cuckoos but it is quite simple, powerful and especially requires less parameters to define optimal solutions than a genetic algorithm (GA) and a particle swarm optimization (PSO) algorithm. Though numerical results, the CS algorithm shows its effectiveness in finding out MPPs in the DFIG-WECS. Furthermore, the obtained results using the CS algorithm are also compared with those using the HCS and PSO algorithms. The comparison demonstrates that the convergence value and speed of the CS algorithm are always better than those of the HCS and PSO algorithms in the MPPT application in the DFIG-WECS.

Bee-foraging learning particle swarm optimization

Gbest-guided artificial bee colony algorithm for numerical function optimization