Pathfinder Algorithm Research Articles

Evaluation effect climate parameters change on hydropower production and energy demand by RCPs scenarios and the Developed Pathfinder (DPA) algorithm

In the study area, due to the impact of climate change on hydropower generation and energy demand has been investigated the impact of climate change on hydropower generation, and the changes between demand and supply. To predict hydropower generation is utilized Representative Concentration Representative (RCPs) scenarios. Due to the effect of various influential factors such as climate, economy, and social on energy demand, in this study, a proposed model is used to increase the accuracy of energy demand forecasting. This model is an optimal Artificial Neural Network (ANN) model using the Developed Pathfinder Algorithm (DPA) The results of forecasting the climatic parameters showed that in the next 25 years, the temperature will increase and rainfall will decrease. Changes in hydropower generation under RCP2.6 and RCP 8.5 163.3 MW and 188.3 MW will be less than the base time. The results of the energy demand simulation showed that due to the impact of population, Gross Domestic Product (GDP), and rising temperatures, society would face an increasing trend of energy demand. However, this demand will be higher in some seasons due to the increase in the consumption of cooling devices. The electricity demand increased by about 91.42 MW compared to the base period. Examining the changes in demand and energy production, it was found that energy demand would be higher than energy production. Therefore, there is probably a shortage of energy in the future.

Energy-aware task scheduling in cloud compting based on discrete pathfinder algorithm

Task scheduling is one of the fundamental issues that attract the attention of lots of researchers to enhance cloud performance and consumer satisfaction. Task scheduling is an NP–hard problem that is challenging due to the several conflicting objectives of users and service providers. Therefore, meta-heuristic algorithms are the more preferred option for solving scheduling problems in a reasonable time. Although many task scheduling algorithms are proposed, existing strategies mainly focus on minimizing makespan or energy consumption while ignoring other performance factors. In this paper, we propose a new task scheduling algorithm based on the Discrete Pathfinder Algorithm (DPFA) that is inspired by the collective movement of the animal group and mimics the guidance hierarchy of swarms to find hunt. The proposed scheduler considers five objectives (i.e., makespan, energy consumption, throughput, tardiness, and resource utilization) as cost functions. Finally, different algorithms such as Firefly Algorithm (FA), Particle Swarm Optimization (PSO), Grasshopper Optimization Algorithm (GOA), and Bat Algorithm (BA), are used for comparison. The experimental results indicate that the proposed scheduling algorithm can improve up to 9.16%, 38.44%, 3.59%, and 3.44% the makespan in comparison with FA, BA, PSO, and GOA, respectively. Moreover, the results show dramatic improvements in terms of resource utilization, throughput, and energy consumption.

Path-Finder Optimization Based Control of Grid-Tied PV Hybrid Energy Storage System

This paper presents a path-finder algorithm (PFA) of meta-heuristic optimization for the performance enhancement of the bias compensated normalized maximum correntropy criteria (BCNMCC) based voltage source converter (VSC) control for a three-phase three-wire grid-tied dual-stage PV system. The DC link voltage variations during induced dynamic conditions are minimized by PFA optimized PI controller which also generates an accurate loss component of current for enhancing the VSC control performance. The BCNMCC algorithm effectively performs the system identification and echo cancellation in the presence of Gaussian noises. An accurate improves the VSC ability to accurately extract the fundamental load current component and to produce precise weight signals. The BCNMCC performs multifunctional operations, i.e., reactive power compensation, harmonics elimination, load balancing, and power balancing at point of common coupling during steady-state and various dynamic conditions, i.e., grid-islanding & re-synchronization, irradiation variation, unbalanced load and fixed power mode. The proposed system is capable of operating under grid-tied and isolated mode due to the seamless transition of a VSC control from grid current control (GCC) to voltage control. The seamless transition of VSC control does not induce any major transients at the source and on the load side of the system. The hybrid energy storage system (HESS) with a battery, fuel-cell and ultra-capacitor further improves the reliability of the PV system and the stability of the overall system during both grid-tied and isolated operations. The proposed system performs satisfactorily under steady-state and diverse dynamic states as per IEEE519 standards.

Assessing of impact climate parameters on the gap between hydropower supply and electricity demand by RCPs scenarios and optimized ANN by the improved Pathfinder (IPF) algorithm

This study evaluates the effect of climate change on electricity generation, electricity demand, and GHG emissions. For this purpose, using climate scenarios RCPs changes of climatic parameters are predicted. Due to the high importance of energy demand in the management of energy generation resources innovation research is related to forecasting electricity demand. The novelty is the use of an Artificial Neural Network optimized to predict the energy demand. To optimize the ANN method, the Improved Pathfinder algorithm has been used. The use of the optimization method in the ANN method provides a model with more precision and fewer errors for the prediction of energy demand. The results showed that due to the weather changes, hydropower generation for the near future under RCP2.6, RCP4.5, and RCP8.5 increases by about 2.765 MW, 1.892 MW, and 1.219 MW and for the far future increases by about 3.430 MW, 2.475 MW, and 1.827 MW. The electricity demand forecasting by The ANN-IPF model for the near and far future will increase compared to the base period of 391.9 MW and 716.65 MW, respectively. Therefore, the gap between the demand the power supply will increase. Using other resources, the difference between demand and power supply will decrease.

Research on hybrid modified pathfinder algorithm for optimal reactive power dispatch

Research on hybrid modified pathfinder algorithm for optimal reactive power dispatch

Optimal model identification of the PEMFCs using optimized Rotor Hopfield Neural Network

In this paper, a new effective technique is presented for model identification of the Proton-exchange membrane (PEM) fuel cells by a new hybrid model of Rotor Hopfield Neural Network (RHNN). The concept is to lessen the Error between the empirical output voltage and the modeled produced voltage of the PEM fuel cells based on the proposed hybrid RHNN. To improve the structure of the RHNN model, a new metaheuristic, called collective guidance factor-based Pathfinder algorithm (GFPF) has been proposed. To validate the performance of the proposed model, it is performed to a case study and its results are compared with the original RHNN as two Blackbox models. Simulation results indicate 0.5665% and 0.4141% maximum relative error for testing of the original RHNN-based model and the GFPF-RHNN-based model, respectively which is a good accuracy for PEM fuel cells modeling.

Model identification of Solid Oxide Fuel Cell using hybrid Elman Neural Network/Quantum Pathfinder algorithm

In this research, a new efficient method is introduced for model assessment of Solid Oxide Fuel Cell (SOFC) model using a new hybrid Elman Neural Network (ENN). The main purpose of this research is to minimize the Mean Squared Error (MSE) between empirical data and modeling data of the fuel cell output voltage using the suggested hybrid ENN. The designed ENN is indeed a combination of this network with an improved metaheuristic, called Quantum Pathfinder (QPF) algorithm to give an optimal model. The proposed QPF-ENN model is then performed in a SOFC case study to show its efficiency. The results of the suggested method are validated by the reference voltage and also two other methods to show the higher minimum value of the Mean Squared Error (MSE) toward the others. Simulation results are analyzed the mean squared error value of the methods for 5000 samples, where, the voltage is limited between 320 V and 361 V. The results show that the mean square error for the QPF-Elman method, GWO-RHNN method, and PF-Elman method are 0.0014, 0.0017, and 0.0018, respectively. This indicates that the proposed QPF-Elman delivers the minimum value of the mean square error.

Analysis of priority capture method for dynamic requirements of intelligent products based on user scenarios

To solve the problem of the fuzzy and dynamics of requirement caused by users’ cognitive bias, a dynamic requirement and priority capture method based on user scenarios is proposed, aiming at effectively improving user experience. The method consists of the following steps: Firstly, users with similar characteristics are filtered to form a user cluster, then obtain the user’s product experience in different usage scenarios and acquire preliminary requirements by using service design methods. Secondly, the requirement path model tree will be designed and the requirement path matrix will be constructed through the evaluation of the user cluster. Then the pathfinder algorithm will be used to calculate the required correlation of user clusters and prioritize the requirements. Finally, the direction of the product design will be provided. Taking the design of the intelligent office chair as an example, the effectiveness of the method is verified by evaluating the satisfaction of user experience.

Improvement of the computational efficiency of metaheuristic algorithms for the crack detection of cantilever beams using hybrid methods

This study examines the capability of various optimization algorithms and proposes novel hybrid algorithms for more precise prediction of open-edge cracks in cantilever beams. The natural frequencies of the beam with a crack are obtained by modal analysis and experimentally validated by impact testing. The performance of Harris hawk optimization (HHO), electrostatic discharge algorithm (ESDA), pathfinder algorithm (PFA) and Henry gas solubility optimization (HGSO) algorithms from the literature is evaluated to determine the location and depth of an open-edge crack for an Euler–Bernoulli beam. Then, hybrid algorithms (HHO-NM, ESDA-NM and PF-NM) are proposed to improve the results of the aforementioned algorithms. Simulation results show that the proposed hybrid algorithms yield much more precise results with fewer function evaluations than the previously introduced algorithms and, therefore, have superior crack detection capability. Statistical post hoc analysis shows that the proposed hybrid algorithm can be considered a high-performance algorithm, which can significantly improve the efficiency of crack detection applications.

Investigating dynamic performances of fuel cells using pathfinder algorithm

The current work aims at developing an efficient methodology based on pathfinder algorithm (PFA) to study and examine the dynamic performances of fuel cells. At initial stage of this work, the uncertain parameters of fuel cells are defined by using the PFA complete with necessary discussions, comparisons and validations. The best obtained errors of the studied fuel cells modules such as Mark V, and H-12 are equal to 0.853608 V2, and 0.0965364 V2; respectively. Then, the impacts of changeable conditions are performed. At a later stage, a generalized dynamic model of the fuel cell is constructed via Simulink/MATLAB facilities utilizing the cropped parameters produced by the PFA. Just a representative example, various scenarios are demonstrated to indicate the accurateness of the proposed PFA-based methodology on Mark V module. These dynamic scenarios include connections of resistive load and DC motor to the fuel cell under study. At which, various operating parameters such the dynamic responses of inlet pressures, flow rates, and output voltage are observed and analyzed. In addition to that, the detailed responses of the internal voltage declines of fuel cell are traced. It can be confirmed after intensive analyses and verifications that the PFA proves its competency in the accurate modeling of the fuel cells for the purposes of steady-state and dynamic analysis.

An evolutionary approach to implement logic circuits on three dimensional FPGAs

Three Dimensional Field Programmable Gate Arrays (3D FPGAs) recently are presented as the next generation of the FPGA family to continue the integration of more transistors on a single chip seamlessly. The 3D FPGA are fabricated by stacking several layers of semiconductor substrates and the interconnection among layers are realized using Through Silicon Vias (TSVs). Despite their benefits regarding less area and higher speed, 3D FPGAs encounter two major problems; huge size of single TSV and trapping generated heat in inner layers. To handle these problems, we propose a complete Computer Aided Design (CAD) flow to implement an arbitrary logic circuit on 3D FPGA. Prtitioning, Placement, and Routing are primary stages of the proposed CAD flow. The partitioning and placement stages of the flow are based on Simulated Annealing algorithm. Furthermore, the routing stage is a modified version of the Pathfinder algorithm. Unbalanced SA based partitioning tremendously reduces the required TSVs along with distribution of highly active circuit’s modules on the bottom layers and constructing thermal channels facilitate transferring the generated heat in intermediate layers. Simulation results show more than 60%, 65%, and 23% reduction in TSV count, heat transfer performance, and area respectively, along with 4% increase in critical path delay. In addition, comparison between 2D FPGA and 3D FPGA with our proposed architecture (including 2 tier), shows that the circuit speed increases by 28.61%, and minimum channel width decreases by 30.47%. Finally, the results of comparison between 2-tier and 4-tier in 3D FPGA show that circuit speed and minimum channel width increase by 15.95% and 15.92% in 4-tier, respectively.

An enhanced pathfinder algorithm for engineering optimization problems

The pathfinder algorithm (PFA) is a new population-based optimizer, it divides the search agents of the algorithm into leaders and followers, imitating the leadership level of the group movement to find the best food area or prey. In PFA, followers follow the new position according to the position of the leader and their own consciousness makes the algorithm easy to fall into local optimum. To overcome this shortcoming, the following stage is complicated in this paper, and the acceptance operator, the exchange operator and the mutation mechanism are introduced into the algorithm. To further balance the mining ability and exploration ability of the algorithm, the article regards the leader as a guide and introduces a guide mechanism. To verify the performance of the improved algorithm, it is applied to nine real-life engineering case problems. The simulation results of the real-life engineering design problems exhibit the superiority of the improved PFA (IMPFA) algorithm in solving challenging problems with constrained and unknown search spaces when compared to the basic PFA algorithm or other available solutions.

Teaching-learning-based pathfinder algorithm for function and engineering optimization problems

Pathfinder algorithm (PFA) for finding the best food area or prey based on the leadership of collective action in animal groups is a new metaheuristic algorithm for solving optimization problems with different structures. PFA is divided into two stages to search: pathfinder stage and follower stage. They represent the exploration phase and mining phase of PFA respectively. However, the original algorithm also has the problem of falling into a local optimum. In order to solve this problem, the teaching phase in the teaching and learning algorithm is added to the pathfinder stage in the text. In order to balance the exploration and mining capabilities of the algorithm, the learning phase of the teaching and learning algorithm is added to the follower phase in the article. In order to further enhance the depth search ability of the algorithm and increase the convergence speed, the exponential step is given to the followers. Therefore, a teaching-learning-based pathfinder algorithm (TLPFA) is proposed. 19 benchmark functions of four different types and six engineering design problems are used to test of the TLPFA exploration and exploiting capabilities. The experimental results show that the proposed TLPFA algorithm is superior to the state-of-the-art metaheuristic algorithms in terms of the performance measures.

A new meta-heuristic pathfinder algorithm for solving optimal allocation of solar photovoltaic system in multi-lateral distribution system for improving resilience

A new meta-heuristic Pathfinder Algorithm (PFA) is adopted in this paper for optimal allocation and simultaneous integration of a solar photovoltaic system among multi-laterals, called interline-photovoltaic (I-PV) system. At first, the performance of PFA is evaluated by solving the optimal allocation of distribution generation problem in IEEE 33- and 69-bus systems for loss minimization. The obtained results show that the performance of proposed PFA is superior to PSO, TLBO, CSA, and GOA and other approaches cited in literature. The comparison of different performance measures of 50 independent trail runs predominantly shows the effectiveness of PFA and its efficiency for global optima. Subsequently, PFA is implemented for determining the optimal I-PV configuration considering the resilience without compromising the various operational and radiality constraints. Different case studies are simulated and the impact of the I-PV system is analyzed in terms of voltage profile and voltage stability. The proposed optimal I-PV configuration resulted in loss reduction of 77.87% and 98.33% in IEEE 33- and 69-bus systems, respectively. Further, the reduced average voltage deviation index and increased voltage stability index result in an improved voltage profile and enhanced voltage stability margin in radial distribution systems and its suitability for practical applications.

Application of a New Fusion of Flower Pollinated With Pathfinder Algorithm for AGC of Multi-Source Interconnected Power System

As the world's population grows and energy demand increases, it is necessary to increase the scale of the electrical system, which is more complicated. Consequently, adopting automatic generation control (AGC) scheme to meet the demand becomes inevitable. In this article, the fusion of flower pollinated algorithm (FPA) and pathfinder algorithm (PFA), named hereafter as hFPAPFA, is proposed to achieve maximum control efficiency by combining the exploitation of FPA with the exploration capacity of PFA. The proposed hFPAPFA is meant to regulate two unequal multi-area interconnected power system with different generating units such as thermal, hydro, wind power and diesel plants. The proposed control scheme aims to achieve this by using the new algorithm to optimize the fractional-order set-point weighted PID (FOSWPID) parameters under time domain-based fitness functions namely, integral time square error (ITSE) and integral time absolute error (ITAE) while simultaneously minimizing the power losses. Employing the same interconnected power systems, a comparative study with some recent approaches in renowned journals is conducted. The performance of the proposed method is observed under diverse load conditions scenarios. Moreover, three nonlinearities including boiler dynamics, the governor dead band (GDB) and generation rate constraints (GRC) are further integrated into the system from a pragmatic context. Finally, sensitivity tests involving various parameter changes and the introduction of random step load perturbations are carried out. From the results, the proposed approach outperformed other approaches under different load condition scenarios, incorporation of nonlinearities and random load perturbation, demonstrating the proposed technique's efficacy and reliability.

Evaluation Effect Climate Parameters Change on Hydropower Production and Energy Demand by Rcps Scenarios and the Developed Pathfinder (DPA) Algorithm

Evaluation Effect Climate Parameters Change on Hydropower Production and Energy Demand by Rcps Scenarios and the Developed Pathfinder (DPA) Algorithm

Binary pathfinder algorithm for bus splitting optimisation problem

This study proposes a binary version of the pathfinder algorithm (BPFA) inspired by both the collective movement of animals and the leadership hierarchy of swarms in order to solve the existing bus splitting optimisation (BSO) problem encountered by transmission system operators. Some existing efficient algorithms, such as particle swarm optimisation, binary grey wolf optimiser, and salp swarm algorithms, are also utilised to obtain a suitable solution in the BSO problem and to compare it with the BPFA. The objective function of the BSO problem is built to restrict the short-circuit current and to provide the N − 1 security criteria. A new mathematical approach is proposed so as to assess the security performance of the transmission system during the solving of the BSO problem. The efficacy of the proposed algorithm is tested on well-known benchmark functions as well as IEEE 57 and IEEE 118 bus systems. Various transfer functions and position updating rules are implemented to each of the algorithms to acquire a better transition from continuous context to the binary format. The results obtained of the statistical indicators and the pairwise comparisons validate the efficiency and reliability of the BPFA algorithm in solving the compelling real-world problem.

Solution of optimal reactive power dispatch problem using pathfinder algorithm

The optimal reactive power dispatch (ORPD) problem, as a subproblem of optimal power flow, has significant effects in providing reliability and economic operation. In this article, a modified version of the pathfinder algorithm (PFA), which is inspired by the collective movement of a swarm led by one member, is proposed for solving the ORPD problem. The objective of this study is to minimize the power losses by adjusting the control variables. Numerical analyses are performed on 57-bus and 118-bus power systems. To show the performance and effectiveness of the modified pathfinder algorithm (mPFA), some well-known methods are used for comparison. Statistical tests are performed to assess the consistency and ranking of the proposed method. The results show that the mPFA achieved competitive results and obtained a competitive ranking with statistical analyses. The simulations show that the proposed method could be a superior algorithm for solving the ORPD problem.

Real‐time speech enhancement using optimised empirical mode decomposition and non‐local means estimation

In this study, the authors present a novel speech enhancement method by exploring the benefits of non-local means (NLM) estimation and optimised empirical mode decomposition (OEMD) adopting cubic-spline interpolation. The optimal parameters responsible for improving the performance are estimated using the path-finder algorithm. At first, the noisy speech signal is decomposed into many scaled signals called intrinsic-mode functions (IMFs) through the use of a temporary decomposition method is called sifting process in OEMD approach. The obtained IMFs are processed by NLM estimation technique in terms of non-local similarities present in each IMF, to reduce the ill-effects caused by interfering noise. The proposed NLM-based method is effective to eliminate the noise of less-frequency. Each IMF contains essential information about the signals, on some scale or frequency band. Field programmable gate array architecture is implemented on a Xilinx ISE 14.5 and the result of the proposed method offers good performance with a high signal-to-noise ratio (SNR) and low mean-square error compared to other approaches. The performance evolution is carried out for different speech signals taken from the TIMIT database and noises taken from the NOISEX-92 database in different SNR stages of 0, 5 and 10 dB, respectively.

Optimal hydrogen consumption of fuel cell-based locomotive using speed trajectory optimization by Improved Pathfinder algorithm

This paper proposes a new optimization methodology for presenting an efficient result of the hydrogen consumption and speed trajectory in a PEM fuel cell (PEMFC)-based locomotive energy system for transportation applications. After modeling the system, it has been linearized for simplification. The efficiency of the hydrogen consumption is maximized based on a new version of the Pathfinder (IPF) algorithm and the speed trajectory. The reason for designing IPF is to resolve the local optimum and fast convergence shortcomings during the maximization. Two scenarios including normal and extreme characteristics are performed to the system. The simulation results specified that the total traction energy for the normal mode and the extreme mode is 4.27 MJ and 4.80 MJ, respectively, and the optimal hydrogen consumption for these two modes are 0.0764 kg and 0.0628 kg, respectively. The final results showed that the locomotive improves significantly the efficiency of the system for hydrogen saving.