Kidney-inspired Algorithm Research Articles

Implementation of a Binary Kidney-Inspired Algorithm Based on a Parallel Method for Solving the Job Shop Scheduling Problem

Implementation of a Binary Kidney-Inspired Algorithm Based on a Parallel Method for Solving the Job Shop Scheduling Problem

Microgrid Frequency Regulation Based on a Fractional Order Cascade Controller

Nowadays, the participation of renewable energy sources (RESs) and the integration of these sources with traditional power plants in microgrids (MGs) for providing demand-side power has rapidly grown. Although the presence of RESs in MGs reduces environmental problems, their high participation significantly affects the system’s whole inertia and dynamic stability. This paper focuses on an islanded MG frequency regulation under the high participation of RESs. In this regard, a novel fractional order cascade controller (FOCC) is proposed as the secondary frequency controller. In the proposed FOCC controller structure, a fractional order proportional-integral controller is cascaded with a fractional order tilt-derivative controller. The proposed FOCC controller has a greater degree of freedom and adaptability than integer order controllers and improves the control system’s efficiency. The adjustable coefficients of the proposed controller are tuned via the kidney-inspired algorithm. An energy storage system equipped with virtual inertia is also employed to improve the system inertia. The proposed FOCC controller efficiency is compared with proportional-integral-derivative (PID), tilt-integral-derivative (TID), and fractional order proportional-integral-derivative (FOPID) controllers under different disturbances and operating conditions. The results demonstrate that the presented controller provides better frequency responses compared to the other controllers. Moreover, the sensitivity analysis is performed to show the proposed controller robustness versus the parameters’ changes in the system.

Optimal PID Controller Autotuning Design for MIMO Nonlinear Systems Based on the Adaptive SLP Algorithm

In this paper, an adaptive swarm learning process (SLP) algorithm for designing the optimal proportional integral and derivative (PID) parameter for a multiple-input multiple-output (MIMO) control system is proposed. The SLP algorithm is proposed to improve the performance and convergence of PID parameter autotuning by applying the swarm algorithm and the learning process. The adaptive SLP algorithm improves the stability, performance and robustness of the traditional SLP algorithm to apply it to a MIMO control system. It can update the online weights of the SLP algorithm caused by the errors in the settling time, rise time and overshoot of the system based on a stable learning rate. The gradient descent is applied to update the weights. The stable learning rate is verified based on the Lyapunov stability theorem. Additionally, simulations are performed to verify the superiority of the algorithm in terms of performance and robustness. Results that compare the adaptive SLP algorithm with the traditional SLP, a neural network (NN), the genetic algorithm (GA), the particle swarm and optimization (PSO) algorithm and the kidney-inspired algorithm (KIA) based on a two-wheel inverted pendulum system are presented. With respect to performance and robustness, the adaptive SLP algorithm provides a better response than the traditional SLP, NN, GA, PSO and KIA.

Dual adaptive robust control for uncertain nonlinear active suspension systems actuated by asymmetric electrohydraulic actuators

This study investigates the vibration control issue of active suspension systems. Unlike previous results that neglect the actuator dynamics or consider the impractical symmetrical hydraulic cylinder model, this paper incorporates more reasonable asymmetric electrohydraulic actuator into active suspension system and derives its dynamic model. However, whether active suspension or electrohydraulic actuator suffers from nonlinearities (e.g. nonlinear spring, nonlinear damper and nonlinear actuator dynamics) and parameters uncertainties (e.g. the variations of sprung mass and hydraulic fluid’s bulk modulus as well as hydraulic cylinder original control volumes) , which were rarely synthetically considered in the existing researches.To address these issues, we develop a novel dual adaptive robust controller (ARC). An ARC is firstly designed for main-loop system for stabilizing the car body and improving ride comfort in the presence of nonlinearities and parameter uncertainties as well as road disturbances. In order to meet the constraints requirements of suspension system, the tunable parameters in main-loop control law are optimized by solving linear matrix inequality with kidney-inspired algorithm. Another ARC is further synthesized for sub-loop system to deal with the nonlinear and uncertain dynamics in electrohydraulic actuator for ensuring the force tracking performance. Meanwhile, the uncertain parameters are estimated online to compensate the model deviation. The terminal control law is able to guarantee the asymptotic stability of close-loop system within Lyapunov framework. Finally, the effectiveness and robustness of the proposed controller are demonstrated via excessive simulation experiments over different road conditions.

Dual Kidney-Inspired Algorithm for Water Quality Prediction and Cancer Detection

The kidney-inspired algorithm (KA) was presented in a recent research paper as a metaheuristic search algorithm. The KA imitates the physiological process of the kidneys in the human body. The second kidney in the human body filters all the solutes if the other kidney fails. If the second kidney also gets damaged, dialysis can be performed as a treatment. The failure of a kidney is proved by the Glomerular Filtration Rate (GFR) calculation. In this paper, a Dual-population Kidney-inspired Algorithm (Dual-KA), which contributes to the enhancement of a diversity of solutions and, subsequently, better exploration of the search space, is proposed, as a research objective, in the form of a novel simulation of cooperation between kidneys in human body. If GFR is greater than 60 in each iteration, the process in Dual-KA is continued as normal. If this number is between 15 and 60, some treatment is performed. Else, if the GFR is less than 15 and the current sub-population is the first sub-population with a GFR of less than 15, all the solutions in the current sub-population are sent to the other sub-population in the current iteration. Otherwise, a dialysis or transplant process is carried out. Dual-KA is tested on 12 known test functions, standard classification and time series datasets. Water quality prediction and cancer detection are two real-world applications of this algorithm. The mentioned algorithm is run on these problems and its high ability is proved.

Hybridization of Kidney-Inspired and Sine–Cosine Algorithm for Multi-robot Path Planning

A hybridization of kidney-inspired and sine–cosine algorithm has been proposed for a path planning of multiple mobile robots in the environment where obstacles are either static or moveable. In this novel approach, each robot computes its collision-free optimal path from their corresponding start position to goal position through hybridization of kidney-inspired algorithm (KA) and sine–cosine algorithm (SCA). The proposed KA–SCA employs the selection of subsequent optimal position for each robot from their current position by escaping the collision with dynamic obstacles and teammates. In the present work, SCA is used to accelerate the convergence rate of KA, to preserve a good equilibrium between the intensification and diversification, and to compute an optimal path for each robot by minimizing the path distance, path deviation, number of rotation for each robot, and running time required to reach their destination. Finally, the effectiveness and robustness of the proposed algorithm have been verified with the result of KA and SCA in the same environment. The result obtained from the real platform and simulation environment reveals that the proposed KA–SCA outperforms KA and SCA.

Kidney-inspired algorithm with reduced functionality treatment for classification and time series prediction

Optimization of an artificial neural network model through the use of optimization algorithms is the common method employed to search for an optimum solution for a broad variety of real-world problems. One such optimization algorithm is the kidney-inspired algorithm (KA) which has recently been proposed in the literature. The algorithm mimics the four processes performed by the kidneys: filtration, reabsorption, secretion, and excretion. However, a human with reduced kidney function needs to undergo additional treatment to improve kidney performance. In the medical field, the glomerular filtration rate (GFR) test is used to check the health of kidneys. The test estimates the amount of blood that passes through the glomeruli each minute. In this paper, we mimic this kidney function test and the GFR result is used to select a suitable step to add to the basic KA process. This novel imitation is designed for both minimization and maximization problems. In the proposed method, depends on GFR test result which is less than 15 or falls between 15 and 60 or is more than 60 a particular action is performed. These additional processes are applied as required with the aim of improving exploration of the search space and increasing the likelihood of the KA finding the optimum solution. The proposed method is tested on test functions and its results are compared with those of the basic KA. Its performance on benchmark classification and time series prediction problems is also examined and compared with that of other available methods in the literature. In addition, the proposed method is applied to a real-world water quality prediction problem. The statistical analysis of all these applications showed that the proposed method had a ability to improve the optimization outcome.

Improved Kidney-Inspired Algorithm Approach for Tuning of PID Controller in AVR System

This paper proposes a novel tuning design of proportional integral derivative (PID) controller via an improved kidney-inspired algorithm (IKA) with a new objective function. The main objective of the proposed approach is to optimize the transient response of the AVR system by minimizing the maximum overshoot, settling time, rise time and peak time values of the terminal voltage, and eliminating the steady state error. After obtaining the optimal values of the three gains of the PID controller (KP, KI, and KD) with the proposed approach, the transient response analysis was performed and compared with some of the current heuristic algorithms-based approaches in literature to show the superiority of the optimized PID controller. In order to evaluate the stability of the automatic voltage regulator (AVR) system tuned by IKA method, the pole/zero map analysis and Bode analysis are performed. Finally, the robustness analysis of the proposed approach has been carried out with variations in the parameters of the AVR system. The numerical simulation results demonstrated that the proposed IKA tuned PID controller has better control performances compared to the other existing approaches. The essence of the presented study points out that the proposed approach may successfully be applied for the AVR system.

A new method for choosing the biasing parameter in ridge estimator for generalized linear model

Multicollinearity problem arises frequently in several modern applications, such as chemometrics, biology, and other scientific fields. The common feature of the multicollinearity problem is that a large number of predictors are highly correlated. Generalized linear model is a powerful and a popular approach for modeling a large variety of regression data. It is well known that the existence of multicollinearity can inflate the variance of the maximum likelihood estimator. To reduce the effects of multicollinearity, the ridge estimator has been efficiently demonstrated to be an attractive method. However, the choice of the biasing parameter of the ridge estimator is critical. Our aim is to efficiently estimate such a biasing parameter. Towards this aim, a kidney-inspired algorithm, which is a population-based algorithm inspiring by the kidney process in the human body, is proposed. Extensive comparisons with different classical biasing parameter estimating methods are conducted through simulation and real data application. The results demonstrate that our proposed approach is able to find the best biasing parameter value with high prediction accuracy. Further, the results indicate that the performance of our proposed approach is superior to that of other competitor methods.

Two-factor high-order fuzzy-trend FTS model based on BSO-FCM and improved KA for TAIEX stock forecasting

Fuzzy time series has been an effective and attractive forecasting model for solving the problem of stock index forecasting. In particular, fuzzy-trend fuzzy time series models have been proposed recently to address complex cases and perform well in terms of forecasting accuracy. Nonetheless, they have just explored the two-factor second-order forecasting but cannot satisfy the complex stock system. In this study, we proposed a new two-factor high-order fuzzy-trend fuzzy time series model to explore the more complex situation on the TAIEX stock index forecasting. We presented the backtracking search optimization-fuzzy c-means method to obtain the optimal intervals of the data sets. In addition, an improved kidney-inspired algorithm is employed to integrate the high-order forecasting values. The proposed model shows outstanding forecasting accuracy than the benchmark methods on the TAIEX. Besides, we combined two other stock indexes (NASDAQ and the Dow Jones) as the secondary factors, respectively. It provides a useful method for two-factor high-order fuzzy-trend fuzzy time series stock index forecasting.

Parameter optimization of power system stabilizers via kidney-inspired algorithm

This article describes the application of a new population-based meta-heuristic optimization algorithm inspired by the kidney process in the human body for the tuning of power system stabilizers (PSSs) in a multi-machine power system. The tuning problem of PSS parameters is formulated as an optimization problem that aims at maximizing the damping ratio of the electromechanical modes and the kidney-inspired algorithm (KA) is used to search for the optimal parameters. The efficacy of the KA-based PSS design was successfully tested on a well-known 16-machine, 68-bus power system. The obtained results are evaluated and compared with the other results obtained by the original particle swarm optimization (PSO) and the bat algorithm (BA) methods. From the detailed eigenvalue analysis, the nonlinear simulation studies and some performance indices it has been found out that for damping oscillations, the performance of the proposed KA approach in this study is better than that obtained by other intelligent techniques (PSO and BA). Moreover, the efficiency and the superior performance of the proposed method over the other two algorithms in terms of computation time, convergence rate and solution quality are confirmed.

Terminal sliding mode control for full vehicle active suspension systems

In current study, a terminal sliding mode control approach different from the conventional sliding mode control is proposed for active suspension system, which has an ability to reach the sliding surface in a finite time to achieve a high control accuracy. A full vehicle active suspension model is adopted with consideration of system uncertainties. The terminal sliding mode controller (TSMC) is systematically designed to force motion trajectories of vehicle body to accurately track the ideal reference model, and the controller parameters are tuned by a novel kidney-inspired algorithm (KA) for better control performance. The thought of designing an adaptive scheme for the reference model is one of the main contribution of this work. Simulation results clearly show the strength of adaptive scheme. The effectiveness and the strong robustness in stabilizing the attitude of the vehicle and improving the ride comfort are the main positive features of the proposed TSMC.

基于缩放因子和协作算子的肾脏算法

基于缩放因子和协作算子的肾脏算法

Optimization of neural network using kidney-inspired algorithm with control of filtration rate and chaotic map for real-world rainfall forecasting

A broad variety of real-world problems have been solved using multilayer perceptron (MLP) artificial neural networks (ANNs). Optimization techniques aid ANNs to select suitable weights and achieve correct results. Recently, the kidney-inspired algorithm (KA) has been proposed for optimization problems. This algorithm is based on the filtration, reabsorption, secretion, and excretion processes that take place in the kidneys of the human body. In the KA, the value of α in the filtration rate formula is a constant value in the range of [0, 1] that is set in the initialization stage of the algorithm. In this paper, an improved KA for optimization of the ANN model is presented in which the filtration rate is controlled by changing the value of α from minimum to maximum during the search process, which helps in achieving a better balance between exploration and exploitation in the algorithm. In this algorithm if more solutes are filtered and moved to filtered blood it means that the algorithm has more exploration. In contrast, if more solutes move to waste it means that more exploitation is performed by the algorithm. In addition, the separate use of three chaotic maps instead of a random number in the movement formula of the modified KA is investigated in order to assess the ability of each map to help to achieve superior results. The proposed method is tested on benchmark classification and time series prediction problems. The method is also applied to a real-world rainfall forecasting problem. The results of a statistical analysis prove the ability of the method.

Assessment Analysis and Forecasting for Security Early Warning of Energy Consumption Carbon Emissions in Hebei Province, China

Against the backdrop of increasingly serious global climate change and the development of the low-carbon economy, the coordination between energy consumption carbon emissions (ECCE) and regional population, resources, environment, economy and society has become an important subject. In this paper, the research focuses on the security early warning of ECCE in Hebei Province, China. First, an assessment index system of the security early warning of ECCE is constructed based on the pressure-state-response (P-S-R) model. Then, the variance method and linearity weighted method are used to calculate the security early warning index of ECCE. From the two dimensions of time series and spatial pattern, the security early warning conditions of ECCE are analyzed in depth. Finally, with the assessment analysis of the data from 2000 to 2014, the prediction of the security early warning of carbon emissions from 2015 to 2020 is given, using a back propagation neural network based on a kidney-inspired algorithm (KA-BPNN) model. The results indicate that: (1) from 2000 to 2014, the security comprehensive index of ECCE demonstrates a fluctuating upward trend in general and the trend of the alarm level is “Severe warning”–“Moderate warning”–“Slight warning”; (2) there is a big spatial difference in the security of ECCE, with relatively high-security alarm level in the north while it is relatively low in the other areas; (3) the security index shows the trend of continuing improvement from 2015 to 2020, however the security level will remain in the state of “Semi-secure” for a long time and the corresponding alarm is still in the state of “Slight warning”, reflecting that the situation is still not optimistic.

Kidney-inspired algorithm for optimization problems

In this paper, a population-based algorithm inspired by the kidney process in the human body is proposed. In this algorithm the solutions are filtered in a rate that is calculated based on the mean of objective functions of all solutions in the current population of each iteration. The filtered solutions as the better solutions are moved to filtered blood and the rest are transferred to waste representing the worse solutions. This is a simulation of the glomerular filtration process in the kidney. The waste solutions are reconsidered in the iterations if after applying a defined movement operator they satisfy the filtration rate, otherwise it is expelled from the waste solutions, simulating the reabsorption and excretion functions of the kidney. In addition, a solution assigned as better solution is secreted if it is not better than the worst solutions simulating the secreting process of blood in the kidney. After placement of all the solutions in the population, the best of them is ranked, the waste and filtered blood are merged to become a new population and the filtration rate is updated. Filtration provides the required exploitation while generating a new solution and reabsorption gives the necessary exploration for the algorithm. The algorithm is assessed by applying it on eight well-known benchmark test functions and compares the results with other algorithms in the literature. The performance of the proposed algorithm is better on seven out of eight test functions when it is compared with the most recent researches in literature. The proposed kidney-inspired algorithm is able to find the global optimum with less function evaluations on six out of eight test functions. A statistical analysis further confirms the ability of this algorithm to produce good-quality results.