JADE Algorithm Research Articles

Multi-UAV Path Planning Based on Cooperative Co-Evolutionary Algorithms with Adaptive Decision Variable Selection

When dealing with UAV path planning problems, evolutionary algorithms demonstrate strong flexibility and global search capabilities. However, as the number of UAVs increases, the scale of the path planning problem grows exponentially, leading to a significant rise in computational complexity. The Cooperative Co-Evolutionary Algorithm (CCEA) effectively addresses this issue through its divide-and-conquer strategy. Nonetheless, the CCEA needs to find a balance between computational efficiency and algorithmic performance while also resolving convergence difficulties arising from the increased number of decision variables. Moreover, the complex interrelationships between the decision variables of each UAV add to the challenge of selecting appropriate decision variables. To tackle this problem, we propose a novel collaborative algorithm called CCEA-ADVS. This algorithm reduces the difficulty of the problem by decomposing high-dimensional variables into sub-variables for collaborative optimization. To improve the efficiency of decision variable selection in the collaborative algorithm and to accelerate the convergence speed, an adaptive decision variable selection strategy is introduced. This strategy selects decision variables according to the order of solving single-UAV constraints and multi-UAV constraints, reducing the cost of the optimization objective. Furthermore, to improve computational efficiency, a two-stage evolutionary optimization process from coarse to fine is adopted.Specifically, the Adaptive Differential Evolution with Optional External Archive algorithm (JADE) is first used to optimize the waypoints of the UAVs to generate a basic path, and then, the Dubins algorithm is combined to optimize the trajectory, yielding the final flight path. The experimental results show that in four different scenarios involving 40 UAVs, the CCEA-ADVS algorithm significantly outperforms the Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and JADE algorithms in terms of path performance, running time, computational efficiency, and convergence speed. In addition, in large-scale experiments involving 500 UAVs, the algorithm also demonstrates good adaptability, stability, and scalability.

A New Hybrid Equivalent Modeling Method of Low-Frequency Radiation Source Based on GS and JADE Algorithms and Phaseless Near-Field Data

A New Hybrid Equivalent Modeling Method of Low-Frequency Radiation Source Based on GS and JADE Algorithms and Phaseless Near-Field Data

Interpretable short-term carbon dioxide emissions forecasting based on flexible two-stage decomposition and temporal fusion transformers

Carbon emissions play a pivotal role in exacerbating the global warming crisis and driving climate change. Accurate and consistent projections of carbon emissions are of utmost importance for nations worldwide, as they shape emission reduction strategies and expedite the pursuit of carbon peaking and carbon neutrality goals. While previous studies have focused on hybrid methodologies for carbon emission forecasting, yielding commendable predictive performance, these approaches often overlook the significance of internal interpretability within the forecasting models. In light of this gap, this study introduces a groundbreaking and elucidating hybrid carbon emission forecasting model that amalgamates the two-stage layer decomposition method, adaptive differential evolution with optional external archive (JADE), and temporal fusion transformers (TFT). To begin with, a series of sub-sequences is derived by employing a flexible two-stage decomposition strategy, which leverages a linear-nonlinear decomposition criterion to thoroughly extract the fluctuating characteristics inherent in the carbon emission series. Subsequently, the JADE algorithm intelligently and efficiently optimizes the parameter combinations within the TFT model, ensuring both stability and reliability of the prediction framework. Empirical investigations conclusively demonstrate the remarkable applicability and efficacy of the proposed model in short-term carbon emission forecasting. By delving into the interpretability of the model's results, the study enhances the capacity of policymakers to devise well-informed strategies based on comprehensive insights gleaned from the forecasting process.

Underwater glider 3D path planning with adaptive segments and optimal motion parameters based on improved JADE algorithm

This paper presents a novel 3D path planning method for the underwater glider (UG) that incorporates adaptive segment strategy, motion parameters optimization, and an improved JADE algorithm. The method aims to generate an energy-efficient path by adapting to ocean currents and seabed topography and selecting favorable motion parameters. We establish an energy consumption model for a blended-wing-body UG, examining the influence of motion parameters and ocean currents on its performance. The proposed method encodes an energy-optimal gliding path through multiple path segments, each defined by a set of path points, pitch angles, and diving depths. The fitness function, based on energy consumption, guides the optimization process. To enhance the optimization, we present an improved JADE algorithm with multi-mutation strategies, which adaptively updates the mutation operation and mean crossover probability. Our method was assessed and compared with a classical UG path planning method on 6 test scenarios. Simulation results confirm that adaptive segments and motion parameter optimization contribute to better adaptation to ocean environments and reduced energy consumption.

Interpretable wind speed forecasting with meteorological feature exploring and two-stage decomposition

Wind speed plays a pivotal role in ensuring the stability of power grid operations. However, the inherent high volatility and non-stationarity of wind patterns pose significant challenges to achieving accurate predictions. To tackle this issue and enhance the interpretability of existing wind speed prediction models, this study proposes an innovative short-term wind speed prediction model that combines two-stage decomposition, meteorological data feature engineering, adaptive differential evolution with optional external archive (JADE) algorithm, and temporal fusion transformers (TFT). To begin, the wind speed data is subjected to decomposition using improved complete ensemble empirical mode decomposition with adaptive noise (IEEMD), yielding multiple eigenmode functions. Subsequently, the nonlinear decomposition subsequence undergoes further decomposition into multiple submodes via empirical wavelet transform (EWT), followed by the careful screening of nonlinear quadratic decomposition submodes. Additionally, meteorological features are ingeniously reconstructed into combined and statistical meteorological features, enhancing the effectiveness of input features. Next, the hyperparameters of the TFT are meticulously optimized using the powerful JADE algorithm. Empirical results unequivocally demonstrate that the IEEMD-EWT-JADE-TFT model achieves remarkably high prediction accuracy. Meanwhile, this interpretative experimental process and its results chart a novel path for decision-makers seeking reliable wind speed forecasting processes and outcomes.

Two-stage decomposition and temporal fusion transformers for interpretable wind speed forecasting

Contemporary wind speed prediction research methodologies often employ two-stage decomposition preprocessing techniques to leverage the temporal correlation of wind speed. However, they frequently neglect to investigate the interpretability within the wind speed prediction model. To this end, a novel and interpretable hybrid forecasting model that combines two-layer decomposition, adaptive differential evolution with optional external archive (JADE), and temporal fusion transformers (TFT) is proposed. Primarily, on the basis of the linear-nonlinear decomposition criterion, a set of subcomponents is obtained using two-stage decomposition to fully extract the wind speed series prediction information for high-fluctuation and multi-resolution modes. Utilizing the JADE algorithm for intelligent and efficient optimization of parameter combinations in the TFT model guarantees the stability and reliability of the prediction model. Later, the obtained two-layer decomposition subseries are used as historical variables, and the meteorological and temporal data are entered into the TFT model as future known inputs. Empirical studies show that the proposed model demonstrates remarkable suitability and effectiveness in short-term wind speed forecasting. The utilization of the interpretable model has catalyzed significant advancements in wind speed prediction, while the analysis of its interpretable results empowers managers in formulating effective policies.

Energy-optimal motion planning of underwater gliders accounting for seabed topography and ocean currents

The sawtooth gliding profile of underwater gliders can be determined by motion parameters, including yaw angles, diving depths, and pitch angles. Planning a series of motion parameters can generate a 3D gliding path that helps underwater gliders adapt to ocean currents and seabed topography. This paper presents an offline motion planning method for underwater gliders. By optimizing each gliding profile’s motion parameters, i.e., yaw angle, diving depth, and pitch angle, the seabed topography and ocean currents can be taken into account to generate an energy-optimal 3D path. To solve the motion planning problem, we propose a well-designed encoding method and a piecewise fitness function, which are used with the JADE algorithm to determine the optimal motion parameters. Experimental results on real seabed topography demonstrate that our method significantly reduces energy consumption and has better adaptability to complex ocean environments than traditional underwater glider path planning methods.

A hybrid adaptive Differential Evolution based on Gaussian tail mutation

In this paper, we propose an improved version of JADE by hybridizing the JADE algorithm with a Gaussian tail, a modified hunger games search (HGS) algorithm, and a distance-based multi-population (DbMP) approach named as HJADE-GT. In the proposed algorithm, two sets of operators (modified HGS operator and JADE operator with Gaussian tail) are utilized to generate offspring to further enhance the exploration and exploitation abilities. DbMP approach is proposed to make full use of feedback information from the whole population. In HJADE-GT, the main population is divided into three fixed-size subpopulations: exploration subpopulation, balanced subpopulation, and exploitation subpopulation. Secondly, a modified HGS operator is incorporated into the exploration subpopulation to improve global searchability. Thirdly, the JADE operator with a Gaussian tail is utilized to enhance the ability of exploitation subpopulation. Finally, the DbMP approach is utilized for the balanced subpopulation to choose an appropriate operator for current individuals to make full use of feedback information from the exploration subpopulation and exploitation subpopulation. In the experimental studies, it is demonstrated that the proposed algorithm presents competitive performance with 13 well-known algorithms, including jDE, SaDE, JADE, MPEDE, SHADE, CoDE, SaJADE, iLSHADE, jSO, CMAES, MGFPA, ESSA, and PPSO on CEC2017 benchmark functions. Four engineering problems and three dynamic economic emission dispatch (DEED) problems were utilized to verify the performance of HJADE-GT, and the experiments on DEED problems confirm that HJADE-GT is an efficient algorithm to solve engineering and large-scale constrained DEED problems.

Water-Air Interface Imaging: Recovering the Images Distorted by Surface Waves via an Efficient Registration Algorithm.

Imaging through the wavy water-air interface is challenging since the random fluctuations of water will cause complex geometric distortion and motion blur in the images, seriously affecting the effective identification of the monitored object. Considering the problems of image recovery accuracy and computational efficiency, an efficient reconstruction scheme that combines lucky-patch search and image registration technologies was proposed in this paper. Firstly, a high-quality reference frame is rebuilt using a lucky-patch search strategy. Then an iterative registration algorithm is employed to remove severe geometric distortions by registering warped frames to the reference frame. During the registration process, we integrate JADE and LBFGS algorithms as an optimization strategy to expedite the control parameter optimization process. Finally, the registered frames are refined using PCA and the lucky-patch search algorithm to remove residual distortions and random noise. Experimental results demonstrate that the proposed method significantly outperforms the state-of-the-art methods in terms of sharpness and contrast.

Hybrid modeling of the nonlinear behaviors for magnetorheological energy absorber

Precise forecast of strong nonlinear behaviors of magnetorheological energy absorber (MREA) plays a significant role in its semi-active control and engineering applications. This study proposes a hybrid model based on the support vector regression (SVR) and adaptive differential evolution (i.e., JADE) algorithm for MREA, called the JADE-SVR model. The JADE algorithm is employed to find the optimal hyperparameters of SVR to improve prediction accuracy and generalization ability. The JADE-SVR model is developed using the training data randomly partitioned from an impact test database, and then applied to predict the nonlinear behaviors of MREA in the test set, including force–time, force–displacement and force–velocity characteristics. It is demonstrated that the JADE-SVR model is capable of forecasting strong nonlinear behaviors of MREA under various impact conditions. Moreover, the JADE-SVR model is compared with other SVR-based models to validate its prediction accuracy and convergence rate further. In addition to four statistical indicators, a novel desirability function is used to assess the overall performance of these models. The results show that the JADE-SVR model has the optimal values for all the performance indices, and the largest overall desirability of 0.6274. Therefore, it can be concluded that the JADE-SVR model is the best one for forecasting the dynamic behaviors of MREA under high-impact loadings.

A meta-heuristic assisted underwater glider path planning method

Path planning is a key technology in underwater glider navigation system, however, due to the complex and time variant ocean environment, the development of underwater path planning faces great difficulties. In this paper, a novel path planner for the underwater glider is proposed. Firstly, a three-dimensional ocean environment model is established, including seabed topographies and ocean currents. Secondly, a novel path parameterization method and a new velocity synthesis method are presented. Besides, the constrained optimization model of the underwater glider path planning is established in detail. To solve the problem, a grey wolf enhanced equilibrium optimizer (GWEEO) is put forward. In GWEEO, a grey wolf search phase is designed to improve the quality of the candidate solutions. Moreover, a well-designed competition mechanism is introduced to accelerate the convergence. In the end, different groups of simulation experiments and corresponding statistical analysis results convincingly demonstrate that the proposed algorithm can plan a satisfactory path for the underwater glider and the comprehensive performance of GWEEO is better than EO, GWO, DE and JADE algorithm.

Orthogonal approach to independent component analysis using quaternionic factorization

Independent component analysis (ICA) is a popular technique for demixing multichannel data. The performance of a typical ICA algorithm strongly depends on the presence of additive noise, the actual distribution of source signals, and the estimated number of non-Gaussian components. Often, a linear mixing model is assumed and source signals are extracted by data whitening followed by a sequence of plane (Jacobi) rotations. In this article, we develop a novel algorithm, based on the quaternionic factorization of rotation matrices and the Newton-Raphson iterative scheme. Unlike conventional rotational techniques such as the JADE algorithm, our method exploits 4×4 rotation matrices and uses approximate negentropy as a contrast function. Consequently, the proposed method can be adjusted to a given data distribution (e.g., super-Gaussians) by selecting a suitable non-linear function that approximates the negentropy. Compared to the widely used, the symmetric FastICA algorithm, the proposed method does not require an orthogonalization step and is more accurate in the presence of multiple Gaussian sources.

Visual Control Law Tuning Using the JADE Algorithm Applied to Leaf Detection and Cutting

In the propagation of plant tissue in vitro it is necessary to obtain from the leaf a circular section cut. In order to achieve this type of cuts the visual feedback is used, allowing us to analyze the RGB planes to detect the green color and find the image centroid. In order to fulfill the cut objective a PD+ control is used with self-tuning based on the adaptive differential evolutionary algorithm JADE.

Fast tensorial JADE.

We propose a novel method for tensorial‐independent component analysis. Our approach is based on TJADE and k‐JADE, two recently proposed generalizations of the classical JADE algorithm. Our novel method achieves the consistency and the limiting distribution of TJADE under mild assumptions and at the same time offers notable improvement in computational speed. Detailed mathematical proofs of the statistical properties of our method are given and, as a special case, a conjecture on the properties of k‐JADE is resolved. Simulations and timing comparisons demonstrate remarkable gain in speed. Moreover, the desired efficiency is obtained approximately for finite samples. The method is applied successfully to large‐scale video data, for which neither TJADE nor k‐JADE is feasible. Finally, an experimental procedure is proposed to select the values of a set of tuning parameters. Supplementary material including the R‐code for running the examples and the proofs of the theoretical results is available online.

HKF-SVR Optimized by Krill Herd Algorithm for Coaxial Bearings Performance Degradation Prediction.

In real industrial applications, bearings in pairs or even more are often mounted on the same shaft. So the collected vibration signal is actually a mixed signal from multiple bearings. In this study, a method based on Hybrid Kernel Function-Support Vector Regression (HKF–SVR) whose parameters are optimized by Krill Herd (KH) algorithm was introduced for bearing performance degradation prediction in this situation. First, multi-domain statistical features are extracted from the bearing vibration signals and then fused into sensitive features using Kernel Joint Approximate Diagonalization of Eigen-matrices (KJADE) algorithm which is developed recently by our group. Due to the nonlinear mapping capability of the kernel method and the blind source separation ability of the JADE algorithm, the KJADE could extract latent source features that accurately reflecting the performance degradation from the mixed vibration signal. Then, the between-class and within-class scatters (SS) of the health-stage data sample and the current monitored data sample is calculated as the performance degradation index. Second, the parameters of the HKF–SVR are optimized by the KH (Krill Herd) algorithm to obtain the optimal performance degradation prediction model. Finally, the performance degradation trend of the bearing is predicted using the optimized HKF–SVR. Compared with the traditional methods of Back Propagation Neural Network (BPNN), Extreme Learning Machine (ELM) and traditional SVR, the results show that the proposed method has a better performance. The proposed method has a good application prospect in life prediction of coaxial bearings.

Concurrent Respiration Monitoring of Multiple Subjects by Phase-Comparison Monopulse Radar Using Independent Component Analysis (ICA) With JADE Algorithm and Direction of Arrival (DOA)

While non-contact monitoring of human respiration has been demonstrated using Doppler radar, the concurrent monitoring of multiple equidistant subjects remains a significant technological challenge. Reported research has so far been limited to maintaining 1-m subject separation, based on the radar antenna beam-width. Proposed here is a hybrid method consisting of an SNR-based intelligent decision algorithm which integrates two different approaches to isolate respiratory signatures of two subjects within the radar beam-width separated by less than 1 meter. Using Independent Component Analysis with the JADE algorithm (ICA-JADE) and Direction of Arrival (DOA), this SNR-based decision algorithm works with an accuracy above 93%. In addition, angular location of each subject is estimated by phase-comparison monopulse and an integrated beam switching capability is demonstrated to optimally extract respiratory information. The proposed method coherently combines two separation methods to overcome multiple-subject monitoring limits which can lead to practical adoption for many respiration monitoring applications.

Active Chatter Suppression in Turning by Simultaneous Adjustment of Amplitude and Frequency of Spindle Speed Variation

Abstract In machining processes, chatter suppression is very important for achieving a high material removal rate, good dimensional accuracy, and surface finish. With the merits of effectiveness and easy implementation, spindle speed variation (SSV) is regarded as a promising approach for chatter suppression. However, there is little research on the selection of SSV parameters for adaptive chatter suppression. Although the effectiveness of adaptively adjusting SSV amplitudes has been recently examined, the simultaneous adjustment of the SSV amplitude and frequency is expected to exhibit stronger adaptability since it achieves greater flexibility. In this paper, an active chatter suppression strategy is presented by simultaneously adjusting the amplitude and frequency of spindle speed variation. The effect of SSV parameters on stability improvement in turning processes including the tool wear is first investigated to demonstrate the necessity of simultaneously adjusting the amplitude and frequency for chatter suppression. Then, the proposed chatter suppression system is introduced, where two SSV parameters are simultaneously adjusted with optimal fractional-order proportional integral differential (FOPID) controllers to keep the chatter indicator close to a target value. Moreover, the FOPID controller is optimally tuned with the JADE algorithm. The effectiveness of the proposed method is verified by comparing simulated results of different SSV parameters adjusting strategies. Finally, machining tests are conducted to validate that the proposed chatter suppression method outperforms the existing SSV method in flexibility and effectiveness.

A new multi-stage perturbed differential evolution with multi-parameter adaption and directional difference

A new multi-stage perturbed differential evolution (MPDE) is proposed in this paper. A new mutation strategy “multi-stage perturbation” is implemented with directivity difference information strategy and multiple parameters adaption. The DE/current-to-pbest is introduced to increase the population diversity while remaining its elitist learning behavior in this architecture. The multi-stage perturbation-based mutation operation utilizes the Normal random distribution with adjustable variance to perturb the chosen solutions. Multiple parameters are adaptively adjusted to appropriate values to match the current search status of algorithm. It is thus helpful to enhance the performance and the robustness of algorithm. Simulation results show that the newly proposed MPDE is better than, or at least comparable to CLPSO, SPSO2011, NGHS, jDE, CoDE, SaDE and JADE algorithms in terms of optimization performance based on CEC2015 benchmark function.

Step-by-step improvement of JADE and SHADE-based algorithms: Success or failure?

Abstract For about two decades Differential Evolution (DE) algorithms belong to the most successful optimization metaheuristics. Among plentiful DE versions proposed so far those that are based on mutation strategies and control parameter adaptation methods introduced within JADE and SHADE variants show especially encouraging performance. However, many modifications of JADE or SHADE are developed simultaneously by various researchers, and are rarely compared or discussed against each other. As most of JADE or SHADE-based variants are tested on recently introduced sets of artificial benchmark functions with a single, pre-specified maximum number of function calls, it remains unclear how they would perform on real-world problems, or when the number of allowed function calls differs from the standard values. In this study in-deep insight into the performance of twenty two JADE/SHADE-based variants on a large sets of artificial benchmarks and real-world problems is presented. The impact of the pre-assumed maximum number of function calls and the algorithm population size on the results is verified and discussed. Finally, overall comparison of algorithms that originate from JADE or SHADE against other kinds of metaheuristics is presented. The main aim of the study is to point out these among recently introduced JADE or SHADE-based operators that turn out to be especially successful, and to determine conditions under which they either achieve desired results or fail.

Non-contact detection of human heart rate with Kinect

Non-contact detection of heart rate has been addressed by researchers from very different fields. However, the low accuracy of measuring results in the difficulties in methodology deployment. This paper introduces the principle of heartbeat detection. A detection scheme by using Kinect is proposed. Further, the signal processing approach based on JADE algorithm is developed to efficiently remove the clutter in mixture signals, and it enables accurate transforming via Z-score normalization. Due to the significances presented in this work, the detection error is 1.79%, when processed with the proposed algorithm. Experimental results are statistically analyzed, which makes it a promising basis for the realization of heart rate detection.