Parameter Adaptation Scheme Research Articles

Adaptive backstepping event‐triggered attitude tracking fault‐tolerant control for spacecraft systems

SummaryThis article is dedicated to study the problem of attitude tracking fault‐tolerant control for fractionated spacecraft via wireless communication. The practical case is that the spacecraft system always subject to external disturbances, uncertain inertia parameters, and even unknown actuator faults. Under the backstepping control scheme framework, an adaptive attitude tracking fault‐tolerant controller is constructed, where the triggering strategy is set not only in the sensor‐to‐controller channel, but also in the controller‐to‐actuator channel. Hence, continuous communication and Zeno phenomenon are successfully avoided. Then, a fuzzy‐logic system is used to deal with system parameter uncertainties and adaptive control schemes are constructed to estimate unknown parameters. By using Lyapunov function theory, the uniform ultimate boundedness of the closed‐loop system is proved. Finally, the validity of the developed method is demonstrated via simulation results.

Differential evolution for population diversity mechanism based on covariance matrix

Differential evolution (DE) is a heuristic global search algorithm based on population. It has exhibited great adaptability in solving continuous-domain problems, but sometimes suffered from insufficient local search ability and being trapped in local optimum when dealing with complicated optimization problems. To solve these problems, an improved differential evolution algorithm with population diversity mechanism based on covariance matrix (CM-DE) is proposed. First, a new parameter adaptation strategy is used to adapt the control parameters, in which the scale factor F is updated according to the improved wavelet basis function in the early stage and Cauchy distribution in the later stage and the crossover rate CR is generated according to normal distribution. The diversity of population and convergence speed are improved by employing the method above. Second, the perturbation strategy is incorporated into crossover operator to enhance the search ability of DE. Finally, the covariance matrix of the population is constructed, where the variance in the covariance matrix is used as indicator to measure the similarity between individuals in the population in order to prevent the algorithm from falling into local optimum resulted by low population diversity. The CM-DE is compared with the state-of-art DE variants including LSHADE (Tanabe and Fukunaga, 2014), jSO [1], LPalmDE [2], PaDE [3] and LSHADE-cnEpSin [4] under 88 test functions from CEC2013 [5], CEC2014 [6] and CEC2017 (Wu et al., 2017) test suites. From the experiment results, it is obvious that among 30 benchmark functions from CEC2017 on 50D optimization, the CM-DE algorithm has 22, 20, 24, 23, 28 better performances comparing with LSHADE, jSO, LPalmDE, PaDE, and LSHADE-cnEpsin. For CEC2017 on 30D optimization, the proposed algorithm secures better performance on 19 out of 30 benchmark functions in terms of convergence speed. In addition, a real-world application is also used to verify the feasibility of the proposed algorithm. The experiment results validate the highly competitive performance in terms of solution accuracy and convergence speed.

Inspection path planning of free-form surfaces based on improved cuckoo search algorithm

To address the problems of long run times, long path length and low efficiencies of traditional intelligent algorithms to optimise free-form surface inspection path algorithms, this paper proposes a method based on an improved cuckoo search algorithm. Since the basic cuckoo search algorithm suffers from problems such as low search efficiency and the tendency to fall into local optimum solutions, the basic cuckoo search algorithm is improved by using a parameter adaptive adjustment strategy and dynamic neighbourhood search strategy, so that the improved cuckoo search algorithm can obtain the optimised inspection path stably and quickly. The local composition of the free-form surface inspection path and the corresponding mathematical model are first analysed, and then traditional intelligent algorithms and the improved cuckoo search algorithm are applied to optimise the mathematical model. The results of inspection experiments conducted with an engine impeller showed that the improved cuckoo search algorithm reduced the length of the optimised inspection path by at least 8.6%, reduced the algorithm run time by at least 35%, and improved the inspection efficiency by at least 1.2% compared to those of the genetic algorithm, simulated annealing algorithm, and ant colony Optimisation algorithm. The improved cuckoo search algorithm allows for effective free-form surface inspection path Optimisation and an improved inspection efficiency.

DCC-IACJS: A novel bio-inspired duty cycle-based clustering approach for energy-efficient wireless sensor networks

Clustering routing is one of the most significant mechanisms applied to extend the lifetime of battery-powered wireless sensor networks (WSNs). However, most existing clustering approaches fail to exploit the feature of node redundancy in WSNs, which as a result leads to unnecessary energy waste. In this regard, a new clustering model named DCCM, based on the duty cycle method, was proposed to reduce the number of working nodes to save energy. This model enables nodes to work alternately to slow energy depletion by offering a novel designed coverage relationship matrix (CRM) and cover sets (CSs). Furthermore, an improved adaptive clone jellyfish search (DCC-IACJS) algorithm was designed to optimize the proposed model to obtain the most desirable clustering scheme. To increase its superiority over the clustering schemes, a new adaptive parameter strategy, as well as a new clone scheme, was devised in the DCC-IACJS. Subsequently, to verify the effectiveness of the proposed clustering approach, comprehensive simulation experiments were conducted to compare it with other state-of-the-art counterparts, namely, GA-CSO-LBCM, FGF, and O-LEACH. Simulation results showed that DCC-IACJS can provide 10.24%, 7.04%, and 8.54% longer network lifetime than O-LEACH, FGF, and GA-CSO-LBCM, respectively.

Application of a Parallel Adaptive Cuckoo Search Algorithm in the Rectangle Layout Problem

The meta-heuristic algorithm is a global probabilistic search algorithm for the iterative solution. It has good performance in global optimization fields such as maximization. In this paper, a new adaptive parameter strategy and a parallel communication strategy are proposed to further improve the Cuckoo Search (CS) algorithm. This strategy greatly improves the convergence speed and accuracy of the algorithm and strengthens the algorithm’s ability to jump out of the local optimal. This paper compares the optimization performance of Parallel Adaptive Cuckoo Search (PACS) with CS, Parallel Cuckoo Search (PCS), Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Differential Evolution (DE) and Artificial Bee Colony (ABC) algorithms by using the CEC-2013 test function. The results show that PACS algorithm outperforms other algorithms in 20 of 28 test functions. Due to the superior performance of PACS algorithm, this paper uses it to solve the problem of the rectangular layout. Experimental results show that this scheme has a significant effect, and the material utilization rate is improved from 89.5% to 97.8% after optimization.

Dual-Population Adaptive Differential Evolution Algorithm L-NTADE

This study proposes a dual-population algorithmic scheme for differential evolution and specific mutation strategy. The first population contains the newest individuals, and is continuously updated, whereas the other keeps the top individuals throughout the whole search process. The proposed mutation strategy combines information from both populations. The proposed L-NTADE algorithm (Linear population size reduction Newest and Top Adaptive Differential Evolution) follows the L-SHADE approach by utilizing its parameter adaptation scheme and linear population size reduction. The L-NTADE is tested on two benchmark sets, namely CEC 2017 and CEC 2022, and demonstrates highly competitive results compared to the state-of-the-art methods. The deeper analysis of the results shows that it displays different properties compared to known DE schemes. The simplicity of L-NTADE coupled with its high efficiency make it a promising approach.

Feasibility in real-time optimization under model uncertainty. The use of Lipschitz bounds

In real-time optimization (RTO), feedback information from the plant is used to compensate for model uncertainty. Feasibility upon convergence can be guaranteed by simply adding bias correction terms to the constraints predicted by the model. However, the RTO solutions obtained prior to convergence may violate the plant constraints in the presence of model uncertainty. The use of constraint upper-bounding functions based on Lipschitz continuity assumptions has been proposed as a means to ensure the satisfaction of constraints. This paper presents a comparative study between three different types of Lipschitz bounds for providing theoretical feasibility guarantees in different RTO schemes. Based on a novel Lipschitz bound on the constraint modeling error, robust RTO algorithms are proposed for the two model adaptation strategies that are most commonly employed in industrial RTO practice, which are the constraint–adaptation and parameter-adaptation schemes. A robust modifier-adaptation algorithm is also studied.

Distributed Fusion Estimation for the Measurements with Bounded Disturbances

Abstract The information fusion problem is studied for multi-sensor systems in the presence of bounded disturbances. In this paper, a distributed fusion estimation algorithm is proposed based on the set-membership theory, which obtains the overall estimates based on multi-ellipsoids intersection. A parameter adaptive adjustment scheme is derived to guarantee the performance of the algorithm. The feedback mechanism is also introduced to enhance the estimation procedure. Through theoretical analysis and simulation, the performance of the proposed algorithm is analyzed, and some interesting properties of the proposed algorithm are proved. Results show that the proposed algorithm improves the point estimation accuracy. Compared with the algorithm without feedback, the one with feedback has better local estimation. Meanwhile, the effectiveness of the proposed algorithm in improving state estimation accuracy has been proved by the simulation results.

Adaptive Estimation of K-Distribution Shape Parameter Based on Fuzzy Statistical Normalization Processing

It is difficult to estimate the parameters of K-distribution in inhomogeneous reverberation or clutter, which usually consists of interferences such as strong scattering objects in sonar data or sea-spikes in radar data. In this article, a shape parameter adaptive estimation scheme based on fuzzy statistical normalization processing is proposed to improve the estimation accuracy by adaptively suppress the interferences. Influences imposed by the interferences were studied and analyzed. Performance analysis based on simulated data and real sonar data showed that the proposed adaptive estimation method can suppress the interferences adaptively and, thus, improve the performance of K-distribution shape parameter estimation remarkably.

Dynamic hybrid mechanism-based differential evolution algorithm and its application

In order to effectively schedule railway train delay, an adaptive cooperative co-evolutionary differential evolution with dynamic hybrid mechanism of the quantum evolutionary algorithm and genetic algorithm, named QGDECC is designed in this paper. In the QGDECC, the quantum variable decomposition strategy is designed by utilizing qubit string to decompose variables adaptively according to the coevolution performance. Then the increment mutation method is proposed to improve the convergence speed which make full use of searched evolution information. Besides, the parameter adaptive strategy is deeply explored for strengthening the robust of the algorithm. The QGDECC with global search capability is employed to realize a railway train delay scheduling method for effectively eliminating the impact of train delay. Finally, several benchmark functions and actual train operation data are selected to verify the optimization performance of QGDECC. The experimental results show that QGDECC has higher adaptability, faster convergence speed and accuracy. The train delay scheduling method can effectively eliminate the impact of delay on the railway network, and minimize the gap between the rescheduled train schedule and the original train schedule.

Three-Dimensional Path Planning for AUVs Based on Standard Particle Swarm Optimization Algorithm

This paper proposes an improved standard particle swarm optimization 2011 for autonomous underwater vehicles (AUVs). A mutation operator with a threshold is introduced to solve the problem of particles falling into the local extreme, and a nonlinear adaptive parameter strategy is introduced to accelerate the convergence speed. The proposed algorithm considers “path length”, “path safety”, “path smoothness” and “physical constraints” synthetically. For the specific navigation environment of AUVs, the path planning simulation is conducted based on MATLAB/Simulink, and the navigation guidance and control closed-loop simulation system is established. Simulation results show the effectiveness of the proposed algorithm.

A novel self-adaptation and sorting selection-based differential evolutionary algorithm applied to water distribution system optimization

Abstract The differential evolution (DE) algorithm has been demonstrated to be the most powerful evolutionary algorithm (EA) to optimally design water distribution systems (WDSs), but issues such as slow convergence speed, limited exploratory ability, and parameter adjustment remain when used for large-scale WDS optimization. This paper proposes a novel self-adaptation and sorting selection-based differential evolutionary (SA-SSDE) algorithm that can solve large-scale WDS optimization problems more efficiently while having the greater ability to explore global optimal solutions. The following two unique features enable the better performance of the proposed SA-SSDE algorithm: (1) the DE/current-to-pbest/n mutation and sorting selection operators are used to speed up the convergence and thus improve the optimization efficiency; (2) the parameter adaptation strategy in JADE (an adaptive differential evolution algorithm proposed by Zhang & Sanderson 2009) is introduced and modified to cater for WDS optimization, and it is capable of dynamically adapting the control parameters (i.e., F and CR values) to the fitness landscapes characteristic of larger-scale WDS optimization problems, allowing for greater exploratory ability. The proposed SA-SSDE algorithm found new best solutions of $7.068 million, €1.9205 million, and $30.852 million for three well-known large networks (ZJ164, Balerma454, and Rural476), having the convergence speed of 1.02, 1.92, and 5.99 times faster than the classic DE, respectively. Investigations into the searching behavior and the control parameter evolution during optimization are carried out, resulting in a better understanding of why the proposed SA-SSDE algorithm outperforms the classic DE, as well as the guidance for developing more advanced EAs.

Influence of Binomial Crossover on Approximation Error of Evolutionary Algorithms

Although differential evolution (DE) algorithms perform well on a large variety of complicated optimization problems, only a few theoretical studies are focused on the working principle of DE algorithms. To make the first attempt to reveal the function of binomial crossover, this paper aims to answer whether it can reduce the approximation error of evolutionary algorithms. By investigating the expected approximation error and the probability of not finding the optimum, we conduct a case study comparing two evolutionary algorithms with and without binomial crossover on two classical benchmark problems: OneMax and Deceptive. It is proven that using binomial crossover leads to the dominance of transition matrices. As a result, the algorithm with binomial crossover asymptotically outperforms that without crossover on both OneMax and Deceptive, and outperforms on OneMax, however, not on Deceptive. Furthermore, an adaptive parameter strategy is proposed which can strengthen the superiority of binomial crossover on Deceptive.

Association Rule Mining Based on Hybrid Whale Optimization Algorithm

Association Rule Mining(ARM) is one of the most significant and active research areas in data mining. Recently, Whale Optimization Algorithm (WOA) has been successfully applied in the field of data mining, however, it easily falls into the local optimum. Therefore, an improved WOA based adaptive parameter strategy and Levy Flight mechanism (LWOA) is applied to mine association rules. Meanwhile, a hybrid strategy that blends two algorithms to balance the exploration and exploitation phases is put forward, that is, grey wolf optimization algorithm (GWO), artificial bee colony algorithm (ABC) and cuckoo search algorithm (CS) are devoted to improving the convergence of LWOA. The approach performs a global search and finds the association rules sets by modeling the rule mining task as a multi-objective problem that simultaneously meets support, confidence, lift, and certain factor, which is examined on multiple data sets. Experimental results verify that the proposed method has better mining performance compared to other algorithms involved in the paper.

An adaptive differential evolution algorithm based on belief space and generalized opposition-based learning for resource allocation

Differential evolution (DE) algorithm is prone to premature convergence and local optimization in solving complex optimization problems. In order to solve these problems, the belief space strategy, generalized opposition-based learning strategy and parameter adaptive strategy are introduced into DE to propose an improved adaptive DE algorithm, namely ACDE/F in this paper. In the ACDE/F, the idea of cultural algorithm and different mutation strategies are introduced into belief space to balance the global exploration ability and local optimization ability. A generalized opposition-based learning strategy is designed to improve the convergence speed of local optimization and increase the population diversity. A parameter adaptive adjustment strategy is developed to reasonably adjust the mutation factor and crossover factor to avoid to fall into local optimum. In order to test and verify the optimization performance of the ACDE/F, the unimodal functions and multimodal functions from CEC 2005 and CEC 2017 are selected in here. The experiment results show that the ACDE/F has better optimization performance than the DE with different strategies, WMSDE, DE2/F, GOAL-RNADE and DE/best/1. In addition, the actual gate allocation problem is selected to verify the practical application ability of the ACDE/F. The ACDE/F obtains the maximum allocation rate and average allocation rate of 98% and 96.8%, respectively. Therefore, the experimental results show that the ACDE/F can effectively solve the gate allocation problem and obtain ideal gate allocation results.

Field-factory hybrid service mode and its resource scheduling method based on an enhanced MOJS algorithm

At present, there are mainly two service modes in the field of industrial service, referring to field service and factory service, in which all the service processes occur in user-specified places and the factory workshops, respectively. Their compulsive constraints on the service sites inevitably result in the limited service areas, high service costs, and long service cycles. However, with the market demands on quality and efficiency of industrial services increase, especially with the extensive use of cloud platform, the service schemes of the traditional industrial service modes have gradually been unable to meet user requirements. Therefore, this paper proposes a field-factory hybrid service (FFHS) mode, in which service providers are allowed to transport service resources to the user-specified places for providing field services, and they also can establish temporary factories at certain user sites to provide factory services. FFHS removes the constraints on service locations, so it can generate better industrial service schemes. On this basis, the FFHS process is analyzed and a bi-objective resource scheduling model considering the emergence of cloud platform is established for it. A two- segment code is designed and an enhanced multi-objective jellyfish search (EMOJS) algorithm is developed for solving the above model. In EMOJS, the elitist preservation strategy, a parameter adaptive adjustment strategy and an opposition-based learning strategy are developed to improve the search performance. Comparison experiments with several state-of-the-art algorithms on 16 typical bi-objective instances are carried out and prove that EMOJS possesses better search performance. Case studies on 9 real industrial service instances of different sizes show that the scheduling schemes generated by the FFHS mode have better qualities and faster response speeds, so the its superiority in engineering practice is verified.

Full-Coverage PM2.5 Mapping and Variation Assessment during the Three-Year Blue-Sky Action Plan Based on a Daily Adaptive Modeling Approach

Owing to a series of air pollution prevention and control policies, China’s PM2.5 pollution has greatly improved; however, the long-term spatial contiguous products that facilitate the analysis of the distribution and variation of PM2.5 pollution are insufficient. Due to the limitations of missing values in aerosol optical depth (AOD) products, the reconstruction of full-coverage PM2.5 concentration remains challenging. In this study, we present a two-stage daily adaptive modeling framework, based on machine learning, to solve this problem. We built the annual models in the first stage, then daily models were constructed in the second stage based on the output of the annual models, which incorporated the parameter and feature adaptive tuning strategy. Within this study, PM2.5 concentrations were adaptively modeled and reconstructed daily based on the multi-angle implementation of atmospheric correction (MAIAC) AOD products and other ancillary data, such as meteorological factors, population, and elevation. Our model validation showed excellent performance with an overall R2 = 0.91 and RMSE = 9.91 μg/m3 for the daily models, along with the site-based cross-validation R2s and RMSEs of 0.86–0.87 and 12–12.33 μg/m3; these results indicated the reliability and feasibility of the proposed approach. The daily full-coverage PM2.5 concentrations at 1 km resolution across China during the Three-Year Blue-Sky Action Plan were reconstructed in this study. We analyzed the distribution and variations of reconstructed PM2.5 at three different time scales. Overall, national PM2.5 pollution has significantly improved with the annual average concentration dropping from 33.67–28.03 μg/m3, which demonstrated that air pollution control policies are effective and beneficial. However, some areas still have severe PM2.5 pollution problems that cannot be ignored. In conclusion, the approach proposed in this study can accurately present daily full-coverage PM2.5 concentrations and the research outcomes could provide a reference for subsequent air pollution prevention and control decision-making.

A novel channel selection scheme for olfactory EEG signal classification on Riemannian manifolds

Objective. The classification of olfactory-induced electroencephalogram (olfactory EEG) signals has potential applications in disease diagnosis, emotion regulation, multimedia, and so on. To achieve high-precision classification, numerous EEG channels are usually used, but this also brings problems such as information redundancy, overfitting and high computational load. Consequently, channel selection is necessary to find and use the most effective channels. Approach. In this study, we proposed a multi-strategy fusion binary harmony search (MFBHS) algorithm and combined it with the Riemannian geometry classification framework to select the optimal channel sets for olfactory EEG signal classification. MFBHS was designed by simultaneously integrating three strategies into the binary harmony search algorithm, including an opposition-based learning strategy for generating high-quality initial population, an adaptive parameter strategy for improving search capability, and a bitwise operation strategy for maintaining population diversity. It performed channel selection directly on the covariance matrix of EEG signals, and used the number of selected channels and the classification accuracy computed by a Riemannian classifier to evaluate the newly generated subset of channels. Main results. With five different classification protocols designed based on two public olfactory EEG datasets, the performance of MFBHS was evaluated and compared with some state-of-the-art algorithms. Experimental results reveal that our method can minimize the number of channels while achieving high classification accuracy compatible with using all the channels. In addition, cross-subject generalization tests of MFBHS channel selection show that subject-independent channels obtained through training can be directly used on untrained subjects without greatly compromising classification accuracy. Significance. The proposed MFBHS algorithm is a practical technique for effective use of EEG channels in olfactory recognition.

Using spatial neighborhoods for parameter adaptation: An improved success history based differential evolution

Differential Evolution (DE) has been widely appraised as a simple yet robust population-based, non-convex optimization algorithm primarily designed for continuous optimization. Two important control parameters of DE are the scale factor F, which controls the amplitude of a perturbation step on the current solutions and the crossover rate Cr, which limits the mixing of components of the parent and the mutant individuals during recombination. We propose a very simple, yet effective, nearest spatial neighborhood-based modification to the adaptation process of the aforesaid parameters in the Success-History based adaptive DE (SHADE) algorithm. SHADE uses a historical archive of the successful F and Cr values to update these parameters and stands out as a very competitive DE variant of current interest. Our proposed modifications can be extended to any SHADE-based DE algorithm like L-SHADE (SHADE with linear population size reduction), jSO (L-SHADE with modified mutation) etc. The enhanced performance of the modified SHADE algorithm is showcased on the IEEE CEC (Congress on Evolutionary Computation) 2013, 2014, 2015, and 2017 benchmark suites by comparing against the DE-based winners of the corresponding competitions. Furthermore, the effectiveness of the proposed neighborhood-based parameter adaptation strategy is demonstrated by using the real-life problems from the IEEE CEC 2011 competition on testing evolutionary algorithms on real-world numerical optimization problems.

Maximum reweighted-kurtosis deconvolution: a fully blind and adaptive method for restoration of gear fault impulse trains

Deconvolution based on vibration signals has been proven to be an effective tool in gear fault diagnosis. However, for many common methods, precisely restoring the fault impulse train is still a challenging task due to the great dependence on prior knowledge and the empirical determination of filter parameters. In this paper, a fully blind and adaptive method termed maximum reweighted-kurtosis deconvolution (MRKD) is proposed. A new deconvolution criterion, i.e., reweighted-kurtosis, is defined. This criterion possesses great robustness to impulse interferencesand thus has great potential to solve the problem of previous kurtosis-based methods in which a single dominant impulse is deconvolved instead of the impulse train induced by a localized fault. Furthermore, a parameter-adaptive strategy is developed to adaptively determine the appropriate filter parameters. As such, the proposed method does not require any prior knowledge of the target fault impulse train and addresses the critical issue of many common methods specifying filter parameters empirically. The proposed method is validated through simulated and real vibration signals. Comparison with the most popular deconvolution methods indicates that MRKD outperforms other methods for the restoration of a gear fault impulse train.