Hierarchical Optimization Algorithm Research Articles

Loading and Multi-trip Routing Problem Using Hierarchical Ant Colony Optimization Algorithm.

Loading and Multi-trip Routing Problem Using Hierarchical Ant Colony Optimization Algorithm.

A hierarchical chain-based Archimedes optimization algorithm.

The Archimedes optimization algorithm (AOA) has attracted much attention for its few parameters and competitive optimization effects. However, all agents in the canonical AOA are treated in the same way, resulting in slow convergence and local optima. To solve these problems, an improved hierarchical chain-based AOA (HCAOA) is proposed in this paper. The idea of HCAOA is to deal with individuals at different levels in different ways. The optimal individual is processed by an orthogonal learning mechanism based on refraction opposition to fully learn the information on all dimensions, effectively avoiding local optima. Superior individuals are handled by an Archimedes spiral mechanism based on Levy flight, avoiding clueless random mining and improving optimization speed. For general individuals, the conventional AOA is applied to maximize its inherent exploration and exploitation abilities. Moreover, a multi-strategy boundary processing mechanism is introduced to improve population diversity. Experimental outcomes on CEC 2017 test suite show that HCAOA outperforms AOA and other advanced competitors. The competitive optimization results achieved by HCAOA on four engineering design problems also demonstrate its ability to solve practical problems.

Adaptive connected hierarchical optimization algorithm for minimum energy spacecraft attitude maneuver path planning

Adaptive connected hierarchical optimization algorithm for minimum energy spacecraft attitude maneuver path planning

Maritime traffic clustering to capture high-risk multi-ship encounters in complex waters

Maritime traffic situational awareness is fundamental to the safety of maritime transportation. The state-of-the-art research primarily attaches importance to collision risk estimation and evaluation between/among ships but encounters the challenges of capturing the high-risk traffic clusters in complex waters. This paper develops a systematic traffic clustering approach to enhance traffic pattern interpretability and proactively discover high-risk multi-ship encounter scenarios, in which both the conflict connectivity and spatial compactness of encounter ships are considered. Specifically, a novel hybrid clustering approach that integrates a composite distance measure, a constrained Shared Nearest Neighbour clustering, and a fine-tuning strategy is developed to segment maritime traffic into multiple conflict-connected and spatially compact clusters. Meanwhile, a hierarchical bi-objective optimization algorithm is introduced to search for optimal clustering solutions. Through maritime traffic data obtained from the Ningbo-Zhoushan Port, a thorough methodology performance evaluation is carried out through application demonstration and validation. Experiment results reveal that the new approach: 1) can effectively capture the high-risk/density traffic clusters; 2) is robust with respect to various traffic scenarios; and 3) can be extended to assist in collision risk management. It therefore offers new insights into enhancing maritime traffic surveillance capabilities and eases the design of risk management strategy.

Whole-Body Control for a Torque-Controlled Legged Mobile Manipulator

The task of performing locomotion and manipulation simultaneously poses several scientific challenges, such as how to deal with the coupling effects between them and how to cope with unknown disturbances introduced by manipulation. This paper presents an inverse dynamics-based whole-body controller for a torque-controlled quadrupedal manipulator capable of performing locomotion while executing manipulation tasks. Unlike existing methods that deal with locomotion and manipulation separately, the proposed controller can handle them uniformly, which can take into account the coupling effects between the base, limbs and manipulated object. The controller tracks the desired task–space motion references based on a hierarchical optimization algorithm, given a set of hierarchies that define strict priorities and the importance of weighting each task within a hierarchy. The simulation results show the robot is able to follow multiple task–space motion reference trajectories with reasonable deviation, which proved the effectiveness of the proposed controller.

Distributed hierarchical deep optimization for federated learning in mobile edge computing

Deep learning has recently attracted great attention in many application fields, especially for big data analysis in the field of edge computing. Federated learning, as a promising machine learning technology, applies training data on distributed edge nodes to design shared learning systems to protect data privacy. Due to the system update in federated learning is at the expense of parameter exchange between edge nodes, it is extremely bandwidth consuming. A novel distributed hierarchical tensor depth optimization algorithm is proposed, which compresses the model parameters from the high-dimensional tensor space to a union of low-dimensional subspaces to reduce bandwidth consumption and storage demands of federated learning. In addition, an update method based on hierarchical tensor back propagation is developed by directly calculating the gradient of low-dimensional parameters to reduce the memory requirement and improve the training efficiency caused by edge node training. Finally, a large number of simulation experiments were performed to evaluate performance based on classical data sets with different local data distributions. Experimental results show that the proposed algorithm reduces the burden of communication bandwidth and the energy consumption of edge nodes.

LigninGraphs: lignin structure determination with multiscale graph modeling

Lignin is an aromatic biopolymer found in ubiquitous sources of woody biomass. Designing and optimizing lignin valorization processes requires a fundamental understanding of lignin structures. Experimental characterization techniques, such as 2D-heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) spectra, could elucidate the global properties of the polymer molecules. Computer models could extend the resolution of experiments by representing structures at the molecular and atomistic scales. We introduce a graph-based multiscale modeling framework for lignin structure generation and visualization. The framework employs accelerated rejection-free polymerization and hierarchical Metropolis Monte Carlo optimization algorithms. We obtain structure libraries for various lignin feedstocks based on literature and new experimental NMR data for poplar wood, pinewood, and herbaceous lignin. The framework could guide researchers towards feasible lignin structures, efficient space exploration, and future kinetics modeling. Its software implementation in Python, LigninGraphs, is open-source and available on GitHub.Graphical abstract

Study of distributed model predictive control for radiant floor heating systems with hydraulic coupling

Radiant floor heating (RFH) is an advanced technology that can couple with renewable low-temperature sources and improve occupant thermal comfort. However, due to the large thermal inertia, the conventional control strategies based on heuristic rules have difficulty handling a room temperature response to changes in weather or set-points. Because of the excellent coping ability, model predictive control (MPC) has aroused increasing attention as a promising solution to manage RFH. This research is focused on the MPC scheme for RFH systems, especially large-scale RFH systems with hydraulic coupling. First, we develop a novel control-oriented thermodynamic model for an RFH system. A new calculating method for the floor surface temperature is adopted to avoid defining the thermal resistance and thermal capacity of the virtual core temperature layer, and the heat transfer along water flow and weather elements like solar radiation and wind are considered. Then an experiment is conducted to validate that the accuracy can be adequate for the prediction of MPC. Next, we design a distributed MPC scheme for RFH systems with hydraulic coupling. A supply reset rule for central equipment is improved, and a hierarchical optimization algorithm is proposed to implement the distributed MPC scheme. Then a simulation test is carried out with results showing that the scheme can achieve hydraulic decoupling between occupants, thus avoiding room temperature fluctuations and decreasing the root-mean-square error by 0.24 °C. An appropriate supply set-point is recommended that meets thermal comfort demands with minimum energy consumption.

Soft-sensing of effluent total phosphorus using adaptive recurrent fuzzy neural network with Gustafson-Kessel clustering

To address the issue of soft-sensing of effluent total phosphorus in wastewater treatment processes (WWTPs), a soft-sensing system based on an adaptive recursive fuzzy neural network with Gustafson-Kessel (GK) clustering and hierarchical adaptive second-order optimization algorithm (HAS) is proposed in this paper. In GK-ARFNN, first, the GK clustering algorithm was utilized to cluster the input–output dataset. Thus, the establishment of the initial fuzzy rule base and the determination of the parameter value of the fuzzy set membership function was realized. Then, the recursive layer was added into FNN to improve the dynamic mapping ability of the system. Finally, the HAS algorithm was developed based on the improved Levenberg-Marquardt (LM) optimization algorithm, and all the free parameters of the GK-ARFNN were adjusted online using HAS to improve the generalization capability and prediction accuracy of the soft-sensing system. In addition, the convergence of the proposed GK-ARFNN algorithm was also analyzed in this paper, which can ensure the effectiveness of the solutions to modelling issues for practical industrial processes. The simulation results demonstrate that the GK-ARFNN-based soft-sensing system introduced in this paper achieved satisfactory accuracy in the prediction of effluent total phosphorus in WWTPs. The source codes of GK-ARFNN and some competitors can be downloaded from https://github.com/hyitzhb/GK-ARFNN.

Hybrid Active-and-Passive Relay Network Optimization of mmWave Communications for Industrial Internet

<p>Multi-hop relay selection is regarded as a key technology to overcome severe path loss and adapt complex environments in millimeter-wave (mmWave) and terahertz communication. Meanwhile, new hardware called reconfigurable intelligent surface (RIS) has emerged and is used to boost the spectral and energy efficiency of future wireless communication systems. Then, to solve the challenge that the numerous energy consumption caused by the increasing number of relays in the future industrial internet a promising architecture with a hybrid active–and-passive relay network is proposed in this paper. Then, the optimization joint system delay and energy efficiency based on the simulated annealing (SA) algorithm is considered in this paper. A hierarchical heuristic optimization algorithm with lower complexity is proposed and proved to be feasible under good communication conditions compared with other algorithms. Finally, numerical results demonstrate that the proposed hybrid relay system and joint optimization model can improve the energy efficiency of the network and reduce the system delay.</p> <p> </p>

A model-free method for identifying time-delay characteristics of HVAC system based on multivariate transfer entropy

The time delays in the HVAC system are common, which seriously affect time series prediction and pre-control command implementation, so identifying them is promising research. The existing methods of identifying time-delays mainly focus on establishing physical or dynamical models first and then conducting control variable experiments, which don't facilitate engineering. Therefore, we develop a model-free identification method rooted in an information-theoretic framework by introducing transfer entropy (TE) into the HVAC field. The suggested multivariate TE method can pick out each time-delay characteristic by mining monitoring data. Compared with correlation coefficients, it can filter redundant information between variables and discern the nonlinearity. Among them, to estimate multivariate TE well, a kernel estimator is improved. It performs the precise detection ability, low computational burden and strong robustness, compared with the original and k-nearest-neighbor (KNN) estimator. Besides, for an unacquainted HVAC system, a hierarchical optimization algorithm combining the Nash-optimization algorithm with a second-order oscillatory particle swarm optimization (SOPSO) algorithm is proposed to identify its time delays, where the accuracy and time cost are improved. Lastly, the above-mentioned methods are validated with simulated and real time series. This work is enlightening and has a further reference to identifying time delays in HVAC systems.

Hierarchical Optimization Algorithm and Applications of Spacecraft Trajectory Optimization

The pursuit of excellent performance in meta-heuristic algorithms has led to a myriad of extensive and profound research and achievements. Notably, many space mission planning problems are solved with the help of meta-heuristic algorithms, and relevant studies continue to appear. This paper introduces a hierarchical optimization frame in which two types of particles—B-particles and S-particles—synergistically search for the optima. Global exploration relies on B-particles, whose motional direction and step length are designed independently. S-particles are for fine local exploitation near the current best B-particle. Two specific algorithms are designed according to this frame. New variants of classical benchmark functions are used to better test the proposed algorithms. Furthermore, two spacecraft trajectory optimization problems, spacecraft multi-impulse orbit transfer and the pursuit-evasion game of two spacecraft, are employed to examine the applicability of the proposed algorithms. The simulation results indicate that the hierarchical optimization algorithms perform well on given trials and have great potential for space mission planning.

Multi-Robot Multi-Station Cooperative Spot Welding Task Allocation Based on Stepwise Optimization: An Industrial Case Study

The complicated task allocation, scheduling and planning problem with multiple stations and multiple robots commonly seen in spot welding production line design is studied in this paper. To deal with the highly coupled model combined with several task planning sub-problems, including robot cells design, robots allocation among cells, welding allocation among cells and robots, and welding scheduling for each robot, as well as numerous internal and external constraints, the traditional multi-robot task allocation (MRTA) framework is extended to a novel and uniform multi-station multi-robot (MS-MRTA) framework, and a sophisticated hierarchical optimization algorithm is proposed. Firstly, to establish the optimization model based on MS-MRTA framework as a whole, constraints such as reachability constraint, maximum speed and acceleration constraint, collision constraint and welding operation time constraint are considered, and the optimization objective is established based on the balance of welding tasks of each robot and each cell. Then, in order to solve the highly coupled model, a hierarchical optimization algorithm is proposed to divide the problem into three layers from top to bottom: the path planning of a single robot, welding task allocation among robots, and welding task allocation among cells. The path planning of a single robot is analogous to the Travelling Salesman Problem (TSP) solved by iterating the Lin-Kernighan-Helsgaun (LKH) solver with the trapezoidal acceleration and deceleration motion. To solve the welding task allocation among robots with numerous constraints, a regional assignment method was proposed which simplify the model and eliminate the accessibility constraint and collision constraint, and combined with genetic algorithm to solve the sub-problem iteratively. The welding task allocation among cells is solved based on the principle of balanced welding of each cell. Genetic algorithm is used to obtain the nested iterative solution of three sub-problems. The cases of actual door welding tasks are studied to verify the effectiveness of the proposed optimization algorithm. Compared with the method of long-term trial and error by experienced experts and two other more advanced algorithms, the proposed optimization algorithm results in a task assignment scheme with less welding time, less waiting time and an increase of welding operation productivity, which shows the effectiveness and feasibility of the multi-robot multi-station task allocation algorithm based on stepwise optimization.

A Novel Fault Identification Method Driven by Knowledge and Data

In the field of intelligent manufacturing, fault identification is an effective way to improve product service by identifying the cause of failures. For addressing it, the Generalized Bayesian Network (GBN) model is extended based on the traditional Bayesian Network in this paper, which redefines the directed edges and probability parameters among nodes. Compared with Bayesian Network, the GBN model has the ability to simultaneously define causality and correlation of variables. In addition, the structure of network is not only based on statistical data but also driven by expert knowledge. In order to achieve the collaboration of data and knowledge while maintaining the consistency, a hierarchical collaborative framework is designed including the data layer and knowledge layer. Furthermore, a hierarchical multi-objective optimization algorithm, namely Hierarchical Non-dominated Sorting Genetic Algorithm II (HNSGA-II), is advanced to solve the proposed model. Finally, an industrial case study for fault cause identification targeting the product service helps illustrate all details.

Game Theory-Based Optimal Cooperative Path Planning for Multiple UAVs

This paper presents new cooperative path planning algorithms for multiple unmanned aerial vehicles (UAVs) using Game theory-based particle swarm optimization (GPSO). First, the formation path planning is formulated into the minimization of a cost function that incorporates multiple objectives and constraints for each UAV. A framework based on game theory is then developed to cast the minimization into the problem of finding a Stackelberg-Nash equilibrium. Next, hierarchical particle swarm optimization algorithms are developed to obtain the global optimal solution. Simulation results show that the GPSO algorithm can generate efficient and feasible flight paths for multiple UAVs, outperforming other path planning methods in terms of convergence rate and flexibility. The formation can adjust its geometrical shape to accommodate a working environment. Experimental tests on a group of three UAVs confirm the advantages of the proposed approach for a practical application.

Multiple Buried Target Reconstruction Using a Multiscale Hybrid of Diffraction Tomography and CMA-ES Optimization

In this article, a hierarchical stochastic optimization algorithm for profiling of multiple high-contrast buried objects in large investigation domains (IDs) is presented. As this problem is highly non-linear and ill-posed, a combination of different profiling modalities is required to tackle the challenges. First, an initialization step using qualitative diffraction tomography (DT) method is performed to not only limit the ID to scatterers’ locations but also obtain an approximation of their dielectric permittivity range. Then, an algorithm which combines the iterative multi-scaling approach (IMSA) with the reconstruction capabilities of Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is implemented. IMSA is a multistep strategy, which starts with coarse meshes in lower frequencies, then, step by step, tightens the ID to the newly found domain of scatterers and uses finer meshes for partitioning them. This procedure enhances the resolution without increasing the number of unknowns. In each step, the inversion process is executed using the global optimization technique of CMA-ES. The proposed technique uses the full advantages of global optimization technique and at the same time, by executing it on a multi-scaling scheme, and using the initializing step, reduces the number of unknowns, the degree of freedom in the search space, and the required measured data. The Numerical assessments for various scenarios are performed that clearly shows an acceptable dielectric profile retrieval, even for inhomogeneous dielectric distributions or noisy measurements. Moreover, a comparison between CMA-ES and other global optimization algorithms is performed, which reveals the outperformance of CMA-ES in these scenarios.

Adaptive hierarchical update particle swarm optimization algorithm with a multi-choice comprehensive learning strategy

Adaptive hierarchical update particle swarm optimization algorithm with a multi-choice comprehensive learning strategy

Collaborative design of multi-type parameters for design and operational stage matching in fuel cells

A collaborative design method for multi-types of graphite end plates geometric parameters, membrane electrolyte assembly physical parameters, and operating parameters, was novelly developed to break bounds of parameter types for design stage and operational stage matching in fuel cells. A multi-step design method, assisted by a surrogate-assisted hierarchical particle swarm optimization algorithm, was used for the optimal design of multi-type parameters. During this multi-step design process, the initial experiments are implemented following the design of experiments, and further experiments are adaptively carried out under the guidance of the layered optimization method. Experimental evidence of cell performance response to the coupled effects of multi-parameters was firstly provided and discussed in details. The collaborative design of multi-type parameters realized using the proposed multi-step design method was proven to improve the energy conversion efficiency by 10.9%. Different performance requirements, from single to multiple objectives in various types of fuel cells, were also shown to be fulfilled using this collaborative design method.

Adaptive Pose Estimation for Gait Event Detection Using Context-Aware Model and Hierarchical Optimization

To understand daily events accurately, adaptive pose estimation (APE) systems require a robust context-aware model and optimal feature selection methods. In this paper, we propose a novel gait event detection (GED) system that consists of saliency silhouette detection, a robust body parts model and a 2D stick-model followed by a hierarchical optimization algorithm. Furthermore, the most prominent context-aware features such as energy, 0–180° intensity and distinct moveable features are proposed by focusing on invariant and localized characteristics of human postures in different event classes. Finally, we apply Grey Wolf optimization and a genetic algorithm to discriminate complex postures and to provide appropriate labels to each event. In order to evaluate the performance of proposed GED, two public benchmark datasets, UCF101 and YouTube, are examined via the n-fold cross validation method. For the two benchmark datasets, our proposed method detects the human body key points with 82.4% and 83.2% accuracy respectively. Also, it extracts the context-aware features and finally recognizes the gait events with 82.6% and 85.0% accuracy, respectively. Compared with other well-known statistical and state-of-the-art methods, our proposed method outperforms other similarly tasked methods in terms of posture detection and recognition accuracy.

Hierarchical Multi-Agent Optimization for Resource Allocation in Cloud Computing

In cloud computing, an important concern is to allocate the available resources of service nodes to the requested tasks on demand and to make the objective function optimum, i.e., maximizing resource utilization, payoffs and available bandwidth. This paper proposes a hierarchical multi-agent optimization (HMAO) algorithm in order to maximize the resource utilization and make the bandwidth cost minimum for cloud computing. The proposed HMAO algorithm is a combination of the genetic algorithm (GA) and the multi-agent optimization (MAO) algorithm. With maximizing the resource utilization, an improved GA is implemented to find a set of service nodes that are used to deploy the requested tasks. A decentralized-based MAO algorithm is presented to minimize the bandwidth cost. We study the effect of key parameters of the HMAO algorithm by the Taguchi method and evaluate the performance results. When compared with genetic algorithm (GA) and fast elitist non-dominated sorting genetic (NSGA-II) algorithm, the simulation results demonstrate that the HMAO algorithm is more effective than the existing solutions to solve the problem of resource allocation with a large number of the requested tasks. Furthermore, we provide the performance comparison of the HMAO algorithm with the first-fit greedy approach in on-line resource allocation.