Hierarchical Genetic Algorithm Research Articles

A Quantum Genetic Algorithm for Building a Semantic Textual Similarity Estimation Framework for Plagiarism Detection Applications.

The majority of the recent research on text similarity has been focused on machine learning strategies to combat the problem in the educational environment. When the originality of an idea is copied, it increases the difficulty of using a plagiarism detection system in practice, and the system fails. In cases like active-to-passive conversion, phrase structure changes, synonym substitution, and sentence reordering, the present approaches may not be adequate for plagiarism detection. In this article, semantic extraction and the quantum genetic algorithm (QGA) are integrated in a unified framework to identify idea plagiarism with the aim of enhancing the performance of existing methods in terms of detection accuracy and computational time. Semantic similarity measures, which use the WordNet database to extract semantic information, are used to capture a document's idea. In addition, the QGA is adapted to identify the interconnected, cohesive sentences that effectively convey the source document's main idea. QGAs are formulated using the quantum computing paradigm based on qubits and the superposition of states. By using the qubit chromosome as a representation rather than the more traditional binary, numeric, or symbolic representations, the QGA is able to express a linear superposition of solutions with the aim of increasing gene diversity. Due to its fast convergence and strong global search capacity, the QGA is well suited for a parallel structure. The proposed model has been assessed using a PAN 13-14 dataset, and the result indicates the model's ability to achieve significant detection improvement over some of the compared models. The recommended PD model achieves an approximately 20%, 15%, and 10% increase for TPR, PPV, and F-Score compared to GA and hierarchical GA (HGA)-based PD methods, respectively. Furthermore, the accuracy rate rises by approximately 10-15% for each increase in the number of samples in the dataset.

Optimization method for a moving-coil electromagnetic linear actuator using an improved Macaca thibetana behavior-based hierarchical GA–PSO algorithm

Optimization method for a moving-coil electromagnetic linear actuator using an improved Macaca thibetana behavior-based hierarchical GA–PSO algorithm

Research Progress of Polycyclic Aromatic Hydrocarbons Pretreatment Methods and Application of Computer Simulation Technology for Prediction and Degradation of Electrochemical Concentration Detection

Polycyclic aromatic hydrocarbons (PAHs) are a class of organic compounds that are composed of aromatic rings containing only carbon and hydrogen atoms. They are one of the widespread environmental pollutants in the world. In recent years, many scholars have focused on the inhibition, formation mechanism, content of active components, and biodegradation effect of polycyclic aromatic hydrocarbons. They summarized the research progress of pretreatment methods for detection, but rarely discussed the experimental dataset for comprehensive analysis of pollution sources and the impact of different pretreatment technologies on the extraction of different substrates. What is more, computer simulation has not been mentioned. In this study, the pollution sources of polycyclic aromatic hydrocarbons (PAHs) are reviewed, and the related applications of various pretreatment methods such as gel permeation chromatography (GPC) are summarized. Finally, the computer simulation of the response surface method is introduced. The concentration of polycyclic aromatic hydrocarbons is tested or predicted by combining the neural network with the alternating trilinear decomposition (ATLD) algorithm, artificial population algorithm (ABC), and hierarchical genetic algorithm (HGA). Its future development trend is discussed and prospected, which provides a reference for solving the pollution problem. We look forward to providing help for the follow-up research of scholars in this field.

Hierarchical genetic optimization of convolutional neural models for diabetic retinopathy classification

Diabetic retinopathy (DR) is one of the worse conditions caused by diabetes mellitus (DM). DR can leave the patient completely blind because it may have no symptoms in its initial stages. Expert physicians have been developing technologies for early detection and classification of DR to prevent the increasing number of patients. Some authors have used convolutional neural networks for this purpose. Pre-processing methods for database are important to increase the accuracy detection of CNN, and the use for an optimization algorithm can further increase that accuracy. In this work, four pre-processing methods are presented to compare them and select the best one. Then the use of a hierarchical genetic algorithm (HGA) with the pre-processing method is done with the intention of increasing the classification accuracy of a new CNN model. Using the HGA increases the accuracies obtained by the pre-processing methods and outperforms the results obtained by other authors. In the binary study case (detection of DR) a 0.9781 in the highest accuracy was achieved, a 0.9650 in mean accuracy and 0.007665 in standard deviation. In the multi-class study case (classification of DR) a 0.7762 in the highest accuracy, 0.7596 in mean accuracy and 0.009948 in standard deviation.

Hydrodynamic interactions among wave energy converter array and a hierarchical genetic algorithm for layout optimization

This paper focuses on the layout optimization and hydrodynamic investigation of wave energy converter (WEC) array. For hydrodynamic analysis, each buoy is modeled as a truncated floating cylinder that can move with five degrees of freedom. The hydrodynamic response of such an array is analyzed by a semi-analytical method. Then, the power extraction by the array is used as the objective function for layout optimization. To improve efficiency, especially for an array with a large number of WECs, a novel hierarchical optimization method based on matrix coded genetic algorithm is proposed. Within this method, a large-scale array is firstly divided into several levels of small-scale arrays which are easy to handle, then, the genetic algorithm developed in this paper is used to optimize the small array of each level. An example of two-level optimization is given in this paper and compared with the result by conventional optimization routine. Finally, we investigate the influence of introducing other degrees of freedom, besides the heave mode, on the performance of arrays. Results show that as the constraints get weaker in non-heave directions, the effect of other degrees of freedom on the WEC array interaction factor becomes more and more obvious.

Cooperative Automotive Radars with Multi-Aperture Multiplexing MIMO Sparse Array Design

In this paper, a multi-aperture multiplexing multiple-input multiple-output (MAM-MIMO) sparse array is presented for cooperative automotive radars (CARs). The proposed sparse array composed of multiple subarrays can simultaneously cover a wide field-of-view (FOV) and achieve the required azimuth resolution at different ranges. To validate this idea, an optimization model for the MAM-MIMO sparse array is derived based on the example of CARs. This optimization model has been found by combining the peak-to-sidelobe ratio (PSLR) at all beams pointing within the constraints of different detection ranges. In addition, a hierarchical genetic algorithm based on the multi-objective decomposition method has been developed to obtain the optimized sparse array. The proposed method has been evaluated through both simulations and experiments. It is demonstrated that the optimized MAM-MIMO sparse array can effectively suppress sidelobes of its subarrays, yet with reasonably high azimuth resolutions and large FOVs.

Calibration of geometric parameters and error compensation of non-geometric parameters for cable-driven parallel robots

Accuracy is an important performance indicator that affects the research and industrial application of cable-driven parallel robots (CDPRs). The error sources of CDPRs include geometric parameters (GPs) and non-geometric parameters (NGPs). Typically, GPs can be calibrated by external measurement devices whose position is dependent on coordinate system parameters. To improve the calibration accuracy and robustness, an iterative calibration method is proposed to calibrate the coordinate system parameters and GPs, and the asymptotic convergence is proven. Moreover, considering the tight coupling and non-linearity of NGPs, we design an artificial neural network to compensate for the residual position errors caused by NGPs. Based on the hierarchical genetic algorithm, a synchronization optimization algorithm is developed to improve the approximate accuracy and generalization to residual position errors of unknown trajectories. With theoretical initial parameters, experiments for the calibration of GPs and error compensation of NGPs were performed on a 3-DOFs CDPR. Finally, the average position error of the end-effector is reduced to 1.3 mm and the maximum error is 1.9 mm.

A hierarchical genetic algorithm and mixed-integer linear programming-based stochastic optimization of the configuration of integrated trigeneration energy systems

Facing the growing pressure of climate change and environmental protection, integrated energy systems (IESs), which comprise different energy sources, have become promising candidates for future energy systems. However, the capacity configuration of each source remains challenging due to the various couplings, randomness of renewables and numerical optimization difficulty. In this paper, a hierarchical optimization framework is proposed to determine the component capacities of trigeneration IESs, i.e., systems involving combined cooling, heating and power (CCHP) generation. The potential variation in the demand and renewable resource availability are considered stochastic factors and captured as scenarios generated according to a probability function. In the first level, with the component capacities and scenarios defined, a mixed-integer linear programming (MILP) problem is formulated to minimize the total system cost. Then, in the second level, the Monte Carlo method is applied to calculate the expectation by feeding different scenarios into the MILP and sampling the minimal costs. Finally, as the second level returns the expected value of the system cost considering the given component capacities, a genetic algorithm is adopted in the third level to search the optimal component capacities. Compared to the conventional deterministic optimization method, the proposed stochastic optimization method reduces the annual operational cost while allowing a wider operational range. In addition, it is revealed that the inclusion of heat storage and grid connections yields notable benefits in terms of IES cost reduction.

Time-resolved estimation of strength of motor imagery representation by multivariate EEG decoding

Objective. Multivariate decoding enables access to information encoded in multiple brain activity features with high temporal resolution. However, whether the strength, of which this information is represented in the brain, can be extracted across time within single trials remains largely unexplored. Approach. In this study, we addressed this question by applying a support vector machine (SVM) to extract motor imagery (MI) representations, from electroencephalogram (EEG) data, and by performing time-resolved single-trial analyses of the multivariate decoding. EEG was recorded from a group of healthy participants during MI of opening and closing of the same hand. Main results. Cross-temporal decoding revealed both dynamic and stationary MI-relevant features during the task. Specifically, features representing MI evolved dynamically early in the trial and later stabilized into a stationary network of MI features. Using a hierarchical genetic algorithm for selection of MI-relevant features, we identified primarily contralateral alpha and beta frequency features over the sensorimotor and parieto-occipital cortices as stationary which extended into a bilateral pattern in the later part of the trial. During the stationary encoding of MI, by extracting the SVM prediction scores, we analyzed MI-relevant EEG activity patterns with respect to the temporal dynamics within single trials. We show that the SVM prediction score correlates to the amplitude of univariate MI-relevant features (as documented from an extensive repertoire of previous MI studies) within single trials, strongly suggesting that these are functional variations of MI strength hidden in trial averages. Significance. Our work demonstrates a powerful approach for estimating MI strength continually within single trials, having far-reaching impact for single-trial analyses. In terms of MI neurofeedback for motor rehabilitation, these results set the ground for more refined neurofeedback reflecting the strength of MI that can be provided to patients continually in time.

HGHA: task allocation and path planning for warehouse agents

PurposeWith the rapid development of e-commerce, logistics demand is increasing day by day. The modern warehousing with a multi-agent system as the core comes into being. This paper aims to study the task allocation and path-planning (TAPP) problem as required by the multi-agent warehouse system.Design/methodology/approachThe TAPP problem targets to minimize the makespan by allocating tasks to the agents and planning collision-free paths for the agents. This paper presents the Hierarchical Genetic Highways Algorithm (HGHA), a hierarchical algorithm combining optimization and multi-agent path-finding (MAPF). The top-level is the genetic algorithm (GA), allocating tasks to agents in an optimized way. The lower level is the so-called highways local repair (HLR) process, avoiding the collisions by local repairment if and only if conflicts arise.FindingsExperiments demonstrate that HGHA performs faster and more efficient for the warehouse scenario than max multi-flow. This paper also applies HGHA to TAPP instances with a hundred agents and a thousand storage locations in a customized warehouse simulation platform with MultiBots.Originality/valueThis paper formulates the multi-agent warehousing distribution problem, TAPP. The HGHA based on hierarchical architecture solves the TAPP accurately and quickly. Verifying the HGHA by the large-scale multi-agent simulation platform MultiBots.

Study on the nursing of patients with early dementia based on a hierarchical genetic algorithm (GA‐RBF) neural network system

AbstractObjectiveIn order to improve the efficiency and prediction accuracy of BP neural network for disease diagnosis, a genetic algorithm was proposed to optimize BP neural network for the diagnosis of Alzheimer's disease to verify the more practical and dementia diagnosis. Methods: Taking the electronic medical record data mining in hospital as an example, a predictive model was established for the diagnosis of Alzheimer's disease. The method firstly uses the genetic algorithm's search optimization technology to carry out feature reduction, and then uses the reduced feature as the input variable of BP neural network to train and construct the BP neural network model. The simulation experiment was carried out on the MATLAB software platform. Results: Compared with single BP neural network, genetic algorithm optimization BP neural network can reduce the training time of the model and improve the prediction accuracy. It is a feasible auxiliary diagnosis method for Alzheimer's disease. Conclusion: The application of hierarchical genetic algorithm GA‐RBF neural network system and early prediction of senile dementia and cognitive status can help to accurately predict the cognitive status of the elderly, prevent it in advance, and improve the quality of care for patients with Alzheimer's disease clinical promotion.

Research on Radial Basis Function Neural Network Based on Improved Hierarchical Genetic Algorithm in Early Diagnosis of Alzheimer’s Disease

With the increasing aging level of China’s population, the probability of suffering from Alzheimer’s disease also increases. Genetic Algorithm-Back-Propagation (GA-BP) were introduced and compared, and the effectiveness of Genetic Algorithm-Radial Basis Function (GA-RBF) algorithm in diagnosis and prediction of Alzheimer’s disease was verified by simulation experiments. The results showed that GA-RBF modeling time increased by about 75%, It showed that GA-RBF algorithm can be used helpful to accurately predict the cognitive status of the elderly.

Cloud Storage Strategy of Blockchain Based on Genetic Prediction Dynamic Files

With the rapid expansion of data volume, traditional data storage methods have been unable to meet the practical application requirements of blockchain cloud storage. Aiming for the cloud storage problem of blockchain, a new storage access method for predicting dynamic file load is proposed. By predicting the load status of cloud storage files in advance, the load of each blockchain data node at the next moment is first estimated. A hierarchical genetic algorithm is used to construct the connection weights between the hidden layer and the output layer, which makes the data network converge faster and more accurate, thereby effectively predicting the node load. In addition, based on the file allocation, an evaluation analysis model is constructed to obtain the time response capability of each file during the allocation process. The node’s periodic load prediction value is used to calculate the corresponding weight of the node and it is continuously updated, retaining the advantages of the static weighted polling algorithm. Combined with the genetic algorithm to help predict the file assignment access strategy of the later load of each node, it can meet the system requirements under complex load conditions and provide a reasonable and effective cloud storage method. The experimental evaluation of the proposed new strategy and new algorithm verifies that the new storage method has a faster response time, more balanced load, and greatly reduced energy consumption.

Optimization strategies for Microgrid energy management systems by Genetic Algorithms

Grid-connected Microgrids (MGs) have a key role for bottom-up modernization of the electric distribution network forward next generation Smart Grids, allowing the application of Demand Response (DR) services, as well as the active participation of prosumers into the energy market. To this aim, MGs must be equipped with suitable Energy Management Systems (EMSs) in charge to efficiently manage in real time internal energy flows and the connection with the grid. Several decision making EMSs are proposed in literature mainly based on soft computing techniques and stochastic models. The adoption of Fuzzy Inference Systems (FISs) has proved to be very successful due to their ease of implementation, low computational run time cost, and the high level of interpretability with respect to more conventional models. In this work we investigate different strategies for the synthesis of a FIS (i.e. rule based) EMS by means of a hierarchical Genetic Algorithm (GA) with the aim to maximize the profit generated by the energy exchange with the grid, assuming a Time Of Use (TOU) energy price policy, and at the same time to reduce the EMS rule base system complexity. Results show that the performances are just 10% below to the ideal (optimal) reference solution, even when the rule base system is reduced to less than 30 rules.

Hierarchical measurement strategy for cost-effective interpolation of spatiotemporal data in manufacturing

High-resolution spatiotemporal data is crucial for characterizing, modeling, and monitoring the space–time dynamics of complex systems in manufacturing. However, the acquisition of such data is generally expensive and time-consuming. Spatiotemporal interpolation aims to predict the values at unmeasured locations using measured data, and emerges as a promising solution to cost-effectively characterizing spatiotemporal processes. Since the interpolation performance is largely influenced by the available measurement data, an intelligent measurement strategy is an important prerequisite to the success of interpolation methods. In this paper, a hierarchical measurement strategy is developed to achieve a balance between interpolation precision and measurement cost in spatiotemporal interpolation. A hierarchical decision-making problem is formulated to determine the observation times and measurement locations at each observation. To expedite the solution search process, hierarchical genetic algorithm is adopted and implemented using high-performance computing. Moreover, a new form of the covariance function is developed using a Bessel additive periodic variogram to more accurately model the periodic spatial variations in spatiotemporal processes. Case studies using real-world data collected from ultrasonic metal welding are reported to demonstrate the effectiveness of the proposed method.

On Rough Set-Based Modeling and with Application to Process Decision for Forming Plate by Line Heating

In shipbuilding, forming plate by line heating is atypical complex manufacturing process involving many uncertain factors, so it is difficult to establish an accurate mathematical model. How to establish a knowledge model that can reflect technological laws is the key to the development of an intelligent decision system for forming plate by line heating. In this article, rough set (RS) theory is applied to the modeling of the line heating process. By defining variable-inclusion RSs, an algorithm of knowledge reduction is proposed, which enhances the noise immunity and fault tolerance of the model, and improves the efficiency of knowledge acquisition. Through introducing fuzzy logic, a method of modeling the line heating process based on RSs and fuzzy logic is proposed, which effectively extracts the technological rules of plate formation. Finally, rapid decision-making for process parameters is implemented by fuzzy inference technology. 1. Introduction For some complex manufacturing technologies in modern shipbuilding, such as line heating and welding processes, it is difficult to establish an exact mathematical model because of high nonlinearity, multivariable coupling, and uncertainties of the system. With the development of computer technology and artificial intelligence, soft computing methods such as artificial neural networks, genetic algorithms, fuzzy logic, and rough set (RS) theory have been applied successively in ship manufacturing process modeling, which shows good prospects for intelligent technology in shipbuilding. Shin et al. (1999) at Seoul National University used a single-curvature plate model to simulate the formation of saddle-type shells and deduce the technological parameters of line heating by an artificial neural network. They also proposed a comprehensive algorithm for automatically curving plate by line heating, and further developed an application system that can simulate the deformation of double-curved plates (Shin et al. 2004a, 2004b). Liu et al. (2006) applied a hierarchical genetic algorithm to optimize the technological parameters of an automatic line heating process. In the field of ship welding, fuzzy logic technology has been used to establish a fuzzy model of the relationship between welding variables and weld forming-parameters (Su 2009). Feng (2012) set up a knowledge base of a ship-welding process by a RS method and then implemented ship-welding production design through uncertainty reasoning. Based on RS theory, Chen and Lv (2013) developed a data-driven knowledge base for quality control of ship hull welding.

Parallel Hierarchical Genetic Algorithm for Scattered Data Fitting through B-Splines

Curve fitting to unorganized data points is a very challenging problem that arises in a wide variety of scientific and engineering applications. Given a set of scattered and noisy data points, the goal is to construct a curve that corresponds to the best estimate of the unknown underlying relationship between two variables. Although many papers have addressed the problem, this remains very challenging. In this paper we propose to solve the curve fitting problem to noisy scattered data using a parallel hierarchical genetic algorithm and B-splines. We use a novel hierarchical structure to represent both the model structure and the model parameters. The best B-spline model is searched using bi-objective fitness function. As a result, our method determines the number and locations of the knots, and the B-spline coefficients simultaneously and automatically. In addition, to accelerate the estimation of B-spline parameters the algorithm is implemented with two levels of parallelism, taking advantages of the new hardware platforms. Finally, to validate our approach, we fitted curves from scattered noisy points and results were compared through numerical simulations with several methods, which are widely used in fitting tasks. Results show a better performance on the reference methods.

Multi-Objective Bi-Level Programming for the Energy-Aware Integration of Flexible Job Shop Scheduling and Multi-Row Layout

The flexible job shop scheduling problem (FJSSP) and multi-row workshop layout problem (MRWLP) are two major focuses in sustainable manufacturing processes. There is a close interaction between them since the FJSSP provides the material handling information to guide the optimization of the MRWLP, and the layout scheme affects the effect of the scheduling scheme by the transportation time of jobs. However, in traditional methods, they are regarded as separate tasks performed sequentially, which ignores the interaction. Therefore, developing effective methods to deal with the multi-objective energy-aware integration of the FJSSP and MRWLP (MEIFM) problem in a sustainable manufacturing system is becoming more and more important. Based on the interaction between FJSSP and MRWLP, the MEIFM problem can be formulated as a multi-objective bi-level programming (MOBLP) model. The upper-level model for FJSSP is employed to minimize the makespan and total energy consumption, while the lower-level model for MRWLP is used to minimize the material handling quantity. Because the MEIFM problem is denoted as a mixed integer non-liner programming model, it is difficult to solve it using traditional methods. Thus, this paper proposes an improved multi-objective hierarchical genetic algorithm (IMHGA) to solve this model. Finally, the effectiveness of the method is verified through comparative experiments.

An Efficient Computation Offloading Management Scheme in the Densely Deployed Small Cell Networks With Mobile Edge Computing

To tackle the contradiction between the computation intensive applications and the resource-hungry mobile user equipments (UEs), mobile edge computing (MEC) has been provisioned as a promising solution, which enables the UEs to offload the tasks to the MEC servers. Considering the characteristics of small cell networks (SCNs), integrating MEC into SCNs is natural. But in terms of the high interference, multi-access property, and limited resources of small cell base stations (SBSs), an efficient computation offloading scheme is essential. However, there still lack comprehensive studies on this problem in the densely deployed SCNs. In this paper, we study the energy-efficient computation offloading management scheme in the MEC system with SCNs. The aim of this paper is to minimize the energy consumption of all UEs via jointly optimizing computation offloading decision making, spectrum, power, and computation resource allocation. Specially, the UEs need not only to decide whether to offload but also to determine where to offload. First, we present the computation offloading model and formulate this problem as a mix integer non-linear programming problem, which is NP-hard. Taking advantages of genetic algorithm (GA) and particle swarm optimization (PSO), we design a suboptimal algorithm named as hierarchical GA and PSO-based computation algorithm to solve this problem. Finally, the convergence of this algorithm is studied by simulation, and the performance of the proposed algorithm is verified by comparing with the other baseline algorithms.

Optimization of type-1, interval type-2 and general type-2 fuzzy inference systems using a hierarchical genetic algorithm for modular granular neural networks

In this paper, a new method for fuzzy inference system optimization is presented. The proposed method performs type-1, interval type-2 and general type-2 fuzzy inference systems design using a hierarchical genetic algorithm as optimization method. This method is an improvement of a fuzzy system optimization approach presented in previous works where only the optimization of type-1 and interval type-2 fuzzy inference systems was performed using a human recognition application. The human recognition is performed using three biometric measures namely iris, ear, and voice, where the main idea is to perform the combination of responses in the modular neural networks using an optimized fuzzy inference system to improve the final results without and with noisy conditions. The main contribution of the proposed method is an optimization able to select type of fuzzy logic, granulation of each fuzzy variable and fuzzy rules selection to design optimal fuzzy inference systems applied in combining modular neural networks responses. The results obtained show the effectiveness of the proposed method to design structures of fuzzy inference systems. Statistical comparisons are performed with previous results, where better results can be observed using the proposed method. The design of optimal structures of fuzzy inference systems include among other parameters; type of fuzzy logic (type-1, interval and general type-2 fuzzy logic), type of inference model (Mamdani model or Sugeno model), and consequents of the fuzzy if–then rules.