Hybrid Intelligent Optimization Research Articles

Hybrid Intelligent Optimization of Nonlinear Switched Systems With Guaranteed Feasibility

Hybrid Intelligent Optimization of Nonlinear Switched Systems With Guaranteed Feasibility

Evaluating the Geo-Environmental Conditions within a Working Face Using a Hybrid Intelligent Optimization Model

Geological anomalies within the working face likely induce geological disasters, such as water, gas, and coal mine roof fall, directly impacting the rational planning and safe mining of underground resources. Constrained by the conditions of underground closed spaces, effective reconstruction under incomplete and highly sparse projection is the central challenge when evaluating geo-environmental conditions. This work proposes a new hybrid intelligent optimization model (MPGA-SIRT) that integrates a multiple-population genetic algorithm (MPGA) with the simultaneous iterative reconstruction technique (SIRT) to finely reconstruct the geo-environmental conditions within a working face based on electromagnetic wave tomography theory. MPGA-SIRT can provide a more precise initial inversion model for the conventional linear reconstruction technique of SIRT, incorporating a local search property by leveraging the robust global search capacity of MPGA. The advantages of MPGA-SIRT have been demonstrated through numerical modeling, theoretical testing, and engineering practices on the 8208 working face in the Datong mining area, Shanxi Province. In comparison to individual SIRT inversion models, MPGA-SIRT reconstruction yields more accurate and stable performance, as demonstrated by the evolution curve of the objective function and the corresponding convergence tomography results. Consequently, the geomagnetic wave absorption coefficient within the area of reconstruction can be precisely ascertained through the use of our proposed technique. This innovation represents a groundbreaking strategy for assessing geological anomaly zones within a working face. The introduced method stands as a valuable theoretical instrument for confronting the complexities associated with geo-environmental reconstruction in underground engineering.

A hybrid intelligent optimization algorithm to select discriminative genes from large-scale medical data

A hybrid intelligent optimization algorithm to select discriminative genes from large-scale medical data

A Scalable Multi-FPGA Platform for Hybrid Intelligent Optimization Algorithms

The Intelligent Optimization Algorithm (IOA) is widely focused due to its ability to search for approximate solutions to the NP-Hard problem. To enhance applicability to practical scenarios and leverage advantages from diverse intelligent optimization algorithms, the Hybrid Intelligent Optimization Algorithm (H-IOA) is employed. However, IOA typically requires numerous iterations and substantial computing resources, resulting in poor execution efficiency. In complex optimization scenarios, IOA traditionally relies on population partitioning and periodic communication, highlighting the feasibility and necessity of parallelization. To address the challenges above, this paper proposes a general hardware design approach for H-IOA based on multi-FPGA. The approach includes the hardware architecture of multi-FPGA, inter-board communication protocols, population storage strategies, complex hardware functions, and parallelization methodologies, which enhance the computing capabilities of H-IOA. To validate the proposed approach, a case study is conducted, in which an H-IOA integrating genetic algorithm (GA), a simulated annealing algorithm (SA), and a pigeon-inspired optimization algorithm (PIO) are implemented on a multi-FPGA platform. Specifically, the flexible job-shop scheduling problem (FJSP) is employed to verify the potential in industrial applications. Two Xilinx XC6SLX16 FPGA chips are used for hardware implementation, encoded in VHDL, and an AMD Ryzen 7 5800U was used for the software implementation of Python programs (version 3.12.4). The results indicate that hardware implementation is 13.4 times faster than software, which illustrates that the proposed approach effectively improves the execution performance of H-IOA.

A Hybrid Intelligent Optimization Algorithm Based on a Learning Strategy

To overcome the limitations of single-type intelligent optimization algorithms prone to becoming stuck in local optima for complex problems, a hybrid intelligent optimization algorithm named SDIQ is proposed. This algorithm combines simulated annealing (SA), differential evolution (DE), quantum-behaved particle swarm optimization (QPSO), and improved particle swarm optimization (IPSO) into a unified framework. Initially, SA is used to explore the solution space and guide individuals toward preliminary optimization. The individuals are then ranked by fitness and divided into three subpopulations, each optimized by DE, QPSO, and IPSO, respectively. After each iteration, probabilistic learning based on fitness logarithms facilitates mutual learning among subpopulations, enabling global information sharing and improvement. The experimental results demonstrate that SDIQ exhibits strong global search capability and stability in solving both standard test functions and real-world engineering problems. Compared to traditional algorithms, SDIQ enhances global convergence and solution efficiency by integrating multiple optimization strategies and leveraging inter-individual learning, providing an effective solution for complex optimization problems.

Evaluating nano-metal oxide mixed matrix membranes for whey protein separation using hybrid intelligent optimization learning

Ultrafiltration (UF) membranes offer cost-efficient and sustainable techniques for the extraction of whey protein in cheese whey wastewater. This strategy substantially aids in waste minimization and fosters the progression of a circular economy paradigm within the dairy industry. The emerging discipline of machine learning has benefited UF by enhancing the separation performance and deepening the filtration mechanism. In this study, adaptive neuro-fuzzy inference systems (ANFIS) coupled chemometric learning was implemented for the performance evaluation of whey protein recovery in nano metal oxide embedded mixed matrix membranes (MMMs) UF from whey wastewater. The selected input variables are nano metal oxides concentration (NC), pore radius (S), and contact angle (CA). The targeted responses for the parametric dependency analysis are individual whey protein permeability (lysozyme (PLys), lactalbumin (PLA) and bovine serum albumin (PBSA)) and protein rejection (%) (lysozyme (RLys), lactalbumin (RLA) and bovine serum albumin (RBSA)). The Jarque-Bera indicated that the input variables did not deviate from normality, and S and CA have significantly influenced the whey protein rejection and permeability. ANFIS models demonstrate superior predictive capabilities with minimal error for flux under various transmembrane pressure (TMP) in MMMs due to their inherent gradient descent optimization. Among the proteins, a Pearson correlation coefficient approaching unity denotes a significant positive relationship between lysozyme flux (FLys). The ANFIS models exhibited excellent predictive performance in real-time cheese whey effluent flux and rejection data, achieving a mean square error of 0.0003 for experimental flux. This study provides valuable insights for the development of models tailored to small datasets, offering a foundation for predicting and enhancing membrane performance.

Hybrid Intelligent Optimization Techniques for Grid Integration with Renewable Systems: Review

Hybrid Intelligent Optimization Techniques for Grid Integration with Renewable Systems: Review

Hybrid Intelligent Dynamic Optimization of Switched Systems

In this article, a hybrid intelligent dynamic optimization method integrating of particle swarm optimization (PSO) and gradient-based optimization (GBO) is proposed for optimal control of switched systems. The method both improves the solution accuracy of PSO and avoids falling into local minima generated by the deterministic optimization. First, the PSO algorithm with ring topology is used to explore the whole search space to detect the global optimum area. Secondly, the GBO algorithm is deployed in the detected global optimum area to achieve faster convergence rate and higher precision solution than those of pure PSO. Finally, the simulation results show that the algorithm outperforms both PSO and GBO in terms of solution accuracy and computational cost.

When architecture meets RL+EA: A hybrid intelligent optimization approach for selecting combat system-of-systems architecture

In recent decades, various domains such as military, aerospace and supply chain are becoming increasingly complex, forming system-of-systems (SoSs). The effectiveness of SoSs is mainly determined by their architecture, thus selecting proper architecture for SoSs is crucial to maximize their effectiveness. While most studies focus on specific issues, such as mission planning and SoSs meta-architecture selection, few papers deal with the two issues together. This paper introduces the task oriented SoSs architecture selection problem (TSASP) and proposes an combination of reinforcement learning (RL) and evolutionary algorithm (EA) (CRE) to address this problem. CRE comprises two layers: the inner layer utilizes a DQN-based algorithm to solve the mission planning problem, and the outer layer with improved genetic algorithm optimizes the SoSs architecture selection based on the output of inner one. A synthetic air and missile defense (AMD) example is conducted to test the effectiveness of proposed method, and results show that CRE can improve the mission planning effect by select a feasible architecture.

Multi-objective optimization design of coupled wall structure with hybrid coupling beams using hybrid machine learning algorithms

A new hybrid coupling beam incorporating buckling restrained energy dissipaters and viscous dampers was previously recommended. The beam can effectively reduce the floor acceleration and interstory drift ratio of coupled wall structure, and has great application prospects. However, the optimal design of a structure with hybrid coupling beams needs to consider multiple objectives, and these objectives often conflict with each other. At the same time, multiple parameters need to be optimized, resulting in a corresponding increase in the time-cost. This usually brings challenges to structural design. To address this issue, this study presents a hybrid intelligent optimization framework based on multi-objective particle swarm optimization (MOPSO) and machine learning techniques. The developed framework uses machine learning technology to quickly predict the seismic performance of structures with hybrid coupling beams, and obtain a design solution set through multi-objective particle swarm optimization. The proposed framework is demonstrated by considering a case study of a 12-story coupled wall structure with hybrid coupling beams, and it is shown that compared with the initial stage of optimization, the seismic performance of optimized structure is simultaneously improved at close cost. The floor acceleration and interstory drift ratio of the optimized structure are reduced by 11.5% and 38.7%, respectively. Finally, a formula is proposed to quantify the optimization result of pareto solution, so as to make an optimal structural design scheme. In practice, the proposed framework can provide guidance for realizing rapid and accurate about multi-objective optimization of the coupled wall structure with hybrid coupling beams.

Intelligent multiobjective optimization for high-performance concrete mix proportion design: A hybrid machine learning approach

The concrete mix proportion design process is complex but important, especially in cold, ocean, underground and other complex engineering environments. In this study, a hybrid intelligent optimization method based on the random forest (RF), recursive feature elimination (RFE), Bayesian optimization (BO), least squares support vector machine (LSSVM) and nondominated sorting genetic algorithm (NGSA)-III was proposed to optimize the concrete mix proportion and rapidly and accurately predict the frost resistance, chloride ion penetration resistance and concrete strength (CS). Adopting a key project in Jilin Province as an example, the RF-RFE-BO-LSSVM-NSGA-III algorithm achieved a significant optimization effect in terms of the chloride ion permeability coefficient (CIPC), relative dynamic elastic modulus (RDEM) and 28-day CS. After optimization, the chloride ion penetration resistance, frost resistance and CS increased by 34.6%, 4.1% and 3.7%, respectively, over the average levels of the sample data. This study can provide basis for concrete mix proportion design in complex environment.

Overview of Logistics Demand Forecasting Methods

Accurate forecasting of logistics demand is of great theoretical significance and practical application value for the formulation of national policies and the satisfaction of actual demand in the logistics industry. From the modeling form, the existing logistics demand forecasting methods are divided into four categories: single traditional forecasting method, single intelligent forecasting method, combined forecasting method and mixed forecasting method. Among them, single traditional forecasting methods mainly include simple time series method, regression analysis, mathematical and statistical methods, etc.; single intelligent forecasting methods mainly involve gray forecasting method, neural network, support vector machine and their improved forms; combined forecasting methods are mainly summarized into three combined forms: linear combination of single forecasting results, nonlinear combination of single forecasting results, modified single forecasting results; mixed forecasting methods are mainly summarized into three hybrid forms: hybrid intelligent optimization algorithms with single prediction methods, hybrid data dimensionality reduction techniques with intelligent prediction methods, and hybrid data mining techniques with intelligent prediction methods. The four major types of forecasting methods are reviewed, and each forecasting model in the four major types of methods is evaluated in terms of modeling principles, advantages and disadvantages, and applicability, in order to find forecasting methods suitable for different logistics demand forecasting tasks for logistics demand researchers.

Shear Capacity Prediction Model of Deep Beam Based on New Hybrid Intelligent Algorithm

Accurate shear load capacity predictions are crucial to achieving the load-bearing requirements of concrete deep beams in a variety of construction structures. Conventional BP neural networks have the drawbacks of being prone to local optimums and having a sluggish rate of convergence for predicting the shear load capacity of reinforced concrete deep beams. To overcome this problem, this study incorporated the black widow optimization algorithm (BWO) and principal component analysis (PCA) into a BP neural network to create a unique Hybrid Intelligent Optimization Algorithm (PCA-BWO-BP). Firstly, PCA was used to reduce the dimensionality of the input variables of the shear load capacity prediction model of reinforced concrete deep beams. Secondly, BWO was introduced to optimize the weights and thresholds of the BP neural network. Finally, the four algorithms were compared and validated through the use of five model evaluators. The results showed that the PCA-BWO-BP model can explore the intrinsic relationship between member size, bottom longitudinal reinforcement, hoop reinforcement, concrete strength and the shear load capacity of reinforced concrete deep beams and generate reasonable prediction values, and the complexity of the prediction model can be effectively reduced by introducing the PCA algorithm, whereas the BWO algorithm can optimize the weights and thresholds of the BP neural network to improve the convergence and global search ability of the model. The mean absolute percentage error (MAPE) of the PCA-BWO-BP algorithm is 5.126, and the Nash efficiency coefficient (NS) is 0.989. The generalization ability and prediction accuracy are significantly better than those of the BP neural network, which can solve the problem relating to the fact that BP neural networks are prone to falling into the local optimum. The PCA-BWO-BP model has strong prediction ability, stability, generalization ability and robustness, which can predict the shear load capacity of reinforced concrete deep beams more accurately. It provides a new method and case support for further research on the shear bearing capacity of reinforced concrete deep beams.

Multi-Swarm Co-Evolution Based Hybrid Intelligent Optimization for Bi-Objective Multi-Workflow Scheduling in the Cloud

Many scientific applications can be well modelled as large-scale workflows. Cloud computing has become a suitable platform for hosting and executing them. Workflow scheduling has gained much attention in recent years. However, since cloud service providers must offer services for multiple users with various QoS demands, scheduling multiple applications with different QoS requirements is highly challenging. This work proposes a Multi-swarm Co-evolution-based Hybrid Intelligent Optimization (MCHO) algorithm for multiple-workflow scheduling to minimize total makespan and cost while meeting the deadline constraint of each workflow. First, we design a multi-swarm co-evolutionary mechanism where three swarms are adopted to sufficiently search for various elite solutions. Second, to improve global search and convergence performance, we embed local and global guiding information into the updating process of a Particle Swarm Optimizer, and develop a swarm cooperation technique. Third, we propose a Genetic Algorithm-based elite enhancement strategy to exploit more non-dominated individuals, and apply the Metropolis Acceptance rule of Simulated Annealing to update the local guiding solution for each swarm so as to prevent it from being stuck into a local optimum at an early stage. Extensive experimental results demonstrate that MCHO outperforms the state-of-art scheduling algorithms with better distributed non-dominated solutions.

Design and Application of Cloud Resource-Based Ideological and Political Online Course Resource Platform

Online teaching platforms have been popularized and promoted as a result of the development of network information technology, and colleges and universities encourage teachers to use a variety of network teaching platforms to innovate teaching models and improve teaching effectiveness. Using an ideological and political online course as an example, it analyzes the teaching design concepts, instructional effects, and existing problems on the online learning platform, and extracts recommendations for online course construction that have a specific reference for online course teaching. Additionally, aiming at the multiobjective cloud resource scheduling problem, this article aims to optimize the total completion time and total execution cost of the task. It does so by utilizing fuzzy mathematics, establishing a fuzzy cloud resource scheduling model, and proposing a hybrid intelligent optimization algorithm CO. The CO algorithm is validated by randomly generating cloud-computing resource scheduling data using the CloudSim simulation platform. The experimental results indicate that the CO algorithm outperforms traditional cloud resource scheduling algorithms in terms of optimization and load balancing performance.

Green Communication in Internet of Things: A Hybrid Bio-Inspired Intelligent Approach.

Clustering is a promising technique for optimizing energy consumption in sensor-enabled Internet of Things (IoT) networks. Uneven distribution of cluster heads (CHs) across the network, repeatedly choosing the same IoT nodes as CHs and identifying cluster heads in the communication range of other CHs are the major problems leading to higher energy consumption in IoT networks. In this paper, using fuzzy logic, bio-inspired chicken swarm optimization (CSO) and a genetic algorithm, an optimal cluster formation is presented as a Hybrid Intelligent Optimization Algorithm (HIOA) to minimize overall energy consumption in an IoT network. In HIOA, the key idea for formation of IoT nodes as clusters depends on finding chromosomes having a minimum value fitness function with relevant network parameters. The fitness function includes minimization of inter- and intra-cluster distance to reduce the interface and minimum energy consumption over communication per round. The hierarchical order classification of CSO utilizes the crossover and mutation operation of the genetic approach to increase the population diversity that ultimately solves the uneven distribution of CHs and turnout to be balanced network load. The proposed HIOA algorithm is simulated over MATLAB2019A and its performance over CSO parameters is analyzed, and it is found that the best fitness value of the proposed algorithm HIOA is obtained though setting up the parameters, number of rooster, number of hen’s and swarm updating frequency . Further, comparative results proved that HIOA is more effective than traditional bio-inspired algorithms in terms of node death percentage, average residual energy and network lifetime by 12%, 19% and 23%.

Modeling and Control of Hysteresis Characteristics of Piezoelectric Micro-Positioning Platform Based on Duhem Model

The hysteresis characteristic of piezoelectric micro-positioning platforms seriously affects its positioning accuracy in precision positioning. It is important to design an effective hysteresis model and control scheme. Based on the analysis of the Duhem model, this paper proposes to divide the hysteresis curve into two parts, the step-up section and the step-down section, to identify the model parameters, respectively, and a hybrid intelligent optimization algorithm based on the artificial fish swarm algorithm and the bat algorithm is proposed. The simulation experiment verified that the error of the improved model was reduced by 48.97%, which greatly improved the identification accuracy of the Duhem model. Finally, an inverse model of the Duhem model for the segmental identification of the improved artificial fish swarm algorithm is established, and a composite controller integrating feedforward, feedback and decoupling control is designed on the basis of the inverse model, and an experimental verification is carried out. The results show that the displacement errors of the composite controller under different voltage signals are all within 0.25%. Therefore, the established model can accurately express the hysteresis characteristics of the platform, and the use of the composite controller can effectively reduce the accuracy error caused by the hysteresis characteristics.

Improved model-free adaptive predictive control method for direct data-driven control of a wastewater treatment process with high performance

In this paper, an improved model-free adaptive predictive control (IMFAPC) method with controller parameters sensitivity discrimination and optimization is proposed, to solve the problem that using conventional methods is difficult to effectively control the nitrate concentration and dissolved oxygen concentration of wastewater treatment process (WWTP). First, aiming at the nonstationary dynamic characteristics of WWTP and the limitation of control accuracy of conventional MFAPC method in the nonlinear dynamic system control, a novel improved MFAPC method is proposed considering the effect of the WWTP output tracking error variation on the control of nitrate concentration and dissolved oxygen concentration. Meanwhile, in order to obtain the optimum control performance, the sensitivity discrimination and optimization for the multi-parameter set of the proposed IMFAPC method are performed by combining the Monte Carlo experiment based multi-parameter sensitivity analysis and the hybrid intelligent optimization. Then, the stability of the proposed IMFAPC scheme is analyzed and proved by the BIBO method. Finally, various data experiments are carried out to verify the proposed method. The experimental results demonstrate that the proposed method can control the nitrate nitrogen and dissolved oxygen concentration rapidly and accurately under different operating conditions.

Chaos numbers based a new representation scheme for evolutionary computation: Applications in evolutionary association rule mining

AbstractIn some practical situations, new computational methods are required for appropriately representing systems and their variables with inaccuracies, uncertainties, or variability. The chaos numbers seem to efficiently represent a set or range of values with lower and upper bounds for variables. In this article, a new chaos‐enhanced representation scheme that is based on the notion of chaos numbers is proposed for evolutionary optimization methods. As a first application of chaos‐based new encoding type, it is integrated into the novel hybrid intelligent optimization method proposed that is adapted as numerical association rules miner for the first time. The proposed hybrid method can also be used for complex types of search and optimization problems. The method is designed as a multiobjective rule miner that simultaneously handles different conflicting objectives and finds the accurate and comprehensible rules automatically. Based on the chaotic encoding, the proposed method easily and effectively adjusts the intervals of the attributes and automatically mines the rules without any preprocess. The performance of the proposed method was tested in real quantitative data sets and results were compared with the other association rule mining methods. According to the obtained results, the proposed method seems promising with respect to different metrics.

Wind turbine power curve modeling using an asymmetric error characteristic-based loss function and a hybrid intelligent optimizer

Wind energy is one of the most promising solutions to energy crisis and environmental pollution, so it is being developed rapidly. Wind turbine power curves (WTPCs) play an important role in wind energy assessment, turbine condition monitoring, and power grid dispatching. However, there are two challenges in WTPC modeling: model selection and parameter optimization. Many parametric and non-parametric models have been developed to characterize WTPCs, but none can always perform the best due to the complex wind regimes. In this paper, considering the simple structure and interpretability of model parameters, a set of parametric WTPC models is constructed to adapt to the variability of wind regimes, and the optimal candidate will be selected from the set according to their performances. As to the process of parameter optimization, a novel loss function, which considers the asymmetric error characteristic of WTPC modeling, is proposed, and a hybrid intelligent optimization method named GWO-BSA, which makes full use of the advantages of grey wolf optimizer and backtracking search algorithm, is designed. Finally, a novel WTPC modeling strategy, which combines the candidate model set, error characteristic-based loss function, and GWO-BSA, is proposed to obtain better power curves. Experimental results show that (1) GWO-BSA shows faster convergence speed and higher optimization accuracy than single optimization algorithms; (2) the proposed error characteristic-based loss function has better performance than the commonly used symmetric loss functions; and (3) compared with some popular artificial intelligence-based models, the designed WTPC modeling strategy produces better WTPCs under different wind regimes.