Global Optimization Research Articles

Reliability-based optimization for concrete-filled steel tubular columns incorporating multi-advanced techniques and model constraints

This work presents an advanced structural reliability analysis of circular concrete-filled steel tubular (CFST) members through machine learning and global optimization, together with numerical Monte Carlo simulations (MCS). The artificial neural network (ANN) model is developed and validated against available experimental data. On the basis of monetary cost of materials, the optimal design of CFST members with reliability constraint is formulated and solved by balancing composite motion optimization (BCMO) algorithm. Based on the MCS, reliability analysis is then carried out to calculate the coefficient of variation of optimal design under different loads. Subsequently, a comprehensive prediction simulation, taking account of the uncertainty of experimental data and potential errors from models (ANN, MCS, and BCMO), is performed to generate the statistics on the axial capacity of CFST members. The critical value for the optimal solution is also investigated as a function of the input variables. The results of this study can be applied to achieve a better reliability-based design optimization with minimum cost for the circular CFST columns.

Exomod: A Python Library for Exoplanet Transit Light Curve Fitting and Stacking with Background Simulation and Optimization

Abstract From the light curve taken during exoplanet transit, important parameters such as the radius ratio of the star-planet, impact parameter, inclination, and relative tangential velocity could be derived. A module was developed in the form of a Python library based on modeling using background simulation and fitting by utilizing global and local optimization, such as differential evolution and the Broyden–Fletcher–Goldfarb–Shanno (BFGS) method. For more accurate fitting, the effects of linear limb darkening on exoplanets are also included. The performance testing for the program was done by using dummy data to test its accuracy, followed by validation tests using WASP-12b transit curves to find out any discrepancies between our model and real exoplanet data. While performing a characterization test on WASP-12b, we found an interesting result regarding its impact parameter and the exoplanet’s velocity, which might be an interesting subject for further analysis.

Voice recognition enhancement by genetic algorithm

This research paper investigates the application of Genetic Algorithms (GA) in optimizing Artificial Neural Networks (ANN) for phoneme recognition. The study examines the formalism of GA, their parameters, and operators, and describes the genetic strategy adopted for phoneme recognition using the TIMIT sound database. The paper presents the outcomes of experiments conducted on the phonemes of the test base and the DR1 dialect learning base of the TIMIT base, and compares the recognition rates obtained by learning and testing with those guided by Self-Organizing Map (SOM) experiments. The findings suggest that the GA algorithm provides improved recognition rates and extends the search space to a global optimum, but can sometimes produce unacceptable results due to rapid and premature convergence and the overfitting problem. The study proposes a hybrid model between SOM and GA to take advantage of each of their properties and improve recognition rates. The results show that the proposed approach achieves an average recognition rate of over 95%.

A Novel Reinforcement Learning-Based Particle Swarm Optimization Algorithm for Better Symmetry between Convergence Speed and Diversity

This paper introduces a novel Particle Swarm Optimization (RLPSO) algorithm based on reinforcement learning, embodying a fundamental symmetry between global and local search processes. This symmetry aims at addressing the trade-off issue between convergence speed and diversity in traditional algorithms. Traditional Particle Swarm Optimization (PSO) algorithms often struggle to maintain good convergence speed and particle diversity when solving multi-modal function problems. To tackle this challenge, we propose a new algorithm that incorporates the principles of reinforcement learning, enabling particles to intelligently learn and adjust their behavior for better convergence speed and richer exploration of the search space. This algorithm guides particle learning behavior through online updating of a Q-table, allowing particles to selectively learn effective information from other particles and dynamically adjust their strategies during the learning process, thus finding a better balance between convergence speed and diversity. The results demonstrate the superior performance of this algorithm on 16 benchmark functions of the CEC2005 test suite compared to three other algorithms. The RLPSO algorithm can find all global optimum solutions within a certain error range on all 16 benchmark functions, exhibiting outstanding performance and better robustness. Additionally, the algorithm’s performance was tested on 13 benchmark functions from CEC2017, where it outperformed six other algorithms by achieving the minimum value on 11 benchmark functions. Overall, the RLPSO algorithm shows significant improvements and advantages over traditional PSO algorithms in aspects such as local search strategy, parameter adaptive adjustment, convergence speed, and multi-modal problem handling, resulting in better performance and robustness in solving optimization problems. This study provides new insights and methods for the further development of Particle Swarm Optimization algorithms.

A hierarchical adaptive federated reinforcement learning for efficient resource allocation and task scheduling in hierarchical IoT network

The increasing demand for processing numerous data from IoT devices in a hierarchical IoT network drives researchers to propose different resource allocation methods in the edge hosts efficiently. Traditional approaches often compromise on one of these aspects: either prioritizing local decision-making at the edge, which lacks global system insights or centralizing decisions in cloud systems, which raises privacy concerns. Additionally, most solutions do not consider scheduling tasks at the same time to effectively complete the prioritized task accordingly. This study introduces the hierarchical adaptive federated reinforcement learning (HAFedRL) framework for robust resource allocation and task scheduling in hierarchical IoT networks. At the local edge host level, a primal–dual update based deep deterministic policy gradient (DDPG) method is introduced for effective individual task resource allocation and scheduling. Concurrently, the central server utilizes an adaptive multi-objective policy gradient (AMOPG) which integrates a multi-objective policy adaptation (MOPA) with dynamic federated reward aggregation (DFRA) method to allocate resources across connected edge hosts. An adaptive learning rate modulation (ALRM) is proposed for faster convergence and to ensure high performance output from HAFedRL. Our proposed HAFedRL enables the effective integration of reward from edge hosts, ensuring the alignment of local and global optimization goals. The experimental results of HAFedRL showcase its efficacy in improving system-wide utility, average task completion rate, and optimizing resource utilization, establishing it as a robust solution for hierarchical IoT networks.

Maritime Inventory Routing with Speed Optimization: A MIQCP Formulation for a Tanker Fleet Servicing FPSO Units

This paper presents a continuous-time formulation for the management of deep-sea floating production storage and offloading (FPSO) units, which process and store crude oil from nearby oil platforms while waiting for a shuttle tanker to arrive and collect it. A heterogeneous fleet of tanker vessels travelling between the FPSO units and a port refinery is available, with the optimization deciding on the number and type of vessels to use, their route and travelling speed. The goal is to achieve an environmentally friendly solution featuring vessels travelling at lower speeds to minimize fuel consumption (a quadratic function of speed), which may require renting additional shuttle tankers to maintain production. The resulting mixed-integer quadratically constrained problems (MIQCP) can be solved by GUROBI, but not to global optimality, whereas a mixed-integer linear programming (MILP) relaxation that underestimates the total operating cost can tackle moderate problem sizes (5 FPSOs and 4 shuttle tankers).

A Novel Accurate Peak Extraction Algorithm of Mass Spectrometry Based on Iterative Adaptive Curve Fitting.

In the analysis of mass spectrometry, the peak identification from the overlapped region is necessary yet difficult. Although various methods have been developed to identify these peaks, especially the continuous wavelet transformation, their applications are still limited and it is hard to deal with the complex overlapped peaks. In this study, a novel peak extraction algorithm of mass spectrometry based on iterative adaptive curve fitting is proposed to address these challenges. It fully utilizes the global optimization characteristics of adaptive curve fitting. Initial peak parameters are obtained using a window searching method, and the residuals between the adaptive fitting peak and the original data indicate the fit's effectiveness and provide information about the peaks in overlap. Using this information, we performed iterative adaptive fitting, continuously updating the overlapped peaks until the residuals met the completion criteria. All of the peaks within the overlapped region can be successfully extracted by the final fitting. The proposed method is evaluated by the simulated data, the real signal from a public data set, and the spectra of two different mass spectrometry instruments. The results demonstrate that this method can more effectively extract peaks with severe overlap and multiple overlapped peaks, resist noise interference, and offer the potential to process peaks with a high dynamic range. More importantly, the proposed method accurately identifies overlapped peaks in the actual spectra from various mass spectrometry instruments, which helps the qualitative and quantitative analyses to a great extent.

Gaussian error loss function for image smoothing

Edge-preserving image smoothing plays an important role in the fields of image processing and computational photography, and is widely used for a variety of applications. The edge-preserving filters based on global optimization models have attracted widespread attention due to their nice smoothing quality. According to existing research, the edge-preserving capability is strongly correlated to the penalty function used for gradient regularization. By analyzing the edge-stopping function of existing penalties, we demonstrate that existing image smoothing models are not adequately edge-preserving. In this paper, based on a Gaussian error function (ERF), we propose a Gaussian error loss function (ERLF), which shows stronger edge-preserving capability. We embed the proposed loss function into a global optimization model for edge-preserving image smoothing. In addition, we propose an efficient solution based on additive half-quadratic minimization and Fourier-domain optimization that is capable of processing 720P color images (over 20 fps) in real-time on an NVIDIA RTX 3070 GPU. We have experimented with the proposed filter on a number of low-level vision tasks. Both quantitative and qualitative experimental results show that the proposed filter outperforms existing filters. Therefore, it can be practical for real applications.

Toward digitally twinning the process of creating machine controller digital twins – A G-code generation scenario

This paper proposes BatchEGO, an extension of Efficient Global Optimization (EGO), in an active learning context for creating and maintaining digital twins (DT). The motivation for this study is to address the challenges of obtaining high-quality yet cost and time effective data for developing accurate DTs of machine tool controllers that are robust to noise and physical system changes. BatchEGO is employed to generate manufacturing recipes (G-codes) recursively and iteratively, based on the spatial constraints of the physical machine, providing G-code based data for DTs. BatchEGO Interm is also proposed that extends BatchEGO by sampling additional intermediate points on the generated trajectory, to improve convergence and accuracy of DTs. To account for the stochastic nature of proposed methods, a total of 70 experiments based around repetitions of 4 correlation functions, 3 acquisition functions, and 4 sampling schemes are used to evaluate the performance of both methods. The results showcase impressive and faster convergence, in terms of sum of squared errors, for BatchEGO Interm with specific settings producing highly accurate and smooth G-codes. For instance, BatchEGO Interm with Matérn 3/2 correlation function and acquisition function of lower confidence bound can reach an error range of 0.0026 within 3-5 iterations. The work demonstrates the novelty and potential of such frameworks for enhancing data quality and quantity for DTs. The importance of G-code generation for DTs is also highlighted, as it can enable better control and optimization of machine tools in various industrial applications. The paper also discusses the different approaches for stitching the sampled points into G-code and compares their accuracy and efficiency.

Construction Method of Information Security Detection based on Clustering Algorithm

A method for K-prototype clustering, which can process mixed data types, is proposed first. An algorithm for information security evaluation of hybrid clusters based on K-prototypes was constructed. This method makes full use of the excellent global optimization performance of PSO and effectively solves the defect that the K-protection function quickly falls into local optimization. Simulation results show that the proposed method can effectively prevent local extreme values and improve overall performance.

ReLU surrogates in mixed-integer MPC for irrigation scheduling

Efficient water management in agriculture is important for mitigating the growing freshwater scarcity crisis. Mixed-integer Model Predictive Control (MPC) has emerged as an effective approach for addressing the complex scheduling problems in agricultural irrigation. However, the computational complexity of mixed-integer MPC still poses a significant challenge, particularly in large-scale applications. This study proposes an approach to enhance the computational efficiency of mixed-integer MPC-based irrigation schedulers by employing Rectified Linear Unit (ReLU) surrogate models to describe the soil moisture dynamics of the agricultural field. By leveraging the mixed-integer linear representation of the ReLU operator, the proposed approach transforms the mixed-integer MPC-based scheduler with a quadratic cost function into a mixed-integer quadratic program, which is the simplest class of mixed-integer nonlinear programming problems that can be efficiently solved using global optimization solvers. The effectiveness of this approach is demonstrated through comparative studies conducted on a large-scale agricultural field across two growing seasons, involving other machine learning surrogate models, specifically Long Short-Term Memory (LSTM) networks, and the triggered irrigation scheduling method. The ReLU-based approach significantly reduces solution times—by up to 99.5%—while achieving comparable performance to the LSTM approach in terms of water savings and Irrigation Water Use Efficiency (IWUE). Moreover, the ReLU-based approach achieves enhanced performance in terms of irrigation water savings and IWUE compared to the triggered approach.

A novel locally statistical active contour model for SAR image segmentation and faster algorithm

Abstract In this paper, a novel locally statistical active contour model (LACM) based on the Aubert-Aujol (AA) denoising model and variational level set method is proposed, which can be used for SAR image segmentation with intensity inhomogeneity. By simple transformation, a fast segmentation algorithm with a global optimization solver can be obtained. Experiments using some challenging synthetic images and Envisat SAR images demonstrate the superiority of our proposed models with respect to classic models. Further, we can find that the structure of the proposed algorithm is the same as that of the Transformer or RNN. We will study the nature of the above two types of algorithms to propose a more effective segmentation algorithm based on the energy model.

Hepatitis C virus infection and Parkinson’s disease: insights from a joint sex-stratified BioOptimatics meta-analysis

Hepatitis C virus (HCV) infection poses a significant public health challenge and often leads to long-term health complications and even death. Parkinson’s disease (PD) is a progressive neurodegenerative disorder with a proposed viral etiology. HCV infection and PD have been previously suggested to be related. This work aimed to identify potential biomarkers and pathways that may play a role in the joint development of PD and HCV infection. Using BioOptimatics-bioinformatics driven by mathematical global optimization-, 22 publicly available microarray and RNAseq datasets for both diseases were analyzed, focusing on sex-specific differences. Our results revealed that 19 genes, including MT1H, MYOM2, and RPL18, exhibited significant changes in expression in both diseases. Pathway and network analyses stratified by sex indicated that these gene expression changes were enriched in processes related to immune response regulation in females and immune cell activation in males. These findings suggest a potential link between HCV infection and PD, highlighting the importance of further investigation into the underlying mechanisms and potential therapeutic targets involved.

Code and Data Repository for An Efficient Optimization Model and Tabu Search-Based Global Optimization Approach for the Continuous p-Dispersion Problem

The software and data in this repository are a snapshot of the software and data that were used in the research reported in the paper An efficient optimization model and tabu search-based global optimization approach for continuous p-dispersion problem by L.J. Lai, Z.H. Lin, J.K. Hao, and Q.H. Wu.

Research on multi-wave joint elastic modulus inversion based on improved quantum particle swarm optimization

Young's modulus and Poisson's ratio are crucial parameters for reservoir characterization and rock brittleness evaluation. Conventional methods often rely on indirect computation or approximations of the Zoeppritz equations to estimate Young's modulus, which can introduce cumulative errors and reduce the accuracy of inversion results. To address these issues, this paper introduces the analytical solution of the Zoeppritz equation into the inversion process. The equation is re-derived and expressed in terms of Young's modulus, Poisson's ratio, and density. Within the Bayesian framework, we construct an objective function for the joint inversion of PP and PS waves. Traditional gradient-based algorithms often suffer from low precision and the computational complexity. In this study, we address limitations of conventional approaches related to low precision and complicated code by using Circle chaotic mapping, Lévy flights, and Gaussian mutation to optimize the quantum particle swarm optimization (QPSO), named improved quantum particle swarm optimization (IQPSO). The IQPSO demonstrates superior global optimization capabilities. We test the proposed inversion method with both synthetic and field data. The test results demonstrate the proposed method's feasibility and effectiveness, indicating an improvement in inversion accuracy over traditional methods.

Improved ACO algorithm fused with improved Q-Learning algorithm for Bessel curve global path planning of search and rescue robots

Addressing issues with traditional ant colony and reinforcement learning algorithms, such as low search efficiency and the tendency to produce insufficiently smooth paths that easily fall into local optima, this paper designs an improved ant colony optimization algorithm fusion with improved Q-Learning (IAC-IQL) algorithm for Bessel curve global path planning of search and rescue (SAR) robots. First, the heuristic function model in the ant colony algorithm is improved, the elite ant search strategy and the adaptive pheromone volatility factor strategy are introduced, and the initial path is searched in realize the motion environment with the help of the improved ant colony algorithm, and the initialized pheromone matrix is constructed. Second, the improved ant colony algorithm and Q-Learning (QL) algorithm are fused by utilizing the similarity between the pheromone matrix in the improved ant colony algorithm and the Q-matrix in the QL algorithm. A heuristic learning evaluation model is designed to dynamically adjust the learning factor and provide guidance for the search path. Additionally, a dynamic adaptive greedy strategy is introduced to balance the exploration and exploitation of the robot in the environment. Finally, the paths are smoothed using third-order Bessel curves to eliminate the problem of excessive steering angles. Through three sets of comparative simulation experiments conducted in Pycharm platform, the effectiveness, superiority, and practicality of the IAC-IQL algorithm were verified. The experimental results demonstrated that the IAC-IQL algorithm integrates the strong search capability of ant colony algorithm and the self-learning characteristics of QL algorithm. SAR robots equipped with the improved IAC-IQL algorithm exhibit significantly enhanced iterative search efficiency in grid simulation environment and image sampling simulation environment. The global path optimization indicators demonstrate high efficiency, and the paths are smoother.

Modular design and structural optimization of CubeSat separation mechanism

In order to achieve the design requirements of light weight and high stiffness of the CubeSat modular separation mechanism, a BPNN-GA-PSO size optimization method is proposed to optimize the design of the separation mechanism. Firstly, the separation mechanism of modularization and standardization is analyzed to select the optimization objects. Then, a hierarchical optimization strategy of topology optimization followed by size optimization is adopted to find the global optimum using a hybrid GA-PSO optimization algorithm. Meanwhile, the BPNN surrogate model is introduced to improve the optimization efficiency. The results show that the mass proportion of the optimized separation mechanism is reduced to 18 %, and the maximum deformation of the separation mechanism is 0.123 mm, which meets the design requirements of light weight and high stiffness of the separation mechanism. It proves the applicability of the optimization method to the optimal design of the separation mechanism. The CubeSat modular separation mechanism designed in this paper has been verified by ground verification with overload, vibration, and shock mechanical tests, and successfully deployed the BY-03 satellite in-orbit, which can provide reference for the design and development of subsequent CubeSat modular separation mechanisms.

An Intelligent Decision-Making Approach for Multi-Ship Traffic Conflict Mitigation from the Perspective of Maritime Surveillance

Potential multi-ship conflict situations in coastal or near-shore port areas have always been one of the important factors affecting ship navigation safety and a key target of maritime traffic regulatory authorities. In recent years, with the continuous development and integration of various emerging technologies in the maritime field, maritime traffic supervision has also shown a trend of intelligent and autonomous development. The traditional supervision method dominated by human experience is evolving towards data and model-driven practices. In order to solve the problem of ship navigation safety supervision under multi-ship conflict scenarios, it is urgent to build an intelligent conflict mitigation decision-making model. Therefore, this paper designs a novel risk mitigation decision-making model for multi-ship conflict scenarios from the perspective of maritime supervision. The model proposed in this paper first extracts high-density ship clusters based on AIS (Automatic Identification System) data and uses the MCD (Mean Core Density) and PRM (Proportion of Relative Motion) as feature indicators to further mine potential multi-ship conflict scenarios. Finally, a global optimization decision-making model is constructed to effectively mitigate conflict risks. Experimental verification shows that the intelligent decision-making model for the mitigation of maritime traffic conflict proposed in this paper can autonomously identify conflict scenarios and make reasonable decisions in real time. It can effectively ensure the navigation safety of ships in multi-ship conflict scenarios and further improve the supervision level of maritime departments.

Decentralized Stochastic Recursive Gradient Method for Fully Decentralized OPF in Multi-Area Power Systems

This paper addresses the critical challenge of optimizing power flow in multi-area power systems while maintaining information privacy and decentralized control. The main objective is to develop a novel decentralized stochastic recursive gradient (DSRG) method for solving the optimal power flow (OPF) problem in a fully decentralized manner. Unlike traditional centralized approaches, which require extensive data sharing and centralized control, the DSRG method ensures that each area within the power system can make independent decisions based on local information while still achieving global optimization. Numerical simulations are conducted using MATLAB (Version 1.0.0.1) to evaluate the performance of the DSRG method on a 3-area, 9-bus test system. The results demonstrate that the DSRG method converges significantly faster than other decentralized OPF methods, reducing the overall computation time while maintaining cost efficiency and system stability. These findings highlight the DSRG method’s potential to significantly enhance the efficiency and scalability of decentralized OPF in modern power systems.

Analysis and evaluation of the effectiveness of program codes for particle swarm and ant colony methods for lower semi-continuous functions

In this paper, a comparative analysis of two stochastic methods of global optimization is carried out: the Particle Swarm Optimization method and the Ant Colony Optimization method. The analysis and evaluation of efficiency were carried out using parameters such as the number of calls to the objective function, the rate of convergence, resources, computational efficiency, sensitivity to parameters, and resistance to initial conditions. The article presents the program codes of these methods in the Python environment, designed to calculate the global extremum of semi-continuous functions from below. Well- known test functions were used to construct (by gluing) semi-continuous functions at different values of input parameters.