Global Optimization Research Articles

A New Class of Filled Functions with Two Parameters for Solving Unconstrained Global Optimization Problems

A New Class of Filled Functions with Two Parameters for Solving Unconstrained Global Optimization Problems

Short-Term Electricity Load Forecasting Based on Improved Data Decomposition and Hybrid Deep-Learning Models

Short-term power load forecasting plays a key role in daily scheduling and ensuring stable power system operation. The problem of the volatility of the power load sequence and poor prediction accuracy is addressed. In this study, a learning model integrating intelligent optimization algorithms is proposed, which combines an ensemble-learning model based on long short-term memory (LSTM), variational modal decomposition (VMD) and the multi-strategy optimization dung beetle algorithm (MODBO). The aim is to address the shortcomings of the dung beetle optimizer algorithm (DBO) in power load forecasting, such as its time-consuming nature, low accuracy, and ease of falling into local optimum. In this paper, firstly, the dung beetle algorithm is initialized using a lens-imaging reverse-learning strategy to avoid premature convergence of the algorithm. Second, a spiral search strategy is used to update the dynamic positions of the breeding dung beetles to balance the local and global search capabilities. Then, the positions of the foraging dung beetles are updated using an optimal value bootstrapping strategy to avoid falling into a local optimum. Finally, the dynamic-weighting coefficients are used to update the position of the stealing dung beetle to improve the global search ability and convergence of the algorithm. The proposed new algorithm is named MVMO-LSTM. Compared to traditional intelligent algorithms, the four-quarter averages of the RMSE, MAE and R2 of MVMO-LSTM are improved by 0.1147–0.7989 KW, 0.09799–0.6937 KW, and 1.00–13.05%, respectively. The experimental results show that the MVMO-LSTM proposed in this paper not only solves the shortcomings of the DBO but also enhances the stability, global optimization capability and information utilization of the model.

An Improved Football Team Training Algorithm for Global Optimization

The football team training algorithm (FTTA) is a new metaheuristic algorithm that was proposed in 2024. The FTTA has better performance but faces challenges such as poor convergence accuracy and ease of falling into local optimality due to limitations such as referring too much to the optimal individual for updating and insufficient perturbation of the optimal agent. To address these concerns, this paper presents an improved football team training algorithm called IFTTA. To enhance the exploration ability in the collective training phase, this paper proposes the fitness distance-balanced collective training strategy. This enables the players to train more rationally in the collective training phase and balances the exploration and exploitation capabilities of the algorithm. To further perturb the optimal agent in FTTA, a non-monopoly extra training strategy is designed to enhance the ability to get rid of the local optimum. In addition, a population restart strategy is then designed to boost the convergence accuracy and population diversity of the algorithm. In this paper, we validate the performance of IFTTA and FTTA as well as six comparison algorithms in CEC2017 test suites. The experimental results show that IFTTA has strong optimization performance. Moreover, several engineering-constrained optimization problems confirm the potential of IFTTA to solve real-world optimization problems.

Accelerating structure search using atomistic graph-based classifiers.

We introduce an atomistic classifier based on a combination of spectral graph theory and a Voronoi tessellation method. This classifier allows for the discrimination between structures from different minima of a potential energy surface, making it a useful tool for sorting through large datasets of atomic systems. We incorporate the classifier as a filtering method in the Global Optimization with First-principles Energy Expressions (GOFEE) algorithm. Here, it is used to filter out structures from exploited regions of the potential energy landscape, whereby the risk of stagnation during the searches is lowered. We demonstrate the usefulness of the classifier by solving the global optimization problem of two-dimensional pyroxene, three-dimensional olivine, Au12, and Lennard-Jones LJ55 and LJ75 nanoparticles.

Compound Attitude Control Strategy for Reusable Launch Vehicle Based on Improved Particle Swarm Optimization Algorithm

This study introduces an advanced dual-mode compound attitude control framework for reusable launch vehicles (RLVs), underpinned by an enhanced particle swarm optimization (PSO) algorithm. This innovative strategy is tailored to meet the stringent demands for precision and robust anti-interference capabilities across the entire flight envelope of RLVs. The research commences with the formulation of a comprehensive attitude dynamics model and diverse heterogeneous actuator representations, meticulously crafted to reflect the distinct phases of RLV flight. Building upon this foundation, a synergistic control paradigm is engineered, integrating PID and fuzzy PID controllers and dynamically adjusting the inertia weights and learning factors of the PSO algorithm to achieve the balance between global and local optimization performance, complemented by a refined fitness evaluation function. The crux of the study is the application of an upgraded PSO algorithm to fine-tune the controllers’ weighting coefficients, culminating in an optimized dual-mode compound attitude control system. A series of comparative simulation analyses are thoroughly executed to appraise the system’s responsiveness, stability, precision, and resilience to interference. The simulation outcomes demonstrate an average reduction of 42.21% in step response overshoot, an 18.52% decrease in settling time, a 53.18% decline in steady-state error, a 56.80% drop in the maximum deviation value, a 55.82% improvement in recovery speed, and a 75.61% enhancement in tracking precision for the proposed controller. The findings clearly verify the superior performance of the proposed control system, affirming its contribution to the advancement of RLV attitude control. The proposed controller holds promising potential for real application in attitude control systems and is poised to augment the reliability and mission success rate of RLVs under intricate flight scenarios.

MILP-based framework for seismic resilience evaluation and enhancement of substations

In order to minimize earthquake impacts, substation rehabilitation requires an optimal sequence for defining repair priorities. In this paper, a continuous rehabilitation process is discretized to reduce the nonlinearity of the seismic resilience evaluation. Thus, a newly proposed post-earthquake rehabilitation strategy based on mixed integer linear programming (MILP) can be applied to find the optimal rehabilitation sequence. In particular, this strategy can evaluate the global optimality of the obtained rehabilitation sequence. A differentiated retrofitting strategy for substation equipment based on iterated local search (ILS) is also proposed to improve the seismic robustness. Case study shows that the MILP rehabilitation strategy can find out the globally optimal rehabilitation sequences in most simulated scenarios, which obviously increases the restorative capacity. Meanwhile, the proposed retrofitting strategy improves the seismic robustness more significantly than other strategies. Thus, the seismic resilience of the substation was enhanced comprehensively by the proposed method.

Force Field X: A computational microscope to study genetic variation and organic crystals using theory and experiment.

Force Field X (FFX) is an open-source software package for atomic resolution modeling of genetic variants and organic crystals that leverages advanced potential energy functions and experimental data. FFX currently consists of nine modular packages with novel algorithms that include global optimization via a many-body expansion, acid-base chemistry using polarizable constant-pH molecular dynamics, estimation of free energy differences, generalized Kirkwood implicit solvent models, and many more. Applications of FFX focus on the use and development of a crystal structure prediction pipeline, biomolecular structure refinement against experimental datasets, and estimation of the thermodynamic effects of genetic variants on both proteins and nucleic acids. The use of Parallel Java and OpenMM combines to offer shared memory, message passing, and graphics processing unit parallelization for high performance simulations. Overall, the FFX platform serves as a computational microscope to study systems ranging from organic crystals to solvated biomolecular systems.

Ant Colony Optimization for solving Directed Chinese Postman Problem

AbstractThe Chinese Postman Problem (CPP) is a well-known optimization problem involving determining the shortest route, modeling the system as an undirected graph, for delivering mail, ensuring all roads are traversed while returning to the post office. The Directed Chinese Postman Problem (DCPP) extends the Chinese Postman Problem (CPP), where the underlying graph representing the system incorporates exclusively directed edges. Similarly to CPP, this problem has plenty of applications in route optimization, interactive system analysis, and circuit design problems. However, due to the added constraint (directionality of edges), DCPP results are more challenging to solve. Although methods to solve it in literature are proposed, typically by using minimum-cost-flow algorithms, the meta-heuristics approaches proposed to deal with it are very limited. In this paper, we propose an innovative meta-heuristic approach to solve DCPP by using an ant colony optimization (ACO) algorithm, i.e., an algorithm that simulates in a simplified way the behavior of some species of ants to solve optimization problems. The efficiency of our ant colony optimization for solving the Directed Chinese Postman Problem (ACO-DCPP) is measured by comparing the ACO outcomes with the results obtained by a recursive algorithm that explores all the possible solutions. Results show that ACO-DCPP is stable and gets the global optimum frequently by using an extremely limited number of solutions explored.

Image smoothing method based on global gradient sparsity and local relative gradient constraint optimization

Removing texture while preserving the main structure of an image is a challenging task. To address this, this paper propose an image smoothing method based on global gradient sparsity and local relative gradient constraints optimization. To reduce the interference of complex texture details, adopting a multi-directional difference constrained global gradient sparsity decomposition method, which provides a guidance image with weaker texture detail gradients. Meanwhile, using the luminance channel as a reference, edge-aware operator is constructed based on local gradient constraints. This operator weakens the gradients of repetitive and similar texture details, enabling it to obtain more accurate structural information for guiding global optimization of the image. By projecting multi-directional differences onto the horizontal and vertical directions, a mapping from multi-directional differences to bi-directional gradients is achieved. Additionally, to ensure the consistency of measurement results, a multi-directional gradient normalization method is designed. Through experiments, we demonstrate that our method exhibits significant advantages in preserving image edges compared to current advanced smoothing methods.

GLIM: 3D range-inertial localization and mapping with GPU-accelerated scan matching factors

This article presents GLIM, a 3D range-inertial localization and mapping framework with GPU-accelerated scan matching factors. The odometry estimation module of GLIM employs a combination of fixed-lag smoothing and keyframe-based point cloud matching that makes it possible to deal with a few seconds of completely degenerated range data while efficiently reducing trajectory estimation drift. It also incorporates multi-camera visual feature constraints in a tightly coupled way to further improve the stability and accuracy. The global trajectory optimization module directly minimizes the registration errors between submaps over the entire map. This approach enables us to accurately constrain the relative pose between submaps with a small overlap. Although both the odometry estimation and global trajectory optimization algorithms require much more computation than existing methods, we show that they can be run in real-time due to the careful design of the registration error evaluation algorithm and the entire system to fully leverage GPU parallel processing.

A Novel Squirrel-Cat Optimization Based Optimal Expansion Planning for Distribution System

AimIn recent years, renewable distributed generation (DG) has grown to deliver sustainable electricity with minimal environmental impact. However, renewable DG poses new provocation in the distribution system expansion planning problem (DSEP). To address those problems, this paper suggests a new mathematical model for distribution system expansion plans using a novel hybrid optimization strategy. MethodThe optimal expansion plan for the distribution system is achieved using the hybrid optimization model, named Squirrel Search Insisted Cat Swarm Optimization (SSI-CS) algorithm. The proposed hybrid optimization algorithm is developed with the incorporation of the characteristics features of Squirrel search optimization (SSA) algorithm and Cat Swarm Optimization (CSO) Algorithm to optimize the solar capacity, wind capacity and biomass capacity. This combination aims to strike a balance between global and local optimization, ultimately leading to better cost-effective results. The Distribution systems variables like DG type, size/capacity, location, real power, reactive power, and the solar and wind capacity during load demand uncertainty act as the input to the proposed hybrid optimization algorithm. The main objective of attaining minimal cost for the expansion plan of the distribution system is checked, and the cycle is repeated until obtaining the optimal solution (minimum cost). ResultThe experimental analysis using an IEEE-33 bus system with 5 system states is executed in MATLAB/Simulink. The suggested SSI-CS model attained a minimal operational cost of 7433.4 which better than GWO, SSA and CSO. ConclusionHence, the proposed SSI-CS shows promise as an efficient and effective approach for distribution system expansion planning.

Chaotic Artificial Algae Algorithm for Solving Global Optimization With Real-World Space Trajectory Design Problems

AbstractThe artificial algae algorithm (AAA) is a recently introduced metaheuristic algorithm inspired by the behavior and characteristics of microalgae. Like other metaheuristic algorithms, AAA faces challenges such as local optima and premature convergence. Various strategies to address these issues and enhance the performance of the algorithm have been proposed in the literature. These include levy flight, local search, variable search, intelligent search, multi-agent systems, and quantum behaviors. This paper introduces chaos theory as a strategy to improve AAA's performance. Chaotic maps are utilized to effectively balance exploration and exploitation, prevent premature convergence, and avoid local minima. Ten popular chaotic maps are employed to enhance AAA's performance, resulting in the chaotic artificial algae algorithm (CAAA). CAAA's performance is evaluated on thirty benchmark test functions, including unimodal, multimodal, and fixed dimension problems. The algorithm is also tested on three classical engineering problems and eight space trajectory design problems at the European Space Agency. A statistical analysis using the Friedman and Wilcoxon tests confirms that CAA demonstrates successful performance in optimization problems.

Detailed Reaction Kinetics for Hydrocarbon Fuels: The Development and Application of the ReaxFFCHO-S22 Force Field for C/H/O Systems with Enhanced Accuracy.

Efficient and accurate reactive force fields (e.g., ReaxFF) are pivotal for large-scale atomistic simulations to comprehend microscopic combustion processes. ReaxFF has been extensively utilized to describe chemical reactions in condensed phases, but most existing ReaxFF models rely on quantum mechanical (QM) data nearly two decades old, particularly in hydrocarbon systems, constraining their accuracy and applicability. Addressing this gap, we introduce a reparametrized ReaxFFCHO-S22 for C/H/O systems, tailored for studying the pyrolysis and combustion of hydrocarbon fuel. Our approach involves high-level QM benchmarks and large database construction for C/H/O systems, global ReaxFF parameter optimization, and molecular dynamics simulations of typical hydrocarbon fuels. Density functional theory (DFT) computations utilized the M06-2X functional at the meta-generalized gradient approximation (meta-GGA) level with a large basis set (6-311++G**). Our new ReaxFFCHO-S22 model exhibits a minimum 10% enhancement in accuracy compared to the previous ReaxFF models for a large variety of hydrocarbon molecules. This advanced ReaxFFCHO-S22 not only enables efficient large-scale studies on the microscopic chemical reactions of more complex hydrocarbon fuel but also can extend to biofuels, energetic materials, polymers, and other pertinent systems, thus serving as a valuable tool for studying chemical reaction dynamics of the large-scale hydrocarbon condensed phase at an atomistic level.

Prediction of Drilling Efficiency for Rotary Drilling Rig Based on an Improved Back Propagation Neural Network Algorithm

Accurately predicting the drilling efficiency of rotary drilling is the key to achieving intelligent construction. The current types of principle analysis (based on traditional interactive experimental methods) and efficiency prediction (based on simulation models) cannot meet the requirements needed for the efficient, real-time, and accurate drilling efficiency predictions of rotary drilling rigs. Therefore, we adopted a method based on machine learning to predict drilling efficiency. The extremely complex rock fragmentation process in drilling conditions also brings challenges to predicting drilling efficiency. Therefore, this article went through a combination of mechanism and data analysis to conduct correlation analysis and to clarify the drilling characteristic parameters that are highly correlated with drilling efficiency, and it then used them as inputs for machine learning models. We propose a rotary drilling rig drilling efficiency prediction model based on the GA-BP neural network to construct an accurate and efficient drilling efficiency prediction model. Compared with traditional BP neural networks, it utilizes the global optimization ability of a genetic algorithm to obtain the initial weights and thresholds of a BP neural network in order to avoid the defect of ordinary BP neural networks, i.e., that they easily fall into local optimal solutions during the training process. The average prediction accuracy of the GA-BP neural network is 93.6%, which is 3.1% higher than the traditional BP neural network.

A sequence decomposition genetic algorithm for petrophysical inversion with spatial correlation

Predicting petrophysical properties based on seismic attributes is essential for accurate subsurface characterization. Typically, a forward model is first established to relate the petrophysical properties to geophysical measurements, and then the petrophysical properties are inverted from the actual measurements. Imposing prior constraints of the spatial relationship is also desirable to regularize the ill-posed inverse problem. However, petrophysical inversion involving spatial correlation presents a high-dimensional optimization problem with multiple local extrema. This problem can be challenging to solve, particularly when discrete variables (such as lithofacies) and continuous variables (such as porosity and fluid saturation) are considered together. Therefore, we develop a global optimization algorithm that uses the Markov chain decomposition and a genetic algorithm for a joint discrete-continuous petrophysical inversion with spatial correlation. The innovation of this algorithm is to spatially decouple the sequence optimization problem into multiple low-dimensional subproblems and overcome the initial value dependence to converge to the global optimal solution. Furthermore, an approximation operation is introduced to expedite the optimization algorithm. Numerical experiments show that after algorithmic acceleration, the spatially correlated inversion requires the same computational time as a simple spatially uncorrelated inversion and achieves significantly better accuracy than the latter. Finally, we apply the method to a case study in East China, wherein the lithofacies, clay content, porosity, and water saturation are inferred from the data of the wave velocities and density. The inversion results around the wells generally match the well-logging data, verifying the feasibility of the new method.

Dam Deformation Prediction Model Based on Multi-Scale Adaptive Kernel Ensemble

Dam Deformation Prediction Model Based on Multi-Scale Adaptive Kernel Ensemble

Unified scheme design and control optimization of flapping wing for next-generation manta ray robot

An efficient global optimization (EGO) algorithm has been used in the geometry design and motion for a manta ray-like flapping wing to optimize the best swimming performance. To this end, we combine a three-dimensional simulation using STAR-CCM+ with this framework to generate the optimal flapping wing geometry that maximizes the lift-to-drag ratio. The drag and moment coefficients between the original and optimized flapping wings were further compared in a servo-driven towing tank to validate the optimization effects. In addition, by associating the force experiments in a water tunnel, we successfully predict the motion trajectory for the largest thrust with the same EGO algorithm. A free-swimming test is carried out to verify the optimization. This study gives a unified scheme design optimization for the next-generation manta ray robot.

Dynamic Security Rule Optimization Based on Deep Learning and Adaptive Algorithms

Compared with traditional computer network security identification techniques, deep learning algorithms are based on their own distributed network structure for storage, processing, classification, comparison, and automatic identification functions. The distribution and dynamism of cloud databases increase the difficulty of route prediction and recognition in the cloud, affecting the efficiency of cloud computing. In response to the above issues, the author proposes a dynamic path optimization process for cloud databases based on adaptive immune grouping polymorphic ant colony algorithm. By setting up two states of ant colony, reconnaissance ant and search ant, and introducing an adaptive polymorphic ant colony competition strategy, the defect of general ant colony algorithms being prone to falling into local optima is improved; On this basis, an artificial immune algorithm with fast global search capability is further integrated to improve the search ant path optimization process, improving search speed and accuracy. Simulation experiments show that the IPANT algorithm outperforms the other three algorithms, maintaining a throughput of 1000 kbps and relatively stable; The data of OSPF, SPF, and FR are not significantly different, significantly lower than IPANT. The immune polymorphic ant colony algorithm (IPANT) has the lowest time delay for packet routing and performs better than the other three algorithms, with FR and SPF having higher latency. It has been proven that this algorithm can better solve convergence speed and global optimization problems, and can quickly and reasonably find the database to be accessed in the cloud.

Quality-centered production and maintenance scheduling for multi-machine manufacturing systems under variable operating conditions

Production, maintenance, and quality represent the three primary tasks in manufacturing systems. Previous studies have predominantly focused on optimizing these tasks separately or jointly optimizing two of them. However, it is worth noting that these tasks exhibit mutual interaction, and models failing to consider them simultaneously cannot achieve global optimality. Therefore, this paper aims to develop a quality-centered production and maintenance scheduling methodology (QPMSM) for multi-machine and multi-product manufacturing systems. Specifically, a delivery time-based production scheduling strategy is introduced to emphasize the impact of maintenance activities. A quality loss model is developed to characterize the mapping relationship between quality deterioration and component degradation. Furthermore, a well-designed maintenance strategy determines the optimal timing and frequency of maintenance activities to improve system performance and reduce quality deterioration. Building upon this foundation, an integrated model is proposed to concurrently optimize production scheduling, maintenance planning, and quality control tasks to minimize the overall system cost. The effectiveness of the proposed QPMSM in terms of cost-savings has been verified using a designed hybrid meta-heuristic algorithm, providing a viable approach for simultaneous optimization.

Revisiting CEC 2022 ranking: A new ranking method and influence of parameter tuning

Optimization competitions give an impulse to develop optimization algorithms. However, there is no common agreement on how to rank the contestants. This paper proposes a method of assessing the performance of the algorithms. The proposed mark is easily interpretable by humans and can be compared with previously published marks. The proposed method was used to create a ranking for contestants of the CEC 2022 competition on single objective bound constrained numerical optimization. The resulting ranking is different from the official one. The ranks of some algorithms differ by up to five. As the proposed ranking is more focused on the results at the end of the budget, winning algorithms are better suited for most real-world applications. Since the tuning effort influences the algorithm’s results, the paper also examines the influence of parameter tuning on the place achieved in both rankings by the top four algorithms. The parameters to tune were extracted from the papers that introduced the algorithms and from the source codes. The results showed that all considered algorithms were not carefully tuned for CEC. Generally, tuning using the target performance metric helps, but tuning using the other metric is harmful. The profit of tuning is up to a 33% increase in the number of trails that found the global optimum. The ablation analyses of the algorithms’ parameters showed that only a few parameters strongly influence the results. Frequently, parameters not listed in the papers are among the most important.