Global Optimization Research Articles

Solving the storage location assignment of large-scale automated warehouse based on dynamic vortex search algorithm

This paper establishes the mathematical model for Storage Location Assignment (SLA) problem in large-scale automated warehouses by combining three objectives: efficiency, shelf stability, and stacker load balancing. Along with a novel repair strategy to handle the complex constraints of large-scale problems. Additionally, a coding method and solution approach suitable for practical application scenarios are developed. In order to solve large-scale SLA problem, an improved vortex search algorithm is proposed based on attraction operation in flow field, dimension-by-dimension dynamic radius and leadership decision-making mechanism (FDVSA). In the experimental part, the algorithm effectiveness experiment of FDVSA was first conducted using the large-scale global optimization test sets IEEE congress on evolutionary computation 2010 and 2013 (CEC2010, CEC2013). The results show that: (1) Compared with other comparison algorithms, the comprehensive average optimization rate of FDVSA in CEC2010 and CEC2013 is 88 % and 78 %, respectively. (2) The experimental results of FDVSA showed that each improvement strategy has advantages in dealing with large-scale problems. (3) The post-hoc analysis showed that there are significant differences between FDVSA and other comparison algorithms, and FDVSA is significantly better. Finally, FDVSA and other comparison algorithms are solved on three different scale and complexity of SLA cases. The results show that: (1) FDVSA has significant advantages in solving large-scale SLA problem, and the comprehensive average optimization rate is 19 %. (2) The convergence curve and boxplot showed that FDVSA has good convergence speed and solving stability. (3) The effectiveness of the repair strategy was verified by experiments in the large-scale SLA problems.

Visual-Preserving Mesh Repair.

Mesh repair is a long-standing challenge in computer graphics and related fields. Converting defective meshes into watertight manifold meshes can greatly benefit downstream applications such as geometric processing, simulation, fabrication, learning, and synthesis. In this work, by assuming the model is visually correct, we first introduce three visual measures for visibility, orientation, and openness, based on ray-tracing. We then present a novel mesh repair framework incorporating visual measures with several critical steps, i.e., open surface closing, face reorientation, and global optimization, to effectively repair meshes with defects (e.g., gaps, holes, self-intersections, degenerate elements, and inconsistent orientations) and preserve visual appearances. Our method reduces unnecessary mesh complexity without compromising geometric accuracy or visual quality while preserving input attributes such as UV coordinates for rendering. We evaluate our approach on hundreds of models randomly selected from ShapeNet and Thingi10K, demonstrating its effectiveness and robustness compared to existing approaches.

Real-time energy management strategy for a plug-in hybrid electric bus considering the battery degradation

The energy conservation remains the key issue for the hybrid electric vehicles (HEVs) today. However, most existing energy management strategies (EMS) only focus on the fuel consumption or battery preservation, and little considers the battery health. Besides, the global energy optimality and real-time execution are two trade-off counterparts for the EMS application. To this end, this paper proposed a real time EMS of the HEVs considering the battery health. Explicitly, the battery health status and SOC values are predicted with the space vector machine algorithm and adaptive Kalman filter algorithm, respectively. Then, the offline energy optimization is realized with the Pontryagin's minimum principle; thereby, the offline energy conversion coefficient can be further extracted into the simple rules. On this basis, the online equivalent consumption minimization strategy (ECMS) is adopted to output the optimal control variables including the motor torque, engine torque, clutch states and gear information. Besides, to apply the proposed EMS in the practical vehicles, the energy conversion coefficients are further modified according to the estimated battery state of the charge (SOC). The simulation and experimental tests have validated the superiority of the proposed EMS in terms of energy economy and maneuverability. Explicitly, the proposed EMS considering the battery degradation has effectively prevented the SOC trajectory into charge-sustaining stage earlier and meanwhile the fuel utilization is improved by 4.1% than that without considering the battery degradation. Besides, compared with the existing ECMS method, the proposed EMS can reduce the fuel consumption by about 8%∼14% and meanwhile enhance the handling adaptiveness by about 15%∼20%. In general, the proposed EMS is of significance to the vehicular energy conservation and the real-time executability for the HEVs.

A fixed point evolution algorithm based on expanded Aitken rapid iteration method for global numeric optimization

Evolution algorithms based on mathematical models that their reproduction operators are derived from mathematical models are a promising branch of metaheuristic algorithms. Aitken rapid iteration method, as a fixed point iteration technique for solving nonlinear equations, performs a procedure of progressive display of a root. Inspired by the idea of viewing the progressive display procedure of a fixed point iteration as an evolutionary optimization process, a fixed point evolution algorithm based on expanded Aitken rapid iteration method (FPEea) is proposed. To develop the FPEea, an expanded Aitken rapid model is firstly constructed; Then, three polynomials which are derived from the expanded Aitken rapid model are used as the reproduction operator of the FPEea to produce offsprings. The performance of the FPEea is investigated on CEC2019, CEC2020 benchmark function sets as well as four engineering design problems. Experimental results show that the FPEea is a effective and competitive algorithm compared with several classical evolution algorithms and state-of-the-art algorithms.

Enhancing stochastic optimization: investigating fixed points of chaotic maps for global optimization

<p>Chaotic maps, despite their deterministic nature, can introduce controlled randomness into optimization algorithms. This chaotic map behaviour helps overcome the lack of mathematical validation in traditional stochastic methods. The chaotic optimization algorithm (COA) uses chaotic maps that help it achieve faster convergence and escape local optima. The effective use of these maps to find the global optimum would be possible only with a complete understanding of them, especially their fixed points. In chaotic maps, fixed points repeat indefinitely, disrupting the map's characteristic unpredictability. While using chaotic maps for global optimization, it is crucial to avoid starting the search at fixed points and implement corrective measures if they arise in between the sequence. This paper outlines strategies for addressing fixed points and provides a numerical evaluation (using Newton's method) of the fixed points for 20 widely used chaotic maps. By appropriately handling fixed points, researchers and practitioners across diverse fields can avoid costly failures, improve accuracy, and enhance the reliability of their systems.</p>

Particle swarm optimization for hybrid mutant slime mold: An efficient algorithm for solving the hyperparameters of adaptive Grey-Markov modified model

Thegrey prediction model is a scientific and effective prediction method for small amounts of incomplete data. In this paper, we proposed a particle swarm optimization for mixed mutant slime mold (namely SCPSO) combined with an extended adaptive Grey-Markov modified model called AOPGM(1,1,λ,µ), which constructs an AOPGM(1,1,λ,µ) based hyperparameters optimization for SCPSO. Firstly, the values of the exponential cumulative generation operator coefficient λ and the background value µ of the adaptive Grey-Markov modified model are refined, and λ and µ are also calculated using SCPSO. Secondly, a competitive SCPSO is formed by introducing a good point set, identifying attack strategy, mutation slime mold algorithm, and Sigmoid function into particle swarm optimization. It was also compared with the improved optimization algorithms on the CEC2020 test set and with classical and newer intelligent algorithms on the CEC2022 test set. The results of numerical experiments show that SCPSO can be considered a competitive and promising global optimization algorithm. Finally, SCPSO is used to optimize the hyperparameters of the AOPGM(1,1,λ,µ) and applied to the prediction of oil production and proven reserves in China. In terms of optimizing the hyperparameters of the AOPGM(1,1,λ,µ), the numerical experimental results of this method were better than the prediction results of 7 intelligent algorithms and the results of 4 prediction models. The validity of the proposed methodology was verified through five evaluation indicators.

Globalization of distributed parameter self‐optimizing control

AbstractNumerous nonlinear distributed parameter systems (DPSs) operate within an extensive range due to process uncertainties. Their spatial distribution characteristic, combined with nonlinearity and uncertainty, poses challenges in optimal operation under two‐step real‐time optimization (RTO) and economic model predictive control (EMPC). Both methods necessitate substantial computational power for prompt online reoptimization. Recent local distributed parameter self‐optimizing control (DPSOC) achieves optimality without repetitive optimization. However, its effectiveness is confined to a narrow range around a nominal operation. Here, globalized DPSOC is introduced to surmount the limitation of the local DPSOC. A global loss functional concerning controlled variables (CVs) is formulated using linear operators and Fubini's theorem. Minimizing the loss with a numerical optimization procedure yields CVs exhibiting global optimality. Maintaining these CVs at constants ensures such a process has a minimal average loss in a large operating space. The effectiveness of the proposed method is substantiated through a transport reaction simulation.

An effective channel allocation designed using hybrid memory dragonfly with imperialist competitive algorithm in distributed mobile adhoc network

SummaryThe channel availability problem reaches a higher degree in mobile ad hoc networks (MANETs) and garners a lot of attention in communication networks. Because increased mobile usage might result in a lack of channel allocation, an improved channel allocation technique is presented to tackle the availability problem. The distributed dynamic channel allocation (DDCA) model is built in this paper using the hybrid memory dragonfly with imperialist competitive (HMDIC) method. Based on optimization logic, this strategy assigns the channel to mobile hosts. The MANET provides a dispersed network within the coverage region in the absence of base station infrastructure. The HMDIC optimizer approach in this circumstance randomly begins every respective node to update and store their pbest value utilizing RAM dragonfly employing satellite images. The constraint values are then used to construct the cost function, which results in a strong kind of global optimum solution. The channels are therefore distributed in an effective manner. The HMDIC algorithm is used in this research to build a novel channel allocation system. It makes advantage of the exploration capabilities to successfully explore the individual node using MDA (Modified Dragonfly Algorithm) and locate the global best solution using imperialist competitive algorithm (ICA). Both of these combined tactics are more effective in accelerating the convergence of the allocation model. To validate the performance, the HMDIC‐based DDCA system provides promising results in terms of assigning available channels, thereby enhancing channel reuse efficiency and fractional interference.

Deep-UEO: Empowering medical image registration with hybrid strategy based on deep learning and united equilibrium optimizer

Traditional optimization methods encounter challenges in determining the optimal geometric transformation parameters and often lacking to achieve global optimum which leads to extended computational time required for convergence. To address this challenge, a dual-phase strategy is proposed for multimodal biomedical image registration by amalgamating the united equilibrium optimizer (UEO) with Visual Geometry Group (VGG-19). In first phase, the input images are aligned by identifying the optimal values of rigid transformation parameters through UEO, with mutual information (MI) maximization serving as an objective function. In second phase, VGG19 is employed to enhance the reliability of resulting registered image obtained through UEO optimization for extracting both the low- and high-level features of reference and target images. Following this, dynamic inlier selection is employed to refine the matching process, integrating expectation maximization for iterative updating of inlier selection, thus improving registration accuracy. Finally, thin plate spline interpolation is utilized to calculate the transformation matrix, ensuring precise alignment between reference and UEO registered image. Extensive experiments are conducted on multimodal medical images sourced from publicly available repositories and clinical(in-house) datasets to validate the reliability of the proposed scheme. These experiments showcase substantial improvements in RMSE from 22.21094 to 10.983654, SSIM from 0.7190832 to 0.8409321, PSNR from 30.176044 to 34.095471, and CC from 0.9304633 to 0.9809432, outperforming state-of-the-art methods.

Optimisation of multiple clustering based undersampling using artificial bee colony: Application to improved detection of obfuscated patterns without adversarial training

Attack detection is one of the main features required in modern defence systems. Despite the ongoing research, it remains challenging for a typical mechanism like network-based intrusion detection system (NIDS) to catch up with evolving adversarial attacks. They specifically aim to confuse a machine-learning based predictor. Without the knowledge of adversarial patterns, the best approach is generalising signatures learned from a dataset of legitimate connections and known intrusions. This work focuses on analysing non-payload traffics so that the resulting techniques can be exploited to a range of network-based applications. It investigates a novel means to deal with the problem of imbalanced classes. An optimised undersampling method is introduced to select a subset of majority-class representatives initially created through an ensemble clustering procedure. A weighted combination of criteria representing distributions within and between classes is proposed as the objective function for a global optimisation using the artificial bee colony (ABC). This approach usually outperforms its baselines and other state-of-the-art undersampling models, with ABC being more effective using the global best strategy than a random selection of solutions or an iterative greedy search. The paper also details the parameter analysis offering a heuristic guide for potential taking up of the proposed techniques.

Exploring interpretable evolutionary optimization via significance of each constraint and population diversity

Evolutionary algorithms (EAs) have been widely employed to solve complex constrained optimization problems (COPs). However, numerous EAs treat constraints as a collective black box, employing a uniform processing technique for all constraints. Generally, there exists variability in the significance of each constraint within COPs. To address this issue, this paper is the first attempt to investigate the significance of each constraint spontaneously during the evolution process, and then proposes a co-directed evolutionary algorithm (CdEA-SCPD) for exploring interpretable COPs. First, CdEA-SCPD develops an adaptive penalty function designed to assign different weights to constraints based on their violation severity, thereby varying the significance of each constraint to enhance interpretability and facilitate the algorithm to converge more rapidly toward the global optimum. In addition, a dynamic archiving strategy and a shared replacement mechanism are developed to improve the population diversity of CdEA-SCPD. Extensive experiments on benchmark functions from IEEE CEC2006, CEC2010, and CEC2017 and three engineering problems demonstrate the superiority of the proposed CdEA-SCPD compared to existing competitive EAs. Specifically, on the benchmark functions from IEEE CEC2010, the proposed method yields ρ values lower than 0.05 in the multiple-problem Wilcoxon's signed rank test and ranks first in the Friedman's test. Furthermore, ablation analysis and parameter analysis have demonstrated the beneficial effects of the proposed strategies.

A new filled function method for solving constrained global optimization problems

ABSTRACT Filled function methods have been considered as effective algorithms for solving global optimization problems. However, their effectiveness is greatly affected by the selection of parameters, the noncontinuous or non-differentiable properties of the constructed filled function. In addition, many of the constructed filled functions are only for unconstrained optimization problems, and they are unable to solve constrained optimization problems. In this paper, a new filled function is constructed for solving constrained global optimization problems. The new filled function has only one parameter which needs to be adjusted, and, when the objective functions and constrained functions are all continuously differentiable functions, the constructed filled function is also a continuously differentiable function. Then, the classical local optimization methods can be used to find a better minimum of the proposed filled function and a few parameter adjustments are needed. At last, a new filled function algorithm for constrained global optimization is developed based on the proposed filled function. The new algorithm is applied to several test examples. The results of the numerical experiments show that the new filled function algorithm is effective and efficient.

BA-CLM: A Globally Consistent 3D LiDAR Mapping Based on Bundle Adjustment Cost Factors

Constructing a globally consistent high-precision map is essential for the application of mobile robots. Existing optimization-based mapping methods typically constrain robot states in pose space during the graph optimization process, without directly optimizing the structure of the scene, thereby causing the map to be inconsistent. To address the above issues, this paper presents a three-dimensional (3D) LiDAR mapping framework (i.e., BA-CLM) based on LiDAR bundle adjustment (LBA) cost factors. We propose a multivariate LBA cost factor, which is built from a multi-resolution voxel map, to uniformly constrain the robot poses within a submap. The framework proposed in this paper applies the LBA cost factors for both local and global map optimization. Experimental results on several public 3D LiDAR datasets and a self-collected 32-line LiDAR dataset demonstrate that the proposed method achieves accurate trajectory estimation and consistent mapping.

MPPT control of photovoltaic array based on improved marine predator algorithm under complex solar irradiance conditions.

In practical engineering applications, factors like dust adhesion and environmental changes can cause photovoltaic arrays to exhibit multiple peaks in output power. An optimization algorithm with global optimization capability is needed to track its maximum power. In this regard, this paper proposes an improved marine predator algorithm (IMPA) to extract the maximum power point of photovoltaic system under complex solar irradiation conditions. To overcome the issues in the traditional marine predator algorithm (MPA), the opposition-based learning(OBL) strategy is introduced in IMPA, and the sine cosine algorithm (SCA) is integrated into the iteration stage to enhance the search ability of the algorithm. Furthermore, the low-order converter in the traditional MPPT control system is replaced by the Zeta converter, which increases the operating voltage range. Ultimately, simulation results demonstrate that the MPPT based on IMPA has higher tracking efficiency and shorter response time.The experimental results also indicate the practical feasibility of this method, as well as its high level of stability and robustness.

PSAO: An enhanced Aquila Optimizer with particle swarm mechanism for engineering design and UAV path planning problems

Metaheuristic algorithms have become increasingly significant in solving complex optimization problems. To address the limitations of the original Aquila Optimizer (AO), such as insufficient local exploitation ability, low optimization precision, and slow convergence rate, an enhanced Aquila Optimizer (PSAO) for global optimization has been proposed. PSAO uses a better ergodic good point set to initialize the Aquila population and modifies the search method by employing the golden sine operator and the mechanism of self-learning and social learning based on particle swarm, followed by designing a nonlinear balance factor γ as the switching condition of the algorithm. The simulation experiments on benchmark functions and CEC2017 functions have verified that the PSAO has better global optimization ability and stronger robustness compared with other intelligent algorithms. Meanwhile, the contribution of each component that belongs to PSAO has been validated by ablation experiments for the CEC2022 test functions. To further illustrate the practical application potential of PSAO, PSAO is successfully applied to four typical engineering design problems, three of which reach the best fitness values. In addition, utilizing PSAO to solve the UAV trajectory planning problem by considering the objectives of trajectory length, altitude and corner of the flight process, the total flight cost is reduced by 60.22 %, 27.94 %, and 22.41 % compared with AO, PSO and Gold-SA, respectively, which proves its applicability and superiority in solving the real optimization problems.

A novel multi-loss dynamic fusion-enhanced image segmentation model for welding spatter measurement

Quantitative detection of welding spatter is not only critical for evaluating and optimizing welding energy consumption, but also significant for improving welding quality. Inspired by the welder's visual estimation of the amount of spatter, a novel welding spatter measurement method based on image segmentation is proposed. Considering the challenges of welding spatter such as small object, aggregation, and unclear boundary, four loss functions, namely, Focal loss, Dice loss, Boundary loss, and Count loss, are designed to achieve global optimization for complex welding spatter. Furthermore, for the different spatter characteristics with different optimization difficulties, a multi-loss dynamic fusion method with differential optimization capability is designed. Finally, a welding spatter segmentation dataset is established and a comprehensive ablation and comparison test of the proposed methodology is carried out. The results indicate that the proposed method has an average F1-score metric of 83.52 % and a mean intersection over union metric of 75.11 %. These findings suggest the potential for vision-based quantitative estimation of welding spatter.

An ionic liquid in Core-shell structure: Halogen-free, metal-free bifunctional catalyst for olefin epoxidation and CO2 cycloaddition

We synthesized a core-shell resin structure with abundant architecture and excellent thermal stability through polymerization. Imidazole ionic liquid with varying carbon chain lengths was immobilized on the surface, resulting in the preparation of metal-free, halogen-free core-shell catalysts with different carbon chain lengths via HCO3- exchange. Characterization using FT-IR, XPS, and various techniques revealed the exceptional performance of our synthetic catalyst in terms of its internal structure. After extensive experimentation, we discovered that the synthesized catalyst exhibits dual functionality for epoxidation and CO2 cycloaddition reactions without requiring solvents or co-catalysts. The epoxidation system demonstrated remarkable conversion rates and selectivity while also exhibiting strong recyclability in heterogeneous reactions, according to kinetic parameters, the reaction order of styrene, TBHP and catalyst during epoxidation is approximately 1, the reference factor for this reaction was calculated to be 6.7×109 (L2·mol−2·min−1), with an activation energy of 48.9 kJ/mol obtained from analyzing reaction rates at different temperatures. In the CO2 cycloaddition reaction, our catalyst exhibited an advantage in catalyzing ring-opening reactions, achieving a conversion rate of 95 % for styrene oxide within six hours along with over 99 % selectivity towards cyclic carbonate formation. It is suggested that the epoxide reaction is carried out in steps, and it is inferred that the catalyst PS-ImC4HCO3 and TBHP are produced into peroxy intermediate active species TBA and HCO4, and the reaction between HCO4- and styrene is the determination step of the total reaction. The reaction rate constant k=0.009 of the absolute step is calculated based on the global optimization algorithm

Exploring effects of reactant’s chemical environments on adsorption and reaction mechanism by global optimization and IR spectrum calculation, using NO oxidation as a case

Reactant’s chemical environments derived from the type and concentration of surface species on heterogeneous catalysts do great effects on the structures of reactants and reaction pathway. Unraveling the arrangement of surface species and reaction mechanism at different reactant’s chemical environments in theory remains a substantial challenge due to the difficulty in obtaining the global stable configuration of surface species on catalysts and the lack of other theoretical evidences for the identification of surface species and reaction pathway. To solve this problem, a protocol combining the global optimization of surface species at different chemical environments with electronic structure analysis and IR spectrum calculation including frequency and intensity was proposed to unravel the arrangement of surface species and their reaction pathway in this work. Using NO adsorption and oxidation on Pt(111) and O-covered Pt(111) as a case, their stable structures were obtained by the global optimization; then the origins of the structural transformation of NO and NO2 at different environments were elucidated by electronic structure analysis; then the structures and reaction pathway were confirmed by comparing the experimental and calculated IR spectra of NO and NO2 with frequency and intensity. Utilizing this protocol, the stable structures of NO and its transformation, as well as oxidation pathway were revealed under varying chemical environments at the molecular level. It provides solid supports for identifying the stable arrangement of surface species, their transformation and reaction pathway, and can be easily extended to heterogeneous catalysis.

Vision-based autonomous robots calibration for large-size workspace using ArUco map and single camera systems

The low positioning accuracy of industrial robots limits their application in industry. Vision-based kinematic calibration, known for its rapid processing and economic efficiency, is an effective solution to enhance this accuracy. However, most of these methods are constrained by the camera's field of view, limiting their effectiveness in large workspaces. This paper proposes a novel calibration framework composed of monocular vision and computer vision techniques using ArUco markers. Firstly, a robot positioning error model was established by considering the kinematic error based on the Modified Denavit-Hartenberg model. Subsequently, a calibrated camera was used to create an ArUco map as an alternative to traditional single calibration targets. The map was constructed by stitching images of ArUco markers with unique identifiers, and its accuracy was enhanced through closed-loop detection and global optimization that minimizes reprojection errors. Then, initial hand-eye parameters were determined, followed by acquiring the robot's end-effector pose through the ArUco map. The Levenberg-Marquardt algorithm was employed for calibration, involving iterative refinement of hand-eye and kinematic parameters. Finally, experimental validation was conducted on the KUKA kr500 industrial robot, with laser tracker measurements as the reference standard. Compared to the traditional checkerboard method, this new approach not only expands the calibration space but also significantly reduces the robot's absolute positioning error, from 1.359 mm to 0.472 mm.

Variation-aware automated design and optimization of sub-1V bandgap voltage reference

A robust systematic gm/ID-based design procedure for a CMOS low-voltage bandgap reference is introduced. The proposed approach is technology node independent, and it eliminates invoking the simulator in the loop by using precomputed lookup tables (LUTs) generated once. The proposed methodology is capable of addressing the impact of PVT corners and random mismatch. The proposed procedure is verified against Spectre simulations and yields very accurate results. Moreover, the bandgap reference automated synthesis procedure is fully vectorized, enabling the concurrent synthesis of multiple design points in a short time. As a result, large datasets can be generated to span the whole design space, which enables global optimization. Next, local optimization algorithms can be utilized to quickly determine the degrees of freedom of an optimal design point that meets a set of specifications. The speedup of the proposed methodology is around 140x compared to simulation-based optimization in addition to accomplishing better results.