Local Optimization Research Articles

Omnidirectional AGV Path Planning Based on Improved Genetic Algorithm

To address the issues with traditional genetic algorithm (GA) path planning, which often results in redundant path nodes and local optima, we propose an Improved Genetic Algorithm that incorporates an ant colony algorithm (ACO). Firstly, a new population initialization method is proposed. This method adopts a higher-quality random point generation strategy to generate random points centrally near the start and end of connecting lines. It combines the improved ACO algorithm to connect these random points quickly, thus greatly improving the quality of the initial population. Secondly, path smoothness constraints are proposed in the adaptive function. These constraints reduce the large-angle turns and non-essential turns, improving the smoothness of the generated path. The algorithm integrates the roulette and tournament methods in the selection stage to enhance the searching ability and prevent premature convergence. Additionally, the crossover stage introduces the edit distance and a two-layer crossover operation based on it to avoid ineffective crossover and improve convergence speed. In the mutation stage, we propose a new mutation method and introduce a three-stage mutation operation based on the idea of simulated annealing. This makes the mutation operation more effective and efficient. The three-stage mutation operation ensures that the mutated paths also have high weights, increases the diversity of the population, and avoids local optimality. Additionally, we added a deletion operation to eliminate redundant nodes in the paths and shorten them. The simulation software and experimental platform of ROS (Robot Operating System) demonstrate that the improved algorithm has better path search quality and faster convergence speed. This effectively prevents the algorithm from maturing prematurely and proves its effectiveness in solving the path planning problem of AGV (automated guided vehicle).

Improved Crab Algorithm Based on Chaos Theory and Its Application in Parameter Identification of Fresnel Refractive Index

The Crab Algorithm is an evolutionary algorithm inspired by the behavior of crabs, which balances global search and local optimization by simulating the behavioral strategies of crabs. This paper introduces chaos theory and proposes a chaos disturbance strategy. Chaos variables are introduced into the iteration process of the Crab Algorithm to perturb the position and velocity of individuals, thereby escaping local optima and achieving global optimization. Several test functions are used to compare the new algorithm with the original one, yielding better results. In the application of parameter identification of Fresnel refractive index, an optimization model is constructed, and the improved Crab Algorithm is applied to solve the model. The experimental results show that compared with the traditional Crab Algorithm, the improved algorithm proposed in this paper has significantly improved identification accuracy and convergence speed. In addition, by comparing with other optimization algorithms, the superiority of this paper's algorithm is verified.

Short-term Electricity Load Forecasting Based on Improved Seagull Algorithm Optimized Gated Recurrent Unit Neural Network

INTRODUCTION: The complexity of the power network, changes in weather conditions, diverse geographical locations, and holiday activities comprehensively affect the normal operation of power loads. Power load changes have characteristics such as non stationarity, randomness, seasonality, and high volatility. Therefore, how to construct accurate short-term power load forecasting models has become the key to the normal operation and maintenance of power.OBJECTIVES: Accurate short-term power load forecasting helps to arrange power consumption planning, optimize power usage and largely reduce power system losses and operating costs.METHODS: A hybrid decomposition-optimization-integration load forecasting method is proposed to address the problems of low accuracy of current short-term power load forecasting methods.RESULTS: The original power load time series is decomposed using the complete ensemble empirical modal decomposition method, while the correlation of power load influencing factors is analysed using Pearson correlation coefficients. The seagull optimisation algorithm is overcome to fall into local optimality by using the random adaptive non-linear adjustment strategy of manipulated variables and the differential variational Levy flight strategy, which improves the search efficiency of the algorithm. Then, the The gated cyclic unit hidden layer parameters are optimised by the improved seagull optimisation algorithm to construct a short-term electricity load forecasting model.The effectiveness of the proposed method is verified by simulation experimental analysis. The results show that the proposed method has improved the accuracy of the forecasting model.CONCLUSION: The CEEMD method is used to decompose the original load time series, which improves the accuracy of the measurement model. The GRU prediction model based on improved SOA optimization not only has better prediction accuracy than other prediction models, but also consumes the least amount of time compared to other prediction models.

Hybrid Strategy Improved Beetle Antennae Search Algorithm and Application

The multi-dimensional optimization of mechanisms is a typical optimization problem encountered in mechanical design. Herein, the Hybrid strategy improved Beetle Antennae Search (HSBAS) algorithm is proposed to solve the multi-dimensional optimization problems encountered in structural design. To solve the problems of local optimization and low accuracy of the high-dimensional solution of the Beetle Antennae Search (BAS) algorithm, the algorithm adopts the adaptive step strategy, multi-directional exploration strategy, and Lens Opposition-Based Learning strategy, significantly reducing the probability of the algorithm falling into the local optimum and improving its global search capability. Comparative experiments of the improved algorithm are carried out by selecting eleven benchmark test functions. HSBAS can reach 1 × 10−22 accuracy from the optimal value when dealing with low-dimensional functions. It can also obtain 1 × 10−2 accuracy when dealing with high-dimensional functions, significantly improving the algorithm’s capability. According to Friedman’s ranking test result, HSBAS ranks first, which proves that HSBAS is superior to the other three algorithms. The HSBAS algorithm is further used to optimize the design of the altitude compensation module of the gravity compensation device for solar wings, controlling the fluctuation of bearing capacity within 0.25%, which shows that the algorithm can be used as an effective tool for engineering structural optimization problems.

Application of Big Data Analysis and Intelligent Algorithm in Power System Operation Optimization

The power communication system provides powerful technical support for realizing the intelligent operation and information management of the power grid and improving the operation efficiency and power supply quality of the power grid. Quantum key distribution (QKD) is considered one of the most promising technologies for commercialization. QKD uses a single photon to encrypt data to produce a more secure and reliable password. This paper intends to study the hierarchical, centralized control architecture of power dispatching based on quantum essential supply (QKD). The performance indexes of MDI-QKD under symmetric and asymmetric conditions were studied by local optimization. The optimal key formation rate of the algorithm is analyzed. From the perspective of quantum critical utilization, a quantum key utilization scheme for grid backbone dispatching service is proposed. The dynamic adjustment test of multi-node time slot and service key update rate is carried out. Experiments show that the MDI scheme can effectively improve the effectiveness of a multi-node QKD system. Thus, the security of data transmission of the core business of power dispatching data networks can be ensured to the greatest extent. AMDI can effectively reduce the transmission timeout of low-priority data streams because the delay of high-priority data streams reaches the proportion. It can be an excellent solution to the power system and the password requirements.

Optimisation-based strategies to identification of material parameters of hygro-thermo-mechanical problems

Applications involving hygro-thermo-mechanical models are found in engineering fields as diverse as electronic packaging, concrete structures, composite materials and wood structures. Within the framework of wood processing, hygro-thermo-mechanical problems account for the cross-dependence of moisture content, temperature and dimensional changes of wooden components. Numerical simulation plays an important role in predicting the behaviour of wooden structures and, therefore, use of appropriate process and material parameters is essential for a successful prediction. The present work is inserted in this context and addresses application of optimisation techniques to identification of heat transfer, moisture diffusion and moisture-dependent swelling/shrinkage parameters. The direct problem is formulated based on a fully coupled transient solution of the energy, moisture and momentum transfer conservation laws. A global–local hybrid optimisation technique is proposed combining swarm intelligence and deterministic approaches, respectively based on particle swarm optimisation and the Nelder–Mead (NM) technique. The first stage aims at reducing the search space, so that the NM algorithm is able to determine the global minimum with acceptable accuracy. The strategy is both robust and efficient, being able to avoid local minima with reduced number of fitness computations. An illustrative example featuring a moisture-gaining process is also discussed, in which special attention is placed on the elastic and material swelling coupled effects caused by moisture transfer.

A Resampling Ant Colony Optimization with Elite Exploration and Convergence Mechanism for Multithreshold Segmentation of Breast Cancer Images

Previous studies have emphasized the potential of threshold image segmentation for early breast cancer detection. However, traditional methods encounter challenges regarding low segmentation efficiency and accuracy. Addressing this, the ant colony optimization algorithm for continuous optimization (ACOR) shows promise. Yet, existing ACOR variants still grapple with poor initial population quality, affecting convergence speed and avoiding local optimization. These issues impact segmentation efficiency and accuracy. To tackle them, this study introduces RESACO, an enhanced ACOR version integrating three novel optimization strategies: resampling initialization (RIS), elite exploration (EES), and strengthened convergence mechanism (SCM). RIS enhances initial population quality by resampling regions with individuals demonstrating superior fitness and segmentation efficiency. EES promotes exploration across the search space, preventing local optima entrapment and enhancing model stability. SCM expediting convergence, segmentation efficiency, and precision. RESACO's performance is assessed through extensive experiments using IEEE CEC 2014 and IEEE CEC 2022 benchmark functions, including ablation experiments and comparisons with basic and improved algorithms and ACOR variants. Subsequently, the threshold image segmentation model based on RESACO is compared with other models using metaheuristic algorithms for segmenting realistic breast cancer medical images. Results demonstrate the proposed model's faster convergence and higher segmentation accuracy, preserving more lesion tissue details.

Certifying Optimality of Bell Inequality Violations: Noncommutative Polynomial Optimization through Semidefinite Programming and Local Optimization

Certifying Optimality of Bell Inequality Violations: Noncommutative Polynomial Optimization through Semidefinite Programming and Local Optimization

Local and global optimization methods for optical line control based on quality of transmission

The ever-increasing demand for data traffic in recent decades has pushed network operators to give importance to the aspect of infrastructure control to facilitate its scalability and maximize its capacity. A generic lightpath (LP) is deployed starting from a traffic request between a given pair of nodes in a network. LPs are operated in the network based on an estimate of the quality of transmission (QoT), which is derived from the physical layer characteristics of a selected route. Regardless of the model used to estimate QoT, it is necessary to calibrate the model to maximize its accuracy and define minimum design margins. The model calibration process depends significantly on the type of data that can be collected in the field (i.e., type of metric, resolution) and therefore on the available monitoring devices. In this work, a systematic evaluation of the QoT estimation is carried out on a multi-span erbium-doped-fiber-amplified optical line system (OLS) using in the first case only total power monitors and in the second experimentally emulating optical channel monitors (OCMs). Given the type of monitoring devices available, three different physical models are calibrated, and six optimization methods are used to define the optimal configuration of the target gain and tilt parameters of the optical amplifiers, jointly optimizing the working point of all amplifiers (global approach) or proceeding span by span (local approach). Subsequently, the OLS was set in each configuration obtained, and the generalized signal-to-noise ratio (GSNR) profile was measured at the end.

Local Bayesian optimization for controller tuning with crash constraints

Abstract Controller tuning is crucial for closed-loop performance but often involves manual adjustments. Although Bayesian optimization (BO) has been established as a data-efficient method for automated tuning, applying it to large and high-dimensional search spaces remains challenging. We extend a recently proposed local variant of BO to include crash constraints, where the controller can only be successfully evaluated in an a-priori unknown feasible region. We demonstrate the efficiency of the proposed method through simulations and hardware experiments. Our findings showcase the potential of local BO to enhance controller performance and reduce the time and resources necessary for tuning.

HVAC energy cost minimization in smart grids: A cloud-based demand side management approach with game theory optimization and deep learning

In this paper, we present a novel cloud-based demand side management (DSM) optimization approach for the cost reduction of energy usage in heating, ventilation and air conditioning (HVAC) systems in residential homes at the district level. The proposed approach achieves optimization through scheduling of HVAC energy usage within permissible bounds set by house users. House smart home energy management (SHEM) devices are connected to the utility/aggregator via a dedicated communication network that is used to enable DSM. Each house SHEM can predict its own HVAC energy usage for the next 24 h using minimalistic deep learning (DL) prediction models. These predictions are communicated to the aggregator, which will then do day ahead optimizations using the proposed game theory (GT) algorithm. The GT model captures the interaction between aggregator and customers and identifies a solution to the GT problem that translates into HVAC energy peak shifting and peak reduction achieved by rescheduling HVAC energy usage. The found solution is communicated by the aggregator to houses SHEM devices in the form of offers via DSM signals. If customers’ SHEM devices accept the offer, then energy cost reduction will be achieved. To validate the proposed algorithm, we conduct extensive simulations with a custom simulation tool based on GridLab-D tool, which is integrated with DL prediction models and optimization libraries. Results show that HVAC energy cost can be reduced by up to 36% while indirectly also reducing the peak-to-average (PAR) and the aggregated net load by up to 9.97%.

A recursive framework for improving the performance of multi-objective differential evolution algorithms for gene selection

Gene selection is a pivotal process in machine-learning-driven medical diagnostics, where the goal is to identify a subset of genes from microarray expression profiles that can enhance the predictive accuracy of classifiers for disease diagnosis. The two key objectives of gene selection are to reduce the dimensionality of the data and to improve the accuracy of disease diagnosis, which is typically a multi-objective optimization problem. In recent years, multi-objective evolutionary algorithms (MOEAs) have gained wide attention in feature selection research, and several related algorithms have been produced. However, most algorithms tend to get stuck in local optimality when searching for solutions from a high-dimensional space. To solve the gene selection problem effectively, this study introduces a recursive multi-objective differential evolution algorithm with elite recursive strategy (RMODE-E) and a recursive multi-objective differential evolution algorithm with Pareto front recursive strategy (RMODE-P). RMODE-E amalgamates the features selected by the top E elite individuals, RMODE-P consolidates the features selected by the Pareto front set, and the combined features then serve as the foundation for subsequent recursive rounds of searching. The proposed feature subspace combination strategy not only reduces the recursive search space but also improves the capacity to find globally optimal feature subsets. Extensive experiments were conducted to compare our proposed algorithms with eight state-of-the-art evolutionary algorithms to validate their effectiveness. Experimental results demonstrate that RMODE-P has better global search capability as it achieves better best classification accuracy, mean classification accuracy, and minimal gene subset size.

Design of fruit fly optimization algorithm based on Gaussian distribution and its application to image processing

The Fruit Fly Optimization Algorithm (FOA) has strong applicability, which can be optimized directly after the objective is determined not by building a complex model. Due to the problems of the algorithm such as easy prematureness, low solution accuracy, and easy to fall into local optimality. Therefore, the Gaussian Distribution Fruit Fly Optimization Algorithm (GaussFOA) based on Gaussian distribution was first proposed to solve the shortcomings of FOA. Then GaussFOA was applied to image segmentation processing. Finally, the experimental results were compared with FOA, the improved Fruit Fly Optimization Algorithm with Changing Step and Strategy (CSSFOA), and the Linear Generation Mechanism of Candidate Solution of Fruit Fly Optimization Algorithm (LGMSFOA). The results showed that GaussFOA had 100 % success rate compared with FOA, CSSFOA, and LGMSFOA under the same function. This algorithm also had the best finding mean and standard deviation. The low and high threshold division was compared in terms of the number of segmentation thresholds. The GaussFOA had the best value of both the average and the standard deviation of the search for merit. The segmentation results under high threshold were more obvious when compared with the segmentation results of low threshold GaussFOA. The image immunity of GaussFOA was 8.57 %, 10 %, and 29.97 % higher than that of FOA, Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). This indicated that the model constructed based on GaussFOA had improved the image segmentation effect and stability compared with other algorithms. The findings of the research can offer a new path for the processing techniques of images.

Two-stream neural network with different gradient update strategies

Deep neural networks will be affected by various noises in different scenes. Traditional deep neural networks often use gradient descent algorithms to update parameter weights. When the gradient falls to a certain range, it is easy to fall into the local optimal solution. Although the impulse method and other methods can escape from local optimization in some scenarios, they still have some limitations, which will greatly reduce the application effect of the actual scenes. To solve the above problems, a two-stream neural network with different gradient update strategies was proposed. Combined with the gradient ascent algorithm, this method alleviated the disadvantage of deep neural networks falling into local optimality and increased the robustness of neural networks to a certain extent. The experimental results on the CIFAR10 dataset verify that the proposed method can improve the accuracy of various gradient descent optimizers by about 1%, such as SGD, Adagrad, RM-Sprop and Adam. The experimental results on the COCO dataset show that the accuracy of the proposed method is also improved compared with the baseline models PAA and EfficientDet. The method proposed can be widely used in various neural network structures and has good practical significance and application prospects.

Tailoring Localized Electrolyte via a Dual-Functional Protein Membrane toward Stable Zn Anodes.

Considerable attention has been by far paid to stabilizing metallic Zn anodes, where side reactions and dendrite formation still remain detrimental to their practical advancement. Electrolyte modification or protected layer design is widely reported; nonetheless, an effective maneuver to synergize both tactics has been rarely explored. Herein, we propose a localized electrolyte optimization via the introduction of a dual-functional biomass modificator over the Zn anode. Instrumental characterization in conjunction with molecular dynamics simulation indicates local solvation structure transformation owing to the limitation of bound water with intermolecular hydrogen bonds, effectively suppressing hydrogen evolutions. Meanwhile, the optimized nucleation throughout the protein membrane allows uniform Zn deposition. Accordingly, the symmetric cell exhibits an elongated lifespan of 3280 h at 1.0 mA cm-2/1.0 mAh cm-2, while the capacity retention of the full cell sustains 91.1% after 2000 cycles at 5.0 A g-1. The localized electrolyte tailoring via protein membrane introduction might offer insights into operational metal anode protection.

Dense Contrastive-based Federated Learning for Dense Prediction Tasks on Medical Images.

Deep learning (DL) models have achieved remarkable success in various domains. But training an accurate DL model requires large amounts of data, which can be challenging to obtain in medical settings due to privacy concerns. Recently, federated learning (FL) has emerged as a promising solution that shares local models instead of raw data. However, FL in medical settings faces challenges of client drift due to the data heterogeneity across dispersed institutions. Although there exist studies to address this challenge, they mainly focus on the classification tasks that learn global representation of an entire image. Few have been studied on the dense prediction tasks, such as object detection. In this study, we propose dense contrastive-based federated learning (DCFL) tailored for dense prediction tasks in FL settings. DCFL introduces dense contrastive learning to FL, which aligns the local optimization objectives towards the global objective by maximizing the agreement of representations between the global and local models. Moreover, to improve the performance of dense target prediction at each level, DCFL applies multi-scale contrastive representation by utilizing multi-scale representations with dense features in contrastive learning. We evaluated DCFL on a set of realistic datasets for pulmonary nodule detection. DCFL demonstrates an overall performance improvement compared with the other federated learning methods in heterogeneous settings-improving the mean average precision by 4.13% and testing recall by 6.07% in highly heterogeneous settings.

Gorilla optimization algorithm combining sine cosine and cauchy variations and its engineering applications

To address the issues of lacking ability, loss of population diversity, and tendency to fall into the local extreme value in the later stage of optimization searching, resulting in slow convergence and lack of exploration ability of the artificial gorilla troops optimizer algorithm (AGTO), this paper proposes a gorilla search algorithm that integrates the positive cosine and Cauchy's variance (SCAGTO). Firstly, the population is initialized using the refractive reverse learning mechanism to increase species diversity. A positive cosine strategy and nonlinearly decreasing search and weight factors are introduced into the finder position update to coordinate the global and local optimization ability of the algorithm. The follower position is updated by introducing Cauchy variation to perturb the optimal solution, thereby improving the algorithm's ability to obtain the global optimal solution. The SCAGTO algorithm is evaluated using 30 classical test functions of Test Functions 2018 in terms of convergence speed, convergence accuracy, average absolute error, and other indexes, and two engineering design optimization problems, namely, the pressure vessel optimization design problem and the welded beam design problem, are introduced for verification. The experimental results demonstrate that the improved gorilla search algorithm significantly enhances convergence speed and optimization accuracy, and exhibits good robustness. The SCAGTO algorithm demonstrates certain solution advantages in optimizing the pressure vessel design problem and welded beam design problem, verifying the superior optimization ability and engineering practicality of the SCAGTO algorithm.

Local-coordinated voltage optimal control strategy of integrated PV and ESS system in distribution networks with π-type network simplification

With the increasing penetration of distributed photovoltaics (PVs) in distribution networks (DNs), the voltage violation and PV absorption difficulty have become great challenges for the security operation of DNs. In this context, the integrated photovoltaic and energy storage system (PESS) plays an increasingly important role in enhancing the DNs operational security and economy with its capability of flexible power regulation and energy transfer. Considering the incomplete online measurement of DNs, this paper proposes a local-coordinated voltage optimal control strategy that combines local optimization and real-time coordinated control. Firstly, a new π-type network simplification method is proposed, which is less demanding on the measurement device and doesn't add new virtual nodes for the convenience of modeling. Then the local rolling optimization model for PESS is established based on simplified network to solve the optimal local instruction, which only uses the measurement data of local node and the slack bus rather than global measurement. Next, a real-time coordinated voltage control method based on network loss sensitivity is proposed to eliminate voltage violation caused by random fluctuations of PV and load power. Finally, the IEEE 33-bus system and modified 123-bus system are used to verify the effectiveness and accuracy of the proposed voltage optimal control strategy, which is also compared with centralized optimization method with global measurement. The simulation results indicate that the proposed control strategy can effectively solve the voltage violation and improve the system operation economy, especially only relying on partial measurement.

Was There a 3.5 keV Line?

The 3.5 keV line is a purported emission line observed in galaxies, galaxy clusters, and the Milky Way whose origin is inconsistent with known atomic transitions and has previously been suggested to arise from dark matter decay. We systematically reexamine the bulk of the evidence for the 3.5 keV line, attempting to reproduce six previous analyses that found evidence for the line. We only reproduce one of the analyses; in the other five, we find no significant evidence for a 3.5 keV line when following the described analysis procedures on the original data sets. For example, previous results claimed 4σ evidence for a 3.5 keV line from the Perseus cluster; we dispute this claim, finding no evidence for a 3.5 keV line. We find evidence for background mismodeling in multiple analyses. We show that analyzing these data in narrower energy windows diminishes the effects of mismodeling but returns no evidence for a 3.5 keV line. We conclude that there is little robust evidence for the existence of the 3.5 keV line. Some of the discrepancy of our results from those of the original works may be due to the earlier reliance on local optimizers, which we demonstrate can lead to incorrect results. For ease of reproducibility, all code and data are publicly available.

Continuous Space Wireless Communication Tower Placement by Hybrid Simulated Annealing

Wireless communication tower placement arises in many real-world applications. This paper investigates a new emerging wireless communication tower placement problem, namely, continuous space wireless communication tower placement. Unlike existing wireless communication tower placement problems, which are discrete computational problems, this new wireless communication tower placement problem is a continuous space computational problem. In this paper, we formulate the new wireless communication tower placement problem and propose a hybrid simulated annealing algorithm that can take advantage of the powerful exploration capacity of simulated annealing and the strong exploitation capacity of a local optimization procedure. We also demonstrate through experiments the effectiveness of this hybridization technique and the good performance and scalability of the hybrid simulated annulling in this paper.