Global Search Research Articles

Optimal control design for power system stabilizer using a novel crow search algorithm dynamic approach

This paper proposes the first application of a novel improved metaheuristic optimization technique, dynamic crow search algorithm (DCSA) to find the optimal design of the power system stabilizer (PSS). Using the novel DCSA adaptive approach provides a global search capability as well as fastness and effectiveness to get the PSS best parameters under the search space constraints and local optimums. Moreover, PSS parameters has a crucial effect on power system stability that is a major nowadays engineering concern, where its best parameters could make a difference by damping small-signal stability oscillations effectively and in the smallest amount of time if they are obtained through a robust algorithm. As a result, PSS will prevent large types of instabilities which could reach up to some generation stations shedding. The novel proposed algorithm is for first time used and tested under different power system instabilities scenarios by using single-machine infinity bus model over multiple operating conditions to damp out perturbation signals effectively by means of getting optimal PSS design parameters, where Integral absolute error (IAE) performance index is used as an objective function to be minimized. Additionally, results obtained by the proposed approach are compared with those obtained by other methods. It is observed that the proposed method has better convergence characteristics and robustness compared to the original version and other comparison methods. It is revealed that the proposed adaptive method is able to improve the power system stability dynamics and damp out perturbation oscillations successfully.

Improved Sparrow Search Algorithm for Rectangular Planar Array Synthesis

To optimize sparse rectangular planar array antennas under multiple constraints, including array aperture, number of elements, and minimum element spacing, an improved sparrow search algorithm (ISSA) is proposed. Initially, a position distribution matrix is generated using the Blackman window weighting method. The sparrow search algorithm (SSA) is then enhanced by incorporating Kent mapping for population initialization, which improves the initial population’s diversity. Additionally, the strategy for updating the discoverer’s position integrates elements from the sine cosine algorithm (SCA), along with a nonlinear sine learning factor, thereby enhancing global search capability. Finally, a crossover strategy is embedded into the SSA to refine the optimization accuracy by improving the search methodology used by the vigilantes. To verify the efficacy of our approach, we carried out simulation experiments. The results demonstrate that this method significantly enhances the performance of the array antenna by reducing the peak sidelobe level and minimizing the zero-trap depth. These findings validate the reliability and effectiveness of the suggested method.

GP4ESP: a hybrid genetic algorithm and particle swarm optimization algorithm for edge server placement

Edge computing has attracted wide attention due to its ultra-low latency services, as well as the prevalence of smart devices and intelligent applications. Edge server placement (ESP) is one of the key issues needed to be addressed for effective and efficient request processing, by deciding which edge stations to equip with limited edge resources. Due to NP-hardness of ESP, some works have designed meta-heuristic algorithms for solving it. While these algorithms either exploited only one kind of meta-heuristic search strategies or separately perform two different meta-heuristic algorithms. This can result in limit performance of ESP solutions due to the “No Free Lunch” theorem. In addition, existing algorithms ignored the computing delay of edge servers (ESs) on request process, resulting in overestimation of the service quality. To address these issues, in this article, we first formulate ESP problem with the objective of minimizing the overall response time, considering heterogeneous edge servers with various service capacity. Then, to search effective or even the best ESP solutions, we propose a hybrid meta-heuristic algorithm (named GP4ESP) by taking advantage of both the powerful global search ability of genetic algorithm (GA) and the fast convergence of particle swarm optimization (PSO). GP4ESP effectively fuses the merits of GA and PS by integrating the swarm cognition of PSO into the evolutionary strategy of GA. At last, we conducted extensive simulation experiments to evaluate the performance of GP4ESP, and results show that GP4ESP achieves 18.2%–20.7% shorter overall response time, compared with eleven up-to-date ESP solving algorithms, and the performance improvement is stable as the scale of ESP is varied.

Efficient Productivity-Aware Control Parameter Optimization in Cutter Suction Dredger Construction Using Machine Learning with Parallel Global Search

This paper proposes an efficient productivity-aware optimization framework that utilizes hybrid machine learning with parallel global search to timely and appropriately adjust the critical control parameters (CCPs) of a cutter suction dredger (CSD) during construction. This optimization framework consists of three main parts. First, a hybrid Jaya–multilayer perceptron (MLP) algorithm was developed to rapidly construct a model that captures the interaction between construction parameters and slurry concentration. Next, the preliminary coarse results for the CCPs are determined through multi-parameter sensitivity analysis. Finally, the proposed resilient-zone parallel global search algorithm was employed to further optimize the CCPs, yielding more precise optimization results. To validate the proposed optimization framework and implement the in-situ service, it is applied to a real-world case study involving “Tianda” CSD construction. The results demonstrated that the average optimization duration is 6.7 s, which is shorter than the data acquisition interval of 8 s. Our approach improves the computational efficiency by 9.4 times compared with traditional optimization control methods. Additionally, there is a significant increase in the slurry concentration, with the maximum growth rate reaching 81.64%.

Research on hybrid strategy Particle Swarm Optimization algorithm and its applications

The increasing complexity and high-dimensional nature of real-world optimization problems necessitate the development of advanced optimization algorithms. Traditional Particle Swarm Optimization (PSO) often faces challenges such as local optima entrapment and slow convergence, limiting its effectiveness in complex tasks. This paper introduces a novel Hybrid Strategy Particle Swarm Optimization (HSPSO) algorithm, which integrates adaptive weight adjustment, reverse learning, Cauchy mutation, and the Hook-Jeeves strategy to enhance both global and local search capabilities. HSPSO is evaluated using CEC-2005 and CEC-2014 benchmark functions, demonstrating superior performance over standard PSO, Dynamic Adaptive Inertia Weight PSO (DAIW-PSO), Hummingbird Flight patterns PSO (HBF-PSO), Butterfly Optimization Algorithm (BOA), Ant Colony Optimization (ACO), and Firefly Algorithm (FA). Experimental results show that HSPSO achieves optimal results in terms of best fitness, average fitness, and stability. Additionally, HSPSO is applied to feature selection for the UCI Arrhythmia dataset, resulting in a high-accuracy classification model that outperforms traditional methods. These findings establish HSPSO as an effective solution for complex optimization and feature selection tasks.

A multi-strategy improved sparrow search algorithm for indoor AGV path planning

To address the problems of weak search ability, easily falling into local optimal solutions and poor path quality of sparrow search algorithm in AGV path planning, a multi-strategy improved sparrow search algorithm (MISSA) is proposed in this paper. MISSA improves the global search ability by improving the discoverer position update operator and introducing the sine cosine algorithm; adopts the adaptive number of vigilantes and adaptive adjustment step size to improve the convergence speed; introduces the Levy flight variation strategy to reduce the probability of falling into any local optimal solution; optimizes the boundary handling mechanism to prevent the loss of population diversity at a later stage; finally, uses the large-scale neighborhood search strategy and path smoothing mechanism for path optimization to further improve the path quality. The superiority-seeking ability of MISSA was verified by 12 standard test functions, and then 30 simulation experiments were conducted in grid maps with two specifications of 20×20 and 30×30. The experimental results showed that, by using MISSA, the path length was reduced by 44.1% and 63.1%, the number of turns was reduced by 68.4% and 78.4%, and the risk degree was reduced by 61.3% and 77.2%, which verifies the superiority of MISSA in path planning. Finally, MISSA was ported to the QBot2e mobile robot for physical verification to prove its feasibility in practical applications.

CDO-TCN-BiGRU: An Optimized Hybrid Deep Learning Model for Shared Bicycles Demand Forecasting

<div>Accurate prediction of the demand for shared bicycles is not only conducive to the operation of relevant enterprises, but also conducive to improving the image of the city, facilitating people’s travel, and solving the balance between supply and demand of bicycles in the region. To precisely predict the demand of shared bicycles, a model combining temporal convolution network (TCN) and bidirectional gating recurrent unit (BiGRU) model is proposed, and the Chernobyl disaster optimizer (CDO) is used to optimize its hyperparameters. It has the ability of TCN to extract sequence features and gated recurrent unit (GRU) to mine time series data and combine the characteristics of CDO with fast convergence and high global search ability, so as to reduce the influence of model hyperparameters. This article selects the shared bicycles travel data in Washington, analyzes its multi-characteristics, and trains it as the input characteristics of the model. In the experiments, we performed comparison study and ablation study. The results show that the prediction error of the proposed model is less than other comparative models. Therefore, CDO-TCN-BiGRU model has the characteristics of high prediction precision and good stability.</div>

A Coupled Model for Forecasting Spatiotemporal Variability of Regional Drought in the Mu Us Sandy Land Using a Meta-Heuristic Algorithm

Vegetation plays a vital role in terrestrial ecosystems, and droughts driven by rising temperatures pose significant threats to vegetation health. This study investigates the evolution of vegetation drought from 2010 to 2024 and introduces a deep-learning-based forecasting model for analyzing regional spatial and temporal variations in drought. Extensive time-series remote-sensing data were utilized, and we integrated the Temperature–Vegetation Dryness Index (TVDI), Drought Severity Index (DSI), Evaporation Stress Index (ESI), and the Temperature–Vegetation–Precipitation Dryness Index (TVPDI) to develop a comprehensive methodology for extracting regional vegetation drought characteristics. To mitigate the effects of regional drought non-stationarity on predictive accuracy, we propose a coupling-enhancement strategy that combines the Whale Optimization Algorithm (WOA) with the Informer model, enabling more precise forecasting of long-term regional drought variations. Unlike conventional deep-learning models, this approach introduces rapid convergence and global search capabilities, utilizing a sparse self-attention mechanism that improves performance while reducing model complexity. The results demonstrate that: (1) compared to the traditional Transformer model, test accuracy is improved by 43%; (2) the WOA–Informer model efficiently handles multi-objective forecasting for extended time series, achieving MAE (Mean Absolute Error) ≤ 0.05, MSE (Mean Squared Error) ≤ 0.001, MSPE (Mean Squared Percentage Error) ≤ 0.01, and MAPE (Mean Absolute Percentage Error) ≤ 5%. This research provides advanced predictive tools and precise model support for long-term vegetation restoration efforts.

Pixel-level Terrain Processing of the Martian Surface from Moderate Resolution Images of Tianwen-1

The moderate-resolution camera (MoRIC) of the Tianwen-1 orbiter, China’s first Mars exploration mission, is a frame-imaging scheme color camera. However, generating a precise digital elevation model (DEM) from these images faces challenges due to the presence of featureless and repetitive regions on the Martian surface. Traditional photogrammetric methods often struggle to achieve pixel-level resolution in these areas. To address this, we employ a shape from shading (SFS) algorithm that leverages shading patterns to reconstruct fine-scale terrain details, optimizing photogrammetric initial DEM. This paper presents a novel global bisection search algorithm, based on the Pearson correlation coefficient to determine the smoothing parameter, a crucial element in the SFS process. Comparisons with higher-resolution data from the Mars Express high-resolution stereo camera and Mars Reconnaissance Orbiter CTX images validate our approach, highlighting the potential of SFS to significantly enhance the scientific value of Tianwen-1 MoRIC data by generating high-resolution, three-dimensional maps of Mars.

Multi‐Objective Optimization and Experimental Research of Ship Form Based on Improved Bare‐Bones Multi‐Objective Particle Swarm Optimization Algorithm

ABSTRACTShip form optimization poses a complex and high‐dimensional engineering challenge. Therefore, when conducting multi‐objective optimization research of ship forms, traditional intelligent optimization algorithms are prone to falling into local optima solution and difficult to converge. In order to effectively improve the diversity and convergence performance of the algorithm, this paper improves the bare‐bones multi‐objective particle swarm optimization (BBMOPSO) algorithm by dynamically adjusting the local and global search step sizes, and verifies the algorithm's reliability through standard function testing. Then, a multi‐objective optimization design framework with high efficiency and high integration is constructed. Taking DTMB 5512 as the research case, Free Form Deformation (FFD) method is used for hull deformation, and the proposed algorithm is used for multi‐objective optimization of resistance performance and motion response. Ship model tests were conducted on the DTMB 5512's original hull. And the numerical simulations were compared with the ship model tests. Finally, under the constructed multi‐objective optimization design framework, satisfactory solutions were obtained through the improved algorithm, which confirms the effectiveness and practicality of the improved algorithm. The results show that the algorithm improved in this paper can provide some theoretical basis and technical support for green ship design and low‐carbon shipping.

Dynamic motion based evolutionary algorithm for enhancement of the search capability for global search space

Dynamic motion based evolutionary algorithm for enhancement of the search capability for global search space

A Passive Defensive Tactic in Target‐Attacker‐Defenders Game Against Fast Attacker

ABSTRACTThe target‐attacker‐defenders game (TADs) is an important topic in multi‐agent systems, concerning group cooperation and decision‐making. To effectively counter fast attackers, this study introduces a passive defensive approach, where a group of defenders position themselves around a formation point, rather than intercept the attacker actively. An evaluation model is developed to determine the optimal formation point, and a comprehensive analysis of the model is conducted, demonstrating that the formation point changes at a slower rate than the attacker, allowing the defenders to effectively track the attacker's movements. To enhance the global search capabilities, a backbone exchange mechanism is integrated into the original pigeon‐inspired optimization (PIO) algorithm. Finally, a series of comparative experiments are conducted, providing a comprehensive assessment of the defense strategy and the improved algorithm. The simulation results indicate the defenders can block the attacker with a slower speed following the proposed strategy, while the UAVs using the comparison method fail to follow the attacker, and the attacker escape the encirclement of defenders. The results prove the effectiveness of the proposed passive method in the article.

An Improved Cuckoo Search Algorithm and Its Application in Robot Path Planning

This manuscript introduces an improved Cuckoo Search (CS) algorithm, known as BASCS, designed to address the inherent limitations of CS, including insufficient search space coverage, premature convergence, low search accuracy, and slow search speed. The proposed improvements encompass four main areas: the integration of tent chaotic mapping and random migration in population initialization to reduce the impact of random errors, the guidance of Levy flight by the directional determination strategy of the Beetle Antennae Search (BAS) algorithm during the global search phase to improve search accuracy and convergence speed, the adoption of the Sine Cosine Algorithm for local exploitation in later iterations to enhance local optimization and accuracy, and the adaptive adjustment of the step-size factor and elimination probability throughout the iterative process to convergence. The performance of BASCS is validated through ablation experiments on 10 benchmark functions, comparative experiments with the original CS and its four variants, and application to a robot path planning problem. The results demonstrate that BASCS achieves higher convergence accuracy and exhibits faster convergence speed and superior practical applicability compared to other algorithms.

Improvement of Criminisi’s Stripe Noise Suppression Method for Side-Scan Sonar Images

In response to the problem of stripe noise significantly reducing the clarity and details of side-scan sonar images due to various factors, the authors of this paper propose an improved Criminisi method for stripe noise suppression. To address the issues encountered in the Criminisi algorithm during the suppression of stripe noise in side-scan sonar images, the following steps are suggested: firstly, introduce dynamic weights in the priority calculation to adaptively adjust the confidence and data term weights based on the current patch’s texture complexity; secondly, utilize the Sobel operator in the data term calculation to capture the image edge information more accurately; and, thirdly, optimize the matching block search process by introducing the Manhattan distance in addition to the Sum of Squared Differences (SSD) criterion to further select the best matching block while transitioning from a global search to a local search. Experimental validation was conducted using simulated stripe noise images, comparing the proposed method with four traditional denoising techniques. The results demonstrate that the denoising effectiveness of the proposed method is superior, effectively restoring texture in noisy regions while preserving texture structure integrity. Ablation experiments validate the effectiveness of the proposed improvements. Denoising experiments on real noisy images show satisfactory results with this method, and combining it with Fourier transform for additional smoothing in certain cases may yield even better results.

Application of spiral enhanced whale optimization algorithm in solving optimization problems

The Whale Optimization Algorithm (WOA) is regarded as a classic metaheuristic algorithm, yet it suffers from limited population diversity, imbalance between exploitation and exploration, and low solution accuracy. In this paper, we propose the Spiral-Enhanced Whale Optimization Algorithm (SEWOA), which incorporates a nonlinear time-varying self-adaptive perturbation strategy and an Archimedean spiral structure into the original WOA. The Archimedean spiral structure enhances the diversity of the solution space, aiding the algorithm in escaping local optima. The nonlinear time-varying self-adaptive optimization dynamic perturbation strategy improves the algorithm’s local search capability and enhances solution accuracy. The effectiveness of the proposed algorithm is validated from multiple perspectives using CEC2014 test functions, CEC2017 test functions, and 23 benchmark test functions. The experimental results demonstrate that the enhanced Whale Optimization Algorithm significantly improves population diversity, balances global and local search, and enhances solution accuracy. Additionally, SEWOA exhibits excellent performance in solving three engineering design problems, showcasing its value and wide range of potential applications.

Integrating reinforcement learning and supervisory control theory for optimal directed control of discrete-event systems

Directed control is crucial for implementing controllers on programmable logic controllers. A directed controller is one that actively selects at most one controllable event (control command) to execute at any instant. This study investigates the numerical optimization problem of directed control for discrete-event systems, such as traffic systems and robotic systems. By integrating supervisory control theory and reinforcement learning, an algorithmic procedure is designed to synthesize an optimal directed controller, which minimizes the total cost associated with event execution while ensuring the safety and liveness of the controlled system. Two improved Q-learning algorithms incorporating dynamic parameter adaptation strategies are developed to enhance the global search ability of basic Q-learning. To demonstrate applicability and effectiveness, we apply the proposed method to control a guideway system and a multi-train traffic system, respectively. The experimental results indicate the superiority of the proposed method over the comparison methods.

Fault diagnosis method of rolling bearing of BN ball mill based on AESL-GA

Aiming at the shortcomings of incomplete and inaccurate knowledge-based Bayesian network (BN) construction methods, a BN structure construction method based on knowledge guidance and data mining is proposed. Aiming at the problem of inaccurate single signal fault diagnosis results and the uncertainty problem in fault information, the current signal and the vibration signal are fused to establish the feature nodes of the BN network, and the fault feature parameters of the two signals are extracted respectively. The feature basket is selected using the discrimination index method and used as the node of the BN structure feature layer. The initial BN structure constructed by expert knowledge is combined with the adaptive elite structure genetic algorithm (AESL-GA) for structural optimization. By adaptively restricting the search space in the evolution process, reducing the number of free parameters, improving its global search ability, and obtaining the optimal BN structure. The method is verified by the measured data of the ball mill rolling bearing of Jinchuan Company and the Paderborn University data set, which proves the effectiveness of the proposed method.

Agent path planning based on adaptive polymorphic ant colony optimization

In the path planning of intelligent agents, ant colony algorithm is a popular path solving strategy and has been widely used. However, the traditional ant colony algorithm has problems of local optimum and redundant turning points. The adaptive polymorphic ant colony algorithm greatly improves the search and convergence speed through multi-group division and cooperation mechanism, which helps to enhance the global search ability and avoid falling into the local optimal solution. This paper proposes an improved pheromone update strategy and path selection record table construction to further improve the accuracy of path planning. Finally, the path is processed by cubic B-Spline smoothing curve to effectively reduce the turning points and realize the smoothing of the path. After MATLAB and ROS (Robot Operating System)-Gazebo simulation verification, the results show that the algorithm has good feasibility in complex environments. In summary, the improved adaptive polymorphic ant colony algorithm in this paper brings significant optimization and improvement to the global search of intelligent agents.

Evolving the Whale Optimization Algorithm: The Development and Analysis of MISWOA.

This paper introduces an enhanced Whale Optimization Algorithm, named the Multi-Swarm Improved Spiral Whale Optimization Algorithm (MISWOA), designed to address the shortcomings of the traditional Whale Optimization Algorithm (WOA) in terms of global search capability and convergence velocity. The MISWOA combines an adaptive nonlinear convergence factor with a variable gain compensation mechanism, adaptive weights, and an advanced spiral convergence strategy, resulting in a significant enhancement in the algorithm's global search capability, convergence velocity, and precision. Moreover, MISWOA incorporates a multi-population mechanism, further bolstering the algorithm's efficiency and robustness. Ultimately, an extensive validation of MISWOA through "simulation + experimentation" approaches has been conducted, demonstrating that MISWOA surpasses other algorithms and the Whale Optimization Algorithm (WOA) and its variants in terms of convergence accuracy and algorithmic efficiency. This validates the effectiveness of the improvement method and the exceptional performance of MISWOA, while also highlighting its substantial potential for application in practical engineering scenarios. This study not only presents an improved optimization algorithm but also constructs a systematic framework for analysis and research, offering novel insights for the comprehension and refinement of swarm intelligence algorithms.

FTDZOA: An Efficient and Robust FS Method with Multi-Strategy Assistance.

Feature selection (FS) is a pivotal technique in big data analytics, aimed at mitigating redundant information within datasets and optimizing computational resource utilization. This study introduces an enhanced zebra optimization algorithm (ZOA), termed FTDZOA, for superior feature dimensionality reduction. To address the challenges of ZOA, such as susceptibility to local optimal feature subsets, limited global search capabilities, and sluggish convergence when tackling FS problems, three strategies are integrated into the original ZOA to bolster its FS performance. Firstly, a fractional order search strategy is incorporated to preserve information from the preceding generations, thereby enhancing ZOA's exploitation capabilities. Secondly, a triple mean point guidance strategy is introduced, amalgamating information from the global optimal point, a random point, and the current point to effectively augment ZOA's exploration prowess. Lastly, the exploration capacity of ZOA is further elevated through the introduction of a differential strategy, which integrates information disparities among different individuals. Subsequently, the FTDZOA-based FS method was applied to solve 23 FS problems spanning low, medium, and high dimensions. A comparative analysis with nine advanced FS methods revealed that FTDZOA achieved higher classification accuracy on over 90% of the datasets and secured a winning rate exceeding 83% in terms of execution time. These findings confirm that FTDZOA is a reliable, high-performance, practical, and robust FS method.