Local Optimization Problem Research Articles

Multi-UAV Path Planning for Air-Ground Relay Communication Based on Mix-Greedy MAPPO Algorithm

With the continuous development of modern UAV technology and communication technology, UAV-to-ground communication relay has become a research hotspot. In this paper, a Multi-Agent Reinforcement Learning (MARL) method based on the ε-greedy strategy and multi-agent proximal policy optimization (MAPPO) algorithm is proposed to address the local optimization problem, improving the communication efficiency and task execution capability of UAV cluster control. This paper explores the path planning problem in multi-UAV-to-ground relay communication, with a special focus on the application of the proposed Mix-Greedy MAPPO algorithm. The state space, action space, communication model, training environment, and reward function are designed by comprehensively considering the actual tasks and entity characteristics such as safe distance, no-fly zones, survival in a threatened environment, and energy consumption. The results show that the Mix-Greedy MAPPO algorithm significantly improves communication probability, reduces energy consumption, avoids no-fly zones, and facilitates exploration compared to other algorithms in the multi-UAV ground communication relay path planning task. After training with the same number of steps, the Mix-Greedy MAPPO algorithm has an average reward score that is 45.9% higher than the MAPPO algorithm and several times higher than the multi-agent soft actor-critic (MASAC) and multi-agent deep deterministic policy gradient (MADDPG) algorithms. The experimental results verify the superiority and adaptability of the algorithm in complex environments.

A genetic algorithm-based solution for multi-type maximal covering location problem (MMCLP): application to defense and deterrence

PurposeWe study the problem of finding optimal locations for a suite of defense assets in order to protect high-value tactical and strategic infrastructure across a vast geographical area. To this end, we present a multi-type with non-overlapping coverage requirement as an extension to the classical formulation for the maximal covering location problem (MCLP).Design/methodology/approachIn our case study, we use open source geographic and demographic data from Canadian sources as inputs to our optimization problem. Due to the complexity of the MIP formulation, we propose a hybrid metaheuristic solution approach, for which a genetic algorithm (GA) is proposed and integrated with local and large neighborhood search operators.FindingsExtensive numerical experiments over different instances of the proposed problem indicate the effectiveness of the GA-based solution in reducing the solution time by a factor of ten compared to the CPLEX commercial solver while both approaches obtain solutions of similar quality.Research limitations/implicationsThis research is limited to location planning of defense assets leveraging geospatial data of Canada. However, the diverse Canadian geography is among the most challenging given broad variability in population density and the vast size of the country leading to a large search space having substantial variability in fitness performance.Practical implicationsOur findings demonstrate that for large-scale location searches, the GA with a local neighborhood search performs very well in comparison to CPLEX but at a fraction of the execution time.Originality/valueOur findings provide insight into how to make improved decisions for the placement of deterrence and defense systems and the effectiveness of a hybrid metaheuristic in addressing associated computational challenges.

Efficient path planning for autonomous vehicles based on RRT* with variable probability strategy and artificial potential field approach.

With the rapid development of autonomous driving technology, path planning has gained significant attention as it holds great potential for improving safety. The Rapidly-exploring Random Tree star(RRT*) algorithm has attracted much attention because of its good adaptability and expansibility. However, how solving problems in the RRT* algorithm such as slow convergence time, significant search range randomness, and unpredictability is a challenge. Therefore, an RRT* enhancement algorithm combining variable probability goal-bias strategy and artificial potential field(APF) method(Improved A-RRT*) is proposed in this paper. Firstly, the variable probability goal-bias strategy is introduced in the sampling process to make random tree expand towards the target direction and improve the directional searchability of the random tree. Secondly, the potential field function in APF is improved to prevent falling into local optimum problems during path generation. Thirdly, improved APF is combined with RRT*, the target generates a gravitational field on random tree, and the obstacle generates a repulsive force on it, leading random tree to grow toward the target region. Finally, the proposed algorithm is compared with RRT* algorithm and its derivative algorithm. The experimental results demonstrate that the proposed algorithm has obvious optimizations in convergence speed and path quality.

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.

Security-Aware Deadline Constraint Task Scheduling using Hybrid Optimization of Modified Flying Squirrel Genetic Chameleon Swarm Algorithm

Cloud computing enables cost-effective resource sharing in hybrid clouds to tackle the problem of insufficient resources by elastically scaling the service capability based on the users’ needs. However, task scheduling (TS) in cloud environments is challenging due to deadline-based performance and security constraints. To remove the existing drawbacks based on deadline and security constraints, a Security-Aware Deadline Constraint TS (SADCTS) approach is presented using a hybrid optimization algorithm of the Modified Flying Squirrel Genetic Chameleon Swarm Algorithm (MFSGCSA). The proposed MFSGCSA is developed by integrating the genetic operators into CSA and combining it with the modified Flying Squirrel Optimization (FSO) algorithm in which the position update and global search equations are enhanced by adaptive probability factor to reduce the local optimum problem. In this SADCTS approach, the task assignment process is modeled into an NP-hard problem concerning a multi-level security model using user authentication, integrity, and confidentiality. This maximizes tasks’ completion rate and decreases the resource costs to process tasks with different deadline limitations. The optimal schedule sequence is obtained by MFSGCSA, where tasks are scheduled concurrently based on security constraints, demand, and deadlines to improve the prominence of cost, energy, and makespan. Experiments are simulated using the CloudSim toolkit, and the comparative outcomes show that the suggested SADCTS approach reduced the makespan, cost, and energy by 5-20% more than the traditional methods. Thus, SADCTS provides less task violation of 0.0001%, high energy efficiency of 700GHz/W, high resource utilization of 92%, less cost efficiency of 72GHz/$, and less makespan of 480minutes to satisfy the necessary security and deadline requirements for TS in shared resource hybrid clouds.

Optimizing Retail Recommendation via Similarity Measures and Machine Learning Approach

Finding a suitable retail business with potential success in a specific location can be challenging for retailers. The process is often lengthy and inconsistent due to the subjective nature of expert opinions. Previous research has demonstrated several techniques that consider numerous influential attributes for location optimization problems. However, while many studies rely on a business's core data for analytical purposes, accessing this information is often a significant constraint. This study aims to address the challenge of extracting valuable location features to enhance the profitability of chosen businesses despite the inaccessibility of core business data. The proposed methodology involves three main steps. First, an analytical dataset must be created by utilizing geographic and demographic information. Second, we conduct similarity measures by applying Manhattan distance to the entire analytical dataset, using an ideal business outlet that contains the footfall information. Through this process, we can identify businesses that share similar characteristics with popular outlets. Finally, several supervised machine learning models are trained to employ the extracted meta-features. Experimental results show that the XGBoost classifier performs best with an 87% accuracy score, outperforming the baseline models. The proposed methodology in this research presents a robust framework that demonstrated remarkable efficiency in achieving the stated objectives and improving the performance of retail business recommendations within a given location. Future work could consider a broader range of features that could potentially enhance model performance by applying ensemble learning.

Prediction of Shale Gas Well Productivity Based on a Cuckoo-Optimized Neural Network

Current shale gas well production capacity predictions primarily rely on analytical and numerical simulation methods, which necessitate extensive calculations and manual parameter tuning and produce lowly accurate predictions. Although employing neural networks yields highly accurate predictions, they can easily fall into local optima. This paper suggests a new way to use Cuckoo Search (CS)-optimized neural networks to make shale gas well production capacity predictions more accurate and to solve the problem of local optima. It aims to assist engineers in devising more effective development plans and production strategies, optimizing resource allocation, and reducing risk. The method first analyzes the factors influencing the production capacity of shale gas wells in a block located in western China through correlation coefficients. It identifies the main factors affecting the gas test absolute open flow as organic carbon content, small-layer passage rate, fracture pressure, acid volume, pump-in fluid volume, brittle mineral content in the rock, and rock density. Subsequently, we used the CS algorithm to conduct the global training of the neural network, avoiding the problem of local optima, and established a neural network model for predicting shale gas well production capacity optimized by the CS algorithm. A comparative analysis with other relevant methods demonstrates that the CS-optimized neural network model can accurately predict production capacity, enabling a more rational and effective exploitation of shale gas resources, which lower development costs and increase the economic returns of oil and gas fields. Compared to numerical simulation, SVM, and BP neural network algorithms, the CS-optimized BP neural network (CS-BP) exhibits significantly lower prediction error. Its correlation coefficient between predicted and actual values reaches as high as 0.9924. Verification experiments conducted on another shale gas well also demonstrate that, in comparison to the BP neural network algorithm, CS-BP offers superior prediction performance, with model validation showing a prediction error of only 0.05. This study can facilitate more rational and efficient exploitation of shale gas resources, reduce development costs, and enhance the economic benefits of oil and gas fields.

Fast Computer Model Calibration using Annealed and Transformed Variational Inference

Computer models play a crucial role in numerous scientific and engineering domains. To ensure the accuracy of simulations, it is essential to properly calibrate the input parameters of these models through statistical inference. While Bayesian inference is the standard approach for this task, employing Markov chain Monte Carlo methods often encounters computational hurdles due to the costly evaluation of likelihood functions and slow mixing rates. Although variational inference (VI) can be a fast alternative to traditional Bayesian approaches, VI has limited applicability due to boundary issues and local optima problems. To address these challenges, we propose flexible VI methods based on deep generative models that do not require parametric assumptions on the variational distribution. We embed a surjective transformation in our framework to avoid posterior truncation at the boundary. Additionally, we provide theoretical conditions that guarantee the success of the algorithm. Furthermore, our temperature annealing scheme and fine-tuning can prevent being trapped in local optima through a series of intermediate posteriors and weight adjustment. We apply our method to infectious disease models and a geophysical model, illustrating that the proposed method can provide fast and accurate inference compared to its competitors.

Improved artificial potential field method based on robot local path information

The artificial potential field (APF) is an important method for robot path planning. However, some information in APF is not fully utilized in practical applications. In this paper, an improved artificial potential field (IAPF) method is presented, in which the local path information is defined and used. And the calculation formulas for various forces in IAPF are given, which include repulsive force (R-force) of obstacle on the robot, the attractive force (A-force) of target on the robot, and the resultant force of R-force and A-force. Then, based on the local path information, a method for solving the robot falling into local optimality problem is proposed and used into IAPF. Finally, IAPF is respectively simulated and discussed in general scenario, complex scenario, and scenarios with the same and different size of circular obstacles. The results show that IAPF has higher efficiency than traditional artificial potential field (TAPF) method and can overcome the local optimality problem. At the same time, IAPF is compared with dynamic window method in the scenarios with the same and different size of circular obstacles. The results show that IAPF is more efficient than the dynamic window approach (DWA) for robot path planning.

A hybrid deep recurrent artificial neural network with a simple exponential smoothing feedback mechanism

Both classical forecasting methods and machine learning approaches are used to solve forecasting problems. Deep artificial neural networks, one of the machine learning methods, are widely used today and give very good results. Recurrent neural networks, a type of deep neural network, are very important in forecasting problems. Simple recurrent artificial neural networks, which are the simplest deep recurrent neural networks, are often preferred in solving forecasting problems due to the small number of parameters they use. Simple exponential smoothing, one of the classical forecasting methods, attracts attention with its performance in solving forecasting problems. The motivation of the study is to create a new forecasting method by combining a classical and simple forecasting method with a deep recurrent artificial neural network in an architecture. In this, a new hybrid deep recurrent artificial neural network with a simple exponential smoothing feedback mechanism is proposed. The architecture of the proposed method is created as a combination of simple recurrent artificial neural networks and simple exponential smoothing methods. In the training of the proposed method, two training algorithms based on sine cosine optimization and particle swarm optimization algorithms are proposed. In these training algorithms, two different solution strategies such as restarting, and early stopping rule are used to avoid overfitting and local optimum problems. The performance of the proposed method is analysed using stock market datasets and compared with both different deep and shallow artificial neural networks and classical forecasting methods. As a result of the analyses, it is concluded that the proposed method is successful in one step ahead of forecasting performance.

Indoor Infrared Sensor Layout Optimization for Elderly Monitoring Based on Fused Genetic Gray Wolf Optimization (FGGWO) Algorithm.

With the increasing aging of the global population, the efficiency and accuracy of the elderly monitoring system become crucial. In this paper, a sensor layout optimization method, the Fusion Genetic Gray Wolf Optimization (FGGWO) algorithm, is proposed which utilizes the global search capability of Genetic Algorithm (GA) and the local search capability of Gray Wolf Optimization algorithm (GWO) to improve the efficiency and accuracy of the sensor layout in elderly monitoring systems. It does so by optimizing the indoor infrared sensor layout in the elderly monitoring system to improve the efficiency and coverage of the sensor layout in the elderly monitoring system. Test results show that the FGGWO algorithm is superior to the single optimization algorithm in monitoring coverage, accuracy, and system efficiency. In addition, the algorithm is able to effectively avoid the local optimum problem commonly found in traditional methods and to reduce the number of sensors used, while maintaining high monitoring accuracy. The flexibility and adaptability of the algorithm bode well for its potential application in a wide range of intelligent surveillance scenarios. Future research will explore how deep learning techniques can be integrated into the FGGWO algorithm to further enhance the system's adaptive and real-time response capabilities.

Optimization Techniques in the Localization Problem: A Survey on Recent Advances

Optimization is a mathematical discipline or tool suitable for minimizing or maximizing a function. It has been largely used in every scientific field to solve problems where it is necessary to find a local or global optimum. In the engineering field of localization, optimization has been adopted too, and in the literature, there are several proposals and applications that have been presented. In the first part of this article, the optimization problem is presented by considering the subject from a purely theoretical point of view and both single objective (SO) optimization and multi-objective (MO) optimization problems are defined. Additionally, it is reported how local and global optimization problems can be tackled differently, and the main characteristics of the related algorithms are outlined. In the second part of the article, extensive research about local and global localization algorithms is reported and some optimization methods for local and global optimum algorithms, such as the Gauss–Newton method, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), and so on, are presented; for each of them, the main concept on which the algorithm is based, the mathematical model, and an example of the application proposed in the literature for localization purposes are reported. Among all investigated methods, the metaheuristic algorithms, which do not exploit gradient information, are the most suitable to solve localization problems due to their flexibility and capability in solving non-convex and non-linear optimization functions.

Decentralized dynamic system for optimal power dispatch in wind farms based on node-dependence nature

Meeting the power demand from the transmission system operator is an important objective for power dispatch, which introduces a power supply-demand equality constraint coupling all the wind turbines among the wind farm into the optimization problem. For a large-scale wind farm, processing the global equality constraint in a centralized or distributed framework is time-consuming and computationally complex. Here we considered the fast and localized execution issue of the power optimal dispatch problems. A completely decentralized dynamic system was designed to optimize power flow while satisfying the electricity supply constraints. A voltage optimization problem with the global power constraints was decoupled into local wind turbine controllers based on the node-dependence nature, which is an inherent characteristic of wind farms and was fitted to the power sensitivity matrix in this paper. The local optimization problem was solved iteratively using the gradient projection method, and the system converged linearly to the equilibrium point. The simulations for the case studies performed in Simulink demonstrate that the proposed method achieves a near-global optimal performance using only local measurements.

Random-guided optimizer: a metaheuristic that shifts random search to guided search through iteration

This study offers a new swarm-based metaheuristic: random-guided optimizer (RGO). RGO has novel mechanics in shifting the random motion into a guided motion strategy during the iteration. In RGO, the iteration is divided into three equal size phases. In the first phase, the unit walks randomly inside the search space to tackle the local optimal problem earlier. In the second phase, each unit uses a unit selected randomly among the population as a reference in conducting the guided motion. In the third phase, each unit conducts guided motion toward or surpasses the best unit. Through simulation, RGO successfully finds the acceptable solution for 23 benchmark functions. Moreover, RGO successfully finds the global optimal solution for four functions: Branin, Goldstein-Price, Six Hump Camel, and Schwefel 2.22. RGO also outperforms slime mold algorithm (SMA), pelican optimization algorithm (POA), golden search optimizer (GSO), and northern goshawk optimizer (NGO) in solving 12, 20, 12, and 1 function consecutively. In the future, improvement can be made by transforming RGO into solid multiple-phase strategy without losing its identity as a metaheuristic with multiple strategy in every iteration.

Data-driven offline reinforcement learning approach for quadrotor’s motion and path planning

Non-learning based motion and path planning of an Unmanned Aerial Vehicle (UAV) is faced with low computation efficiency, mapping memory occupation and local optimization problems. This article investigates the challenge of quadrotor control using offline reinforcement learning. By establishing a data-driven learning paradigm that operates without real-environment interaction, the proposed workflow offers a safer approach than traditional reinforcement learning, making it particularly suited for UAV control in industrial scenarios. The introduced algorithm evaluates dataset uncertainty and employs a pessimistic estimation to foster offline deep reinforcement learning. Experiments highlight the algorithm’s superiority over traditional online reinforcement learning methods, especially when learning from offline datasets. Furthermore, the article emphasizes the importance of a more general behavior policy. In evaluations, the trained policy demonstrated versatility by adeptly navigating diverse obstacles, underscoring its real-world applicability.

Clustering statistical method of high dimensional sparse data based on fuzzy data

Abstract Developing effective clustering and statistical methods for high-dimensional sparse data presents unique challenges compared to traditional low-dimensional data. To address this, a novel approach is proposed, leveraging fuzzy data principles to enhance the clustering and statistical performance of high-dimensional sparse datasets. The method builds upon the fuzzy C-means clustering algorithm, introducing key modifications for better suitability to high-dimensional sparse data. One crucial enhancement involves tackling the local optimization problem by optimizing the initial clustering center, significantly reducing clustering statistical time. Replacing the original Euclidean distance with cosine distance improves the clustering and statistical performance of high-dimensional sparse data. Experimental results have shown that this method has superior clustering statistical performance when the data dimensions are different. When the data dimension is low, and the blocking ratio is 10%, the clustering statistical effect is optimal. When the data dimension is high, and the blocking ratio is 40%, the clustering statistical effect is optimal. This method has higher hit rates and clustering statistical efficiency at different sparsity levels.

Prediction method for power fluctuations in cross regional consumption and transportation under the integration of new energy

Against the backdrop of the increasing development of the new energy industry, the volatility of new energy output poses significant challenges to regional power grid balance and energy absorption. Therefore, this article proposes a prediction method for cross regional transmission power fluctuations under new energy integration conditions. A comprehensive and representative sample dataset was constructed by comprehensively considering factors such as fluctuations in new energy output, capacity confidence, and peak shaving characteristic parameters, combined with numerical weather forecast data. Normalize the sample data to eliminate dimensional differences between parameters. The sparrow search algorithm is used to optimize the weights and thresholds of the double hidden layer BP neural network, effectively avoiding local optimization problems caused by over training. The experimental results show that this method has significant advantages in predicting power fluctuations in cross regional absorption and transportation of new energy, with a predicted power to power ratio of over 0.85.

Optimal Placement and sizing of DG Units Using LCA Algorithm

The motivation of this paper is to present an approach that relies on the league championship algorithm (LCA) to determine the most effective number, placement, and size of distributed generations (DG) within distribution systems. The primary objective of this approach is to minimize the losses of the power system, as well as to enhance the voltage profiles and stability index of the voltage. The optimal location of the DG units is determined through the use of the Loss Sensitivity Factor (LSF), while the optimal size of the DG units is found through the use of LCA. The IEEE 33-bus and 69-bus radial distribution systems were both tested to validate the proposed method, and the results obtained from LCA were compared to those of other methods found in the literature. The simulated results have shown that the LCA method proposed in this paper is highly effective and performs exceptionally well when addressing the problem of optimal location and sizing of DG units in radial networks.

Distributed power system coordination via parametric optimization and ADMM

In this paper we present an efficient model predictive control algorithm for distributed coordination of an electrical power system comprising many spatially distributed controllable units. The coordination problem is formulated using parametric solutions of local optimization problems corresponding to individual subsystems leading to favorable problem structure which can be split across all subsystems and nodes in the network. The structure of the obtained coordination problem is exploited to develop a very efficient solution algorithm based on an ADMM technique. The key features of the overall approach are: (i) private data of individual subsystems are protected, (ii) simple and efficient on-line computations, (iii) parallelization of computation across all subsystems and all nodes in the network. Efficiency of the proposed control strategy is demonstrated on a number of numerical case studies of varying size.

An improved BAT algorithm for collaborative dynamic target tracking and path planning of multiple UAV

It is well known that multi-UAV cooperative dynamic target path planning is a challenging field. In this field, multi-UAV cooperative dynamic target path planning is very important to achieve efficient task completion. However, the existing algorithms have some limitations in solving the problems of insufficient search range, premature convergence, local optimization and insufficient population diversity. In order to solve these problems and improve the efficiency and accuracy of path planning, this paper proposes an innovative method. Firstly, we use A* algorithm to obtain the initial assignment result of UAV target, and then use Hungarian algorithm and genetic algorithm to optimize the assignment result and expand the target assignment range. Secondly, in the path planning stage, the bat algorithm is improved, and sine function, dynamic expansion factor and nonlinear function are introduced to solve the problems of insufficient search range, premature convergence and local optimization. At the same time, genetic algorithm is used to solve the problem of insufficient population. By optimizing target assignment and path planning, the performance of multi-UAV cooperative system is improved, so as to better adapt to the task requirements in dynamic environment and provide more reliable solutions for UAV applications. The experimental results show that the tracking and path planning are more effective than BA, CCWOA and OUMPOA under the condition of good stability. Compared with BA, the fitness function and convergence speed are improved by 34.64% and 30.29% respectively.