Optimization Solution Research Articles

Impact of Balanced Exploration and Exploitation on Highdimensional Feature Selection with Hierarchical Whale Optimisation Algorithm

High-dimensional datasets (HDDs) pose significant challenges in feature selection due to their complex nature. While metaheuristic algorithms like the Whale Optimisation Algorithm (WOA) have shown promise in addressing these challenges, stability issues are least addressed. Through an extensive literature review, it was identified that stability is manifested in the equilibrium of exploration and exploitation. Hence, this study introduced the Hierarchical Whale Optimisation Algorithm (HiWOA), an approach designed to enhance the WOA’s stability and performance in HDD feature selection tasks. The HiWOA incorporates a two-phase strategy comprising a nonlinear control parameter based on the arcsine function and a hierarchical position-update mechanism adapted from the Grey Wolf Optimiser. The proposed HiWOA was evaluated through 23 benchmark optimisation functions and feature selection experiments on 11 medical HDDs. The results indicate that the HiWOA outperformed the WOA and a modified variant (mWOA) in terms of a better fitness value, a more balanced exploration-exploitation ratio, and improved classification accuracy with fewer selected features. These findings demonstrate the HiWOA’s effectiveness in enhancing stability, making it a robust solution for high-dimensional optimisation and feature selection.

CollectiveHLS: A Collaborative Approach to High-Level Synthesis Design Optimization

High-Level Synthesis (HLS) has played a pivotal role in making FPGAs accessible to a broader audience by facilitating high-level device programming and rapid microarchitecture customization through the use of directives. However, manually selecting the right directives can be a formidable challenge for programmers lacking a hardware background. This paper presents CollectiveHLS, an ultra-fast, knowledge-driven approach to optimizing HLS designs. It automates the identification and application of optimal directive configurations from the original source code, focusing on minimizing design latency and ensuring synthesizability. This optimization approach is entirely data-driven, offering a generalized HLS tuning solution without reliance on Quality of Result (QoR) models or meta-heuristics. CollectiveHLS is designed, implemented, and evaluated using around 60 applications sourced from well-established benchmark suites and GitHub repositories, all running on a Xilinx UltraScale + MPSoC ZCU104. It achieves an average geometric mean speedup of up to \(23.1\times\) compared to the official source code without directives, while maintaining synthesizability and feasibility rates of 100% and 96.6%, respectively, matching those of Vitis, the industry-standard framework for FPGA acceleration. Comparisons with resource over-provisioning, traditional genetic algorithm-based Design Space Exploration (DSE), and State-of-the-Art (SotA) approaches demonstrate that CollectiveHLS produces designs of comparable quality \(14.6\times\) faster on average. These results underscore the potential of our approach as an ultra-fast and automated solution for HLS optimization.

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.

Unboundedness of the images of set-valued mappings having closed graphs: application to vector optimization

AbstractIn this paper, we propose criteria for unboundedness of the images of set-valued mappings having closed graphs in Euclidean spaces. We focus on mappings whose domains are non-closed or whose values are connected. These criteria allow us to see structural properties of solutions in vector optimization, where solution sets can be considered as the images of solution mappings associated to specific scalarization methods. In particular, we prove that if the domain of a certain solution mapping is non-closed, then the weak Pareto solution set is unbounded. Furthermore, for a quasi-convex problem, we demonstrate two criteria to ensure that if the weak Pareto solution set is disconnected then each connected component is unbounded.

Route Planning Method of UAV Patrol Based on High Precision Tower Model

The high-precision positioning technology of industrial Unmanned Aerial Vehicle (UAV) plays a vital role in intelligent trajectory planning in power inspection. First, the information on power facilities is collected and processed to form a planning grid to optimize the flight path and correct the trajectory of UAV. However, the preparatory work is more complex, and the algorithm model which needs more detailed and complex terrain database data is not universal enough. To improve the safety of UAV and the efficiency of path planning, we establish a three-dimensional model of the power tower, calculate the coordinate position of the inspection work according to the information of the high-voltage transmission tower, and then determine the corresponding relationship between the inspection point and the flight trajectory combined with the safe distance. Based on the set of random interference semaphores and model rasterization, the discrete error strategy and Euler method are introduced to improve the performance. In each iteration, the best solution is first updated using the execution strategy. We continue to calculate the spatial coordinates of all reference points and provide the coordinate position distribution for the flight mission. The solution of equipment inspection trajectory optimization is realized by using dynamic obstacle perception. The proposed discrete measurement error self-correction algorithm is deployed on the flight platform to guide UAV inspection tasks according to the point cloud coordinate model of transmission equipment in the power system and to correct the flight route in time. To verify the research results, the Algorithm Artemisinin optimization and Whale Optimization Algorithm test verify that the ability of the proposed algorithm to get rid of local optimization is improved by 8.94% and, 12.42% respectively compared with other optimization algorithms. The convergence accuracy is 12.4% and 7.9% higher than other schemes, and it has a better effect in solving numerical problems. The research results using the embedded system to complete the task deployment and unloading provide a reference for trajectory planning and task design in different application scenarios.

Primal-Dual Extrapolation Methods for Monotone Inclusions Under Local Lipschitz Continuity

In this paper, we consider a class of monotone inclusion (MI) problems of finding a zero of the sum of two monotone operators, in which one operator is maximal monotone, whereas the other is locally Lipschitz continuous. We propose primal-dual (PD) extrapolation methods to solve them using a point and operator extrapolation technique, whose parameters are chosen by a backtracking line search scheme. The proposed methods enjoy an operation complexity of [Formula: see text] and [Formula: see text], measured by the number of fundamental operations consisting only of evaluations of one operator and resolvent of the other operator, for finding an ε-residual solution of strongly and nonstrongly MI problems, respectively. The latter complexity significantly improves the previously best operation complexity [Formula: see text]. As a byproduct, complexity results of the primal-dual extrapolation methods are also obtained for finding an ε-KKT or ε-residual solution of convex conic optimization, conic constrained saddle point, and variational inequality problems under local Lipschitz continuity. We provide preliminary numerical results to demonstrate the performance of the proposed methods. Funding: This work was partially supported by the National Science Foundation [Grant IIS-2211491], the Office of Naval Research [Grant N00014-24-1-2702], and the Air Force Office of Scientific Research [Grant FA9550-24-1-0343].

Optimal load distribution control for airport terminal chiller units based on deep reinforcement learning

The building sector consumes substantial energy, with HVAC systems accounting for nearly half of the total energy use. Optimizing chiller unit operations is crucial for reducing energy consumption. This research introduces a novel model-free control method for optimizing central chiller load distribution using deep reinforcement learning (DRL). The aim is to address the challenges posed by the high-dimensional complexity and parameter tuning in traditional meta-heuristic algorithms. The proposed method leverages deep neural networks combined with reinforcement learning to optimize chiller control based on real-world data. Case studies conducted in a large airport terminal demonstrate that this approach achieves a 7.71 % energy savings, closely matching the performance of model-based control methods with only a 0.26 % difference. The results indicate that the DRL method not only outperforms traditional expert control systems but also offers a feasible alternative for optimal control in complex, variable environments with limited data. This study contributes to the field by filling a research gap in applying DRL for optimal chiller load (OCL) control and demonstrates its potential for large-scale public building applications. The novelty of this research lies in its model-free approach, which provides a robust solution for energy optimization in dynamic and uncertain environments.

Dynamic Niches-Based Hybrid Breeding Optimization Algorithm for Solving Multi-Modal Optimization Problem

Some problems exist in classical optimization algorithms to solve multi-modal optimization problems and other complex systems. A Dynamic Niches-based Improved Hybrid Breeding Optimization (DNIHBO) algorithm is proposed to address the multi-modal optimization problem in the paper. By dynamically adjusting the niche scale, it effectively addresses the issue of niche parameter sensitivity. The structure of the algorithm includes three distinct groups: maintainer, restorer, and sterile lines for updating operations. However, the maintainer individuals often stagnate, leading to the risk of the local optima. To overcome this, neighborhood search and elite mutation strategies are incorporated, enhancing the balance between exploration and exploitation. To further improve individual utilization within niches, a niche restart strategy is introduced, ensuring sustained population diversity. The efficacy of DNIHBO is validated through simulations on 16 multi-modal test functions, followed by comparative analyses with various multi-modal optimization algorithms. The results convincingly demonstrate that DNIHBO not only effectively locates multiple global optima but also consistently outperforms other algorithms on test functions. These findings underscore the superiority of DNIHBO as a high-performing solution for multi-modal optimization.

Forward osmosis for concentrating lithium-enriched brine: From membrane performance to system design

Using concentrated salt-lake brine as the draw solution (DS), forward osmosis (FO) has been proposed as the low carbon footprint concentration technology for extracting water from lithium-enriched brine. From membrane modules to a process, there exist tremendous risks when considering total cost in time and investment for a trial. To address this issue, a bridge between the FO membrane/ module characteristics and a FO system design is highly desirable. This study for the first time developed a system design model using hollow fiber (HF) FO membrane modules for FO concentration of lithium-enriched brine. The concentration polarization along the module, the membrane structural parameters (S), crossflow velocity, and DS concentration were the main factors considered in the model. The effect of all factors on module water flux and total membrane area was systematically investigated for a typical industrial output of 10 k ton of Li2CO3. A master curve of the water flux verse feed concentration for the tailor-made HF modules was utilized as the basis. On a module scale, the structural parameter S is of first importance and indicates requirement of FO membranes with low S. The crossflow velocity has relatively small impact to the membrane area, but not negligible. On a system scale, the empirical equation of the structural parameters and crossflow velocity on the theoretical minimum system membrane area was summarized. The results show that increasing the stages can reduce the number of modules and required DS flow. Increasing DS dilution rate in system design efficiently utilizes the osmotic pressure of DS. The present model serves as a practical solution for process optimization and FO membrane selection. The methodology is valuable for other FO applications using HF FO membranes.

Design of Intelligent Financial System Based on Adaptive Learning Algorithm

The high-frequency trading system in the financial domain has long been a focal point of investigation. This study posits an intelligent financial system design framework predicated on a cross-adaptive self-entropy projection clustering model, aimed at enhancing the efficacy of high-frequency trading systems. A composite distribution model of financial data is formulated to derive sequences of financial data activities. And cross-adaptive learning algorithm is employed to ascertain the interrelated attributes of financial data. Following this, the support vector machine algorithm is applied for the classification processing of these interrelated features, yielding a set of financial data feature vectors, which are then fed into the gray correlation-based information feature extraction model. Through extensive empirical evaluations with authentic trading data, the proposed intelligent financial system design framework exhibits commendable performance, furnishing a viable solution for the intelligent optimization of high-frequency trading systems.

Business Solution for Profit Optimization at PBS Clinic

Business Solution for Profit Optimization at PBS Clinic

Collaborative Neural Solution for Time-Varying Nonconvex Optimization With Noise Rejection

Collaborative Neural Solution for Time-Varying Nonconvex Optimization With Noise Rejection

An efficient solution for large offshore wind farm power optimization with the Porté-Agel wake model: Optimality and efficiency

The wake effects may cause significant energy loss in the large offshore wind farm without proper coordination. Therefore, cooperative yaw and induction control is needed to mitigate wake effects and increase power output. This study reveals that conventional methods to solve the power optimization problem may trap into local optimum and have slow computation speed for large offshore wind farm. In this paper, the Porté-Agel wake model is adopted to precisely capture the dynamics and a data-driven method is proposed to calibrate its parameters only using measurements. Then, we propose a distributed hybrid search-BFGS(DHSB) algorithm to solve the power optimization problem. In this algorithm, firstly the yaw angles of some specific turbines are searched coarsely and distributionally to provide a good initial point and escape from the local optimum. Then, a distributed BFGS is developed to solve the optimization problem with very high efficiency, in which the gradient and objective value are updated in distributed manner. Simulation results show that our proposed DHSB can always achieve global optimum with much high efficiency compared to traditional gradient-based methods.

Weakly Supervised AUC Optimization: A Unified Partial AUC Approach.

Since acquiring perfect supervision is usually difficult, real-world machine learning tasks often confront inaccurate, incomplete, or inexact supervision, collectively referred to as weak supervision. In this work, we present WSAUC, a unified framework for weakly supervised AUC optimization problems, which covers noisy label learning, positive-unlabeled learning, multi-instance learning, and semi-supervised learning scenarios. Within the WSAUC framework, we first frame the AUC optimization problems in various weakly supervised scenarios as a common formulation of minimizing the AUC risk on contaminated sets, and demonstrate that the empirical risk minimization problems are consistent with the true AUC. Then, we introduce a new type of partial AUC, specifically, the reversed partial AUC (rpAUC), which serves as a robust training objective for AUC maximization in the presence of contaminated labels. WSAUC offers a universal solution for AUC optimization in various weakly supervised scenarios by maximizing the empirical rpAUC. Theoretical and experimental results under multiple settings support the effectiveness of WSAUC on a range of weakly supervised AUC optimization tasks.

A stable satellite routing algorithm based on a-star algorithm in dynamic networks

Abstract This paper focuses on solving the routing optimization problem under the dynamic topology of the satellite communication network. With the rapid development of low-earth-orbit (LEO) satellite network communication technology, the dynamically changing topology of satellite networks has brought about frequent link switching and triggered a large number of routing updates. This paper proposes an improved routing algorithm named the Stable A-Star Routing (SASR) algorithm. The SASR algorithm is based on the A-star algorithm and aims to reduce path-switching frequency and enhance path stability by optimizing the path selection strategy. Through detailed simulation experiments and performance evaluation, we verified the performance advantages of the SASR algorithm in terms of the number of path switches, the number of path hops, the route convergence time, and the occurrences of congestion in business under different business volumes, and conducted comparative studies with classic algorithms. The results show that the SASR algorithm offers obvious advantages in key performance indicators. Compared with the Dijkstra algorithm and the A-star algorithm, the average number of path switches can be reduced by 22% and 10%, respectively. It shows more prominent advantages under a larger business volume, providing an efficient and reliable solution for routing optimization under the dynamic topology of satellite communication networks.

An adaptive coupled control method based on vehicles platooning for intersection controller and vehicle trajectories in mixed traffic

AbstractConnected and autonomous driving technologies offer a novel solution for intersection control optimization. Connected and autonomous vehicles (CAVs) can access signal plans and optimize trajectories to minimize delays and reduce fuel consumption. However, optimizing trajectories for individual vehicles significantly increases complexity, especially for joint optimization of traffic signals and vehicle trajectories. Given the current technical, regulatory, and policy constraints, a superior intersection management approach is necessary before fully automated driving is achieved. This paper introduces an adaptive coupling control (ACC) method based on vehicle platooning to optimize signal timings and vehicle trajectories in mixed traffic. Initially, vehicle platoon segmentation is conducted, led by CAVs. The study then proposes a single‐layer coupled optimization model based on vehicle platoons, simplifying the joint optimization model. To address logistic constraint difficulties, a linearization of the coupled model (LCM) method is developed. Numerical experiments demonstrate that the ACC method significantly reduces vehicle delay and fuel consumption. At high CAV penetration rates (0.8 < R <1) and high traffic volumes (over 900 pcu/h), vehicle platoon control delivers excellent performance, with delays and fuel consumption even lower than in a fully automated environment (R = 1). This surprising result suggests that the mixed platoon system (ACC method) positively impacts mixed traffic.

Load-aware continuous-time optimization for multi-agent systems: toward dynamic resource allocation and real-time adaptability

In the realm of next-generation mobile communication networks, characterized by dynamic and evolving workloads, the efficient resource allocation becomes paramount for achieving optimal performance. This paper addresses the intricate challenges of multiagent resource management through the integration of stochastic learning automata and continuous-time optimization techniques, presenting an innovative solution for real-time adaptability and dynamic load balancing. The central aim is to achieve optimal values for local time-varying cost functions while taking into account resource constraints and the feasibility of local allocation. In the context of multi-agent resource allocation systems, where conditions vary over time, the algorithm exhibits a dynamic adaptability, continuously adjusting to the evolving optimal solutions tailored for each agent. Utilizing stochastic learning automata, the algorithm effectively addresses the time-varying load-balancing function in response to dynamic user demands. Furthermore, we introduced an scalable solution for mobility robustness optimization based on deep reinforcement learning (DRL-MRO). This method dynamically identifies network-wide optimal parameter values across the entire network to accommodate diverse mobility patterns, ensuring the effectiveness of load balancing parameters that seamlessly adapt to the dynamic configuration of the network. The overarching goal is to maintain a consistent quality of service level for each agent, thereby enhancing the overall system performance. Simulation outcomes unequivocally affirm the superior performance of the proposed load balancing approach, rooted in continuous-time optimization and stochastic learning automata, surpassing existing schemes across diverse system configurations.

Cost optimization of electricity in energy storage system by dynamic programming

This paper presents a dynamic programming solution for the cost optimization of an electric storage system. The objective is to minimize the total cost of meeting electricity demand over a specified time interval, considering energy constraints and costs. The proposed algorithm efficiently determines the optimal energy discharge and charge strategies for the storage system, resulting in reduced overall costs. The effectiveness and efficiency of the algorithm are demonstrated through various test cases, highlighting its potential for real-world applications in energy storage systems and electric grid management. It also provides an overview of different types of electrical storage systems, review recent research on optimization techniques for energy storage, and examines recent studies on the optimization of electrical storage systems for specific applications, such as peak load shaving and grid stability. Through this comprehensive analysis, we hope to shed light on the current state of the field and identify areas for further research and improvement.

Fourier Domain Robust Denoising Decomposition and Adaptive Patch MRI Reconstruction.

The sparsity of the Fourier transform domain has been applied to magnetic resonance imaging (MRI) reconstruction in k -space. Although unsupervised adaptive patch optimization methods have shown promise compared to data-driven-based supervised methods, the following challenges exist in MRI reconstruction: 1) in previous k -space MRI reconstruction tasks, MRI with noise interference in the acquisition process is rarely considered. 2) Differences in transform domains should be resolved to achieve the high-quality reconstruction of low undersampled MRI data. 3) Robust patch dictionary learning problems are usually nonconvex and NP-hard, and alternate minimization methods are often computationally expensive. In this article, we propose a method for Fourier domain robust denoising decomposition and adaptive patch MRI reconstruction (DDAPR). DDAPR is a two-step optimization method for MRI reconstruction in the presence of noise and low undersampled data. It includes the low-rank and sparse denoising reconstruction model (LSDRM) and the robust dictionary learning reconstruction model (RDLRM). In the first step, we propose LSDRM for different domains. For the optimization solution, the proximal gradient method is used to optimize LSDRM by singular value decomposition and soft threshold algorithms. In the second step, we propose RDLRM, which is an effective adaptive patch method by introducing a low-rank and sparse penalty adaptive patch dictionary and using a sparse rank-one matrix to approximate the undersampled data. Then, the block coordinate descent (BCD) method is used to optimize the variables. The BCD optimization process involves valid closed-form solutions. Extensive numerical experiments show that the proposed method has a better performance than previous methods in image reconstruction based on compressed sensing or deep learning.

Bridging operations research and machine learning for service cost prediction in logistics and service industries

AbstractOptimizing shared resources across multiple clients is a complex challenge in the production, logistics, and service sectors. This study addresses the underexplored area of forecasting service costs for non-cooperative clients, which is essential for sustainable business management. We propose a framework that merges Operations Research (OR) and Machine Learning (ML) to fill this gap. It begins by applying the OR model to historical instances, optimizing resource allocation, and determining equitable service cost allocations for each client. These allocations serve as training targets for ML models, which are trained using a combination of original and augmented client data, aiming to reliably project service costs and support competitive, sustainable pricing strategies. The framework’s efficacy is demonstrated in a reverse logistics case study, benchmarked against two traditional cost estimation methods for new clients. Comparative analysis shows that our framework outperforms these methods in terms of predictive accuracy, highlighting its superior effectiveness. The integration of OR and ML offers a significant decision-support mechanism, improving sustainable business strategies across sectors. Our framework provides a scalable solution for cost forecasting and resource optimization, marking progress toward a circular, sustainable economy by accurately estimating costs and promoting efficient operations.