Optimal Allocation Algorithm Research Articles

Hunter–Prey Optimization Algorithm for Optimal Allocation of PV, DSTATCOM, and EVCS in Radial Distribution Systems

ABSTRACTThis research article instigates a seminal approach for optimizing reactive power in renewable energy sources (RES) and electric vehicles (EVs) coalescing distribution systems, using the innovative Hunter–Prey Optimization (HPO) algorithm in conjunction with DSTATCOM as a reactive power compensator. The proposed methodology aims to minimize losses, enhance voltage stability, and improve overall system performance by simultaneously optimizing reactive power flows in photovoltaic RES (PV_DG), EV charging stations (EVCS), and DSTATCOMs within the distribution system. Simulations carried on IEEE‐33, IEEE‐69, and IEEE‐118 test bus systems in MATLAB environment demonstrate that the HPO‐based approach achieves a 91.47% and 96.61% reduction in real power losses and an improvement in voltage profile with a minimum voltage value of 0.991 and 0.994 p.u. (respectively for IEEE‐33 and 69 bus systems), compared to traditional algorithms. These results highlight the lofty performance of the HPO method, effectively addressing the challenges posed by the integration of RES and EVs along with DSTATCOM.

Communication Technology for Renewable Energy Access Grid System Based on Multi-source Heterogeneous Data Fusion

Abstract Incorporating renewable energy solutions into the power grid is essential for facilitating a shift in energy sources. Ensuring the effectiveness and dependability of communication technologies within smart distribution grids is vital, as these grids serve as the link between end users and the central station, thereby contributing to the grid’s stable functioning and the effective dissemination of energy. This study introduces a framework for communication that tackles the challenges associated with the integration of multi-source, heterogeneous data in smart distribution grids. The framework leverages multi-source heterogeneous data fusion and capitalizes on edge computing to efficiently process and consolidate data from diverse sensors and devices. An optimal weight allocation algorithm is proposed to mitigate data redundancy and to improve the energy efficiency of communication. The paper demonstrates, through theoretical examination and empirical tests, the proposed system’s enhanced communication capabilities, especially in terms of data transmission effectiveness and energy usage optimization. The discussion highlights the advantages of smart distribution networks over conventional communication methods and offers insights for further advancements in communication technologies within this sector.

Optimization of resource allocation strategy for high-speed railway based on deep reinforcement learning

With the accelerated development of high-speed railway (HSR), the contradiction between the surge of user services and the demand for resource has become increasingly prominent. Mobile edge computing (MEC) has emerged to improve performance, reduce communication delay and ease network load. In this paper, we design a multi-user MEC system framework that aims to solve the joint optimization problem of computation offloading and resource allocation in HSR communication scenario with deep reinforcement learning algorithm. The framework dynamically allocates computation resource and network bandwidth through the real-time distance between users and base station (BS) to achieve optimal resource utilization and maximize user experience. To achieve this goal, we use a deep reinforcement learning based dynamic computation offloading and resource allocation (DDCORA) optimization algorithm. The algorithm minimizes the system cost by sharing state information among different users and making collaborative decisions to rationally allocate spectrum resource and computation resource. Simulation results show that DDCORA algorithm can significantly decrease the system cost while enhancing the overall system performance and user experience.

Multiple unmanned ship coverage and exploration in complex sea areas

This study addresses the complexities of maritime area information collection, particularly in challenging sea environments, by introducing a multi-agent control model for regional information gathering. Focusing on three key areas—regional coverage, collaborative exploration, and agent obstacle avoidance—we aim to establish a multi-unmanned ship coverage detection system. For regional coverage, a multi-objective optimization model considering effective area coverage and time efficiency is proposed, utilizing a heuristic simulated annealing algorithm for optimal allocation and path planning, achieving a 99.67% effective coverage rate in simulations. Collaborative exploration is tackled through a comprehensive optimization model, solved using an improved greedy strategy, resulting in a 100% static target detection and correct detection index. Agent obstacle avoidance is enhanced by a collision avoidance model and a distributed underlying collision avoidance algorithm, ensuring autonomous obstacle avoidance without communication or scheduling. Simulations confirm zero collaborative failures. This research offers practical solutions for multi-agent exploration and coverage in unknown sea areas, balancing workload and time efficiency while considering ship dynamics constraints.

Performance on massive MIMO enabled mobile edge computing networks: Parallel computing modeling

With the development of communication and computation technologies, mobile edge computing (MEC) has become an emerging technology being deployed in 5G/B5G mobile networks to improve the mobile user experience. In this paper, we study the asymptotic performance of parallel computing in MEC networks with massive multi-input and multi-output (MIMO) in the millimeter wave band. First, a massive MIMO enabled MEC network with a parallel-limited computing model is constructed, and the task completed probability is defined as a new network performance metric. In the proposed model, we derive the task offloading and computing probabilities via the probability generation functional of the Poisson point process, from which, the task completed probability is obtained. Then we propose a computing resource allocation optimization algorithm to maximize the task completed probability within a given delay constraint. The numerical simulation results prove the accuracy of the obtained theoretical results and verify the performance of the proposed algorithm.

Navigation Resource Allocation Algorithm for LEO Constellations Based on Dynamic Programming

Navigation resource allocation for low-earth-orbit (LEO) constellations refers to the optimal allocation of navigational assets when the number and allocation of satellites in the LEO constellation have been determined. LEO constellations can not only transmit navigation enhancement signals but also enable space-based monitoring (SBM) for real-time assessment of GNSS signal quality. However, proximity in the frequencies of LEO navigation signals and SBM can lead to significant interference, necessitating isolated transmission and reception. This separation requires that SBM and navigation signal transmission be carried out by different satellites within the constellation, thus demanding a strategic allocation of satellite resources. Given the vast number of satellites and their rapid movement, the visibility among LEO, medium-earth-orbit (MEO), and geostationary orbit (GEO) satellites is highly dynamic, presenting substantial challenges in resource allocation due to the computational intensity involved. Therefore, this paper proposes an optimal allocation algorithm for LEO constellation navigation resources based on dynamic programming. In this algorithm, a network model for the allocation of navigation resources in LEO constellations is initially established. Under the constraints of visibility time windows and onboard transmission and reception isolation, the objective is set to minimize the number of LEO satellites used while achieving effective navigation signal transmission and SBM. The constraints of resource allocation and the mathematical expression of the optimization objective are derived. A dynamic programming approach is then employed to determine the optimal resource allocation scheme. Analytical results demonstrate that compared to Greedy and Divide-and-Conquer algorithms, this algorithm achieves the highest resource utilization rate and the lowest computational complexity, making it highly valuable for future resource allocation in LEO constellations.

Intelligent control algorithm for dynamic positioning control system

Introduction: The dynamic positioning system resists the environmental forces such as wind, wave and current acting on the ship through the thruster, so that the ship can remain in the position required by the sea level as much as possible, and the operation is very convenient. But its current dynamic positioning ability can not meet people's needs.Methods: A Kalman filter based on untracked optimization was designed for dynamic positioning control system. Then the intelligent control algorithm is designed for the dynamic positioning top-level controller and thrust optimal distribution controller, which occupy an important position in the system, namely the adaptive weight variation particle swarm optimization algorithm and thrust optimal distribution algorithm.Results and Discussion: The average position error of three degrees of freedom after filter processing is 1.53 m. Compared with other mainstream controllers, the mean root error of controllers based on adaptive weight variation particle swarm optimization in environment A and B is 2.295 and 1.8 m, respectively. In environment C, the controller based on thrust optimization allocation algorithm can get the optimal solution when the full rotary thruster reaches the 7 s and the channel thruster reaches the 4 s. The thrust exclusion zone is crossed at 46 s in environment D. In the dynamic positioning capability curve of the system, the experimental hull can balance the different environmental loads at all angles. In summary, the intelligent control algorithm proposed in this paper can effectively improve the positioning ability of the dynamic positioning control system and meet the needs of people for ship navigation today.

Application of fuzzified genetic algorithm for optimal allocation of renewable energy sources into the power system

Application of fuzzified genetic algorithm for optimal allocation of renewable energy sources into the power system

Collaborative optimization model and algorithm for airport capacity and traffic flow allocation.

How to efficiently utilize the existing airport capacity without physical expansion and considerable economic inputs to meet air traffic needs is one of the important tasks of air traffic management. To improve the efficiency of capacity utilization, it is necessary to find the actual airport capacity properly. In this work, taking Shuangliu International Airport as an example, a methodology for capacity estimation is proposed that combines the empirical method with an analytical approach that uses historical performance data from the airport to construct a capacity envelope to approximate the airport's actual capacity to the greatest extent, establishes a collaborative optimization model that reflects the inherent relations between airport capacity and arrival and departure traffic demand, adopts an improved optimization algorithm to solve the model, and generates an optimal flight allocation scheme. Priority ratio is introduced to dynamically adjust management preferences for arrival and departure traffic demand to further reveal the synergy mechanism between departure and arrival traffic flow demand and the airport capacity. The result shows that the Flight On-time Performance rate is lifted by 6% in the case study which proves the feasibility of the proposed method, demonstrating its value for maximizing airport capacity and traffic flow demand without requiring expansions on airport scales.

Height-posture and load coupling control methodology of URAPM and its application in active suspension control of multi-axle vehicles

Unidirectional redundant actuated parallel mechanism (URAPM) is the physical prototype of active suspension system, truck crane outrigger system, etc. As the number of adjustment mechanisms (AMs) increases to more than three, the difficulty of height-posture control of the upper component (UC) increases considerably. The AMs load control is also tough but highly underappreciated, which in turn causes height-posture disturbance. In this work, a convenient height-posture and bearing load coupling control methodology and its application in the active suspension system are presented. Firstly, by characterizing the force-deformation coupling relationship of the URAPM, a basic geometry and load coupling control model is proposed. And for the desired bearing load control, different optimal load allocation algorithms are developed and evaluated. Secondly, coupling control models for the flexible load control under stationary height-posture, and the flexible height-posture control under balancing load are constructed respectively. Finally, bench tests based on a six-legged URAPM (equivalent to a three-axle active suspension vehicle) validate the proposed method can achieve high precision, high convergence, and most attractively, the height-posture and load coupling adjustment with few iterations, thus demonstrating its good application prospect in low-speed off-road maneuvering of multi-axle vehicles and other related fields.

Binary Shuffled Frog Leaping Algorithm for Optimal Allocation of Power Quality Monitors in Unbalanced Distribution System

This paper deals with optimal detection of number and best locations of power quality monitors (PQMs) in an unbalanced distribution network based on the monitor reach area concept. The proposed model uses binary strings which represent the state presence or absence of PQMs in each buses of network. In this paper the binary version of shuffled frog-leaping algorithm (BSFLA) because of having ability to improve the search capability with a fast convergence rate is utilized for optimization process. The overall cost function is formulated to optimize the two indices which are monitor overlapping index and sag severity index. The only optimization constraint in this problem is that, the number of monitors that can detect voltage sags due to a fault at a specific bus must not be zero. In this study, DIGSILENT software is utilized for fault analysis while the optimization problem is handled by the BSFLA. To verify the proposed algorithm, the IEEE 34 Bus unbalanced distribution network, is considered as case studies and results are compared to the similar investigations so as to illustrate the effectiveness of proposed algorithm.

Data-driven allocation of smart grid-connected system based on ant colony optimization algorithm

In the era of advanced technology, integrating and distributing data are crucial in smart grid-connected systems. However, as energy loads continue to increase, practical implementation of these systems faces challenges in resource allocation and lacks efficient data collaboration. In this study, the ant colony optimization algorithm is further investigated for stochastic crossover systems and cluster nodes in intelligent path planning management. To improve the pheromone setting method in smart grid-connected systems, we propose an adaptive intelligent ant colony optimization algorithm called the Group Allocation Optimization Algorithm (GAOA). This algorithm expands the pheromone transmission rate of network nodes, establishes a multi-constrained adaptive model with data mining as the pheromone target, and analyzes the accuracy of resource allocation to import the optimal scheme for smart grid-connected systems. Through experimental results, we demonstrate that the optimized adaptive ant colony algorithm leads to effective improvements in grid-connected systems, pheromone evaluation, data throughput, convergence speed, and data load distribution. These findings provide evidence that the optimized ant colony algorithm is both feasible and effective for resource allocation in smart grid-connected systems.

A joint resource optimization allocation algorithm for NOMA‐D2D communication

AbstractThe AIoT, with its artificial intelligence capabilities, can further enhance Device‐to‐Device (D2D) communication. Based on Non‐Orthogonal Multiple Access (NOMA), D2D technology can effectively alleviate wireless spectrum resource pressure and improve the capacity of heterogeneous cellular networks. However, it also introduces significant system interference issues. In this paper, a resource allocation algorithm is proposed for the NOMA‐D2D heterogeneous cellular network, based on a multi‐agent deep reinforcement learning framework. Firstly, the algorithm allocates appropriate channels to D2D clusters. Then, the power allocation factors and D2D transmit power are jointly optimized to suppress the interference and improve the system performance. Simulation results show that both the channel allocation efficiency and the power control performance of the system can be significantly improved.

A Joint Optimization Algorithm for Trajectory Planning and Resource Allocation of Vehicle Mobile Base Stations for On-Demand Coverage Networks

In today’s urban hotspot regions, service traffic exhibits dynamic variations in both time and location. Traditional fixed macro base stations (FMBSs) are unable to meet these dynamic demands due to their fixed coverage and capacity. Therefore, this paper introduces a novel algorithm for the joint optimization of the placement of terrestrial vehicle-mounted mobile micro base stations (mBSs), the correlation of service clusters (SCs) with mBSs, and resource assignments. The objective is to maximize the matching degree between network capacity and service demands while adhering to constraints related to the power, coverage, and bandwidth of mBSs, as well as the data rate required for the services. Additionally, we investigate the mobility of the mBSs towards the SCs in the spatiotemporal changing service demand network and obtain optimal trajectories for the mBSs. We begin by formulating the problem of maximizing the matching degree by analyzing the capacity provided by the base stations and the network service demand. Subsequently, we derive solutions to the optimization problem using our algorithm. The simulation results demonstrate that the proposed algorithm can effectively meet the capacity demand of dynamically changing hotspot regions and achieve on-demand, resilient coverage of hotspot regions in the network.

KMSSA optimization algorithm for bandwidth allocation in internet of vehicles based on edge computing

KMSSA optimization algorithm for bandwidth allocation in internet of vehicles based on edge computing

GWO-Based Power Allocation Optimization Algorithm for Consumer IoT Networks

GWO-Based Power Allocation Optimization Algorithm for Consumer IoT Networks

DITRA: an efficient event-driven multi-objective optimization algorithm for bandwidth allocation in IoT environments

DITRA: an efficient event-driven multi-objective optimization algorithm for bandwidth allocation in IoT environments

Online Active Fault Detection for Over-Actuated Systems With Prescribed Control Performance

In the field of fault detection, active fault detection for dynamic systems is an emerging research topic. By redesigning the control input, active fault detection can enhance fault detection performance. In this study, the online active fault detection problem is investigated for over-actuated systems, where the control input is designed by considering both the prescribed control performance and the fault detection performance. A two-stage input design architecture is constructed, where the first stage is a virtual controller design and the second stage is a control allocation design. In the first stage, the virtual control input is designed by utilizing a prescribed performance function to fulfill the control requirement. In the second stage, a differential evolution method is adopted to obtain an optimal control allocation algorithm for improving the fault detection performance. The designed control input can both improve fault detection performance and achieve the prescribed control performance. Two cases, including a numerical example and a manned submersible propulsion system, are studied to support theoretical results. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In this study, the aim is to design a control input for over-actuated systems to obtain better fault detection performance while achieving prescribed control performance. To achieve it, a two-stage control input design architecture is constructed, where the first stage is a virtual controller design and the second stage is a control allocation design. In the first stage, choose a proper prescribed performance function and a sliding mode function to accomplish the transient and steady behavioral bounds on the tracking errors. In the second stage, design a special observer to generate the system residual signal, which is used for indicating system faults. Based on the residual signal, construct an index for the control allocation algorithm. Differential evolution algorithm is applied to obtain the optimal control allocation algorithm by maximizing the index. Thus the control allocation algorithm which can improve fault detection performance is designed. In this way, the input can improve fault detection performance without affecting system behavior. The proposed technique of this study can be applied to dynamic systems with input redundancy.

Digital Human Resources Optimization Allocation Model and Algorithm of Artificial Intelligence Technology

From the perspective of optimal allocation of enterprise human resources, in view of the challenges brought by changes in enterprise organizational structure and changes in job requirements in the digital environment to human resource allocation, the article aims to improve the competitiveness of enterprises, combines the characteristics of enterprise organizational structure and job demand, and analyzes the optimal allocation of human resources. The article builds a digital human resources optimization allocation model based on artificial intelligence technology, and designs an artificial intelligence-based digital human resources optimization allocation algorithm. The results show that the resource utilization rate of the digital human resources optimization allocation model based on artificial intelligence technology used in the experiment can reach up to 79.2%.

Cheetah optimization algorithm for simultaneous optimal network reconfiguration and allocation of DG and DSTATCOM with electric vehicle charging station

The potential of Electric Vehicles (EVs) to decarbonize the transportation industry has attracted a lot of attention in recent years in response to growing environmental concerns. Electric Vehicle Charging Stations (EVCSs) need to be properly located for widespread EV integration. The distribution system is facing additional challenges due to inclusion of EVCS. The adverse impacts of EVCS on the Radial Distribution Network (RDN) may be minimized using Distributed Generations (DGs) or Distribution Static Compensators (DSTATCOMs) or by reconfiguring the network. This paper uses a novel optimization technique to solve the problem of simultaneous optimal placement of EVCS with network reconfiguration and optimal planning (siting and sizing) of DGs and DSTATCOMs. The multiple objective functions are considered in order to minimize the active power losses, the voltage deviation, the investment costs for DGs and DSTATCOMs, and to increase the voltage stability of the system. A novel meta-heuristic Cheetah Optimization Algorithm (COA) is used to solve the optimization problem. To examine the effectiveness of the suggested strategy on 33-bus and 136-bus networks, several scenarios of simultaneous incorporation of EVCS, DG, and DSTATCOM installations with network reconfiguration are taken into consideration. The COA results are also compared to the results of grey wolf optimization and genetic algorithms.