Collaborative Optimization Method Research Articles

Collaborative optimization of reference powers and droop coefficients in DC distribution networks considering flexibility enhancement

In droop-controlled DC distribution networks (DCDNs), the uncertainties of loads and renewable energy powers worsen the system performances. This paper proposes a collaborative optimization method of reference powers and droop coefficients to optimize more objectives that including reducing line losses, improving the voltage level, and enhancing the flexibility of DCDNs. Firstly, a fast and accurate flexibility margin evaluation method is presented for DCDNs, in which the allowable power variation intervals under operational constraints are used to quantify the flexibility margins. The analytic relations of allowable power variation intervals and operational constraints are established by the linear power flow model, and the flexibility margins can be calculated quickly and directly. Secondly, the bi-level collaborative optimization method of reference powers and droop coefficients is proposed to optimize more objectives simultaneously. The reference powers are optimized to improve the voltage level and reduce line power losses in the upper-level optimization, and the droop coefficients are optimized to enhance the system flexibility in the lower-level optimization. Finally, multiple test systems with different topologies are simulated in detail. The case study demonstrates that: 1) The flexibility evaluation method can quantify the margins of operational constraints quickly and accurately. Compared with the bisection method, the calculation time of proposed flexibility evaluation method reduce 99.52% with the error of 1.68%. 2) The proposed collaborative optimization of reference powers and droop coefficients can effectively reduce line losses and the risks of voltage off-limit and VSC overload of DCDN under uncertainties.

Data-driven hierarchical collaborative optimization method with multi-fidelity modeling for aerodynamic optimization

The aerodynamic optimization of aircraft based on evolutionary algorithms generally requires thousands or even tens of thousands of iterations. Moreover, it faces challenges such as high-dimensional design variables and high computational costs, leading to low efficiency in algorithm search and poor optimization results. In order to enhance the efficiency of aerodynamic optimization, a data-driven collaborative optimization and multi-fidelity hierarchical modeling method is proposed. Firstly, a Clustering-Enhanced Teaching-Learning-Based Optimization (CETLBO) algorithm is introduced to improve its exploration and exploitation capabilities. By employing a collaborative optimization strategy, a divide-and-conquer paradigm is utilized to efficiently solve high-dimensional, nonlinear, and complex engineering optimization problems. Secondly, a two-stage hierarchical aerodynamic optimization framework is designed to reduce computational costs by integrating data from different fidelity levels. Stage 1 adopts a data-driven approximate optimization method to provide prior knowledge for the high-fidelity optimization of stage 2. Finally, the proposed method is applied to the optimization design of supersonic wing shapes. Compared to traditional optimization design systems, it demonstrates high efficiency in high-dimensional aerodynamic optimization problems, yielding better optimization results.

Researches on the aerodynamic and stealth characteristics for flying wing fighter

Flying wing configuration fighter is very promising for the advanced fighter in the future. This paper studies the aerodynamic and stealth characteristics of flying wing fighter. Firstly, the effect of sharp leading edge on the subsonic, transonic lift and drag characteristics and stealth characteristics of the fighter are studied. The results show that the sharpen of leading edge of the inboard wing could greatly improve the lateral stealth characteristics while maintaining the subsonic and transonic lift characteristics. In order to furtherly improve its aerodynamic and forward stealth characteristics, the aerodynamic/stealth collaborative design optimization method based on discrete adjoint is carried out. And the results presented that there are obvious contradictions in the subsonic, transonic and supersonic lift characteristics of the flying wing fighter, and the improvement of the forward stealth characteristics will also damage the subsonic lift characteristics. Therefore, it is necessary to make a compromise between the subsonic, transonic and supersonic lift characteristics, drag characteristics and stealth characteristics in the shape design of flying wing fighter.

Force-position collaborative optimization of rope-driven snake manipulator for capturing non-cooperative space targets

With the rapid development of space activities, non-cooperative space targets increase swiftly, such as failed satellites and upper stages, threating normal spacecrafts seriously. As there are some problems in the capture process, such as excessive collision and fast tumbling of targets, manipulator with redundant Degrees of Freedom (DOFs) can be used to improve the compliance and therefore solve these problems. The Rope-Driven Snake Manipulator (RDSM) is a combination of hyper-redundant DOFs and better compliance, and therefore it is suitable for capturing mission. In this paper, a snake manipulator mechanism is designed, and the complete kinematic model and system dynamic model considering RDSM, target and contact is established. Then, to obtain the configuration of joint with hyper-redundant DOFs, an improved motion dexterity index is proposed as the joint motion optimization target. Besides, the force-position collaborative optimization index is designed to adjust active stiffness, and the impedance control method based on the modified index is used to capture the space target. Finally, the proposed force-position collaborative optimization method is verified by virtual prototype co-simulation. The results demonstrate that based on the proposed method, the collision force is reduced by about 25% compared to normal impedance control, showing higher safety.

Collaborative Optimization of Dynamic Lane Assignment and Signal Timing for Incident-Affected Intersections

Traffic accidents can lead to rapid changes in the traffic supply and demand at urban intersections, thus causing a severe traffic supply/demand imbalance during specific periods. Responding to accidents requires dynamic and accurate adjustments to optimize intersection resources collaboratively and enhance the real-time reliability and stability of traffic flows during such accidents. Currently, research on traffic incidents rarely considers real-time data-based collaborative optimization theories. Therefore, this study, supported by real-time incident detection technology and accident data, first considers the location and intensity of traffic incidents to update dynamically the changes in intersection traffic demand and supply. Subsequently, a dynamically collaborative optimization method is proposed based on lane assignment and signal timings to minimize the sum of variances of the degree of saturation of various approach lanes. Finally, various traffic demand scenarios are set, and the effectiveness of the proposed model is validated based on numerical and sensitivity analyses. The results demonstrate that compared with signal-only optimization and the highway capacity methods (HCM), the collaborative optimization method presented in this study reduces the average vehicular delay percentages by 8.54% and 16.47%, respectively. Sensitivity analysis indicates that, under various detour rates and detour modes, collaborative optimization methods have effectively mitigated the average vehicular delay at upstream intersections to varying degrees. In the context of real-time accident response, collaborative optimization methods demonstrate a capacity to promptly address the urgency of incidents occurring and maintain a sustained reduction in overall delay levels following detours.

Multi-task collaborative method based on manifold optimization for automated test case generation based on path coverage

Automated test case generation based on path coverage is not only a large-scale black-box optimization problem, but also a key scientific problem in software automatic testing technology. Evolutionary algorithms and other search-based algorithms are representative methods for this problem. However, existing research mainly focuses on the multi-function case, where generating test cases for multiple functions is difficult due to the combinatorial explosion of the path number. In this paper, we propose a multi-task collaborative method based on manifold optimization by considering the topological manifold relationship between the test case space (decision space) and the program path space (target space). This method achieves the goal of collaborative optimization for different function coverage tasks by coordinating the allocation of computing resources and knowledge transfer mechanisms among them. To verify the effectiveness of the proposed method, we compare it with general solution methods for single-function automated test case generation based on path coverage, such as the manifold-inspired search-based algorithm. The experimental results show that the proposed method outperforms the compared single-function optimization algorithms especially on programs with strong coding similarities. This study verifies the feasibility of collaborative optimization methods for solving large-scale black-box optimization problems, such as the automated test case generation based on path coverage, and expands their application scenarios.

Genetic Algorithm Based on Opposite Chromosome Multiple Genetic Operations Combined with Task Load Balancing

A collaborative optimization method for task allocation and path planning in multi-UAV execution of multi-objective cooperative inspection tasks is proposed. The method is based on the Opposite Genetic Algorithms (OGA), which combines the actual task completion time with the balance of UAV inspection flight time, fault downtime, and maximum-minimum time load. A Task Balancing Opposite Chromosome Multiple Mutation Operator Genetic Algorithm (TOMGA) is introduced to solve the task load balancing problem by optimizing the task allocation among multiple UAVs with time as the optimization objective. Simulation results demonstrate that, this algorithm can effectively allocate inspection tasks to UAVs and generate initial flight routes. It resolves the issue of task load imbalance, improves the rationality of task allocation, enhances convergence speed, and overcomes the problem of local optima.

Collaborative optimization on cooling channel flow resistance and winding temperature for integrated cooling of motor and controller

The efficiency and working performance of the motor and controller system in micro-electric vehicles depends mainly on its thermal performance, which is contingent upon effective and active cooling. The working temperature of the motor and controller may be too high during operation, which properly causes motor power reduction or motor breakdowns. Therefore, it is crucial to design a good cooling channel for better cooling the integrated motor and controller system. This work proposes an integrated cooling channel of motor and controller, and the fluid-solid coupling analysis is applied for analyzing the velocity, pressure distributions of the cooling channel, and temperature distributions of motor solid components. Meanwhile, the collaborative optimization method based on the multi-island genetic algorithm (MIGA) is used to attain optimal design parameters of the integrated cooling channel of the motor and controller for obtaining a better cooling channel design with lower flow resistance and lower winding temperatures. Results show that the original cooling channel’s flow resistance and maximum winding temperature are 31.87 kPa and 120.04°C, respectively. The flow resistance and winding temperature are pretty significant. After optimization, the local vortex flow and zero velocity area in optimal cooling channel design are improved, the coolant flow velocity becomes more uniform, and the flow resistance is significantly reduced. The flow resistance and maximum winding temperature of the optimal cooling channel design are 12.5 kPa and 116.76°C, respectively, which are decreased compared with the original cooling channel structure, with a decrease of 39.4% and 2.7%, respectively. The research findings in this work can provide theoretical reference for solid components temperature evaluation of motor and give valuable data to cooling performance evaluation and optimization of integrated cooling channel for motor and controller system.

Research on decentralized resource operation optimization of virtual power plant with 5G base station

Abstract The extensive construction and promotion of 5G base stations (5GBSs) have led to a surge in communication energy consumption, as 5G energy consumption is about three to five times that of 4G. To reduce the energy consumption of 5GBS, this article incorporates 5GBS into power demand side management and proposes a flexible resource collaborative optimization method that integrates 5GBS with virtual power plants (VPPs). Firstly, starting from the basic structure of 5GBS, the adjustable potential of 5GBS and the paths for participating in VPP optimization were analyzed. Secondly, with the minimum operating cost as the optimization objective and considering the constraints of 5GBS and various decentralized resources, a demand side decentralized resource collaborative optimization model for VPP integrating 5GBS was established. This model can be solved using the Non-dominated sorting genetic algorithm II (NSGA-II) algorithm. The results indicate that the participation of 5GBS in power demand side management can reduce their comprehensive energy consumption and operating costs.

Optimal scheduling of microgrids considering real power losses of grid-connected microgrid systems

Energy conservation, emission reduction and vigorous development of new energy are inevitable trends in the development of the power industry, but factors such as energy storage loss, solar energy loss and line loss in real power situations have led the problem to a complex direction. To address these intricacies, we use a more precise modeling approach of power loss and propose a collaborative optimization method integrating the Deep-Q-Network (DQN) algorithm with the multi-head attention mechanism. This algorithm calculates weighted features of the system’s states to compute the Q-values and priorities for determining the next operational directives of the energy system. Through extensive simulations that replicate real world microgrid (MG) scenarios, our investigation substantiates that the optimization methodology presented here effectively governs the distribution of energy resources. It accomplishes this while accommodating uncertainty-induced losses, ultimately achieving the economic optimization of MG. This research provides a new approach to deal with problems such as energy loss, which is expected to improve economic efficiency and sustainability in areas such as microgrids.

A Collaborative Neurodynamic Optimization Approach to Distributed Nash-Equilibrium Seeking in Multicluster Games With Nonconvex Functions.

In this article, we propose a collaborative neurodynamic optimization (CNO) method for the distributed seeking of generalized Nash equilibriums (GNEs) in multicluster games with nonconvex functions. Based on an augmented Lagrangian function, we develop a projection neural network for the local search of GNEs, and its convergence to a local GNE is proven. We formulate a global optimization problem to which a global optimal solution is a high-quality local GNE, and we adopt a CNO approach consisting of multiple recurrent neural networks for scattering searches and a metaheuristic rule for reinitializing states. We elaborate on an example of a price-bidding problem in an electricity market to demonstrate the viability of the proposed approach.

Multi-Source Collaborative Spatial False Target Deception and Optimization Methods for Radar Networks

Multi-Source Collaborative Spatial False Target Deception and Optimization Methods for Radar Networks

A Collaborative Neurodynamic Optimization Approach to Distributed Chiller Loading.

In this article, we present a collaborative neurodynamic optimization approach to distributed chiller loading in the presence of nonconvex power consumption functions and binary variables associated with cardinality constraints. We formulate a cardinality-constrained distributed optimization problem with nonconvex objective functions and discrete feasible regions, based on an augmented Lagrangian function. To overcome the difficulty caused by the nonconvexity in the formulated distributed optimization problem, we develop a collaborative neurodynamic optimization method based on multiple coupled recurrent neural networks reinitialized repeatedly using a meta-heuristic rule. We elaborate on experimental results based on two multi-chiller systems with the parameters from the chiller manufacturers to demonstrate the efficacy of the proposed approach in comparison to several baselines.

A Multi-MILP Model Collaborative Optimization Method for Integrated Process Planning and Scheduling Problem

A Multi-MILP Model Collaborative Optimization Method for Integrated Process Planning and Scheduling Problem

A multi-objective collaborative optimization method for excavator working devices based on knowledge engineering

To enhance the excavator performance considering the digging force and boom lift force under typical working conditions, this paper aims to solve the complex multiobjective optimization of the excavator by proposing a new knowledge-based method. The digging force at multiple key points is utilized to characterize the excavator’s performance during the working process. Then, a new optimization model is developed to address the imbalanced optimization quality among subobjectives obtained from the ordinary linear weighted model. The new model incorporates the loss degree relative to the optimal solution of each subobjective, aiming to achieve a more balanced optimization. Knowledge engineering is integrated into the optimization process to improve the optimization quality, utilizing a knowledge base incorporating seven different types of knowledge to store and reuse the information related to optimization. Furthermore, a knowledge-based multiobjective algorithm is proposed to perform the knowledge-guided optimization. Experimental results demonstrate that the proposed knowledge-based method outperforms existing methods, resulting in an average increase of 15.1% in subobjective values.

Uncertainty-aware error modeling and hierarchical redundancy optimization for robotic surface machining

Industrial robots are commonly utilized in in-situ machining, but their inherent limitations in stiffness and positioning accuracy can pose challenges in achieving precise freeform surface milling. Previous research primarily focused on robot stiffness optimization and positioning error prediction separately, neglecting the uncertainty of robot errors and the modeling of surface profile errors. To address these issues, this work introduces a novel evaluation method for surface profile errors in robotic milling. It combines geometric error prediction models, based on kinematic and stiffness models, with non-geometric errors using Gaussian process regression. Moreover, collaborative optimization of multiple redundancies in robotic surface milling systems is crucial for reducing machining errors effectively. To achieve this, this work proposes a collaborative optimization method for optimizing the robot’s posture change sequence and the workpiece’s orientation, considering the motion constraints and error requirements of the robot. Among them, this work introduces the improved Whale Optimization Star Algorithm (WOA*) method to address the optimization problem of coupling robot posture and workpiece’s orientation. The proposed method’s effectiveness is confirmed through simulations and experiments, which clearly demonstrate its capability in improving error prediction and machining accuracy in robotic milling.

Risk-averse stochastic capacity planning and P2P trading collaborative optimization for multi-energy microgrids considering carbon emission limitations: An asymmetric Nash bargaining approach

The increasing penetration of renewable energy with stochastic characteristics and continuous refinement of carbon emission policies put forward higher requirements for the construction of new power systems. This paper proposes a risk-averse stochastic capacity planning and peer-to-peer (P2P) trading collaborative optimization method for multi-energy microgrids (MEMGs) considering carbon emission limitations. First, a cooperative operation model for MEMGs considering the capacity planning of distributed generation units, uncertainty from renewable energy generations, carbon emission limitations and P2P electricity trading among MEMGs is formulated. Second, a risk-averse stochastic programming method is applied to avoid the potential risk losses caused by randomness and intermittence of renewable energy. Third, an asymmetric Nash bargaining approach is adopted to ensure the fair allocation of benefits and maintain the willingness of individual MEMGs to participate in cooperation. Then, to protect the privacy of individual MEMGs belonging to different stakeholders, the alternating direction method of multipliers is used to solve the two subproblems in a distributed manner. Meanwhile, to further alleviate the computation burdens, a diagonal quadratic approximation method is applied to linearize the quadratic penalty term in the augmented Lagrangian function and realize the parallel solution of all optimization subproblems. Moreover, the influence of different carbon emission targets on the optimal resource combination strategy of the system is investigated by introducing carbon emission factors. Simulations on different models, strategies, and distributed algorithms are conducted to verify the effectiveness and superiority of the proposed method.

Multi-Objective Capacity Optimization of Grid-Connected Wind–Pumped Hydro Storage Hybrid Systems Considering Variable-Speed Operation

The coordination of pumped storage and renewable energy is regarded as a promising avenue for renewable energy accommodation. Considering wind power output uncertainties, a collaborative capacity optimization method for wind–pumped hydro storage hybrid systems is proposed in this work. Firstly, considering the fluctuation of wind power generation caused by the natural seasonal weather and inherent uncertainties of wind power outputs, a combined method based on the generative adversarial network and K-means clustering algorithm is presented to construct wind power output scenarios. Then, a multi-objective wind–pumped storage system capacity optimization model is established with three objectives consisting of minimizing the levelized cost of energy, minimizing the net load peak–valley difference of regional power grids, and minimizing the power output deviation of hybrid systems. An inner and outer nested algorithm is proposed to obtain the Pareto frontiers based on the strength of the Pareto evolutionary algorithm II. Finally, the complementarity of wind power and pumped storage is illustrated through an analysis of numerical examples, and the advantages of variable-speed pumped storage in complementary operation with wind power over fixed-speed units are verified.

Multi-Objective Co-Operative Game-Based Optimization for Park-Level Integrated Energy System Based on Exergy-Economic Analysis

The park-level integrated energy system (PIES) can realize the gradient utilization of energy and improve the efficiency of energy utilization through the coupling between multiple types of energy sub-networks. However, energy analysis and exergy analysis cannot be used to evaluate the economics of PIES. In addition, conflicts of interest among integrated energy suppliers make the economic scheduling of the PIES more difficult. In this paper, we propose a multi-objective collaborative game-based optimization method based on exergy economics, in which the introduction of exergy economics realizes the economic assessment of any link within the PIES, and the optimization model constructed based on the potential game solves the problem of conflict of interest among multiple energy suppliers and improves the benefits of each supplier. Finally, taking a PIES in Guangzhou as an example, the rationality of the optimization scheme proposed in this paper is demonstrated by comparing it with the classical optimization scheme.

Multi-objective optimization design of anti-roll torsion bar using improved beluga whale optimization algorithm

PurposeThis study aims to reduce the redundant weight of the anti-roll torsion bar brought by the traditional empirical design and improving its strength and stiffness.Design/methodology/approachBased on the finite element approach coupled with the improved beluga whale optimization (IBWO) algorithm, a collaborative optimization method is suggested to optimize the design of the anti-roll torsion bar structure and weight. The dimensions and material properties of the torsion bar were defined as random variables, and the torsion bar's mass and strength were investigated using finite elements. Then, chaotic mapping and differential evolution (DE) operators are introduced to improve the beluga whale optimization (BWO) algorithm and run case studies.FindingsThe findings demonstrate that the IBWO has superior solution set distribution uniformity, convergence speed, solution correctness and stability than the BWO. The IBWO algorithm is used to optimize the anti-roll torsion bar design. The error between the optimization and finite element simulation results was less than 1%. The weight of the optimized anti-roll torsion bar was lessened by 4%, the maximum stress was reduced by 35% and the stiffness was increased by 1.9%.Originality/valueThe study provides a methodological reference for the simulation optimization process of the lateral anti-roll torsion bar.