Cooperative Optimization Research Articles

Hybrid multi-group stochastic cooperative particle swarm optimization algorithm and its application to the photovoltaic parameter identification problem

The accurate estimation of model parameters is significant for the simulation, evaluation, control, and optimization of photovoltaic systems. Recently, meta-heuristic algorithms(MHAs) have been proposed to solve the photovoltaic parameter identification problem. However, extracting the accurate and reliable parameters of PV models is still a great challenge, and many HMAs may present unsatisfactory performance due to their premature or slow convergence. Therefore, how to develop algorithms efficiently balancing the exploration and exploitation to improve the accuracy and reliability of the algorithm is extremely important. This paper proposes a Hybrid multi-group stochastic cooperative optimization algorithm (HMSCPSO). In the proposed algorithm, we designed a multi-group cooperation search mechanism to enhance the global search capability: Each group utilized different strategies. The first group used classic velocity and position updates, the second group employed the chaos strategy, and the third group utilized the lévy flight strategy. Through cooperation search between groups to increase the diversity of the population and reduce the possibility of falling into local optimum, but also concentrate some individuals to explore the current global optimum to improve the accuracy of the solution. HMSCPSO and its variants were tested on 27 benchmark functions to verify the proposed algorithm’s effectiveness. Then, the HMSCPSO is applied to solve four PV parameter identification problems of different photovoltaic models. Statistical experiment results demonstrate that the proposed algorithm has excellent advantages compared with other meta-heuristic algorithms in terms of accuracy, reliability, and convergence speed. Therefore, HMSCPSO is expected to be an effective parameter identification method for solar cells and photovoltaic modules.

Global path conflict detection algorithm of multiple agricultural machinery cooperation based on topographic map and time window

Path planning is one of the crucial problems of multi-machine cooperative navigation. In the context of regional farmland operation, multiple agricultural machines often need to complete multiple tasks together. Studies on global path conflict detection algorithms based on topographic maps and time windows have been conducted to solve the global path conflict problem of multiple agricultural machinery cooperation in the farmland operation environment. First, the global path conflict problem of multiple agricultural machinery cooperation was analyzed. The constraints of time window conflict detection and the time parameters to be considered in calculating the time window were proposed. Then, the global path preplanning was performed based on a topographic map and Dijkstra algorithm. According to the path preplanning results, the global path conflict was detected based on the time window. Finally, the global path conflict detection algorithm was simulated on MATLAB with the topographic map of Zhuozhou Experimental Farm as an example. The global path preplanning results show that the four paths overlapped in different degrees at nodes 1, 2, 7, 11, 15, 20, and 27. Path 3 and Path 1 were inclusive interval-type conflicts, and Path 4 and Path 1 were node-type conflicts. Path 3 adopted the change path strategy after the conflicting paths were replanned. The path was changed from [15-11-7-2-1-5-10-14-18-31] to [15-21-24-18-31]. Path 4 adopted the waiting strategy, and its paths did not change. Although the four paths still had different degrees of path overlap, there were no time conflict in the overlapping part of the replanned path. The simulation results show that the global path optimization and path conflict detection of multiple agricultural machinery cooperation could be realized based on the topographic map and time window. Moreover, the conflict resolution strategy with the least time could be obtained to achieve a safe, efficient, and conflict-free global path of multiple agricultural machinery cooperation. Thus, a foundation for further solving the path planning of multiple agricultural machinery cooperation in the complex operation environment of regional farmland is laid.

Research on the Development of China-Indonesia Tourism Cooperation Under the Background of the Belt and Road--Based on the Perspective of Normalization of Public Health Emergency Management 一带一路背景下中印尼旅游合作发展研究——以公共卫生应急管理常态化为视角

In 2022, China and Indonesia will enter the 72nd year of diplomatic relations, and since China's Belt and Road Initiative in 2013, the relationship between China and Indonesia in the fields of humanities, economy, trade and tourism has reached a new level. Tourism cooperation between the two countries is not only an important part of the initiative, but also an important way to meet the needs of tourism development in both countries, deepen trade and economic cooperation, and promote the construction of the Belt and Road. However, the development of tourism cooperation between the two countries has outstanding problems such as security issues, insufficient depth of cooperation and single approach, while the sudden outbreak of the epidemic at the end of 2019 has dealt a heavy blow to the tourism industry of the two countries and has affected the economy and trade of them. Based on the background of the Belt and Road, this article reorganizes the background, dynamics and obstacles of China-Indonesian tourism cooperation from the perspective of the normalization of public health emergency management, and builds a new path model to promote the deepening development of tourism cooperation between the two countries based on this, with a view to becoming an important path choice to promote the optimization and upgrading of bilateral tourism cooperation between China and Indonesia.

Advances in Electricity-Steering Organic Waste Bio-Valorization for Medium Chain Carboxylic Acids Production

Medium chain carboxylic acids (MCCAs, e.g., caproic acid, caprylic acid, etc.) with 6–12 carbon atoms are valuable platform chemicals produced from organic waste via microbial chain elongation metabolism named as reversed β-oxidation and fatty acid-biosynthesis cyclical pathway. Recently, many articles reported that electricity could not only serve as the external electron donor and provide the reduction equivalent required for chain elongation but also regulate the microbiome structure and metabolic behaviors to promote MCCAs formation. Electricity-steering MCCAs bioproduction has become an appealing technique to valorize low-value organic waste, paving an alternative pathway for net-zero carbon emission energy systems and sustainable socio-economic development. However, the MCCAs’ bioproduction from organic waste steered by electric field has not been comprehensively reviewed. From a systematical analysis of publicly available literature, we first covered the basic working principle, fermentation architecture, functional microflora, and metabolic pathway of MCCAs production driven by electricity. The strategies of substrate modulation, applied voltage/current regulation, electrode optimization, and microbial cooperation and stimulation for boosting electricity-driven MCCAs bioproduction are then scrutinized and extensively discussed. Ultimately, the pressing knowledge gaps and the potential path forward are proposed to provide pointers for consistently higher MCCAs yield and the transition from laboratory to market.

A novel framework for optimizing the edge network node for wearable devices

The Multi-access edge computing (MEC) server would provide context-aware capabilities. When edge computing uses high-quality computing performance to supplement edge applications with vast IoT-based data services, substantial constraints are placed on the collaboration of edge nodes. Conversely to cloud computing, situational circumstances in the edge network are more complicated. In this paper, we provide a novel Edge Network (EDN) optimization (EDN-Opt) to boost the efficiency of edge computing jobs. In particular, we initially specify the parameters for cooperative assessment through the Internet of Things (IoT). Furthermore, the effectiveness of the proposed architecture is shown using real datasets collected from elderly individuals and various activity trackers. A comprehensive study on QoC intended with EDN is used to assess collaboration effectiveness. The cooperative optimization method developed provides improved efficiency To assess the effectiveness of EDN optimization, the discrepancy between the proposed equivalent and the real equivalent is examined. Investigation in this sector analyses several practical cases. The Spearman rank correlation factor is +1 or −1 when a perfect monotonic association is attained with no identifying data. The examination of this article demonstrates that trials show that our proposed edge cooperation optimization technique can quickly assess the EDN and then provide information on the collaborative relationship's replacement occurrences that can help the EDN's design.

Hierarchical cooperative eco‐driving control for connected autonomous vehicle platoon at signalized intersections

AbstractVehicles in the platoon can sufficiently incorporate the information via V2X communication to plan ecological speed trajectories and pass the intersection smoothly. Most existing eco‐driving studies mainly focus on the optimal control of a single vehicle at an individual signalized intersection, while rarely involving the cooperative optimization of a group of vehicles at successive signalized intersections. In this study, a hierarchical cooperative eco‐driving control for a connected autonomous vehicle (CAV) platoon is proposed to enhance traffic mobility and energy efficiency, wherein the velocity trajectory of the leading vehicle at each isolated signalized intersection is planned using the pseudo‐spectral method, and then the cooperative optimization of following vehicles in the platoon is conducted via rolling optimization, with the aim of improving driving comfort, safety and energy economy for the platoon. The simulation results highlight that the proposed hierarchical cooperative eco‐driving strategy can lead to preferable vehicle‐following behaviours and platoon driving performance, and the overall energy consumption and trip time of vehicle platoon are respectively reduced by 26.10% and 2.83%, compared with that under manual driving. Furthermore, the overall energy economy is promoted by 4.95% and 4.60%, compared with cooperative adaptive cruise control and intelligent driver model‐based platoon control strategies.

Distributed multi-energy storage cooperative optimization control method for power grid voltage stability enhancement

To solve the problem of grid voltage fluctuation in multi-energy systems, this study proposes a voltage optimization control method based on the coordination of battery storage, heat storage, and gas storage. First, the different operation modes of electricity, heat, and gas in the system were analysed, and a coupling model of an energy system with multi-energy storage was established. Second, according to the energy storage characteristics of distributed energy storage, a collaborative optimization model of distributed energy storage was established by studying the influence mechanism of battery storage, gas storage, and heat storage on grid voltage stability. Taking the minimum voltage fluctuation as the optimization objective, a coordinated operation strategy of distributed energy storage is proposed under the premise of meeting the constraints of distributed energy storage. Combined with the real-time charging and discharging states of each distributed energy storage system, the particle swarm optimization method was used to maintain the node voltage stability. Finally, a regional multi-energy system is taken as an example to verify that the proposed collaborative optimization control method can effectively improve grid voltage stability. Through the results, it can be seen that the node voltage in the regional power grid has increased by 0.012p.u., 0.012p.u. 0.011p.u. 0.014p.u.

Learning-Based Control With Decentralized Dynamic Event-Triggering for Vehicle Systems

The optimal control of multi-input system can be described by a multiplayer nonzero-sum differential game. This article theoretically presents an event-based adaptive learning scheme to approximate the Nash equilibrium, and practically addresses the cruise control problem for Caltech vehicle systems. This design is deployed in two aspects. On one hand, the reinforcement learning is implemented through critic neural network architecture and recalling stored experience data. On the other hand, in view of that each player’s preference is different, the decentralized triggering manner is considered to reduce communication. Based on the continuous state, the local sampled state is defined for each player, and a static triggering mechanism is formulated first. The decentralized dynamic triggering is then promoted by designing an auxiliary variable whose dynamics are constructed using static triggering information. Next, the proposed learning scheme is examined on a four-player numerical system. Finally, the learning-based controller is tested on a single-vehicle system under different tracking commands, and then, it is extended to multivehicle systems to realize cooperative optimization by introducing a novel game-in-game structure.

Spatiotemporal Characteristics and Determinants of Tourism Cooperation Network in Beijing–Tianjin–Hebei Region

The optimization of the cooperation network is a key link to accelerate the high-quality development of regional tourism. Taking the Beijing–Tianjin–Hebei region as an example, this paper measures the tourism cooperation intensity with the modified gravity model, on which the original, binary, and Top networks are generated to identify the spatiotemporal evolution characteristics from the multi-dimensional difference–association–agglomeration model, and provide insight into the determinants by the GeoDetector model. The results show that (1) the cooperation network reveals a diffusion trend with Beijing–Tianjin as the main axis chain, and southward expansion, and the overall differences tend to moderate at a slow pace, among which the north is the weak area. (2) The robustness of the cooperation network association structure is enhanced, showing that the outgoing equilibrium is improved, while the cohesion is strengthened and accessibility changes little. Furthermore, the cities show a core–edge distribution pattern in terms of power roles. (3) The cooperation network shows the phenomenon of hierarchical agglomeration gradually with the expansion of network scale, and eventually evolves into two camps: the Beijing–Tianjin cooperation circle and the Shijiazhuang–Xingtai cooperation circle. (4) Tourism cooperation belongs to the locational traffic constraint type, and making up for the shortcomings of rural development is another key to further enhancing regional tourism cooperation. The future optimization of regional tourism cooperation needs to seek multifactorial promotion paths.

A MOEA/D with global and local cooperative optimization for complicated bi-objective optimization problems

Multi-objective evolutionary algorithm based on decomposition has been well-recognized in addressing bi-objective optimization problems. However, it is a serious challenge for MOEA/D to optimize real-world complicated bi-objective optimization problems which have irregular optimal objective spaces, such as long-tailed, peaked, and disconnected types. Hence, an improved MOEA/D with global and local cooperative mechanisms is proposed in this paper for complicated bi-objective optimization problems. The overall optimization is carried out by the coordination of global and local search phases. Specifically, a bidirectional global search strategy based on reference points is first used to broaden the search regions. Aiming at the irregularity of the Pareto fronts, an adaptive neighborhood search strategy is proposed according to the individual sparsity, which can further explore near the obtained global optimal solutions. The cooperation of these two strategies is designed to improve the distribution and uniformity of the population on complicated bi-objective optimization problems. The performance of the proposed algorithm is investigated on a representative set of benchmark test functions and a real-world water resource allocation problem. The simulation results show that the proposed algorithm is very promising for complicated bi-objective optimization problems.

Research on power-load matching dispatching of Hydro-Wind-PV hybrid system in cascade

With the goal of “double carbon,” wind power and PV are connected to the grid on a large scale, which poses a great challenge to the safe operation of the grid. The synergistic optimization of “hydro-wind-PV hybrid systems” can bring into play the ability of coordination and mutual assistance between different types of power sources, which is one of the important measures to increase the safe operation of the power grid and reduce abandoned wind and photovoltaic power. Based on the analysis of the characteristics of hydro-wind-PV power, the “hydro-wind-PV” integrated network-source cooperative optimization scheduling model is constructed to maximize the source-load matching and the consumption of clean energy. Finally, the results of a case study of a hydropower-wind power-photovoltaic cluster project in southwest China are used to verify the rationality and feasibility of the model.

Enhanced Energy Storage Properties in Lead-Free (Na0.5Bi0.5)0.7Sr0.3TiO3-Based Relaxor Ferroelectric Ceramics through a Cooperative Optimization Strategy.

Although relaxor ferroelectrics have been widely investigated owing to their various advantages, there are still impediments to boosting their energy-storage density (Wrec) and energy-storage efficiency (η). In this paper, we propose a cooperative optimization strategy for achieving comprehensive outstanding energy-storage performance in (Na0.5Bi0.5)0.7Sr0.3TiO3 (NBST)-based ceramics by triggering a nonergodic-to-ergodic transformation and optimizing the forming process. The first step of substituting NaNbO3 (NN) for NBST generated an ergodic state and induced polar nanoregions under the guidance of a phase-field simulation. The second step was to apply a viscous polymer process (VPP) to the 0.85NBST-0.15NN ceramics, which reduced porosity and increased compactness, resulting in a significant polarization difference and high breakdown strength. Consequently, 0.85NBST-0.15NN-VPP ceramics optimized by this cooperative two-step strategy possessed improved energy-storage characteristics (Wrec = 7.6 J/cm3, η = 90%) under 410 kV/cm as well as reliable temperature adaptability within a range of 20-120 °C, outperforming most reported (Na0.5Bi0.5) TiO3-based ceramics. The improved energy-storage performance validates the developed ceramics' practical applicability as well as the advantages of implementing a cooperative optimization technique to fabricate similar high-performance dielectric ceramics.

Advanced control framework of regenerative electric heating with renewable energy based on multi-agent cooperation

Heating, ventilation, and air conditioning (HVAC) systems have accounted for a significant proportion of building energy growth. It is crucial to develop efficient HVAC systems based on renewable energy (RE) utilization to reduce energy costs (EC) and carbon emissions. However, HVAC system regulation is meeting a huge challenge owing to the randomness of RE and fluctuation of demand-side load. Aiming to tackle this problem, a multi-agent cooperative optimization control framework based on deep reinforcement learning was proposed to achieve the optimal match between the supply and demand sides of the regenerative electric heating system. Then, the dueling double deep Q-network and deep deterministic policy gradient method were applied to achieve the discrete and continuous control of independent agents, respectively, and value-decomposition network was exploited to realize the above agents' cooperative optimization. Meanwhile, to further improve the stability, efficiency, and flexibility of the optimization control framework, the feasible action screening mechanism, prioritized experience replay mechanism, and regulation mechanism based on occupant behavior were introduced. According to simulation results, in contrast to the baseline model, the reduction of thermal discomfort duration, unconsumed RE, and EC under the control of the multi-agent cooperative optimization framework can reach 84.86%, 70.79%, and 16.08%, respectively.

Optimal Controller for a Roundabout with Cooperative Optimization

Optimal Controller for a Roundabout with Cooperative Optimization

Sewage Discharging in a Line: Global Optimization and Grand Cooperation

Sewage Discharging in a Line: Global Optimization and Grand Cooperation

Cooperative Optimization Design of Multi-axis Mining Vehicle Steering System

Cooperative Optimization Design of Multi-axis Mining Vehicle Steering System

Intelligent Suppression of Non-Maneuvering Magnetic Interference of Aeromagnetic UAV

In the aeromagnetic survey based on a fixed-wing unmanned aerial vehicle (UAV), the non-maneuvering magnetic interference generated by the magnetic components can significantly reduce the data quality of the airborne magnetometer. Aircraft layout modification is a standard method of addressing this problem. However, the existing layout modification methods rely heavily on personal experience and cannot precisely determine the overall layout of multiple magnetic components quantitatively and cooperatively. Even if the layout of magnetic components is determined through multiple experiments, obtaining an optimal suppression effect of the magnetic interference is difficult. An intelligent suppression method of non-maneuvering magnetic interference is proposed to address this problem. An eccentric multi-magnetic dipole (EMMD) model that can accurately characterize the primary magnetic components is established; then, an intelligent cooperative optimization method for the layout of magnetic components based on the Aquila Optimizer (AO) algorithm is proposed to quantitatively determine the optimal combination of positions and orientations of multiple components. A dedicated experimental platform, including a non-magnetic rotating stage, a three-axis fluxgate magnetic sensor, an optically pumped magnetometer, and an aeromagnetic tester, was built to validate the proposed characterization model and layout optimization method. The experimental results observed in a non-magnetic laboratory demonstrated that the goodness-of-fits of the EMMD model to the total-field and three-component magnetic interference generated by the magnetic components are all above 0.932. Furthermore, the total-field intensities of magnetic interference in the magnetometer areas located at the left and right wingtips were suppressed by 98.7% and 98.9%; the magnetic inhomogeneities in the two areas were reduced by 97.7% and 98.4%; the magnetic imbalance between the two wingtips was reduced by 95.5%.

Significantly enhanced energy storage density in lead-free barium strontium titanate-based ceramics through a cooperative optimization strategy

An ultrahigh Wrec of 5.53 J cm−3 (460 kV cm−1) has been achieved in the 0.91BST–0.09BZT ceramic, together with good temperature stability at 200 kV cm−1 and excellent fatigue resistance.

Encrypted Decentralized Multi-Agent Optimization for Privacy Preservation in Cyber-Physical Systems

Decentralized optimizations have been extensively applied in large-scale industrial cyber-physical systems to achieve control scalability. However, state-of-the-art methods heavily depend on explicit communications between participants, exposing the entire control framework to data confidentiality risks. To overcome this challenge, in this article, a privacy-preserving decentralized multi-agent cooperative optimization paradigm was developed via integrating cryptography into decentralized optimization. The proposed approach can effectively protect participants’ privacy against external eavesdroppers, honest-but-curious agents, and the system operator. Theoretical security and correctness analyses are provided. Simulations of numerical examples and experiments on a real-world platform are given to demonstrate the security, accuracy, and applicability of the proposed method.

Cooperative energy management and eco-driving of plug-in hybrid electric vehicle via multi-agent reinforcement learning

The advanced cruise control system has expanded the energy-saving potential of the hybrid electric vehicle (HEV). Despite this, most energy-saving researches for HEV either only optimize the energy management strategy (EMS) or integrate eco-driving through a hierarchically optimized assumption that optimizes EMS and eco-driving separately. Such kinds of approaches may lead to sub-optimal results. To fill this gap, we design a multi-agent reinforcement learning (MARL) based optimal energy-saving strategy for HEV, achieving a cooperative control on the powertrain and car-following behaviors to minimize the energy consumption and keep a safe following distance simultaneously. Specifically, a plug-in HEV model is regarded as the research object in this paper. Firstly, the HEV energy management problem in the car-following scenario is decomposed into a multi-agent cooperative task into two subtasks, each of which can conduct interactive learning through cooperative optimization. Secondly, the energy-saving strategy is designed, called the independent soft actor–critic, which consists of a car-following agent and an energy management agent. Finally, the performance of velocity tracking and energy-saving are validated under different driving cycles. In comparison to the state-of-the-art hierarchical model predictive control (MPC) strategy, the proposed MARL method can reduce fuel consumption by 15.8% while ensuring safety and comfort.