Cooperative Optimization Problem Research Articles

An Adaptive Large Neighborhood Search Algorithm for Equipment Scheduling in the Railway Yard of an Automated Container Terminal

In container sea–rail combined transport, the railway yard in an automated container terminal (RYACT) is the link in the whole logistics transportation process, and its operation and scheduling efficiency directly affect the efficiency of logistics. To improve the equipment scheduling efficiency of an RYACT, this study examines the “RYACT–train” cooperative optimization problem in the mode of “unloading before loading” for train containers. A mixed-integer programming model with the objective of minimizing the maximum completion time of automated rail-mounted gantry crane (ARMG) tasks is established. An adaptive large neighborhood search (ALNS) algorithm and random search algorithm (RSA) are designed to solve the abovementioned problem, and the feasibility of the model and algorithm is verified by experiments. At the same time, the target value and calculation time of the model and algorithms are compared. The experimental results show that the model and the proposed algorithms are feasible and can effectively solve the “RYACT–train” cooperative optimization problem. The model only obtains the optimal solution of the “RYACT–train” cooperative scheduling problem with no more than 50 tasks within a limited time, and the ALNS algorithm can solve examples of various scales within a reasonable amount of time. The target value of the ALNS solution is smaller than that of the RSA solution.

Balancing lightweight design and crashworthiness in NPR sandwich structures: A multi-objective optimization approach for enhanced crashworthiness

Aiming to address the multi-objective cooperative optimization problem of ship structure lightweight and crashed-resistance, this study proposed a concave reentrant honeycomb-based negative Poisson’s ratio (NPR) sandwich structure to replace the stiffened plate. The feasibility of using the sandwich structure to improve ship crashworthiness is demonstrated through finite element analysis. An intelligent optimization method is established to address the issues of multiple parameters and complex effects associated with composite structures and optimize the crashworthiness of the sandwich structure. The effectiveness of the optimization method and the feasibility of the NPR sandwich structure in ship crashworthiness have been validated, providing a reference for the design of new multi-functional ship structures.

Multiantenna Clustering Collaboration for WPCNs Based on Nonlinear EH

This article considers a wireless-powered communication network (WPCN) composed of a multiantenna hybrid access point (HAP) based on nonlinear energy harvesting (EH). To improve some distant WDs’ throughput performance, one of them is allowed to be selected as a cluster head (CH) to help transfer information from other cluster members (CMs). Nevertheless, the proposed clustering collaboration’s performance is essentially restricted by the CH’s energy-intensive consumption (EC), which requires to transfer every WDs’ information, covering its own. In order to figure out the question, the HAP’s energy beamforming (EB) capability with multiple antennas is utilized that can concentrate greater transmission power into the CH to equilibrate its EC to assist other WDs. To be specific, each WD’s throughput performance is firstly derived under the proposed approach. A high-efficiency optimization algorithm for addressing cooperative optimization problem is put forward. In addition, the simulations are carried out in the actual network environment, and the results demonstrate that our proposed clustering collaboration with multiple antennas can validly enhance the WPCN’s throughput fairness based on nonlinear EH.

Research on Improved BBO Algorithm and Its Application in Optimal Scheduling of Micro-Grid

Aiming at the cooperative optimization problem of economy and environmental protection of the traditional microgrid, including micro gas turbine and diesel engine, carbon capture and storage, and a power to gas system which can consume wind and light and deal with carbon dioxide, is introduced, and three optimization scheduling models of the microgrid based on improved BBO algorithm are proposed. Firstly, a micro-grid with a power to gas system is constructed, and an optimal scheduling model is built which takes into account the system operation cost, environmental governance cost and comprehensive economic benefit. Secondly, the ecological expansion operation is introduced, an improved BBO algorithm is explored by improving the mobility model, and its convergence is derived in detail. Finally, the microgrid system energy optimization scheduling is realized based on the improved BBO algorithm. Compared with the scheduling model that only considers the operation cost or pollution gas control cost, the total cost of the comprehensive economic benefit scheduling model is reduced by 15.5% and 5.5%, respectively, which reflects the reasonableness of the scheduling model and the effectiveness of the improved algorithm.

Cooperative and Competitive Multi-Agent Systems: From Optimization to Games

Multi-agent systems can solve scientific issues related to complex systems that are difficult or impossible for a single agent to solve through mutual collaboration and cooperation optimization. In a multi-agent system, agents with a certain degree of autonomy generate complex interactions due to the correlation and coordination, which is manifested as cooperative/competitive behavior. This survey focuses on multi-agent cooperative optimization and cooperative/non-cooperative games. Starting from cooperative optimization, the studies on distributed optimization and federated optimization are summarized. The survey mainly focuses on distributed online optimization and its application in privacy protection, and overviews federated optimization from the perspective of privacy protection mechanisms. Then, cooperative games and non-cooperative games are introduced to expand the cooperative optimization problems from two aspects of minimizing global costs and minimizing individual costs, respectively. Multi-agent cooperative and non-cooperative behaviors are modeled by games from both static and dynamic aspects, according to whether each player can make decisions based on the information of other players. Finally, future directions for cooperative optimization, cooperative/non-cooperative games, and their applications are discussed.

Distributed cooperative optimization for multiple heterogeneous Euler-Lagrangian systems under global equality and inequality constraints

In this paper, we study the distributed cooperative optimization problem with globally equality and inequality constraints for a multi-agent system, where each agent is modeled by Euler-Lagrangian (EL) dynamics. The optimized function can be represented by the sum of all local cost functions corresponding to each individual agent. Two continuous-time algorithms are proposed to solve such the problem in a distributed manner. In virtue of geometric graph theory, convex analysis, and Lyapunov stability theory, it is proved that all agents achieve consensus on the Lagrangian multipliers associated with constraints while the proposed algorithms converge exponentially to the optimal solution of the problem given in the case that the parameters of EL agents are known, and converge asymptotically to the optimal solution of the problem in the case that the parameters of EL agents are unknown, respectively. Finally, an example is provided to demonstrate the effectiveness of the theoretical results.

Eco-Driving of Freight Vehicles With Signal Priority on Congested Arterial Roads

This paper investigates a cooperative optimization framework of eco-driving and signal priority for freight vehicles in signalized arterial road. Considering the congestion of passenger vehicles at the signalized intersections, the problem is modeled as a multi-stage mixed integer optimization problem containing continuous variables (speed trajectory) and discrete ones (signal timing). The optimal speed trajectory is obtained with the objective of minimizing the fuel consumption of freight vehicles. Meanwhile, freight-priority optimal signal timing is achieved by minimizing the queueing delay of passenger vehicles at the intersections, while ensuring freight vehicles to avoid stopping and idling. The cooperative optimization problem is non-convex due to constraints from the queues and traffic signals in the network; therefore, a sub-optimal strategy restoring convexity is proposed to solve the problem. Comparison studies of the result and some benchmark methods are performed via numerical simulations, showing significant improvement of traffic efficiency and fuel economy with faster calculation.

Cooperative optimization of cutting parameters, process routes, and scheduling considering carbon emissions with analytic target cascading

Cutting parameters, process routes, and scheduling significantly affect carbon emissions and makespan of workshops. However, most current researches carried out cutting parameters, process routes, and scheduling decision independently and ignored their comprehensive effects on carbon emissions. To bridge the gap, this paper is aimed at solving cooperative optimization problem of cutting parameters, process routes, and scheduling (CCPJS) employing analytical target cascading (ATC). Firstly, a CCPJS-ATC model is presented and elements of the model are formulated, where carbon emissions and makespan are as optimization objectives. Secondly, a novel-augmented Lagrangian relaxation method is designed to solve the CCPJS-ATC model in a distributed manner. Finally, a case study is employed to verify the feasibility and effectiveness of the proposed method. It shows that the proposed method could achieve more carbon emissions and solving time savings compared with the traditional all-in-one approaches.

A reinforcement learning approach for optimizing multiple traveling salesman problems over graphs

This paper proposes a learning-based approach to optimize the multiple traveling salesman problem (MTSP), which is one classic representative of cooperative combinatorial optimization problems. The MTSP is interesting to study, because the problem arises from numerous practical applications and efficient approaches to optimize the MTSP can potentially be adapted for other cooperative optimization problems. However, the MTSP is rarely researched in the deep learning domain because of certain difficulties, including the huge search space, the lack of training data that is labeled with optimal solutions and the lack of architectures that extract interactive behaviors among agents. This paper constructs an architecture consisting of a shared graph neural network and distributed policy networks to learn a common policy representation to produce near-optimal solutions for the MTSP. We use a reinforcement learning approach to train the model, overcoming the requirement data labeled with ground truth. We use a two-stage approach, where reinforcement learning is used to learn an allocation of agents to vertices, and a regular optimization method is used to solve the single-agent traveling salesman problems associated with each agent. We introduce a S-samples batch training method to reduce the variance of the gradient, improving the performance significantly. Experiments demonstrate our approach successfully learns a strong policy representation that outperforms integer linear programming and heuristic algorithms, especially on large scale problems.

A Multi-Objective Task Scheduling Method for Fog Computing in Cyber-Physical-Social Services

Fog computing provides users with data storage, computing, and other services by using fog layer devices close to edge devices. Tasks and resource scheduling in fog computing has become a research hotspot. For the multi-objective task-scheduling problem in fog computing, an adaptive multi-objective optimization task scheduling method for fog computing (FOG-AMOSM) is proposed in this paper. In this method, the total execution time and the task resource cost in the fog network are taken as the optimization target of resource allocation, and a multi-objective task scheduling model is designed. Since the objective model is a Pareto optimal solution problem, the global optimal solution can be obtained by using multi-objective optimization theory and the improved multi-objective evolutionary heuristic algorithm. Moreover, to obtain a better distribution of the current task scheduling group, the neighborhood is adaptively changed according to the current situation of the task scheduling group in fog computing, which avoids the problem that the neighborhood value caused by the neighborhood policy in the multi-objective algorithm affects the distribution of the task scheduling population. This algorithm is used to solve the non-inferior solution set of the utility function index of fog computing task scheduling to try to solve the multi-objective cooperative optimization problem in fog computing task scheduling. The results show that the proposed method has better performance than other methods in terms of total task execution time, resource cost and load dimensions.

Demand-driven timetable and stop pattern cooperative optimization on an urban rail transit line

ABSTRACTThis study proposes a modelling framework for the demand-driven train timetable and stop pattern cooperative optimization problem on an urban rail transit line. By embedding the train stop pattern into the timetable optimization process, we consider the minimization of total passenger travel time. A binary variable determination (BVD) method, which can transform complicated linear constraints into simple logical constraints, is proposed to calculate the large number of binary variables easily, and a genetic algorithm (GA) based on the BVD method is designed to solve the proposed model. A case study of the Batong line in the Beijing subway network is conducted to test the proposed model and algorithm. This study can provide beneficial advice for the operator to improve the operational service of urban rail transit lines.

Cooperative Optimization of Electric Vehicles and Renewable Energy Resources in a Regional Multi-Microgrid System

By integrating renewable energy sources (RESs) with electric vehicles (EVs) in microgrids, we are able to reduce carbon emissions as well as alleviate the dependence on fossil fuels. In order to improve the economy of an integrated system and fully exploit the potentiality of EVs’ mobile energy storage while achieving a reasonable configuration of RESs, a cooperative optimization method is proposed to cooperatively optimize the economic dispatching and capacity allocation of both RESs and EVs in the context of a regional multi-microgrid system. An across-time-and-space energy transmission (ATSET) of the EVs was considered, and the impact of ATSET of EVs on economic dispatching and capacity allocation of multi-microgrid system was analyzed. In order to overcome the difficulty of finding the global optimum of the non-smooth total cost function, an improved particle swarm optimization (IPSO) algorithm was used to solve the cooperative optimization problem. Case studies were performed, and the simulation results show that the proposed cooperative optimization method can significantly decrease the total cost of a multi-microgrid system.

Cooperative Distributed Optimization in Multiagent Networks With Delays

In this technical correspondence, we consider a distributed cooperative optimization problem encountered in a computational multiagent network with delay, where each agent has local access to its convex cost function, and jointly minimizes the cost function over the whole network. To solve this problem, we develop an algorithm that is based on dual averaging updates and delayed subgradient information, and analyze its convergence properties for a diminishing step-size by utilizing Bregman-distance functions. Moreover, we provide sharp bounds on the convergence rates as a function of the network size and topology embodied in the inverse spectral gap. Finally, we present a numerical example to evaluate our algorithm and compare its performance with several similar algorithms.

Cooperative adaptive optimization for a class of nonlinear systems

In this paper, we propose an adaptive optimization technique to solve large scale optimization problems for nonlinear systems composed of multiple interacting agents. The framework is based on the concept of cooperative optimization where each agent addresses a specific local optimization problem that requires information from some or all other agents. An inexact penalty function method is used to solve the overall cooperative optimization problem. The closed-loop follows an adaptive gradient flow that leads to the tracking of local critical points with guaranteed transient performance.

Coordinated Route Planning via Nash Equilibrium and Evolutionary Computation

The coordinated route planning problem for multiple unmanned air vehicles (UAVs), a cooperative optimization problem, also a norr cooperative game, is addressed in the framework of game theory. A Nash equilibrium based rout e planner is proposed. The rational is that the structure of U A V subteam usually provides some inherent and implicit preference information, which help to find the optimum coordinated routes and the optimum combination of the various objective functions. The route planner combines the concepts of evolutionary computation with problem specific chromosome structures and evolutionary operators and handles different kinds of mission constraints in hierarchical style. Cooperation and competition among U A V s are reflected by the definition of fitness function. Simulations validate the feasibility and superiority of the game theoretic coordinated routes planner.