162 results about "Combinatorial optimization problem" patented technology

The invention relates to a combined optimization method for agricultural chain-operation logistics delivering and loading-distribution, belonging to the technical field of the combined optimization ofthe logistics delivering and loading-distribution. The technical scheme comprises: proposing a model for the combined optimization of the agricultural chain-operation logistics delivering and loading-distribution and converting the model into the problem of the combined optimization of a single delivering center, a single variety and non-full-loading delivering and loading-distribution; designinga solution algorithm for the model for the combined optimization of the agricultural chain-operation logistics delivering and loading-distribution based on the genetic algorithm principle and solving; and developing a visual vehicle delivering and loading-distribution scheduling management system according to an optimization algorithm and a GIS development platform. The proposed model can actually reflect the interaction of the agricultural loading process and the agricultural delivering process in the operation process of agricultural chain-operation logistics delivering enterprises and embodies that the agricultural loading scheme can determine the selection of agricultural delivering routes to a certain degree. The affection of the agricultural loading and the agricultural delivering on the cost of the agricultural chain-operation logistics delivering enterprises can be comprehensively considered, thereby effectively lowering the loading and delivering cost and the operating cost of agricultural logistics delivering enterprises.

The present disclosure describes a computer-implemented constraint propagation system that supports a variety of different constraint propagation and / or constraint retraction algorithms, including monotonic and / or non-monotonic algorithms. In one embodiment, the system selects particular constraint propagation and / or retraction methods based on the nature of the combinatorial optimization problem (COP) being solved and the attributes of the particular COP application involved. The system may also enable new methods for constraint propagation and / or retraction to be added with relatively little disruptive effect on other components of the system. Embodiments of the system allow the propagation of constraints to be tuned to the semantics of each constraint, the likelihood of significant variable domain reduction, and other problem specific properties. The constraint propagation system is capable of being used as part of a reconfigurable search engine.

The invention discloses a cooperation segmentation method fusing perception information, which is used for performing joint segmentation on a group of image data set containing a common object, and each image can contain a plurality of common objects. The cooperation segmentation method comprises steps of using perception information like the significance, the repeatability, the space position, etc, which are based on the area as a global bound term to introduce an energy model to define the foreground likelihood, fully playing the important role of the perception information, converting the segmentation problem to the combination optimization problem to solve and using the object structural constraint based on the perception to perform iterative solution. Compared with the similar algorithm, the cooperation segmentation method fusing perception information and the solving method can be applicable to various complex occasions and can effectively segment the object.

The invention provides a land use partition method based on tabu search algorithm, aiming to solve the problem that the traditional land use space partition method hardly takes multi-target characteristics of land use into consideration and lacks intelligent and efficient partition decision algorithm. The tabu search algorithm in mathematics field is applied in the land use space partition field; and key issues such as initial plan generation method, neighborhood rule definition, neighborhood generation method, neighborhood search method, memory list update, breadth search, depth search and restarting strategy are modified, a technical method for supporting the land use partition optimization decision is provided, the problem for optimizing spatial unit combination of multi-target in the land use partition is solved better and the problem of hard constraint conditions such as the continuity and integrity of spatial district is considered, so that the partition efficiency is higher and the stability and high efficiency of land use partition process are guaranteed.

The invention discloses - a power electronic circuit optimizing method based on an ant-colony-algorithm, which relates to the fields of the intelligent calculation and the power electron. The invention discretizes the possible values of all elements of the power electronic circuit into a series of standard values, and then maps the standard values into a structural diagram of an ant-colony-algorithm optimizing circuit. The ant-colony algorithm is adopted to select the best route in the structural diagram, namely, a best element value is selected to meet the required circuit performance. The ant-colony algorithm is very applicable to the discretization combinational optimization, thereby can rapidly and effectively optimize the power electronic circuit. The optimized results of the method are the standard values of all elements, so the optimized results can be directly applied to the actual production without any approximation.

The invention discloses a resource-constrained project scheduling method based on a multi-agent evolutionary algorithm, belonging to the technical field of automatic control and information. According to the method, a multi-agent system is combined with evolutionary computation so as to solve the resource-constrained project scheduling problem. The method is characterized by comprising the following steps of: initializing each agent in an agent grid according to two algorithms; and designing a neighborhood competition operator, a neighborhood cross operator, a mutation operator and a self-learning operator for optimizing the agent. The verification result proves that the method has advantages in two aspects for evaluating the effectiveness of the method for solving the resource-constrained project scheduling problem, namely the solving of the optimal solution proportion and the solving of the average error deviating from the optimal solution, so that the method effectively solves the resource-constrained project scheduling problem, and the method can be expanded to solving other combined optimization problems with precedence relationship constraints.

Each of a plurality of subtasks is configured to explore and assess alternative solutions for a combinatorial optimization problem by a reentrant finite state machine is represented. Each of a plurality of threads is configured to perform operations comprising a subtask until either completion or a blocked state is reached and, in the event a blocked state is reached, to move on to performing another subtask that is not currently in a blocked state.

Solving combinatorial optimisation problems using dynamic programming involves automating the integration of bounds propagation into compilation of a dynamic program. This is done by extracting bounds from partial results obtained during dynamic programming, and tightening the bounds throughout execution of the dynamic program. This dramatically reduces the number of “good” solutions that need to be constructed at each stage, improving speed and scalability of algorithms using such dynamic programming.

The invention belongs to the technical field of dynamic resource optimization and specifically relates to a Markov decision process-based dynamic resource optimization method. The method is different from a conventional manufacturing resource selection method; a multi-development-task precision regulation and control cloud manufacture resource problem in a cloud manufacture environment is abstracted into a Markov decision process, mathematical modeling of effects exerted on resource selection by development process uncertainty can be realized, expected development cost is an object function, a cross entropy method is used for calculation, a combinational optimization problem is converted into a random association optimization problem, optimal selection probability of cloud manufacture resources can be obtained, reasonable scheduling and high efficiency utilization of manufacturing resources during cooperative development of complex products can be realized, and product development risk and manufacture cost can be effectively lowered.