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Distributed constraint optimization problem solving method based on ant colony inheritance and application thereof

A technology for constrained optimization and problem solving, applied in the field of distributed constrained optimization problem solving based on ant colony genetics, it can solve problems such as algorithm falling into local optimum, and achieve the effect of expanding search and good quality.

Active Publication Date: 2021-09-17
CHONGQING UNIV OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, due to the pheromone decay mechanism of the ant colony algorithm, the algorithm may fall into local optimum

Method used

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  • Distributed constraint optimization problem solving method based on ant colony inheritance and application thereof
  • Distributed constraint optimization problem solving method based on ant colony inheritance and application thereof
  • Distributed constraint optimization problem solving method based on ant colony inheritance and application thereof

Examples

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Embodiment 1

[0096] Embodiment 1: see figure 2 , a method for solving distributed constraint optimization problems based on ant colony genetics, including the following steps:

[0097] S100: Use a quaternion to represent the distributed constraint optimization problem, wherein:

[0098] A={a 1 ,...,a n} is a collection of agents;

[0099] X={x 1 ,...,x m} is a set of variables, m≤n;

[0100] D = {D 1 ,...,D m} is a collection of ranges, each x i The value range is D i , each Agent starts from the range D i The value in the variable x i assignment, means x i The tth value of ;

[0101] F={f 1 ,...,f p} is a set of constrained cost functions, constrained is from any k variables assignments of to a map with non-negative cost;

[0102] Assuming that an agent only controls one variable and all constraint relations are binary relations, the solution of DCOP is expressed as:

[0103]

[0104] Express DCOP as a constraint graph. In the constraint graph, each node repre...

Embodiment 2

[0160] Embodiment 2: Application of the distributed constraint optimization problem solving method based on ant colony genetics, the distributed constraint optimization problem solving method based on ant colony genetics defined in embodiment 1 is applied to the emergency rescue problem of urban emergencies;

[0161] Considering that the traffic flow in the opposite direction of the same road section is not consistent, given the problem traffic road network G(C,E), C represents the node in the road network, and E represents the connection between the two nodes, that is, the road section. At the same time, the urban burst The event traversable path planning is constructed as a DCOP model, and then the proposed AG_DCOP algorithm is used to solve the model to find several optimal path sets that satisfy the constraints as the best path and alternative path for emergency rescue. The model is described in detail as follows:

[0162] A={car 1 ,..., car n} is a collection of agents,...

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Abstract

The invention discloses a distributed constraint optimization problem solving method based on ant colony inheritance and application thereof; the method can effectively prevent ACO_DCOP from falling into local optimum by combining the ant colony optimization thought and the search advantage of a genetic operator, thereby expanding the search of an algorithm for a solution space, and obtaining a solution with better quality. Besides, the AG_DCOP combines the ant colony optimization thought and the search advantage of the genetic operator, and increases the expansion probability p of dynamic change to trigger the genetic operator to perform expansion optimization on the ant colony traversal result.

Description

technical field [0001] The invention relates to a constraint optimization method, in particular to a method for solving a distributed constraint optimization problem based on ant colony genetics. Background technique [0002] DCOP (Distributed Constraint Optimization Problems, DCOP) is the basic framework of a multi-agent system (Multi-agent System, MAS), in which agents need to cooperate and make decisions together to optimize a global goal. The DCOP model has been successfully applied to various practical problems, such as sensor networks, resource allocation, etc. There are many algorithms for solving DCOP, and the algorithms can be divided into two categories according to different solving goals: complete algorithms and incomplete algorithms. The complete algorithm obtains the optimal solution by traversing all the solution spaces of the problem. Typical search-based complete algorithms include SynchBB [7] 、ADOPT [8] 、BnB-ADOPT [9] etc., the inference-based complete ...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06N3/00G06N3/12
CPCG06Q10/04G06N3/006G06N3/126
Inventor 石美凤肖诗川杨海廖鑫冯欣陈媛
Owner CHONGQING UNIV OF TECH
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