CN117787190A - Optimization method of A star algorithm in PCB wiring environment based on dynamic weight - Google Patents
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Abstract
The invention discloses an optimization method of an A star algorithm in a PCB wiring environment based on dynamic weights, and relates to the technical field of integrated circuit electronic design automation. The method comprises the following steps: constructing an A star algorithm; the A star algorithm is to select path nodes by using the calculation of a total cost function; the total cost function comprises an actual cost function and a heuristic function; optimizing the A star algorithm by using dynamic weights to obtain an A star optimization algorithm; in a PCB simulation wiring environment, solving path nodes of wiring by using the A star optimization algorithm to obtain a path optimal solution; the PCB simulation wiring environment is constructed according to the set starting node, the set target node and the set obstacle; the PCB wiring environment is a grid environment. The invention can reduce search space and redundancy inflection points and optimize wiring effect.
Description
Technical Field
The invention relates to the technical field of integrated circuit electronic design automation, in particular to an optimization method of an A star algorithm in a PCB wiring environment based on dynamic weight.
Background
Printed circuit boards (Printed CircuitBoard, PCB) are widely used in consumer electronics, medical devices, the automotive industry, national defense, aerospace and other related fields. The PCB board in the electronic device electrically interconnects the electronic components according to the design intent through physical wires, thereby realizing the specified functions. The PCB wiring is a critical ring in the design of electronic products, the wiring planning algorithm comprises a maze algorithm, a genetic algorithm, an ant colony algorithm and the like, the most classical wiring algorithm is an A star algorithm, and the total cost calculation function is the core of the A star algorithm.
With the rapid development of integrated circuit technology and the increasing of complex applications, integrated circuits become more and more complex, which makes the wiring process very complex and the wiring result not ideal, and the conventional automatic wiring algorithm using the a star algorithm has a remarkable problem: because the A star algorithm does not have the guidance on path selection in the wiring process, the search space in the PCB wiring result is larger, and more redundant nodes exist, so that the wiring result is not ideal. It is therefore important to optimize the wiring results to reduce the search space and redundancy corners as much as possible.
Disclosure of Invention
The invention aims to provide an optimization method of an A star algorithm in a PCB wiring environment based on dynamic weight, which can reduce search space, reduce redundancy inflection points and optimize wiring effect.
In order to achieve the above object, the present invention provides the following solutions:
an optimization method of an A star algorithm in a PCB wiring environment based on dynamic weight comprises the following steps:
constructing an A star algorithm; the A star algorithm is to select path nodes by using the calculation of a total cost function; the total cost function comprises an actual cost function and a heuristic function;
optimizing the A star algorithm by using dynamic weights to obtain an A star optimization algorithm;
in a PCB simulation wiring environment, solving path nodes of wiring by using the A star optimization algorithm to obtain a path optimal solution; the PCB simulation wiring environment is constructed according to the set starting node, the set target node and the set obstacle; the PCB wiring environment is a grid environment.
Optionally, the total cost function is a sum of an actual cost function and a heuristic function.
Optionally, the optimizing the a-star algorithm by using dynamic weights to obtain an a-star optimization algorithm specifically includes:
adding a dynamic weight to the heuristic function to obtain a heuristic optimization function;
and constructing an A star optimization algorithm according to the actual cost function and the heuristic optimization function.
Optionally, the dynamic weight is a partial formula, and both parts of the partial formula are dynamic numerical values; the dynamic value has three selection directions in the total cost function, namely the total cost value of the current node, the actual cost value of the current node and the estimated cost value of the current node.
Optionally, the calculation formula of the heuristic function is:
wherein h (n) represents the Euclidean distance of the heuristic function; (x) start ，y start )，(x end ，y end ) The coordinates of the start node and the target node, respectively.
Optionally, in the PCB simulation wiring environment, solving path nodes of wiring by using the a star optimization algorithm to obtain a path optimal solution, which specifically includes:
determining an initial node and a target node in a grid environment;
creating two lists of an open table and a close table, simultaneously placing a starting node into the open table as a current point, setting the close table to be empty, placing neighbor nodes of the current point into the open table, calculating the value of each point, taking out the point with the minimum value as a new current node, and adding the point into the close table;
calculating the neighbor nodes reachable by the new current node, generating a set of child nodes, for each child node:
if in close table, discard;
if the child node value is smaller in the open table, updating the child node value when the child node value is smaller, and setting the current point as a father node;
if the node value is not in the open table, adding the node value into the open table, calculating the child node value, repeating the steps of judging whether the child node value is smaller or not if the child node value is in the open table, updating when the judging result is smaller, setting the current point as a father node, and traversing all path nodes until the path optimal solution is obtained.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention discloses an optimization method of an A star algorithm in a PCB wiring environment based on dynamic weight, which comprises the following steps: constructing an A star algorithm; the A star algorithm is to select path nodes by using the calculation of a total cost function; the total cost function comprises an actual cost function and a heuristic function; optimizing the A star algorithm by using dynamic weights to obtain an A star optimization algorithm; in a PCB simulation wiring environment, solving path nodes of wiring by using the A star optimization algorithm to obtain a path optimal solution; the PCB simulation wiring environment is constructed according to the set starting node, the set target node and the set obstacle; the PCB wiring environment is a grid environment. The invention can realize the purposes of reducing the search space and redundancy inflection points and optimizing the wiring effect by adding constraint conditions in the formula to the traditional A star algorithm.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an optimization method of an A star algorithm in a PCB wiring environment of the invention;
FIG. 2 is a schematic diagram of an exemplary PCB wiring environment in this embodiment;
FIG. 3 is a flowchart of a conventional A star algorithm in the present embodiment;
FIG. 4 is a diagram of the wiring result of the conventional A star algorithm in this embodiment;
FIG. 5 (a) is a schematic diagram showing the layout result in the PCB simulation layout environment when the dynamic weight is f (N)/f(s);
FIG. 5 (b) is a diagram showing the layout result in the PCB simulation layout environment when the dynamic weight is g (n)/g(s);
fig. 5 (c) is a schematic diagram of the layout result in the simulated layout environment of the PCB when the dynamic weight is h (n)/h(s) in the present embodiment.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide an optimization method of an A star algorithm in a PCB wiring environment based on dynamic weight, which can reduce search space, reduce redundancy inflection points and optimize wiring effect.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
As shown in fig. 1, the invention provides an optimization method of an a star algorithm in a PCB wiring environment based on dynamic weight, which comprises the following steps:
step 100: constructing an A star algorithm; the A star algorithm is to select path nodes by using the calculation of a total cost function; the total cost function comprises an actual cost function and a heuristic function; the total cost function is the sum of the actual cost function and the heuristic function.
Step 200: optimizing the A star algorithm by using dynamic weights to obtain an A star optimization algorithm; the method specifically comprises the following steps:
adding a dynamic weight to the heuristic function to obtain a heuristic optimization function; and constructing an A star optimization algorithm according to the actual cost function and the heuristic optimization function. Wherein the dynamic weight is a partial formula, and both parts of the partial formula are dynamic numerical values; the dynamic value has three selection directions in the total cost function, namely the total cost value of the current node, the actual cost value of the current node and the estimated cost value of the current node.
The formula structure of the dynamic weight comprises:
in the wiring process, each different current node corresponds to a weight value, the weight value is a proportional value, each current node is an unequal value, in order to achieve the purpose of dynamics, two parts contained in the proportional value must be dynamic values, the two parts of the proportional value have three choices under the constraint, namely the total cost value of the current node, the actual cost value of the current node and the estimated cost value of the current node, and three dynamic weights can be calculated in total. And comparing the wiring results of each weight under different conditions through multiple experimental comparison, and selecting the dynamic weight with the optimal path, wherein the weight is added into a distance formula of a heuristic function as a new constraint condition, so that the total cost function has guiding performance on the wiring results.
The dynamic weights which are most suitable for the PCB wiring environment are selected through various transverse comparisons, and the optimal wiring result improvement effect can be obtained in the proposed weights, and the principle is that under the condition of abandoning searching of an optimal path, the dynamic weights are used for shortening the searching time of an A star algorithm.
Step 300: in a PCB simulation wiring environment, solving path nodes of wiring by using the A star optimization algorithm to obtain a path optimal solution; the PCB simulation wiring environment is constructed according to the set starting node, the set target node and the set obstacle; the method specifically comprises the following steps:
determining an initial node and a target node in a grid environment; creating two lists of an open table and a close table, simultaneously placing a starting node into the open table as a current point, setting the close table to be empty, placing neighbor nodes of the current point into the open table, calculating the value of each point, taking out the point with the minimum value as a new current node, and adding the point into the close table; calculating the neighbor nodes reachable by the new current node, generating a set of child nodes, for each child node:
if in close table, discard; if the child node value is smaller in the open table, updating the child node value when the child node value is smaller, and setting the current point as a father node; if the node value is not in the open table, adding the node value into the open table, calculating the child node value, repeating the steps of judging whether the child node value is smaller or not if the child node value is in the open table, updating when the judging result is smaller, setting the current point as a father node, and traversing all path nodes until the path optimal solution is obtained.
And, the A star optimization algorithm also comprises in the PCB wiring environment:
analyzing the defects of the estimation cost value calculation method in the traditional A star algorithm; the disadvantages include: the estimated cost value is directly calculated by a heuristic function specified by the traditional A star algorithm, the calculation mode of the heuristic function used by the algorithm is Euclidean distance, and in a grid map, the Euclidean distance uses the linear distance between two points as heuristic quantity, so that the distance between the two points is more visual and has reference significance for the total cost value, and redundant corners still exist although the wiring result is relatively ideal, and the requirement of a designer on path optimization is not met.
Based on the above technical solution, the following embodiments are provided.
In this embodiment, the main optimization idea is: since the a star algorithm is an optimization-based search algorithm. The conventional A star algorithm is mainly applied to solving the shortest path by a static grid, such as an automatic wiring function in a PCB. The nodes in the PCB wiring environment during the operation of the A star algorithm are selected by comparing the calculated values according to the total cost function, and the node with the minimum value is selected as a new drop point and finally connected into a new path.
The total cost function is the sum of the actual cost function and the estimated cost function (heuristic function). The actual cost function is obtained by Dijkstra algorithm in traditional A star algorithm, the algorithm is shortest path algorithm from one vertex to other vertexes, and the problem of shortest path in the weighted graph is solved. The Dijkstra algorithm is mainly characterized in that a greedy algorithm strategy is adopted from a starting point, and the adjacent nodes of the current node which are closest to the starting node and are not accessed are traversed each time until the adjacent nodes are extended to a target node.
The search space is reduced and redundancy inflection points are reduced by adding a dynamic weight to the heuristic function on the premise of keeping the total cost formula of the original A star algorithm, so that different wiring purposes can be realized at different stages in the wiring process.
The dynamic weight is a partial formula, two parts of the partial formula are all necessarily dynamic numerical values, the dynamic numerical values have three selection directions in the total cost function, namely the total cost value of the current node, the actual cost value of the current node and the estimated cost value of the current node, and the three numerical values are continuously changed in the wiring process for the A star algorithm, so that the requirement that the heuristic function weight can be dynamic is met.
As a further aspect of the present embodiment: the heuristic function is selected as a Euclidean distance formula which most intuitively reflects the distance between two points, the three different dynamic weights are verified on the basis of the Euclidean distance formula to obtain an optimized wiring result, and the search for non-ideal nodes in the search process is reduced and the wiring process is purposeful by adding constraint conditions in the formula to the traditional A star algorithm, so that the purposes of reducing the search space and reducing redundant inflection points are achieved.
Thus, specific experiments were performed in a grid virtual map environment, but for a better understanding of the solution provided by the present application, the following needs to be understood:
electronic design automation (Electronic Design Automation, EDA) is in the industry upstream of the integrated circuit industry chain. EDA design software encompasses all the processes of functional design, synthesis, verification, physical design (including placement, routing, layout, design rule checking, etc.) through package testing of integrated circuit chips, and is the software tool necessary and most important for integrated circuit design, called the "die mother". EDA includes tools such as circuit design and simulation, printed Circuit Board (PCB) design, integrated circuit design, IP core design, programmable logic device design, etc., while PCB is a bridge carrying electronic components and connecting circuits, called "mother of electronic products", widely used in the fields of communication, consumer electronics, automotive electronics, industrial control, medical instruments, national defense, aerospace, etc.
The PCB board in the electronic device electrically interconnects the electronic components according to the design intent through physical wires, thereby realizing the specified functions. PCB routing is a critical ring in electronic product design, and its goals can be grouped into two layers: according to the connection relation description of the circuit, the required electrical interconnection is 100% completed in a limited area under the conditions of meeting the requirements of design and process rules and meeting the electrical performance; and on the premise of completing wiring, the template of the PCB wiring is also used for further optimizing the wiring result, so that the required PCB area is minimized, and the completed functions and performances of the PCB are better. Among the routing algorithms of PCBs, the one that is common and most widely used is the a star algorithm.
With the rapid development of integrated circuit technology and the increasing of complex applications, integrated circuits become more and more complex, wiring processes become very complex and time-consuming, and a significant problem exists in the traditional automatic wiring algorithm based on the A star algorithm: there are problems of large search space and undesirable redundancy inflection points in the environment of PCB routing, resulting in undesirable routing results.
The definition related to the examples is as follows:
(1) PCB simulation wiring environment: three major elements in the PCB routing include a start node, a target node, and an obstacle. The simulation environment of the exemplary diagram of the present invention is shown in fig. 2, which is built on the qt5.0 platform, by setting the grid properties, three major elements mentioned above are set in these grids, distinguishing unrestricted spaces from obstacles that are not explorable and not traversable during the wiring process.
(2) A star algorithm: the A star algorithm combines the characteristics of the Dijkstra algorithm and the breadth-first search algorithm, in the total cost function f (n) =g (n) +h (n), the actual cost function g (n) is a distance value obtained by the Dijkstra algorithm, the estimated cost h (n) is the breadth-first search algorithm, the wiring result of any algorithm is not ideal, the wiring result of the traditional A star algorithm obtained after the two algorithms are integrated is shown in fig. 3, the wiring result of the A star algorithm is more reasonable, and a larger search space and an undesirable inflection point still exist. The steps of the a star algorithm are shown in fig. 4: determining an initial node and a target node in a grid environment; creating two lists of an open table and a close table, simultaneously placing a starting node into the open table as a current point, setting the close table to be empty, placing neighbor nodes of the current point into the open table, calculating f (n) values of all points, taking out the point with the smallest f (n) value in the open table as a new current node, and adding the point into the close table. Calculating neighbor nodes which can be reached by the current point, generating a group of child nodes, for each child node, discarding the point if the child node is in a close table, considering whether the f (n) value of the child node is smaller or not if the child node is in an open table, updating the f (n) value of the child node if the child node is updated to be smaller, and setting the current point as a father node; if it is not in the open table, it is added to the open table and its f (n) value is calculated, repeating the above operation of updating the current node.
(3) Application of A star algorithm in grid map: the A star algorithm calculates the corresponding total cost value of each searched node in the running process, the value is the sum of the actual cost value and the estimated cost value, as shown in fig. 5, each value is added by two cost values, the algorithm flow introduced above is continuously repeated in the grid map, and finally a wiring result in a specific environment can be obtained. The heuristic function of the A star algorithm is Euclidean distance, and the calculation method comprises the following steps:
wherein h (n) represents the Euclidean distance of the heuristic function; (x) start ，y start )，(x end ，y end ) The coordinates of the start node and the target node, respectively. The present embodiment is not limited to heuristic function improvement based on euclidean distance.
The process of the specific embodiment is as follows:
the embodiment optimizes the heuristic function of the total cost function in the traditional A star algorithm. Application of the a star algorithm in PCB automatic routing planning sometimes fails to meet the intent of PCB designers, such as the occurrence of undesirable corners, etc. In order to solve the problem, the invention takes the dynamic coefficient as the coefficient of the estimated cost function, so that different wiring intentions can be realized at different stages in the wiring process, and the algorithm has a certain guiding property on the wiring result.
The basic principle of the A star algorithm is the comprehensive application of the Dijkstra algorithm and the breadth first search algorithm (BFS), wherein the Dijkstra algorithm can effectively find the current optimal path or the approximate direction thereof by comparing the optimal actual cost, and the BFS can rapidly expand the surrounding nodes of the current point. In short, the Dijkstra algorithm is characterized by having to find the optimal path, but at a slow speed. BFS is fast running, but does not guarantee that the result is necessarily the optimal path. Looking at the principle of both algorithms, the Dijkstra algorithm mainly looks at the cost of the calculated path, while BFS looks at how many steps still reach the end point. It is not difficult to find that the heuristic function can be associated with both algorithms, the Dijkstra algorithm corresponds to the actual cost g (n), and the BFS corresponds to the estimated cost h (n). As shown in table 1, in the original formula of the a-star algorithm, the coefficient of the actual cost and the estimated cost is 1:1, if we change the weight ratio of the actual cost to the estimated cost, the a star algorithm can be controlled to be more biased towards the actual cost or the estimated cost, for example, the cost weight is changed to 1:2, i.e. f (n) =g (n) +2h (n), where f (n) will be more biased to describe the estimated cost, and the routing result will be more similar to the BFS algorithm, when we generalize this conclusion, when g (n) > h (n), it is equivalent to f (n) considering only the actual cost and not considering the estimated cost at all, i.e. degrading to Dijkstra algorithm. Otherwise, the estimation cost is prioritized, namely, when g (n) < h (n), the A star algorithm is degenerated to BFS.
TABLE 1 influence table of heuristic function weights on algorithm performance and routing results
Three different dynamic weights are discussed in this invention, respectively: (1) f (n)/f(s); (2) g (n)/g(s); (3) h (n)/h(s), where h(s) represents the heuristic distance from the start point to the end point. The invention aims at quickly reaching the area where the destination is located when searching is started and aims at obtaining the optimal path reaching the target when searching is finished by adding the dynamic weight mode, thereby achieving the aim of optimizing the wiring result. The results shown in fig. 5 are improved on the basis of fig. 4 to realize optimization of the wiring result, the weight coefficient corresponding to the embodiment of fig. 5 (a) is f (n)/f(s), the cost function corresponding to the embodiment of fig. 5 (b) is g (n)/g(s), the cost function corresponding to the embodiment of fig. 5 (c) is h (n)/h(s), the wiring result is optimal when the dynamic coefficient is set in the group of data, and the result is part of the embodiments in the invention.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the core concept of the invention; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (6)
1. The optimization method of the A star algorithm in the PCB wiring environment based on the dynamic weight is characterized by comprising the following steps:
constructing an A star algorithm; the A star algorithm is to select path nodes by using the calculation of a total cost function; the total cost function comprises an actual cost function and a heuristic function;
optimizing the A star algorithm by using dynamic weights to obtain an A star optimization algorithm;
in a PCB simulation wiring environment, solving path nodes of wiring by using the A star optimization algorithm to obtain a path optimal solution; the PCB simulation wiring environment is constructed according to the set starting node, the set target node and the set obstacle; the PCB wiring environment is a grid environment.
2. The method of optimizing an a-star algorithm in a dynamic weight based PCB routing environment of claim 1, wherein the total cost function is a sum of an actual cost function and a heuristic function.
3. The optimization method of an a star algorithm in a dynamic weight-based PCB routing environment according to claim 1, wherein the optimizing the a star algorithm by using dynamic weights, to obtain the a star optimization algorithm, specifically comprises:
adding a dynamic weight to the heuristic function to obtain a heuristic optimization function;
and constructing an A star optimization algorithm according to the actual cost function and the heuristic optimization function.
4. The optimization method of an a star algorithm in a dynamic weight-based PCB routing environment of claim 3, wherein the dynamic weight is a partial formula, and both parts of the partial formula are dynamic numerical values; the dynamic value has three selection directions in the total cost function, namely the total cost value of the current node, the actual cost value of the current node and the estimated cost value of the current node.
5. The optimization method of an a star algorithm in a dynamic weight-based PCB routing environment of claim 1, wherein the heuristic function has a calculation formula:
wherein h (n) represents the Euclidean distance of the heuristic function; (x) start ，y start )，(x end ，y end ) The coordinates of the start node and the target node, respectively.
6. The optimization method of an a-star algorithm in a PCB wiring environment based on dynamic weight according to claim 1, wherein in the PCB simulated wiring environment, the path nodes of the wiring are solved by using the a-star optimization algorithm to obtain a path optimal solution, and the method specifically comprises:
determining an initial node and a target node in a grid environment;
creating two lists of an open table and a close table, simultaneously placing a starting node into the open table as a current point, setting the close table to be empty, placing neighbor nodes of the current point into the open table, calculating the value of each point, taking out the point with the minimum value as a new current node, and adding the point into the close table;
calculating the neighbor nodes reachable by the new current node, generating a set of child nodes, for each child node:
if in close table, discard;
if the child node value is smaller in the open table, updating the child node value when the child node value is smaller, and setting the current point as a father node;
if the node value is not in the open table, adding the node value into the open table, calculating the child node value, repeating the steps of judging whether the child node value is smaller or not if the child node value is in the open table, updating when the judging result is smaller, setting the current point as a father node, and traversing all path nodes until the path optimal solution is obtained.
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