A heat pipe restraint element layout optimization method and system

Abstract

The application discloses a heat pipe constraint element layout optimization method and system. The method comprises the following steps: on the problem level, considering the non-overlapping constraint of the element, the static stability constraint, the heat dissipation capacity constraint of the heat pipe and the element-heat pipe overlapping constraint, and taking the minimization of the maximum heat pipe power as the optimization target; on the algorithm level, the problem is divided into two stages of heat pipe distribution and position optimization, the double-layer multi-start variable neighborhood search is used for solving in the heat pipe distribution stage, and the genetic algorithm is used for solving in the position optimization stage; according to the problem characteristics, five kinds of neighborhood structures of the heat pipe distribution stage are designed, the position of the element can be effectively adjusted, and the heat concentration is reduced. The application can generate an element layout scheme with better heat performance, can meet the engineering constraint conditions and guarantee the feasibility of the layout scheme.

Description

Technical Field

[0001] This invention belongs to the field of satellite heat dissipation design, and relates to a method and system for optimizing the layout of heat pipe constraint components. More specifically, it relates to a method for solving the heat pipe constraint component layout optimization problem based on a two-layer multi-starting point variable neighborhood search-genetic algorithm. Background Technology

[0002] In satellite design, component layout optimization is a crucial issue. A well-designed layout can improve various satellite performance characteristics, enhance safety and stability during operation, and reduce manufacturing costs. Component layout optimization is also an important aspect of other manufacturing processes, such as PCB design.

[0003] The placement optimization problem of components in a satellite system is a constrained optimization problem. It typically requires consideration of factors such as the size, shape, and power of electronic components, and must simultaneously satisfy constraints including spatial non-overlapping of components, static stability, heat pipe heat dissipation capacity, and component-heat pipe overlap. Furthermore, it necessitates optimization of thermal performance and other indicators. Moreover, the placement optimization problem is a continuous optimization problem, and metaheuristic algorithms are commonly used in current research for solving it.

[0004] Many scholars have studied the optimization of component layout in satellite design. Heat dissipation performance is a problem that must be considered in satellite layout design. A reasonable layout can improve the heat dissipation capacity of the satellite system, reduce heat concentration, and thus ensure the stability of each component during operation.

[0005] Existing technology provides a method for optimizing the layout of heat pipe constraint elements based on genetic algorithms. The core concept involves establishing an x-axis perpendicular to the heat pipe orientation and a y-axis parallel to it. Based on the structure of the heat pipe constraint elements, a subpopulation 1 is established on the x-axis, and a subpopulation 2 is established on the y-axis. The evaluation count, clan size, subpopulation 1, and subpopulation 2 are initialized. A genetic algorithm is used to find the optimal layout of subpopulation 1 on the x-axis, and based on this optimal layout, the optimal layout of subpopulation 2 on the y-axis is found. Combining these optimal layouts yields the optimal feasible solution for the heat pipe constraint element layout. However, because it directly optimizes the position coordinates of the elements, its complexity is high, and its performance is not good enough for complex examples.

[0006] Therefore, a new method for solving the heat pipe constraint element layout optimization problem is urgently needed to address the above issues. Summary of the Invention

[0007] To address at least one deficiency or improvement requirement of the existing technology, this invention provides a solution method for the heat pipe constraint component layout optimization problem based on a two-layer multi-starting point variable neighborhood search-genetic algorithm. Its purpose is to solve the component thermal concentration problem in satellite system design by designing an effective search algorithm, and to calculate a component layout scheme with better thermal performance under the premise of meeting design constraints.

[0008] To achieve the above objectives, according to one aspect of the present invention, a method for optimizing the layout of heat pipe constraint elements is provided, comprising a heat pipe allocation stage and a position optimization stage:

[0009] S1: Heat pipe distribution stage

[0010] S1.1: Randomly generate a heat pipe allocation scheme code as the initial solution s, wherein the heat pipe allocation scheme code includes the number and quantity of heat pipes occupied by each component;

[0011] S1.2: Perform a local search on s within a neighborhood structure of s to obtain the optimal solution s′ in that neighborhood structure;

[0012] S1.3: If the thermal performance of s′ is better than that of s, and the number of iterations in the current neighborhood structure has not reached the upper limit, then let s = s′, and continue to search for s′ locally in the current neighborhood structure until no better solution can be found or the number of iterations in the current neighborhood structure has reached the upper limit. Then switch to the next neighborhood structure to search for the current best solution locally until all neighborhood structures have been used.

[0013] S1.4: Determine if the number of iterations has reached the upper limit. If so, obtain the optimal solution for the heat pipe allocation stage; otherwise, go to S1.1.

[0014] S2: Location Optimization Stage

[0015] S2.1: Based on the optimal solution of the heat pipe allocation scheme obtained in S1.4, randomly generate an initial population of component position coordinate codes;

[0016] S2.2: Retain a subset of individuals with high fitness from the initial population and record the optimal solution;

[0017] S2.3: Perform a local search on the optimal solution obtained in S2.2. If the new optimal solution obtained by the local search is better than the optimal solution obtained in S2.2, then replace it; otherwise, retain the optimal solution obtained in S2.2.

[0018] S2.4: From the population after the search and replacement in S2.3, select individuals for crossover according to the preset crossover probability, and then select individuals for mutation according to the preset mutation probability to update the population;

[0019] S2.5: Determine whether the number of iterations has reached the preset upper limit. If so, output the optimal solution, i.e. the optimal layout of the heat pipe constraint elements, including the optimal heat pipe allocation scheme and the corresponding optimal element position. Otherwise, use the population updated in S2.4 as the initial population and transfer to S2.2.

[0020] Furthermore, in the heat pipe distribution stage, a multi-starting-point variable neighborhood search algorithm is used to optimize the thermal performance of the heat pipe distribution scheme, wherein:

[0021] Multi-starting-point search refers to a local search that starts with a random solution in each iteration, and then performs a local search on a new random solution in the next iteration after the current iteration is completed.

[0022] Variable neighborhood search refers to the process of inserting, exchanging, and / or moving each initial solution during an iteration to obtain different neighborhood structures. Then, the search starts from any neighborhood structure. When no better solution is found in the neighborhood structure or the set maximum number of search attempts is reached, the search is switched to the next neighborhood structure to continue.

[0023] Furthermore, the neighborhood structure includes at least one of three one-step neighborhood structures and two two-step neighborhood structures, wherein:

[0024] The three methods for obtaining the one-step neighborhood structure are as follows:

[0025] Insert the components from the heat pipe with the highest heat output and move them sequentially to the other heat pipes.

[0026] Exchange, which involves sequentially swapping the components on the heat pipe with the highest heat output with the components on other heat pipes;

[0027] Move and change the number of heat pipes occupied by the components on the heat pipe with the highest heat output;

[0028] The two methods for obtaining the two-step neighborhood structure are as follows:

[0029] Select a solution with high fitness from the one-step neighborhood structure, and then perform insertion, swap and / or move operations again;

[0030] For all solutions obtained from the one-step neighborhood structure, perform insertion, swap, and / or shift operations again.

[0031] Further, in step S1.1, the heat pipe allocation scheme H of the component is expressed using integer encoding as follows:

[0032] H={(h i ,q i |i = 1, 2, ..., N c}

[0033] Where i is the component number, N ch represents the total number of components. i Indicates the number of the first heat pipe occupied by component i, q i This indicates the number of heat pipes occupied by component i.

[0034] Further, in step S2.1, the component position coordinate code X is as follows:

[0035] X={(x i ,y i |i = 1, 2, ..., N c}

[0036] Where, x i y i These represent the horizontal and vertical coordinates of the centroid of element i within the layout domain, respectively.

[0037] Furthermore, an evaluation function is used to evaluate the quality of the solution. The upper limit of the number of times the evaluation function can be used is set to βD, where D is the dimension of the decision variable, which is the specific location of each element, including the two dimensions of the centroid of the element in the layout domain: horizontal and vertical coordinates, and β is an empirical value.

[0038] Based on the size ratio of all components to the layout domain, the number of times the evaluation function is used is allocated to the heat pipe allocation stage and the position optimization stage as the upper limit of the iteration count for the corresponding stages. The allocation scheme is as follows:

[0039]

[0040] Where t1 and t2 represent the number of times the evaluation function of the heat pipe distribution model and the location optimization model are used, respectively, and a represents the proportion of the sum of the areas of all components to the area of ​​the layout domain.

[0041] Furthermore, the methods for selecting the optimal solution in the heat pipe allocation stage and the location optimization stage are both based on minimizing the maximum heat pipe power as the optimization objective, while satisfying the following four constraints: 1) non-overlapping constraint; 2) static stability constraint; 3) heat dissipation capacity constraint; 4) component-heat pipe overlap constraint.

[0042] Furthermore, the optimization objective expression is as follows:

[0043]

[0044] in, N represents the actual load power of heat pipe m. hp Number of heat pipes;

[0045] When considering more than two side plates, the optimization objective is expanded to minimize the sum of the maximum heat pipe powers of each plate, i.e.:

[0046]

[0047] This represents the actual load power of heat pipe m on plate 1. The number of heat pipes on plate 1. The actual load power of heat pipe n on plate 2. The number of heat pipes on plate 2;

[0048] The expressions for the four constraints are as follows:

[0049] 1) Non-overlapping constraint g1(X): Components cannot overlap each other and cannot exceed the layout domain, i.e.:

[0050]

[0051] Where, ΔV ij N represents the overlapping area of ​​elements i and j. c The number of components, i,j>0, i≠j;

[0052] 2) Static stability constraint g2(X): The centroid position should be within a given range, i.e.:

[0053] g2(X)=|y c -y e |-δy e ≤0

[0054] Among them, y c The actual y-axis centroid position, y e For the standard y-axis centroid position, δy e This represents the maximum permissible positional deviation.

[0055] 3) Heat dissipation capacity constraint g3(X): The heat power on each heat pipe cannot exceed the maximum heat dissipation capacity, that is:

[0056]

[0057] in, μ is the actual load power of heat pipe j. j P is the set of components occupying heat pipe j. i and These represent the power of component i and the number of heat pipes it occupies. N is the maximum load power of the heat pipe. hp Number of heat pipes;

[0058] 4) Component-Heatpipe Overlap Constraint g4(X): Each component needs to be placed on a heatpipe; when a component uses multiple heatpipes for heat dissipation, it is assumed that these heatpipes distribute the component's heat power evenly, i.e.:

[0059]

[0060] Where, d i denoted as , where is the distance between element i and the nearest heat pipe.

[0061] Furthermore, the method for calculating fitness in step S2.2 is as follows:

[0062] For the solutions in the population, calculate the four constraints, find the solutions that simultaneously satisfy all four constraints, and take the reciprocal of their linear combination as the fitness fit(X), as shown in the following formula:

[0063]

[0064] Where c1, c2, x3, and x4 are empirical constants;

[0065] Based on the fitness values, select a subset of elite solutions with higher fitness as offspring, and use a roulette wheel selection method to select the remaining solutions to obtain a population that meets the constraints.

[0066] The local search method in S2.3 is as follows: perform a local search on the best solution in the current population. Specifically, within the feasible range, move each element sequentially by a preset distance in the four directions of up, down, left, and right; if the optimal solution obtained by the local search is better than the current solution, then replace it.

[0067] In S2.5, the iteration function uses the number of times the evaluation function is used as the benchmark. If the number of times the evaluation function is used reaches the upper limit, the process ends; otherwise, it goes to S2.2.

[0068] To achieve the above objectives, according to another aspect of the present invention, a heat pipe constraint element layout optimization system is provided, comprising a processor and a computer program module, wherein the computer program module, when executed by the processor, implements the heat pipe constraint element layout optimization method as described in any of the preceding claims.

[0069] In general, the above-mentioned technical solutions conceived in this invention can achieve the following beneficial effects compared with the prior art.

[0070] 1. This invention transforms the traditional one-step solution method into a two-stage step-by-step optimization method involving heat pipe allocation and location optimization. First, the optimal heat pipe layout is determined, and then the optimal positions of components are optimized. This significantly reduces the complexity of the solution while improving system heat dissipation performance, calculating a component arrangement with better thermal performance while meeting design constraints. Furthermore, this invention does not focus on the design of a specific evaluation function during execution; any evaluation function is acceptable as long as it allows for comparing the performance of different solutions. In other words, the core concept of this invention lies in the two-stage step-by-step optimization of heat pipe allocation and location optimization, and is not limited by the specific design of the evaluation function. Therefore, it is widely applicable to different design goals and constraints.

[0071] 2. This invention employs a multi-startpoint variable neighborhood search method. Multi-startpoint search expands the search range and avoids getting trapped in local optima. Variable neighborhood search divides the search range into multiple neighborhoods and searches each neighborhood sequentially according to the principle of "if no better solution is found in the current neighborhood structure or the maximum number of searches is reached, switch to the next neighborhood structure to continue searching," thus improving search efficiency. Therefore, this invention's multi-startpoint variable neighborhood search method balances search range and search efficiency, achieving global optimization with high efficiency.

[0072] 3. The five neighborhood structures designed in the heat pipe distribution model of this invention have a good ability to guide the search direction. By adjusting the position of the components, the maximum heat pipe power can be effectively reduced, which can effectively improve the heat concentration problem of the components, improve the heat dissipation performance of the system, and thus improve the stability of the components during system operation.

[0073] 4. In a specific application scenario, the present invention further designs constraints such as spatial non-overlapping constraints, static stability constraints, heat pipe heat dissipation capacity constraints, and component-heat pipe overlap constraints, which can improve the heat dissipation performance of the system while satisfying the above constraints.

[0074] 5. To further improve search efficiency and considering the target focus factors in the industrial optimization process, this invention uses the number of times the evaluation function is used as the design basis for the number of iterations. Furthermore, considering that when the component size accounts for a large proportion, more evaluation iterations need to be allocated to the position optimization model to obtain a feasible layout scheme that meets engineering constraints; conversely, more evaluation iterations should be allocated to the heat pipe allocation model to obtain a heat pipe allocation scheme with better thermal performance. This invention designs a specific allocation scheme for the number of times the evaluation function is used to improve search efficiency while taking into account the target focus of different optimization stages. Attached Figure Description

[0075] Figure 1 This is a schematic diagram of the layout domain of a preferred embodiment of the present invention;

[0076] Figure 2 These are schematic diagrams illustrating five preferred embodiments of the present invention's domain structure design;

[0077] Figure 3 This is a schematic diagram of the optimal layout scheme of a preferred embodiment of the present invention;

[0078] Figure 4 This is a flowchart of the solution scheme based on a two-layer model in a preferred embodiment of the present invention;

[0079] Figure 5This is a flowchart of a preferred embodiment of the present invention based on a multi-starting point variable neighborhood search algorithm in a heat pipe distribution model. Detailed Implementation

[0080] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0081] like Figure 1 As shown, a preferred method of the present invention for solving the heat pipe constraint element layout optimization problem based on variable neighborhood search-genetic algorithm includes:

[0082] Parameter initialization:

[0083] Before using a multi-starting point variable neighborhood search-genetic algorithm to solve the heat pipe constrained component layout optimization problem, parameter initialization is required, specifically including the following parameters:

[0084] (1) Heat pipe information: number of heat pipes, location of each heat pipe, and maximum heat pipe power;

[0085] (2) Layout domain information: size and position of the layout domain;

[0086] (3) Component information: the number of components, the size, mass and power of each component;

[0087] (4) Evaluation function usage count βD;

[0088] (5) Parameters of the multi-startpoint variable neighborhood search algorithm: maximum number of iterations t max ;

[0089] (6) Genetic algorithm parameters: population size, crossover probability, mutation probability, local search step size.

[0090] As an illustrative preference, in this embodiment, β is set to 5000, and the maximum number of iterations for neighborhood structure search in the multi-starting point variable neighborhood search algorithm (i.e., the maximum number of searches in a neighborhood) is set to 20.

[0091] A heat pipe distribution model is designed to perform the corresponding operations in the heat pipe distribution stage, and a location optimization model is designed to perform the corresponding operations in the location optimization stage. Simultaneously, based on the dimensional relationships between components and the layout domain, the number of times the evaluation function can be used in both models is limited. The specific method is as follows:

[0092]

[0093] Where t1 and t2 represent the number of times the evaluation function of the heat pipe distribution model and the location optimization model are used, respectively, and a represents the proportion of the sum of the areas of all components to the area of ​​the layout domain.

[0094] As an illustrative preference, in this embodiment, p1 is 0.1, p2 is 0.4, a1 is 10%, and a2 is 30%.

[0095] coding:

[0096] In the heat pipe distribution model, integer encoding is used to express the heat pipe distribution method of the components, as shown in the equation.

[0097] H={(h i ,q i |i = 1, 2, ..., N c}

[0098] Where i is the component number, N c h represents the number of components. i The q indicates the number of the first heat pipe occupied by the component. i This indicates the number of heat pipes occupied by the component.

[0099] In the location optimization model, the coordinates of each element are used for encoding, as shown in the equation.

[0100] X={(x i ,y i |i = 1, 2, ..., N c}

[0101] Where, x i y i These represent the horizontal and vertical coordinates of the centroid of element i within the layout domain.

[0102] Based on the above settings, the specific steps of this embodiment are designed as follows:

[0103] S1: Heat pipe distribution stage

[0104] S1.1: Randomly generate a heat pipe allocation scheme code as the initial solution s.

[0105] S1.2: The current solution s in the neighborhood structure N k The optimal solution s′ is obtained through a local search within the neighborhood structure N. Specifically, each search is performed within the neighborhood structure N. k The solution is generated in batches, and then the optimal solution s' is found from the batch-generated solutions.

[0106] S1.3: The thermally superior solution between s and s′ is retained and the search continues within the current neighborhood structure. When no better solution is found in the current neighborhood structure, the search switches to the next neighborhood structure. Furthermore, a maximum number of iterations (i.e., the maximum number of searches within that neighborhood) is set for each neighborhood structure; when the maximum number of searches in that neighborhood is reached, the search also switches to the next neighborhood structure.

[0107] As a further preferred approach, the following five neighborhood structures are considered in the local search of the heat pipe allocation model, and their acquisition methods are as follows: 1) Insertion: Move the components on the heat pipe with the highest heat (bottleneck pipe) to other heat pipes in sequence; 2) Exchange: Exchange the components on the bottleneck pipe with the components on other heat pipes in sequence; 3) Move: Change the number of heat pipes occupied by the components on the bottleneck pipe; 4) Perform the above operations 1-3, select a portion of solutions with high fitness, and perform operations 1-3 again; 5) Perform the above operations 1-3, and perform operations 1-3 again for all the obtained solutions. Preferably, by following the search order of first one neighborhood, then two neighborhoods, first insertion, then exchange, and then move, the search range can be gradually narrowed from large to small, which can both take into account search efficiency and avoid getting trapped in local optima.

[0108] S1.4: Determine whether the total number of iterations in the heat pipe allocation stage has reached the upper limit. If so, output the recorded optimal solution; otherwise, go to S1.1. Preferably, in this embodiment, the total number of iterations is set to the number of times the evaluation function is used, t1.

[0109] S2: Location Optimization Stage

[0110] In this embodiment, a genetic algorithm is used to optimize the component position coordinates to satisfy four constraints, including the following steps:

[0111] S2.1: Based on the optimal solution of the heat pipe allocation scheme determined in the heat pipe allocation stage, determine the range of values ​​for the centroid coordinates of each element, and randomly generate an initial population of element position coordinate codes within this range.

[0112] S2.2: Retain a subset of individuals with high fitness from the initial population and record the optimal solution.

[0113] There are many methods for calculating fitness in this field. For example, the value of the evaluation function can be calculated directly, or the fitness value can be scaled to adjust the fitness in order to prevent the fitness value from being distributed irrationally or failing to reflect the characteristics of the individual. The transformation methods include linear transformation, power function transformation, exponential transformation (similar to simulated annealing), Goldberg linear stretching transformation, etc.

[0114] Preferably, the evaluation function and fitness calculation formula set in this embodiment are as follows:

[0115] The evaluation function includes optimization objectives and constraints. In this embodiment, the optimization objective is to minimize the maximum heat pipe power, while satisfying the following four constraints: 1) non-overlapping constraint; 2) static stability constraint; 3) heat dissipation capacity constraint; 4) component-heat pipe overlap constraint.

[0116] The optimization objective expression is:

[0117]

[0118] in, N represents the actual load power of heat pipe m. hp Number of heat pipes;

[0119] In other embodiments, when considering more than two side plates, the optimization objective is expanded to minimize the sum of the maximum heat pipe powers of each plate, i.e.:

[0120]

[0121] This represents the actual load power of heat pipe m on plate 1. The number of heat pipes on plate 1. The actual load power of heat pipe n on plate 2. The number of heat pipes on plate 2;

[0122] The expressions for the four constraints are as follows:

[0123] 1) Non-overlapping constraint g1(X): Components cannot overlap each other and cannot exceed the layout domain, i.e.:

[0124]

[0125] Where, ΔV ij N represents the overlapping area of ​​elements i and j. c The number of components, i,j>0, i≠j;

[0126] 2) Static stability constraint g2(X): The centroid position should be within a given range, i.e.:

[0127] g2(X)=|y c -y e |-δy e ≤0

[0128] Among them, y c The actual y-axis centroid position, y e For the standard y-axis centroid position, δy e This represents the maximum permissible positional deviation.

[0129] 3) Heat dissipation capacity constraint g3(X): The heat power on each heat pipe cannot exceed the maximum heat dissipation capacity, that is:

[0130]

[0131] in, μ is the actual load power of heat pipe j. j P is the set of components occupying heat pipe j. i and These represent the power of component i and the number of heat pipes it occupies. N is the maximum load power of the heat pipe. hp Number of heat pipes;

[0132] 4) Component-Heatpipe Overlap Constraint g4(X): Each component needs to be placed on a heatpipe; when a component uses multiple heatpipes for heat dissipation, it is assumed that these heatpipes evenly distribute the heat power of the component, i.e.:

[0133]

[0134] Where, d i denoted as , where is the distance between element i and the nearest heat pipe.

[0135] The method for calculating fitness in step S2.2 is as follows:

[0136] For the solutions in the population, calculate the four constraints, find the solutions that simultaneously satisfy all four constraints, and take the reciprocal of their linear combination as the fitness fit(X), as shown in the following formula:

[0137]

[0138] Where c1, c2, c3, and c4 are empirical constants;

[0139] In this embodiment, fitness(X) represents the probability that a solution is selected. The higher the fitness, the greater the probability that a solution is selected.

[0140] S2.3: Perform a local search on the optimal solution obtained in S2.2. If the new optimal solution obtained by the local search is better than the optimal solution obtained in S2.2, then replace it; otherwise, retain the optimal solution obtained in S2.2.

[0141] Preferably, the specific method for performing a local search for the best solution in the population in this embodiment is as follows: within feasible limits, each element is moved a certain distance in the four directions (up, down, left, and right). This distance can be set empirically based on actual conditions. A larger distance results in higher search efficiency but may miss better solutions, while a smaller distance results in lower search efficiency but solutions are less likely to be missed. If the optimal solution obtained from the local search is better than the current solution, it is replaced.

[0142] S2.4: From the population after the search and replacement in S2.3, select individuals for crossover according to the preset crossover probability, and then select individuals for mutation according to the preset mutation probability to update the population;

[0143] S2.5: Determine whether the number of iterations has reached the preset upper limit. If so, output the optimal solution, i.e. the optimal layout of the heat pipe constraint elements, including the optimal heat pipe allocation scheme and the corresponding optimal element position. Otherwise, use the population updated in S2.4 as the initial population and transfer to S2.2.

[0144] Following the steps above, in order to verify the algorithm performance, this embodiment selected a case with 40 components, 16 heat pipes, and 2 layout domains for experimentation, as shown in Table 1.

[0145] The layout domains are as follows: Layout domain 1: x∈[100,1900], y∈[-950,-50]; Layout domain 2: x∈[100,1900], y∈[950,50]; The upper limit of the heat pipe power is 120W; The centroid range in the vertical direction is [15,25].

[0146] Table 1

[0147]

[0148] In this example, the average heat pipe power is 99.125W. The optimal arrangement obtained by this algorithm in 30 independent experiments is the arrangement with a maximum heat pipe power of 100W. Figure 3 As shown.

[0149] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing the layout of heat pipe constraint elements, characterized in that, Includes the heat pipe distribution stage and the location optimization stage: S1: Heat pipe distribution stage S1.1: Randomly generate a heat pipe allocation scheme code as the initial solution. The heat pipe allocation scheme code includes the number and quantity of heat pipes occupied by each component; In the heat pipe distribution stage, a multi-starting-point variable neighborhood search algorithm is used to optimize the thermal performance of the heat pipe distribution scheme, wherein: Multi-starting-point search refers to a local search that starts with a random solution in each iteration, and then performs a local search on a new random solution in the next iteration after the current iteration has completed the search. Variable neighborhood search refers to the process of inserting, exchanging, and / or moving each initial solution during an iteration to obtain different neighborhood structures. Then, the search starts from any neighborhood structure. When no better solution is found in the neighborhood structure or the set maximum number of search attempts is reached, the search is switched to the next neighborhood structure to continue. S1.2: Perform a local search on s within a neighborhood structure to obtain the optimal solution within that neighborhood structure. ; S1.3: If thermal performance is better than If the number of iterations within the current neighborhood structure has not reached the upper limit, then let Continue within the current neighborhood structure Perform a local search until no better solution is found or the number of iterations in the current neighborhood structure has reached the limit. Then switch to the next neighborhood structure to perform a local search for the current best solution until all neighborhood structures have been used. S1.4: Determine if the number of iterations has reached the upper limit. If so, obtain the optimal solution for the heat pipe allocation stage; otherwise, go to S1.

1. S2: Location Optimization Stage S2.1: Based on the optimal solution of the heat pipe allocation scheme obtained in S1.4, randomly generate an initial population of component position coordinate codes; S2.2: Retain a subset of individuals with high fitness from the initial population and record the optimal solution; S2.3: Perform a local search on the optimal solution obtained in S2.

2. If the new optimal solution obtained by the local search is better than the optimal solution obtained in S2.2, then replace it; otherwise, retain the optimal solution obtained in S2.

2. S2.4: From the population after the search and replacement in S2.3, select individuals for crossover according to the preset crossover probability, and then select individuals for mutation according to the preset mutation probability to update the population; S2.5: Determine whether the number of iterations has reached the preset upper limit. If so, output the optimal solution, i.e. the optimal layout of the heat pipe constraint elements, including the optimal heat pipe allocation scheme and the corresponding optimal element position. Otherwise, use the population updated in S2.4 as the initial population and transfer to S2.

2.

2. The method for optimizing the layout of heat pipe constraint elements according to claim 1, characterized in that, Neighborhood structures include at least one of three one-step neighborhood structures and two two-step neighborhood structures, wherein: The three methods for obtaining the one-step neighborhood structure are as follows: Insert the components from the heat pipe with the highest heat output and move them sequentially to the other heat pipes. Exchange, which involves sequentially swapping the components on the heat pipe with the highest heat output with the components on other heat pipes; Move and change the number of heat pipes occupied by the components on the heat pipe with the highest heat output; The two methods for obtaining the two-step neighborhood structure are as follows: Select a solution with high fitness from the one-step neighborhood structure, and then perform insertion, swap and / or move operations again; For all solutions obtained from the one-step neighborhood structure, perform insertion, swap, and / or shift operations again.

3. A method for optimizing the layout of heat pipe constraint elements according to any one of claims 1 to 2, characterized in that, In step S1.1, integer encoding is used to assign heat pipes to components. H The expression is as follows: in, For component numbers, The total number of components, Indicator element i The number of the first heat pipe used. Indicator element i The number of heat pipes used.

4. A method for optimizing the layout of heat pipe constraint elements according to any one of claims 1 to 2, characterized in that, In step S2.1, the component position coordinate code X is as follows: in, , Representing components The centroid's horizontal and vertical coordinates within the layout domain, This represents the total number of components.

5. The method for optimizing the layout of heat pipe confinement elements according to any one of claims 1 to 2, characterized in that, An evaluation function is used to assess the quality of the solutions, and the maximum number of times the evaluation function can be used is specified. D, where D is the dimension of the decision variables, which are the specific locations of each element, including the horizontal and vertical coordinates of the element's centroid within the layout domain. Experience value; Based on the size ratio of all components to the layout domain, the number of times the evaluation function is used is allocated to the heat pipe allocation stage and the position optimization stage as the upper limit of the iteration count for the corresponding stages. The allocation scheme is as follows: in, , These represent the number of times the evaluation function is used in the heat pipe allocation model and the location optimization model, respectively. This indicates the proportion of the total area of ​​all components to the total area of ​​the layout domain.

6. A method for optimizing the layout of heat pipe constraint elements according to any one of claims 1 to 2, characterized in that, The methods for selecting the optimal solution in the heat pipe allocation stage and the location optimization stage are both based on minimizing the maximum heat pipe power as the optimization objective, while satisfying the following four constraints: 1) non-overlapping constraint; 2) static stability constraint; 3) heat dissipation capacity constraint; 4) component-heat pipe overlap constraint.

7. The method for optimizing the layout of heat pipe constraint elements according to claim 6, characterized in that, The optimization objective expression is: in, For heat pipes Actual load power Number of heat pipes; When considering more than two side plates, the optimization objective is expanded to minimize the sum of the maximum heat pipe powers of each plate, i.e.: For the heat pipes on plate 1 Actual load power The number of heat pipes on plate 1. For the heat pipes on plate 2 Actual load power This refers to the number of heat pipes on plate 2; The expressions for the four constraints are as follows: 1) Non-overlapping constraints Components cannot overlap each other or exceed the layout area, that is: in, For components and The overlapping area, For the number of components, ; 2) Static stability constraints The location of the centroid should be within the given range, that is: in, For the actual The position of the center of mass of the axis, For standard The position of the center of mass of the axis, This represents the maximum permissible positional deviation. 3) Heat dissipation capacity constraints The heat output of each heat pipe cannot exceed its maximum heat dissipation capacity, that is: in, For heat pipes Actual load power To occupy the heat pipe A collection of components, and Components The power and the number of heat pipes occupied, This represents the maximum load power of the heat pipe. Number of heat pipes; 4) Component-Heatpipe Overlap Constraints Each component needs to be placed on a heat pipe; when a component uses multiple heat pipes for heat dissipation, assuming these heat pipes evenly distribute the component's heat power, that is: in, For components i Distance to the nearest heat pipe.

8. The method for optimizing the layout of heat pipe constraint elements according to claim 7, characterized in that, The method for calculating fitness in step S2.2 is as follows: For each solution in the population, calculate the four constraints, find the solutions that simultaneously satisfy all four constraints, and use the reciprocal of their linear combination as the fitness. As shown in the following formula: in, It is an empirical constant; Based on the fitness values, select a subset of elite solutions with higher fitness as offspring, and select the remaining solutions using a roulette wheel method to obtain a population that meets the constraints. The local search method in S2.3 is as follows: perform a local search on the best solution in the current population. Specifically, within the feasible range, move each element sequentially by a preset distance in the four directions of up, down, left, and right; if the optimal solution obtained by the local search is better than the current solution, then replace it. In S2.5, the iteration function uses the number of times the evaluation function is used as the benchmark. If the number of times the evaluation function is used reaches the upper limit, the process ends; otherwise, it goes to S2.

2.

9. A heat pipe constraint element layout optimization system, characterized in that, It includes a processor and a computer program module, wherein when the computer program module is invoked and executed by the processor, it implements the heat pipe constraint element layout optimization method as described in any one of claims 1 to 8.

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