Two-dimensional ship piping layout method based on artificial bee colony algorithm

Abstract

The present invention relates to the technical field of ship pipeline planning, and in particular to a two-dimensional ship pipeline layout method based on an artificial bee colony algorithm. The method comprises the following steps: step 1: simplifying the expression of an equipment model and a layout space, and building a mathematical model of the pipeline layout problem; step 2: improving the artificial bee colony algorithm by using a chaotic elite inverse learning population to calculate an initial solution of the mathematical model of the pipeline layout problem; and step 3: continuously updating the initial solution by using optimally guided employed bees, spirally searched follower bees, and scout bees until a convergence condition is met, thereby improving path planning efficiency and layout design quality, and reducing design costs.

Description

Technical Field

[0001] The present invention relates to the technical field of ship pipeline planning, and in particular to a two-dimensional ship pipeline layout method based on an artificial bee colony algorithm. Background Art

[0002] Piping design is crucial to the overall design of a ship. Reasonable piping layout reduces energy consumption, and a well-designed piping design facilitates maintenance. Piping design plays a key role in a ship's performance, safety, and economical operation. With the continuous advancement of digitalization and intelligentization, the trend toward intelligentization in the field of piping layout is unstoppable. Currently, many scholars and designers are adopting intelligent algorithms to address piping layout issues. Publication No. CN115587453A discloses an automatic layout calculation method for ship piping. Based on the ship's cabin structure and piping layout patterns, it uses the A* algorithm to solve single-pipe routing paths and employs pipeline priority sorting and cost-value correction methods to solve multi-pipe routing paths. However, when solving two-dimensional piping layout problems, deterministic algorithms such as A* experience exponentially lower search performance as the layout space expands. Among them, the Artificial Bee Colony (ABC) algorithm, a classic heuristic search algorithm, has attracted considerable attention for its efficient search capabilities and has become a hot topic of research. However, the traditional ABC algorithm has several limitations when applied to two-dimensional piping layout. First, the traditional ABC algorithm is significantly affected by the initial solution; second, without modification, the traditional ABC algorithm cannot be directly used to solve pipeline layout problems. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art and provide a two-dimensional ship piping layout method based on an artificial bee colony algorithm.

[0004] The technical solution adopted by this application to solve the technical problem is: a two-dimensional ship piping layout method based on an artificial bee colony algorithm includes the following steps:

[0005] Step 1: Simplify the equipment model and layout space, and build a mathematical model for the pipeline layout problem;

[0006] Step 2: Using the chaotic elite inverse learning population to improve the artificial bee colony algorithm to calculate the initial solution of the mathematical model of the pipeline layout problem;

[0007] Step 3: The initial solution is continuously updated by the optimally guided employed bees, spiral search follower bees, and scout bees until the convergence condition is met;

[0008] Step 4: Calculate the coordinate solution of the pipeline, and convert the orthogonalized coordinate solution into a node solution as the final solution of the pipeline layout problem. Step 1 includes the following sub-steps:

[0009] 1-1: Use the axis-parallel containment box method to simplify the representation of equipment in the ship;

[0010] 1-2: Based on the centerline theory, the connection of the pipeline is simplified to a point, and the complete pipeline is simplified to a centerline;

[0011] 1-3: Determine the objective function of the mathematical model of the pipeline layout problem.

[0012] The objective function of the mathematical model for determining the pipeline layout problem is to minimize the weighted function min F(x), which is expressed as follows:

[0013]

[0014] Where, X i is the i-th optimization objective; w i is the weight factor of the i-th optimization objective; X ij Store information for the pipeline in each optimization target, represented as the jth pipeline node in the i-th solution; X ij_no is the set of infeasible grids; lb is the lower limit of the layout space; ub is the upper limit of the layout space; i, j, k are natural numbers, and j and k are pipeline nodes of different dimensions.

[0015] The initial solution in step 2 is constructed as X i =(X i,1 ,X i,2 ,...X i,j ...X i,D ), x i,j is the jth node of the i-th pipeline solution, D is a natural number, and the step 2 includes the following sub-steps:

[0016] 2-1: Generate a set of basic solutions, and select the elite individuals with the most outstanding performance from this set of basic solutions as the elite solutions.

[0017] 2-2: Perform chaotic inverse learning based on the elite solution to generate another set of solutions.

[0018] 2-3: Select elite individuals from the two sets of solutions again to generate elite initial solutions.

[0019] The step three includes the following sub-steps:

[0020] 3-1: Hired bee stage, guiding the search direction based on the current optimal solution and the global optimal solution, adjusting the influence of the current optimal solution and the global optimal solution based on the weight, controlling the search direction, and updating the solution;

[0021] 3-2: Follower bees perform spiral development around the global optimal solution. At the same time, follower bees communicate with other solutions and search from different angles and directions to increase the possibility of escaping the local optimal solution and updating the solution.

[0022] The 3-1 step includes the following sub-steps:

[0023] 3-1-1: Calculate the current optimal solution;

[0024] 3-1-2: Calculate the global optimal solution;

[0025] 3-1-3: The expression after updating the solution by adjusting the influence of the current optimal solution and the global optimal solution on the search direction based on the weight is as follows:

[0026] X i,j =X i,j +φ(X i,j -X k,j )+λ1(X pbest,j -X i,j )+λ2(X gbest,j -X i,j );

[0027] Where φ is a random number in the interval [-1, 1]; λ1 and λ2 are the influence factors of the current optimal and global optimal individuals, which are 1.5 and 1 in the first 1 / 2 iterations and 1 and 1.5 in the second 1 / 2 iterations; X pbest and X gbest are the current optimal and global optimal individuals respectively.

[0028] The employed bee with the best fitness value in the current iteration number in 3-1-1 is the current optimal solution.

[0029] The optimal solution in the iterative process completed in 3-1-2 is the global optimal solution.

[0030] The expression after updating the solution in 3-2 is as follows:

[0031]

[0032] Where a1 and a2 are weight coefficients that control the tendency of the follower bee to move toward other bees and the global optimal solution, and i and k are the numbers of the current follower bee.

[0033] The coordinate solution after orthogonalization in step 4 is converted into a node solution as the final solution of the pipeline layout problem through a conversion formula. The conversion formula is as follows:

[0034]

[0035] Among them, P x and P yis the solution calculated by the artificial bee colony algorithm, which corresponds to the coordinate information of the pipeline node mapped on the grid map, P is the pipeline node information, mod is the remainder calculation, ceil is the rounding up calculation, d is the grid size, R x and R y are the number of rows and columns of the raster map, respectively.

[0036] Compared with the prior art, this application has the following beneficial effects:

[0037] This paper proposes an improved artificial bee colony algorithm for ship piping layout design, which improves path planning efficiency and layout design quality, reduces design costs, and provides an effective reference for designers. It enables the piping layout to better meet the complex multi-objective and multi-constraint requirements of ship piping design, and has important engineering significance for the intelligent and automated piping design.

[0038] This paper uses an improved artificial bee colony algorithm for two-dimensional ship piping design, which makes the layout efficiency less affected by the size of the layout space. Compared with deterministic algorithms, it is less likely to fall into local optimality, and the layout efficiency and quality are significantly improved.

[0039] The present invention proposes a chaotic elite inverse learning strategy to improve the quality of the initial solution and accelerate the convergence speed;

[0040] The present invention proposes an employed bee search formula that cross-guides the current optimal solution and the global optimal solution, making the search more directional;

[0041] The present invention proposes a follower bee search formula that refers to a random solution and spirally develops around a global optimal solution, thereby improving development efficiency and avoiding falling into a local optimal solution to a certain extent. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 Flowchart of the artificial bee colony algorithm of the present invention;

[0043] Figure 2 This is a schematic diagram of the simulation results of the original artificial bee colony algorithm layout;

[0044] Figure 3 This is a schematic diagram of the layout simulation results for this application. DETAILED DESCRIPTION

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0046] It should be noted that all directional indications in the embodiments of the present invention (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0047] In the present invention, unless otherwise specified or limited, the terms "connection" and "fixation" should be understood in a broad sense. For example, "fixation" can mean fixed connection, detachable connection, or integration; mechanical connection or electrical connection; direct connection or indirect connection through an intermediate medium; internal communication between two elements or interaction between two elements, unless otherwise specified. Those skilled in the art will be able to understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0048] The artificial bee colony algorithm is inspired by the foraging behavior of bees in nature and divides the population into employed bees and follower bees. The traditional artificial bee colony algorithm first initializes the population to obtain an initial solution using the following formula, with the number of updates to the initial solution set to zero. The specific formula is:

[0049] X j =lb j +rand·(ub j -lb j );

[0050] Among them, lb is the lower limit of the layout space; ub is the upper limit of the layout space; rand is a random number between 0 and 1; X j is the solution of the j-th dimension.

[0051] Next, the hired bee stage begins. The hired bees refer to the information of the primary solution and update the solution according to the following formula. If the new solution is better than the old solution, it will be replaced and the number of updates of the solution will be reset to zero; if it is not replaced, the number of updates of the solution will be increased by one.

[0052] X i,j =X i,j +φ(X i,j -X k,j );

[0053] Where φ is a random number in the interval [-1, 1]. i,j 、X k,j are the j-th dimension in the i-th solution and the j-th solution respectively.

[0054] Follower bees select the solution with the richest information for development based on the greedy criterion. The richness of information is determined by the ratio of its fitness value to the sum of all solution fitness values. The information amount calculation and development formula are as follows:

[0055]

[0056] X i,j =X i,j +φ(X i,j -X k,j );

[0057] Among them, p i is the information richness of the i-th solution, fit i is the fitness value of the i-th solution, and SN is the number of solutions.

[0058] The solution updated by the follower bee is also replaced and the number of updates is calculated according to the greedy criterion. When a solution has not been updated for a long time, the bee corresponding to the solution is transformed into a scout bee and a new solution is generated by referring to the initialization formula.

[0059] This paper improves the search mechanism and coding rules of the traditional artificial bee colony algorithm, making the algorithm more suitable for solving the two-dimensional ship pipeline layout problem. Figure 1-Figure 3 ,The 2D ship pipeline layout method based on artificial bee colony algorithm includes the following steps:

[0060] Step 1: Use the grid method to simplify the equipment model and layout space, and build a mathematical model for the pipeline layout problem with the goal of laying length, number of elbows, and easy arrangement of brackets;

[0061] The layout environment of the ship only includes the maximum geometric information of the equipment, which can meet the requirements and improve the search speed of the algorithm. Therefore, the step 1 includes the following sub-steps:

[0062] 1-1: Use the axis-parallel containment box method to simplify the representation of equipment in the ship;

[0063] 1-2: The simplified pipeline model does not affect the expression of the pipeline direction. Therefore, according to the centerline theory, the pipeline connection is simplified to a point, and the complete pipeline is simplified to a centerline.

[0064] 1-3: The objective function of the mathematical model for the pipeline layout problem primarily considers the effects of pipe length and elbows. Furthermore, the objective of facilitating bracket placement is achieved by maximizing the proximity to available bracket locations, as indicated by energy values. Determine the objective function for the mathematical model for the pipeline layout problem.

[0065] The objective function of the mathematical model for determining the pipeline layout problem is to minimize the weighted function minF(x). The linear weighting method is used to simplify the multi-objective problem into a single-objective problem. The specific expression is as follows:

[0066]

[0067]

[0068] Where, X i is the i-th optimization objective; w i is the weight factor of the i-th optimization objective; X i,j Store information for the pipeline in each optimization target, represented as the jth pipeline node in the i-th solution; X i,j_no is the set of infeasible grids; lb is the lower limit of the layout space; ub is the upper limit of the layout space; i, j, k are natural numbers, and j and k are pipeline nodes of different dimensions.

[0069] Step 2: Calculate the initial solution of the mathematical model of the pipeline layout problem by improving the artificial bee colony algorithm through the chaotic elite inverse learning population. To solve the ship pipeline layout problem, all solutions are constructed as follows i =(X i,1 ,X i,2 ,...X i, j ...X i,D ), x i,j is the jth node of the i-th pipeline solution, and D is a natural number;

[0070] The initial solution of this application is generated using the Chaos Elite Inverse Learning Strategy, which has significant advantages. Step 2 includes the following sub-steps:

[0071] 2-1: A set of basic solutions is generated using the traditional artificial bee colony algorithm's initial solution generation formula, laying the foundation for subsequent optimization. Within this set of basic solutions, the fitness values ​​of each are calculated and ranked, and the solutions with the highest fitness values ​​are selected as elite solutions. These elite solutions possess high quality and potential, and are key to further optimization. The fitness value of each basic solution is the sum of the pipeline length and the number of elbows.

[0072] 2-2: Chaotic inverse learning is performed on the elite solution to generate another set of solutions, as shown in the following formula. This inverse learning process fully exploits the potential information of the elite solution, expanding the solution space and providing more possibilities for finding a more optimal solution. In this way, both sets of solutions are valuable.

[0073] Furthermore, define a point X in D-dimensional space e is the elite individual of the initial solution set (corresponding to sub-step 2-1), where X e It is expressed as follows:

[0074]

[0075] in, is the j-th dimension of the elite solution; D is the upper limit of the dimension.

[0076] Reverse solution Expressed as It is defined as follows:

[0077]

[0078] in, is a random number in the interval [0,1] generated using the Tent chaotic map.

[0079] α j , β j is a dynamic boundary condition. Compared with fixed boundary conditions, the elite reverse solution can be constructed in a more accurate search space, where α j =min(X i,j ), β j =max(X i,j ).

[0080] In order to prevent the elite reverse point from crossing the boundary and becoming an infeasible solution, a randomization method is used to reset the expression as follows:

[0081]

[0082] 2-3: Elite individuals are again selected from the two sets of solutions, and these elite individuals together constitute the elite initial solution.

[0083] The Chaos Elite inverse learning strategy significantly improves the quality and diversity of initial solutions. This improved quality makes it more likely that the initial solution will guide the optimization process toward a more optimal solution, reducing ineffective searches and iterations. This increased diversity provides a broader search space for the optimization process, preventing it from being trapped in local optima. This strategy effectively improves the algorithm's search efficiency and solution accuracy, providing strong support for solving complex pipeline layout optimization problems.

[0084] Step 3: The initial solution is continuously updated by the optimally guided employed bees, spiral search follower bees, and scout bees until the convergence condition is met. Step 3 includes the following sub-steps:

[0085] 3-1: Hired bee stage: Based on the current optimal solution and the global optimal solution, the search direction is clearly guided. The influence of the current optimal solution and the global optimal solution is adjusted based on the weight to control the search direction and update the solution.

[0086] In the hired bee stage, in-depth exploration and development are mainly carried out around the optimal solution. The importance of this stage is that it can greatly affect the convergence speed and solution quality of the entire algorithm. Different from the traditional artificial bee colony algorithm, the traditional algorithm often relies on the information of two random solutions for development, which has a certain degree of uncertainty and blindness. In the improved algorithm, the current optimal solution and the global optimal solution are introduced to clearly guide the search direction. The current optimal solution represents the solution that performs best in the current iteration process, and it can provide a strong direction for local search. The global optimal solution is from the perspective of the entire search process, representing the optimal solution found so far, which provides a global vision and goal for the algorithm. Specifically, 3-1 includes the following sub-steps:

[0087] 3-1-1: Calculate the current optimal solution. The employed bee with the best fitness value in the current iteration is the current optimal solution.

[0088] 3-1-2: Calculate the global optimal solution. The optimal solution in the completed iteration process is the global optimal solution.

[0089] 3-1-3: Based on the weight adjustment, the influence of the current optimal solution and the global optimal solution on the search direction is updated. The expression of the updated solution is as follows:

[0090] X i,j =X i,j +φ(X i,j -X k,j )+λ1(X pbest,j -X i,j )+λ2(X gbest,j -X i,j );

[0091] Where φ is a random number in the interval [-1, 1]; λ1 and λ2 are the influence factors of the current optimal and global optimal individuals, which are 1.5 and 1 in the first 1 / 2 iterations and 1 and 1.5 in the second 1 / 2 iterations respectively; X pbest and X gbes are the current optimal and global optimal individuals respectively.

[0092] By adjusting the weights to adjust the influence between the two, the search direction can be more flexibly controlled. When the weights favor the current optimal solution, the algorithm focuses more on local, refined search, enabling deeper exploration near the current optimal solution in the hope of finding a better solution. Conversely, when the weights favor the global optimal solution, the algorithm prefers a more global exploration to avoid being trapped in a local optimal solution. This weight adjustment allows the algorithm to adjust its search strategy based on different problems and search stages, thereby improving its adaptability and solving capabilities.

[0093] 3-2: Follower bees perform spiral development around the global optimal solution. At the same time, follower bees communicate with other solutions and search from different angles and directions to increase the possibility of escaping the local optimal solution and updating the solution.

[0094] After the employed bees complete their updates, the follower bees exhibit distinct characteristics from the traditional ABC algorithm. In the traditional ABC algorithm, the follower bees and employed bees use the same update formula around two random solutions. This approach has certain limitations because the uncertainty of the random solutions can lead to blindness and inefficiency in the search process.

[0095] In the improved algorithm, the follower bees will perform a spiral search around the global optimal solution. As the best known solution in the entire search space, the global optimal solution provides a clear goal and direction for the follower bees. By spiraling around the global optimal solution, the follower bees can more effectively search near it, thereby improving search efficiency and accuracy.

[0096] Follower bees also exchange information with other solutions. This information exchange mechanism enables follower bees to obtain the characteristics and advantages of different solutions, thereby enriching their own search strategies. By interacting with other solutions, follower bees can avoid being trapped in local optimal solutions. Local optimal solutions are a common problem in optimization algorithms. When an algorithm is trapped in a local optimal solution, it is difficult to find a better solution. By exchanging information with other solutions, follower bees can search from different angles and directions, increasing the possibility of jumping out of the local optimal solution. At this time, the updated solution The expression is as follows:

[0097]

[0098] β=e bl ·cos(2πl);

[0099] Where a1 and a2 are weight coefficients that control the tendency of the follower bee to move toward other bees and the global optimal solution, a is a constant used to determine the degree of movement trend, i and k are the numbers of the current follower bee, rand is a function that generates random numbers between 0 and 1, β is the spiral control parameter, T is the total number of iterations, t is the current iteration number, b is a constant that defines the spiral shape, and l is a random number between [-1, 1].

[0100] Step 4: The improved artificial bee colony algorithm calculates the coordinate solution for the pipeline. The orthogonalized coordinate solution is converted into a node solution using a conversion formula, serving as the final solution to the pipeline layout problem. When used in a real-world environment, the node solution can be converted back to a coordinate solution. Referring to the traditional artificial bee colony algorithm, if the solution corresponding to either the employed bee or the follower bee reaches the set update limit without updating, the scout bees are transformed into scout bees, who update the solution according to the formula used to generate the initial solution of the traditional artificial bee colony algorithm, and the update count is reset to zero.

[0101] At the same time, considering the requirements of grid method environment description and pipeline orthogonalization, it is necessary to convert the solution of artificial bee colony algorithm iterative calculation into a node solution that stores pipeline information. The conversion formula is as follows:

[0102]

[0103] Among them, P x and P y is the solution calculated by the artificial bee colony algorithm, which corresponds to the coordinate information of the pipeline node mapped on the grid map, P is the pipeline node information, and is the x above. i,j , mod is the remainder calculation, ceil is the rounding up calculation, d is the grid size, R x and R y are the number of rows and columns of the raster map, respectively.

[0104] For the pipeline node solutions calculated by the improved artificial bee colony algorithm, the path can be orthogonalized by introducing an orthogonalization operation. That is, for two pipeline nodes corresponding to their respective coordinate information (x1, y1) and (x2, y2), you can choose to add the point (x1, y2) or the point (x2, y1). This operation can make the path formed by the original two points orthogonal, meeting the pipeline design requirements.

[0105] The present invention uses chaotic numbers to generate reverse solutions and has a boundary control strategy. Based on improvements to the employed bee and follower bee search equations, the inclusion of other solutions in the employed bee equation, the influence of the current optimal solution and the global optimal solution, and the use of a spiral development formula in the follower bee equation, the present invention improves the exploration and development capabilities of the artificial bee colony algorithm.

[0106] The above descriptions are merely optional embodiments of the present invention and do not limit the patent scope of the present invention. All equivalent structural transformations made using the contents of the present invention specification under the concept of the present invention, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. A two-dimensional ship piping layout method based on artificial bee colony algorithm, characterized in that: The steps include: Step 1: Simplify the equipment model and layout space, and build a mathematical model for the pipeline layout problem; Step 2: Using the chaotic elite inverse learning population to improve the artificial bee colony algorithm to calculate the initial solution of the mathematical model of the pipeline layout problem; The initial solution in step 2 is constructed as X i =(X i,1 ,X i,2 ,...X i,j ...X i,D ), x i,j is the jth node of the i-th pipeline solution, D is a natural number, and the step 2 includes the following sub-steps: 2-1: Generate a set of basic solutions, and select the elite individuals with the most outstanding performance from this set of basic solutions as the elite solutions; 2-2: Perform chaotic inverse learning based on the elite solution to generate another set of solutions; 2-3: Select elite individuals from the two sets of solutions again to generate elite initial solutions; Step 3: The initial solution is continuously updated by the optimally guided employed bees, spiral search follower bees, and scout bees until the convergence condition is met; The step three includes the following sub-steps: 3-1: Hired bee stage, guiding the search direction based on the current optimal solution and the global optimal solution, adjusting the influence of the current optimal solution and the global optimal solution based on the weight, controlling the search direction, and updating the solution; The 3-1 step includes the following sub-steps: 3-1-1: Calculate the current optimal solution; 3-1-2: Calculate the global optimal solution; 3-1-3: The expression after updating the solution by adjusting the influence of the current optimal solution and the global optimal solution on the search direction based on the weight is as follows: X i,j =X i,j +φ(X i,j -X k,j )+λ1(X pbest,j -X i,j )+λ2(X gbest,j -X i,j ); Where φ is a random number in the interval [-1, 1]; λ1 and λ2 are the influence factors of the current optimal and global optimal individuals, which are 1.5 and 1 in the first 1 / 2 iterations and 1 and 1.5 in the second 1 / 2 iterations; X pbest and X gbest are the current optimal and global optimal individuals respectively; 3-2: Follower bees perform spiral development around the global optimal solution. At the same time, follower bees communicate with other solutions and search from different angles and directions to increase the possibility of escaping the local optimal solution and updating the solution. Step 4: Obtain the coordinate solution of the pipeline, and convert the orthogonalized coordinate solution into a node solution as the final solution to the pipeline layout problem.

2. The two-dimensional ship piping layout method based on artificial bee colony algorithm according to claim 1 is characterized in that: The step 1 includes the following sub-steps: 1-1: Use the axis-parallel containment box method to simplify the representation of equipment in the ship; 1-2: Based on the centerline theory, the connection of the pipeline is simplified to a point, and the complete pipeline is simplified to a centerline; 1-3: Determine the objective function of the mathematical model of the pipeline layout problem.

3. The two-dimensional ship piping layout method based on artificial bee colony algorithm according to claim 2 is characterized in that: The objective function of the mathematical model for determining the pipeline layout problem is to minimize the weighted function min F(x), which is expressed as follows: Where, X i is the i-th optimization objective; w i is the weight factor of the i-th optimization objective; X ij Store information for the pipeline in each optimization target, represented as the jth pipeline node in the i-th solution; X ij_no is the set of infeasible grids; lb is the lower limit of the layout space; ub is the upper limit of the layout space; i, j, k are natural numbers, and j and k are pipeline nodes of different dimensions.

4. The two-dimensional ship piping layout method based on artificial bee colony algorithm according to claim 1 is characterized in that: The employed bee with the best fitness value in the current iteration number in 3-1-1 is the current optimal solution.

5. The two-dimensional ship piping layout method based on artificial bee colony algorithm according to claim 1 is characterized in that: The optimal solution in the iterative process completed in 3-1-2 is the global optimal solution.

6. The two-dimensional ship piping layout method based on artificial bee colony algorithm according to claim 1 is characterized in that: The updated solution in 3-2 The following expression is: Where a1 and a2 are weight coefficients that control the tendency of the follower bee to move toward other bees and the global optimal solution, i and k are the numbers of the current follower bee, rand is a function that generates random numbers between 0 and 1, and β is the spiral control parameter.

7. The two-dimensional ship piping layout method based on artificial bee colony algorithm according to claim 6 is characterized in that: The coordinate solution after orthogonalization in step 4 is converted into a node solution as the final solution of the pipeline layout problem through a conversion formula. The conversion formula is as follows: Among them, P x and P y is the solution calculated by the artificial bee colony algorithm, which corresponds to the coordinate information of the pipeline node mapped on the grid map, P is the pipeline node information, mod is the remainder calculation, ceil is the rounding up calculation, d is the grid size, R x and R y are the number of rows and columns of the raster map, respectively.

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