An adaptive multi-objective container ship stowage method based on improved ant colony algorithm

Abstract

The application discloses a container ship stowage method based on an improved ant colony algorithm under self-adaption and multiple targets, and comprises the following steps: first, parameter initialization, a multiple-target ant colony model is constructed, the number of ants, the maximum number of iterations, the pheromone concentration is initialized, the pheromone evaporation coefficient, the pheromone importance coefficient and the heuristic factor coefficient are set, according to the multiple-target characteristics of the container ship stowage problem, a multiple-target weight coefficient vector is constructed, and on the multiple-target optimization level, the edge weight dynamically fuses the box turning penalty (P), the cross-pile area moving cost (C) and the weight deviation (W), and the adaptive weight distribution strategy (such as fuzzy logic dynamic adjustment omega 1-omega 3) is combined, so that the algorithm can flexibly balance the efficiency and safety under complex working conditions, and the multiple-target priorities are balanced through the adaptive weight distribution.

Description

Technical Field

[0001] This invention relates to the field of container ship stowage technology, specifically an adaptive multi-objective container ship stowage method based on an improved ant colony algorithm. Background Technology

[0002] Currently, container ship stowage still relies mainly on manual experience. This method is not only time-consuming and labor-intensive, but also difficult to exhaustively explore feasible solutions when faced with thousands of container slots and multiple stability constraints. Furthermore, it is prone to safety issues such as local overloading of the ship and unstable container stacking, reducing the ship's seaworthiness and operational efficiency. Therefore, existing technologies employ heuristic algorithms to optimize stowage efficiency.

[0003] Limitations of existing algorithms: When applied to container ship stowage, traditional metaheuristic algorithms such as genetic algorithms and particle swarm optimization are prone to premature convergence and getting trapped in local optima. They lack sufficient ability to fully explore the global solution space and cannot meet the stowage requirements under complex and multi-constraint conditions. They have limitations in optimizing the loading sequence, reducing the number of container turnings in the yard, and improving the efficiency of loading and unloading. They are also difficult to accurately balance the weight of each pre-stowage position and cannot effectively ensure the stability of the ship and the smoothness of operations, which greatly reduces the feasibility and effectiveness of the stowage scheme.

[0004] In summary, existing algorithms do not fully consider the multi-objective and multi-constraint collaborative optimization of container ship stowage problems, and there is an urgent need for an efficient ship stowage method that can take into account stability, strength, and operational efficiency. Summary of the Invention

[0005] This invention aims to address the shortcomings of premature convergence and insufficient multi-objective coupling in existing technologies, and provides an adaptive multi-objective container ship loading method based on an improved ant colony algorithm. By optimizing the loading sequence, it effectively reduces the number of container turnings in the yard, improves the yard retrieval efficiency, accurately meets the pre-position weight requirements of the ship bay, and makes the ship bay present a reasonable container dispatch layout.

[0006] This invention is achieved through the following technical solution:

[0007] The present invention provides an adaptive multi-objective container ship stowage method based on an improved ant colony algorithm, characterized by comprising the following steps:

[0008] The first step is parameter initialization, constructing a multi-objective ant colony model, and determining the number of ants based on the ship's specific parameters and container manifest information. Maximum number of iterations Initialize pheromone concentration Set the pheromone volatility coefficient pheromone importance coefficient Heuristic factor coefficients Based on the multi-objective characteristics of the container ship stowage problem, a multi-objective weight coefficient vector is constructed to satisfy... ;

[0009] The second step is to construct a solution, initialize the solution and select a starting node, apply the probability transition rules, and check for constraint adjustments. If a constraint conflict is detected, the most recently added constraint is deleted. Each node, when reselected, narrows its neighborhood to a feasible subset until all containers have been assigned;

[0010] The constraint checking process includes: after each node selection, checking whether the constraint is violated; if it is violated, adjusting the solution list and reselecting the node, triggering heuristic factor correction;

[0011] The third step is pheromone update, which involves local and global elite updates of the pheromone matrix.

[0012] The fourth step involves dynamic constraint handling, calculating constraint factors, adjusting heuristic factors, and adjusting heuristic factors based on the degree of constraint violation if a node violates a constraint. To reduce the probability of a node being selected, the constraint factor is calculated dynamically based on the current solution list and node information each time a node is selected. Determine whether a node violates a hard constraint condition;

[0013] Step 5: Iteration termination determination, check the termination condition: determine whether the current iteration count has reached the maximum iteration count. Output the optimal solution: If the termination condition is met, select the optimal solution from all the solutions of the ants, which is the final loading scheme; if not, continue to the next round of iteration.

[0014] Further technical solutions include multi-target ant colony models comprising nodes. and edge ,node Indicates container Pointing to the ship's container position And the loading sequence number is ,side Represents a node To the node The edge, its weight The objective function and constraints are obtained through joint calculation:

[0015]

[0016] in, As punishment for searching the box, To reduce the cost of moving the gantry crane, This is the weight deviation.

[0017] A further technical solution, the probability transition rule includes the following steps:

[0018] The first step is to select a formula based on the node probability. Calculating ants Select the next node The probability of;

[0019] The second step is pheromone concentration. Heuristic factors derived from the pheromone matrix The dynamic constraint factor is calculated based on the current node's state. Used to determine whether a node is feasible;

[0020] The third step is to select the next node according to the calculated probability and add it to the solution list.

[0021] Further technical solutions, for each ant The path will be selected based on the following probabilities:

[0022]

[0023] pheromone concentration Initial value The dynamic update rules are described in step S3;

[0024] Heuristic factor ;

[0025] Dynamic constraint factor :

[0026]

[0027] Ants The current set of available feasible nodes includes all nodes that meet the following criteria: containers Unassigned, ship container space Not occupied and does not violate the order rules: .

[0028] Further technical solutions include real-time adjustment for weight exceeding limits:

[0029] .

[0030] Further technical solutions and adjustments to continuity conflicts include: if containers Position of already loaded containers Container satisfy ,and Then disable If they are in different heap areas, an additional movement cost penalty will be applied. .

[0031] Further technical solutions, including local updates, include: during the ant colony construction process, selecting each edge... Update the corresponding edges in the pheromone matrix according to the local update formula: ,in The pheromone evaporation coefficient, The initial pheromone concentration.

[0032] A further technical solution, global elite update, includes: calculating the total cost of the solution for each ant after each iteration. The pheromone matrix is ​​fully updated according to the global update formula. ,in The number of ants. , This refers to the total amount of pheromones released. For the first The total cost of finding a solution for an ant is the weighted sum of the objective function.

[0033] Further technical solutions, the objective function includes: ;

[0034] Number of times the box was searched inside the venue :

[0035]

[0036]

[0037] in, Indicates if and If it is in the same storage area slot, the value is 1; otherwise, it is 0.

[0038] Total moving cost of gantry crane :

[0039]

[0040] in:

[0041]

[0042] in This is the movement cost coefficient. By calculating the difference in storage space between the current container and the container with the previous loading sequence number, and then summing these values, the total moving distance of the gantry crane can be obtained, thus minimizing the number of machine movements.

[0043] Weight balance :

[0044]

[0045] in For ship container position Target weight limit, Indicates container The weight.

[0046] Further technical solutions, with constraints including:

[0047] The uniqueness of loading sequence, container position, and on-site container loading constraints:

[0048]

[0049]

[0050]

[0051] Weight limits for the ship's cargo box:

[0052]

[0053] Counterweight constraint:

[0054]

[0055] in:

[0056]

[0057] in This is the global maximum weight limit; Used to indicate the vertical relationship between the ship's containers;

[0058] Container loading sequence constraints:

[0059]

[0060] in Used to indicate the vertical relationship between container storage areas;

[0061] Weight constraints for single-row container loading:

[0062]

[0063] in Indicates container The column weight limit for the column in question.

[0064] The beneficial effects of this invention are: 1. At the constraint processing level, the dynamic constraint factor ( By embedding hard constraints such as weight and counterweight constraints into the anthill selection process, nodes that violate ship stability regulations are eliminated in real time. Compared to the post-event repair mechanisms of existing technologies, this avoids risks at the source. Nodes are defined to include attributes such as bay location / yard container location. With edge It integrates hard constraint rules and dynamic constraint factors to achieve real-time filtering of the feasible solution space.

[0065] II. At the multi-objective optimization level, edge weights are dynamically integrated with the box-flipping penalty. ), cross-reactor area movement cost ( ) and weight deviation ( By combining adaptive weight allocation strategies (such as fuzzy logic to dynamically adjust ω1-ω3), the algorithm can flexibly balance efficiency and safety under complex working conditions, and balance the priorities of multiple objectives through adaptive weight allocation.

[0066] Third, at the algorithmic mechanism level, the dynamic pheromone update strategy maintains search diversity and strengthens high-quality paths through local perturbation (random factor) and global elite stratification (the top 10% release double pheromones), while also implementing local updates. To maintain search diversity, global updates enhance high-quality search paths. Combined with a high-quality solution retention mechanism, convergence speed is improved. Global updates update the pheromone concentration of edges to strengthen the paths corresponding to high-quality solutions.

[0067] Fourth, this invention systematically solves the shortcomings of traditional loading methods in terms of rationality, efficiency and global optimization capabilities through a complete technical link of "constraint embedding algorithm, target quantification modeling, intelligent search guidance and business process optimization". It provides an intelligent solution for container ship transportation that combines safety and economy. The strict constraints on loading order in the same slot and the cost differentiation modeling of cross-stacking area movement reduce the number of container turning times in the yard and the energy consumption of empty running equipment. Attached Figure Description

[0068] For ease of explanation, the present invention will be described in detail below with reference to specific embodiments and accompanying drawings.

[0069] Figure 1 This is a flowchart of an adaptive multi-objective container ship loading method based on an improved ant colony algorithm according to the present invention.

[0070] Figure 2 for Figure 1 Flowchart of the intermediate parameter initialization module;

[0071] Figure 3 for Figure 1 A flowchart of the process of constructing a solution;

[0072] Figure 4 for Figure 1Flowcharts for the pheromone update module, dynamic constraint processing, and iteration termination determination;

[0073] Figure 5 for Figure 2 Flowchart of core parameter configuration;

[0074] Figure 6 for Figure 3 Flowchart for load routing and constraint adjustment;

[0075] Figure 7 for Figure 4 A flowchart illustrating local updates, global elite updates, and the calculation of constraint factors;

[0076] Figure 8 This is a schematic diagram showing the sequence of each process step. Detailed Implementation

[0077] like Figures 1-8 As shown, the present invention will be described in detail. For ease of description, the directions mentioned below are defined as follows: the directions of up, down, left, right, front, and back mentioned below are the same as... Figure 1 Since the projection relationships are consistent in the up, down, left, right, front, and back directions, the present invention provides an adaptive multi-objective container ship loading method based on an improved ant colony algorithm, which includes the following steps:

[0078] S1: Parameter initialization module;

[0079] S2: The process of constructing a solution;

[0080] S3: Pheromone update module;

[0081] S4: Dynamic constraint handling;

[0082] S5: Iteration termination determination.

[0083] Advantageously, the parameter initialization module includes the following steps:

[0084] S100: Determine the number of ants based on the ship's specific parameters (such as the number of container slots, maximum load capacity, size and location distribution of each container slot, etc.) and the container manifest information (including container quantity, weight, size, destination, etc.). Maximum number of iterations ;

[0085] S101: Initialize pheromone concentration ;

[0086] S102: Set pheromone volatility coefficient pheromone importance coefficient Heuristic factor coefficients ;

[0087] S103: Based on the multi-objective characteristics of the container ship stowage problem, construct a multi-objective weight coefficient vector that satisfies... .

[0088] Beneficially, the process of constructing a solution includes the following steps:

[0089] S20: Initialize the solution: for each ant Create an empty solution list to store the ant's loading path;

[0090] S21: Selecting the starting node: Each ant is randomly selected from the set of field boxes using a uniform distribution. Ship container assembly Choose a container and ship container space As the starting node;

[0091] S22: Probability transition rule;

[0092] S23: Check the constraints;

[0093] S24: If a constraint conflict is detected, delete the most recently added one. Each node is reselected, and its neighborhood is narrowed down to a feasible subset until all containers have been assigned.

[0094] Beneficially, the probability transition rule includes the following steps:

[0095] S220: Formula selection based on node probability Calculating ants Select the next node The probability of;

[0096] S221: Pheromones concentration Heuristic factors derived from the pheromone matrix The dynamic constraint factor is calculated based on the current node's state. Used to determine whether a node is feasible;

[0097] S222: Select the next node according to the calculated probability and add it to the solution list.

[0098] Beneficially, checking constraints includes the following steps:

[0099] S230: After each node selection, check for violations of constraints, such as weight limits and loading sequence. If a violation is found, adjust the solution list and reselect the node, triggering heuristic factor correction.

[0100] Beneficially, the pheromone update module includes the following steps:

[0101] S30: Partial update;

[0102] S31: Global Elite Update.

[0103] Beneficially, partial updates include the following steps:

[0104] S300: During the ant construction process, each edge is selected... Update the corresponding edges in the pheromone matrix according to the local update formula: .

[0105] Beneficially, the global elite update includes the following steps:

[0106] S310: After each iteration, calculate the total cost of the solution for each ant. ;

[0107] S311: Perform a comprehensive update of the pheromone matrix according to the global update formula:

[0108] Beneficially, the dynamic constraint processing includes the following steps:

[0109] S40: Calculate the constraint factor;

[0110] S41: Adjusting the heuristic factor: If a node violates a constraint, adjust the heuristic factor according to the degree of violation. This reduces the probability of it being selected.

[0111] Advantageously, the calculation of the constraint factor includes the following steps:

[0112] S400: Dynamically calculate constraint factors based on the current solution list and node information each time a node is selected. Determine whether a node violates a hard constraint.

[0113] Beneficially, the iteration termination determination includes the following steps: S50: Check the termination condition: Determine whether the current iteration count has reached the maximum iteration count. .

[0114] S51: Output the optimal solution: If the termination condition is met, select the optimal solution from all the ant solutions, which is the final loading scheme. If the termination condition is not met, continue to the next iteration.

[0115] Beneficially, the variable definitions include:

[0116] 1. Ship container space: Define a set Location of each ship box Includes attributes:

[0117] a) Maximum / minimum load capacity at the location of the ship's container: ;

[0118] b) Position number: Row number: Column number: ;

[0119] c) High-box markings: ;

[0120] 2. Field box location: Define the set Each container to be loaded Includes attributes:

[0121] a) Box weight: ;

[0122] b) Loading sequence: ;

[0123] c) Storage area slot number: Storage area code: Storage area bay number: Container group number: ;

[0124] d) High box markings: ;

[0125] Constructing a multi-objective ant colony model includes:

[0126] 1. Node Indicates container Pointing to the ship's container position And the loading sequence number is ;

[0127] 2. Side Represents a node To the node The edge, its weight The objective function and constraints are obtained through joint calculation:

[0128]

[0129] in, As punishment for searching the box, To reduce the cost of moving the gantry crane, This is the weight deviation.

[0130] Beneficially, the adaptive path selection strategy includes:

[0131] Each ant The path will be selected based on the following probabilities:

[0132]

[0133] 1. Pheromone concentration Initial value The dynamic update rules are described in step S3;

[0134] 2. Heuristic Factors ;

[0135] 3. Dynamic constraint factor :

[0136]

[0137] Used to dynamically remove infeasible nodes to ensure path validity.

[0138] 4. Ants The current set of available feasible nodes includes all nodes that meet the following criteria: containers Unassigned, ship container space Not occupied and does not violate the order rules: .

[0139] Beneficially, the local update includes: during the ant's solution construction process, for each edge selected, the pheromone concentration of the corresponding edge is updated in real time according to the following local update formula:

[0140]

[0141] in The pheromone evaporation coefficient, The initial pheromone concentration.

[0142] Beneficially, the global update (per iteration) includes:

[0143]

[0144] in The number of ants. , This refers to the total amount of pheromones released. For the first The total cost of finding a solution using only ants is the weighted sum of the objective function. A global update updates the pheromone concentration of edges, strengthening paths corresponding to high-quality solutions.

[0145] Beneficially, the system allows for real-time adjustment when the weight exceeds the limit:

[0146] .

[0147] Beneficially, among other things, the adjustment of continuous conflicts includes: if containers Position of already loaded containers Container satisfy ,and Then disable If they are in different heap areas, an additional movement cost penalty will be applied. .

[0148] Benefically, the objective function includes:

[0149] 1. Number of times boxes were searched inside the venue :

[0150]

[0151]

[0152] in, Indicates if and If it is in the same storage area slot, it is 1; otherwise, it is 0.

[0153] 2. Total moving cost of gantry crane :

[0154]

[0155] in:

[0156]

[0157] in This represents the movement cost coefficient. During ship loading, unnecessary movements of the gantry crane should be avoided. By calculating the difference in storage space between the current container and the container with the previous loading sequence number, and then summing these differences, the total movement distance of the gantry crane can be obtained, minimizing the number of machine movements.

[0158] 3. Weight balance :

[0159]

[0160] in For ship container position Target weight limit, Indicates container The weight.

[0161] Benefically, the constraints include:

[0162] 1. Uniqueness of loading sequence, vessel container position, and on-site container loading constraints:

[0163]

[0164]

[0165]

[0166] 2. Weight limits for the ship's cargo box:

[0167]

[0168] 3. Counterweight constraint:

[0169]

[0170] in:

[0171]

[0172] in This is the global maximum weight limit; Used to indicate the vertical relationship between the ship's containers.

[0173] 4. Container loading sequence constraints:

[0174]

[0175] in Used to indicate the vertical relationship between container storage areas.

[0176] 5. Weight constraints for single-row container loading:

[0177]

[0178] in Indicates container The column weight limit for the column in question.

[0179] Container ship stowage refers to the process of rationally loading containers onto the various bays of a ship to meet the requirements of balance, stability, and operational efficiency. Its main purpose is to maximize the ship's space utilization, reduce operating costs, and improve terminal operation efficiency while ensuring the ship's safe seaworthiness.

[0180] Ant colony optimization (ACO) is a heuristic algorithm that simulates the foraging behavior of ants. It guides the search behavior of the ant colony through pheromones and heuristic information released by the ants, exhibiting strong global search capabilities and good robustness. In practical applications, ACO can effectively solve various combinatorial optimization problems, such as path planning, scheduling, and network routing.

[0181] Multi-objective optimization: The process of simultaneously optimizing multiple objective functions usually requires trade-offs among multiple objectives to find a set of Pareto optimal solutions. A Pareto optimal solution is one in which no other solution is better than the solution in at least one objective, while not inferior to the solution in other objectives.

[0182] Pheromone concentration: A mechanism used in ant colony optimization (ACO) to guide ant path selection. Higher pheromone concentrations increase the probability of ants choosing that path. Pheromone is updated in two ways: local update: ants release pheromones in real-time during solution construction; global update: the optimal path is reinforced after iteration to accelerate convergence. The pheromone evaporation mechanism prevents the algorithm from prematurely falling into local optima, reflecting the dynamic nature of swarm memory.

[0183] Heuristic factors: Another mechanism used in ant colony optimization to guide ants in path selection. They are typically related to the heuristic information of the problem and help ants find high-quality solutions more quickly. Heuristic factors reflect the characteristics and patterns of the problem itself, providing ants with local information about the objective function and constraints, thus assisting them in making effective path selections.

[0184] Dynamic constraint factors: These are used to check in real time whether nodes violate constraints and dynamically adjust heuristic factors to ensure the effectiveness of path selection. During algorithm execution, dynamic constraint factors can eliminate infeasible nodes in real time based on the current solution status, guiding ants to search within the feasible solution space, thereby ensuring the feasibility and effectiveness of the generated solution.

Claims

1. A method for container ship stowage based on improved ant colony algorithm under adaptive multi-objective, characterized in that, The method comprises the following steps: The first step is parameter initialization, constructing a multi-objective ant colony model, determining the number of ants according to the specific parameters of the ship and the list information of the containers , the maximum number of iterations , wherein the specific parameters of the ship include the number of ship box positions, the maximum load, the size and position distribution of each ship box position, the list information of the containers includes the number of containers, the weight, the size, and the destination, the initial pheromone concentration , setting the pheromone evaporation coefficient , the pheromone importance coefficient , the heuristic factor coefficient , constructing a multi-objective weight coefficient vector according to the multi-objective characteristics of the container ship stowage problem, satisfying ; the second step is to construct a solution, initialize the solution and select the starting node, according to the probability transfer rule, and check the constraints and adjust, if a constraint conflict is detected, delete the last added nodes, and narrow the neighborhood to the feasible subset when reselecting until all containers are assigned; checking the constraint conditions includes: checking whether the constraint conditions are violated after selecting a node each time; if violated, adjusting the solution list and reselecting the node, triggering the heuristic factor correction; The third step is pheromone updating, which comprises local updating and global elite updating of the pheromone matrix; The fourth step involves dynamic constraint handling, calculating constraint factors, adjusting heuristic factors, and adjusting heuristic factors based on the degree of constraint violation if a node violates a constraint. To reduce the probability of a node being selected, the constraint factor is calculated dynamically based on the current solution list and node information each time a node is selected. The fifth step is to determine if a node violates a hard constraint. The step involves checking the termination condition: whether the current iteration count has reached the maximum iteration count. Output the optimal solution: If the termination condition is met, select the optimal solution from all the ant solutions, which is the final loading scheme; if not, continue to the next iteration. Each ant Select path with following probabilities: Pheromone concentration : initial value , whose dynamic updating rule is seen in step S3; Heuristic factors ; Number of times of turning in the yard Total moving cost of gantry crane Weight balance degree ; Dynamic constraint factor : representing ants the current set of feasible nodes available for selection, containing all nodes that satisfy the following conditions, container not assigned, container bay not occupied, and do not violate the order rule: ; indicates a container loading sequence number, indicates a container loading sequence number of a previous feasible node, indicates a container storage area slot number of a previous feasible node.

2. The self-adaptive multi-objective container ship stowage method based on improved ant colony algorithm according to claim 1, characterized in that: Multi-objective ant colony model includes nodes and edges , the nodes represent containers pointing to ship bays , and the stowage sequence number is , the edges represent the edges from node to node , and the weight of the edges is obtained by joint calculation of the objective function and constraints: wherein, is a flip penalty, is a gantry movement cost, is a weight deviation amount.

3. The self-adaptive multi-objective container ship stowage method based on improved ant colony algorithm according to claim 2, characterized in that: The probability transition rule The method comprises the following steps: First step, select the next node according to the node probability selection formula Calculate the ant Select the next node The probability; second step, the pheromone concentration From the pheromone matrix, the heuristic factor According to the state of the current node, the dynamic constraint factor Is used to determine whether the node is feasible; third step, select the next node according to the calculated probability, and add it to the solution list.

4. The adaptive multi-objective container ship stowage method based on improved ant colony algorithm according to claim 1, characterized in that: Real-time adjustment of weight overrun ； wherein, denotes a heuristic factor, denotes a ship position of the load, denotes a container of the container weight, denotes a container points to a ship position is a 0-1 variable with a loading sequence number.

5. The adaptive multi-objective based improved ant colony optimization algorithm for container ship stowage method according to claim 2, characterized in that: Continuity conflict resolution includes: if container is in a stowed position and container is full, and , then disable ; if in different stack area, then increase move cost penalty: ; wherein, refers to a container stacking area slot number, refers to a container stacking area slot number, denotes a container loading sequence, denotes a container loading sequence, denotes the total moving cost of gantry crane, denotes the weight coefficient, denotes the stacking area number of container t, denotes the stacking area number of container , denotes the edge of the multi-objective ant colony model.

6. The adaptive multi-objective based improved ant colony algorithm for container ship stowage method according to claim 1, characterized in that: The local update includes: in the process of constructing the solution of the ant, after each selected edge , the corresponding edge in the pheromone matrix is updated according to the local update formula: , wherein is the pheromone evaporation coefficient, is the initial pheromone concentration; Pheromone concentration , Edges representing the multi-objective ant colony model.

7. The adaptive multi-objective based improved ant colony algorithm for container ship stowage method according to claim 1, characterized in that: Global elite update includes: at the end of each iteration, the total cost of each ant solution is calculated ; according to the global update formula, the pheromone matrix is comprehensively updated: , wherein is the number of ants, , is the total amount of pheromone released; is the total cost of the solution found by the th ant, that is, the weighted sum of the objective function; wherein is the pheromone volatility coefficient, denotes the pheromone concentration matrix.

8. The adaptive multi-objective based improved ant colony algorithm for container ship stowage method according to claim 2, characterized in that: The objective function includes: ; represents minimizing the objective function, represents the in-field de-boxing cost of the weight, represents the gantry crane movement cost of the weight, represents the weight balance degree of the weight; Number of times of turning over the box in the field : wherein represents 1 if and 1 if in the same bin, otherwise 0; representing a container of a slot number of a storage area, representing a container of a slot number of a storage area, representing a container and 0-1 variable indicating the up-down relationship, representing a container pointing to a ship container location 0-1 variable indicating the loading sequence number , a container pointing to a ship container location 0-1 variable indicating the loading sequence number , representing a container loading sequence, representing a container loading sequence; gantry crane total moving cost : Wherein: wherein is the moving cost coefficient, the total moving distance of the gantry crane is obtained by calculating the stacking location site difference between the current container and the container of the previous loading sequence number, and then accumulated to minimize the number of machine movements; in, Indicates container Storage area number Indicates container Location of storage area, Indicates container Storage area number Indicates container Location of storage area, Indicates container Pointing to the ship's container position Loading sequence number is 0-1 variables; weight balance : wherein is the target weight limit, for the container position, represents the weight of the container .

9. The self-adapting multi-objective container ship stowage method based on improved ant colony algorithm according to any one of claims 1-8, characterized in that: The constraint conditions include: stowage sequence, container position, uniqueness of in-place container stowage constraint: wherein, represents a container points to a ship bay is the stowage sequence number is a 0-1 variable, represents a set of ship bays, R represents a set of stowage sequence numbers, represents a set of yard bays; Weight upper and lower limits of container position: Overweight constraint: Wherein: wherein is the global pressure weight upper limit value; for identifying the relative position of the containers on the ship. indicates container points to berth stowage sequence number is 0-1 variable; Container stowage sequence constraint: wherein for identifying the up-down relationship of slots in a container yard container pointing to a ship slot loading sequence number 0-1 variable, container loading sequence of containers, container loading sequence of containers; Single-column stowed container weight constraint: wherein indicates a container column weight limit value of the column in which the container is located indicates the weight of the container .

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