Scheduling method for bulk material collecting vehicle of circular cooler based on improved butterfly optimization algorithm

Abstract

The invention relates to an improved butterfly optimization algorithm-based circular cooler bulk material collection vehicle scheduling method, which comprises the following steps of: starting from the angle of efficient and low-frequency operation of a circular cooler bulk material collection vehicle, taking the minimum comprehensive cost of the operation time, the total driving distance and the operation times of the bulk material collection vehicle as a target; according to the method, an annular cooler bulk cargo collection vehicle scheduling problem model is constructed, an improved butterfly optimization algorithm is adopted to help the algorithm jump out of local optimum, and a bulk cargo collection vehicle scheduling optimal scheme with higher precision is solved more quickly. Starting from the actual working condition of the circular cooler, the scheduling of the bulk material collecting vehicle is optimized in real time by adopting the improved butterfly optimization algorithm, and on the basis of shortening the running time, the running distance can be effectively saved, and the running efficiency of the bulk material collecting vehicle is improved.

Description

Technical Field

[0001] The invention relates to the technical field of industrial computer control, and in particular to a method for dispatching a bulk material collection vehicle for an annular cooler based on an improved butterfly optimization algorithm. Background Art

[0002] When the ring cooler is running, the hot pellets and hot sintered ore produced from the previous process slowly rotate on the trolley with grate holes, and the hot ore is cooled by the cooling air blown by the blower at the bottom of the trolley. During cooling, the bulk minerals and dust leak into the bulk material collection hopper below from the gaps between the grate bars of the ring cooler trolley. At present, most of the bulk materials of ring coolers are still discharged by on-site workers based on experience and transported by trolleys, that is, manually opening the double-layer discharge valve of the bulk material collection hopper to unload the bulk materials into the trolley, and then pushing the trolley to the unloading point to unload the bulk materials onto the belt conveyor. This has the problem of untimely unloading, which will block the air duct, and the labor cost is high, and it is easy to cause safety accidents such as red ball burns; or a simple automatic control is adopted, that is, a simple task table is formulated to meet the basic material transportation function of the bulk material collection vehicle on the circular track. This method lacks autonomous learning, and when the rules are not formulated reasonably, it is easy to cause inefficient and high-frequency operation, and the failure rate is high.

[0003] The existing dispatching technology of bulk material collection vehicles for ring coolers is as follows: a control system and control method of a bulk material collection vehicle for a ring cooler (invention patent authorization announcement number: CN104677110B) determines the real-time position of the bulk material collection vehicle by setting proximity switches and limit switches on the circular track, and an electronic belt scale detects the outlet flow of the ring cooler, thereby automatically customizing the unloading task table. However, it only considers the material flow rate and does not meet the low-frequency and high-efficiency operation of the bulk material vehicle; a control method of a bulk material collection vehicle for a ring cooler based on a task table (Automation and Instrumentation Journal Paper) provides an idea of ​​calculating the real-time material amount in the bulk material collection hopper according to the bulk material ratio and formulating a periodic task table of the bulk material collection vehicle according to the weight, and unloading the ash hopper in turn, The unloading efficiency is average; an intelligent bulk material recovery device for annular cooler (utility model authorization announcement number: CN203595411U) receives a start signal from the control cabinet for the bulk material collection vehicle, and stops when the collection vehicle is full, and does not have automatic scheduling capabilities; the design and simulation of an automatic bulk material collection device for annular cooler (degree thesis) calculates the unloading volume and time to formulate an operation plan for the bulk material collection vehicle, and needs to calculate the unloading requirements one by one, which is inefficient; a method for automatic unloading of a bulk material trolley for a sintering annular cooler (invention patent application number: CN202111234958.3) uses a PLC system to control the operation of the bulk material collection hopper and the collection vehicle with a weight data signal, focusing on the unloading device and not involving a scheduling method.

[0004] The butterfly optimization algorithm is a population-based metaheuristic algorithm that has been widely used in path planning and mechanical structure design. However, the butterfly optimization algorithm also has some shortcomings that need to be optimized, such as low convergence speed and accuracy, and easy to fall into local optimality. It is necessary to improve it to improve its solving ability. Summary of the invention

[0005] In order to solve the above technical problems, the purpose of the present invention is to provide a scheduling method for a bulk material collection vehicle for a ring cooler based on an improved butterfly optimization algorithm in order to enable the bulk material collection vehicle for a ring cooler to operate efficiently and at a low frequency.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A method for dispatching a bulk material collection vehicle for an annular cooler based on an improved butterfly optimization algorithm is characterized by comprising the following steps:

[0008] Step 1. Set the constraints of the bulk material collection vehicle for the ring cooler;

[0009] Step 2. Taking the minimization of the comprehensive cost of the bulk material collection vehicle operation time, total driving distance and operation times as the goal, a scheduling problem model for the bulk material collection vehicle of the ring cooler is constructed;

[0010] Step 3. Use the improved butterfly optimization algorithm to solve the scheduling problem model of the bulk material collection vehicle for the ring cooler. The optimal solution obtained is the optimal scheduling solution for the bulk material collection vehicle for the ring cooler.

[0011] Preferably, the step 1 sets the constraints of the bulk material collection vehicle for the ring cooler, specifically including:

[0012] Step 1.1 Variable definition and symbols

[0013] 1.1.1N: Bulk material collection hopper set, N = {1, 2, ..., n};

[0014] 1.1.2V: Bulk material collection hopper needs to take material volume, V = {v1, v2, ..., v n};

[0015] 1.1.3D: Distance from bulk material collection hopper to discharge point 0, d = {d1, d2, ..., d n};

[0016] 1.1.4V e : Rated capacity of bulk material collection hopper;

[0017] 1.1.5V c : Rated capacity of bulk material collection vehicle;

[0018] 1.1.6V i: The volume of the bulk material collection vehicle after taking materials from the bulk material collection hopper i, i∈N;

[0019] 1.1.7x ij : Whether the bulk material collection vehicle starts from the bulk material collection hopper i and goes to the next point j (bulk material collection hopper or unloading point, i.e. j∈N∪0). If yes, then x ij =1, otherwise x ij =0;

[0020] 1.1.8t 0i : The travel time of the bulk material collection vehicle from the unloading point 0 to the bulk material collection hopper i;

[0021] 1.1.9t i : The time taken by the bulk material collection vehicle to retrieve the bulk material collection hopper i;

[0022] 1.1.10t ij : The travel time of the bulk material collection vehicle from the bulk material collection hopper i to the next point j;

[0023] 1.1.11t0: time taken by bulk material collection truck to unload materials;

[0024] 1.1.12t max : The longest time for a single dispatching operation cycle of a bulk material collection vehicle;

[0025] 1.1.13c: Bulk material collection hopper collection that has not been picked up during a single dispatch operation cycle of the bulk material collection vehicle;

[0026] Step 1.2 Constraint Statement

[0027] 1.2.1 The volume of each bulk material collection hopper is less than 90% of the rated volume, that is:

[0028]

[0029] 1.2.2 Bulk material collection vehicles can only take materials from bulk material collection hoppers with a volume that is 30% higher than the rated volume, that is:

[0030]

[0031] 1.2.3 The remaining capacity of the bulk material collection vehicle going to any bulk material collection hopper is greater than the required capacity of the hopper, that is:

[0032]

[0033] 1.2.4 The total duration limit of a single dispatching operation cycle of a bulk material collection vehicle is:

[0034]

[0035] 1.2.5 During a single dispatching operation cycle of a bulk material collection vehicle, materials can only be taken from each bulk material collection hopper once, namely:

[0036]

[0037] 1.2.6 Bulk material collection vehicles are operated at less than rated capacity;

[0038]

[0039] 1.2.7 If the bulk material collection truck exceeds 90% of the rated capacity, it must return to the unloading point to discharge the material.

[0040]

[0041] Preferably, in step 2, the goal is to minimize the comprehensive cost of the bulk material collection vehicle's operating time, total driving distance, and number of operations, and the problem model constructed is specifically described as follows:

[0042] Step 2.1 Bulk material collection vehicle operation time, expressed as:

[0043]

[0044] Step 2.2 The total distance traveled by the bulk material collection vehicle is expressed as:

[0045]

[0046] Step 2.3 The number of times the bulk material collection vehicle runs is expressed as:

[0047] nc(10).

[0048] Preferably, the solution process of the improved butterfly optimization algorithm in step 3 includes:

[0049] Step 3.1 Encoding and decoding

[0050] Integer coding is used to express the bulk material collection vehicle dispatching scheme. Each coding scheme is a butterfly individual. Decoding is to check the constraints of the butterfly individuals generated by the coding and output the butterfly individuals that meet the constraints.

[0051] Step 3.2 Improve the butterfly optimization algorithm

[0052] The butterfly optimization algorithm is improved by introducing a dynamic switching factor and a crossover operation of a genetic algorithm. The dynamic switching factor p is expressed as follows:

[0053]

[0054] Among them, iter is the current iteration number, iter maxis the maximum number of iterations, and the dynamic switching factor p changes with iterations, thus affecting the individual butterfly The update method finally achieves the balance between local and global search. The butterfly individuals formed by coding are updated by introducing the crossover operation of the genetic algorithm. The formula is:

[0055]

[0056] Among them, X gbest is the optimal solution in the current iteration, used to evaluate the butterfly individual corresponding to the minimum value of the function, rand is a random number between [0,1], is the solution corresponding to the mth butterfly in the tth iteration, where m is any number in the population except i, representing a local random search, and cross represents a crossover operation;

[0057] The dynamic switching factor p changes with iteration, which in turn affects the individual butterflies The update method finally achieves the effect of balancing local and global search;

[0058] Step 3.3 Evaluation function based on linear weighting method

[0059] The linear weighted method is used to set the weights, and the running time, total driving distance and number of operations of the bulk material collection vehicle are converted into a single-objective optimization problem with the minimum comprehensive cost. The fitness evaluation function is:

[0060]

[0061] Among them, λ1, λ2, λ3 are weights, λ1+λ2+λ3=1;

[0062] After population initialization and crossover operation, compare the original solution X i The fitness f i And the updated fitness f newi , the butterfly with smaller fitness is the optimal solution. max After iterations, the optimal solution of the butterfly individual obtained by optimization is the optimal solution for scheduling the bulk material collection vehicle for the ring cooler.

[0063] Preferably, the step 3.2 improves the butterfly optimization algorithm by introducing a dynamic switching factor and a crossover operation of a genetic algorithm to solve the scheduling problem of the bulk material collection vehicle for the annular cooler, specifically comprising the following steps:

[0064] 3.2.1 Parameter settings: population size P, maximum number of iterations iter max ;

[0065] 3.2.2 Encode and generate P butterfly individuals to form a population and complete initialization;

[0066] 3.2.3 Calculate the population fitness according to the fitness evaluation function and obtain the current optimal solution;

[0067] 3.2.4 Determine whether to terminate the iteration. If the current number of iterations iter>iter max , then the iteration is terminated and the global optimal solution X of the population obtained in the current iteration is output gbest , redirect to (3.2.6); otherwise execute (3.2.5);

[0068] 3.2.5 Update the dynamic switching factor, generate a random number rand between [0,1] and perform crossover operation to generate new butterfly individuals, perform decoding constraint check and calculate the fitness of each butterfly individual, and update the global optimal solution of the population;

[0069] 3.2.6 The optimal solution output is the optimal solution for dispatching the bulk material collection truck for the ring cooler.

[0070] Advantages of the present invention:

[0071] The present invention aims to minimize the comprehensive cost of the bulk material collection vehicle's operating time, total driving distance and number of operations, constructs a scheduling problem model for a ring cooler bulk material collection vehicle, and adopts an improved butterfly optimization algorithm to help the algorithm jump out of the local optimum, so as to more quickly solve a more accurate optimal scheduling solution for the bulk material collection vehicle, thereby improving the operating efficiency of the bulk material collection vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 It is a flow chart of the method for dispatching bulk material collection vehicles for annular coolers based on the improved butterfly optimization algorithm of the present invention;

[0073] Figure 2 A coding schematic diagram for solving the scheduling problem of bulk material collection vehicles for ring coolers according to the present invention;

[0074] Figure 3 A flow chart of an improved butterfly optimization algorithm for solving the scheduling problem of bulk material collection vehicles for annular coolers according to the present invention;

[0075] Figure 4 Shown is the iterative process diagram of the evaluation function that can be obtained by improving the butterfly optimization algorithm. DETAILED DESCRIPTION

[0076] The following describes the implementation of the present invention in detail with reference to the accompanying drawings, but they do not constitute a limitation of the present invention and are only used as examples. At the same time, the advantages of the present invention will become clearer and easier to understand through the description.

[0077] like Figure 1-Figure 4 As shown, the method for dispatching bulk material collection vehicles for annular coolers based on an improved butterfly optimization algorithm designed by the present invention is characterized in that it includes the following steps:

[0078] Step 1. Set the constraints of the bulk material collection vehicle for the ring cooler;

[0079] The step 1 sets the constraints of the bulk material collection vehicle for the ring cooler, specifically including:

[0080] Step 1.1 Variable definition and symbols

[0081] 1.1.1N: Bulk material collection hopper set, N = {1, 2, ..., n};

[0082] 1.1.2V: Bulk material collection hopper needs to take material volume, V = {v1, v2, ..., v n};

[0083] 1.1.3D: Distance from bulk material collection hopper to discharge point 0, d = {d1, d2, ..., d n};

[0084] 1.1.4V e : Rated capacity of bulk material collection hopper;

[0085] 1.1.5V c : Rated capacity of bulk material collection vehicle;

[0086] 1.1.6V i : The volume of the bulk material collection vehicle after taking materials from the bulk material collection hopper i, i∈N;

[0087] 1.1.7x ij : Whether the bulk material collection vehicle starts from the bulk material collection hopper i and goes to the next point j (bulk material collection hopper or unloading point, i.e. j∈N∪0). If yes, then x ij =1, otherwise x ij =0;

[0088] 1.1.8t 0i : The travel time of the bulk material collection vehicle from the unloading point 0 to the bulk material collection hopper i;

[0089] 1.1.9t i : The time taken by the bulk material collection vehicle to retrieve the bulk material collection hopper i;

[0090] 1.1.10t ij : The travel time of the bulk material collection vehicle from the bulk material collection hopper i to the next point j;

[0091] 1.1.11t0: time taken by bulk material collection truck to unload materials;

[0092] 1.1.12t max : The longest time for a single dispatching operation cycle of a bulk material collection vehicle;

[0093] 1.1.13c: Bulk material collection hopper collection that has not been picked up during a single dispatch operation cycle of the bulk material collection vehicle;

[0094] Step 1.2 Constraint Statement

[0095] 1.2.1 The volume of each bulk material collection hopper is less than 90% of the rated volume, that is:

[0096]

[0097] 1.2.2 Bulk material collection vehicles can only take materials from bulk material collection hoppers with a volume that is 30% higher than the rated volume, that is:

[0098]

[0099] 1.2.3 The remaining capacity of the bulk material collection vehicle going to any bulk material collection hopper is greater than the required capacity of the hopper, that is:

[0100]

[0101] 1.2.4 The total duration limit of a single dispatching operation cycle of a bulk material collection vehicle is:

[0102]

[0103] 1.2.5 During a single dispatching operation cycle of a bulk material collection vehicle, materials can only be taken from each bulk material collection hopper once, namely:

[0104]

[0105] 1.2.6 Bulk material collection vehicles are operated at less than rated capacity;

[0106]

[0107] 1.2.7 If the bulk material collection truck exceeds 90% of the rated capacity, it must return to the unloading point to discharge the material.

[0108]

[0109] Step 2. Taking the minimization of the comprehensive cost of the bulk material collection vehicle operation time, total driving distance and operation times as the goal, a scheduling problem model for the bulk material collection vehicle of the ring cooler is constructed;

[0110] In step 2, the goal is to minimize the comprehensive cost of the bulk material collection vehicle's operating time, total driving distance, and number of operations. The problem model constructed is specifically described as follows:

[0111] Step 2.1 Bulk material collection vehicle operation time, expressed as:

[0112]

[0113] Step 2.2 The total distance traveled by the bulk material collection vehicle is expressed as:

[0114]

[0115] Step 2.3 The number of times the bulk material collection vehicle runs is expressed as:

[0116] nc(10).

[0117] Step 3. Use the improved butterfly optimization algorithm to solve the scheduling problem model of the bulk material collection vehicle for the ring cooler. The optimal solution obtained is the optimal scheduling solution for the bulk material collection vehicle for the ring cooler.

[0118] The solution process of the improved butterfly optimization algorithm in step 3 includes:

[0119] Step 3.1 Encoding and decoding

[0120] Integer coding is used to express the bulk material collection vehicle scheduling scheme. Each coding scheme is a butterfly individual. Decoding is to perform constraint checks on the butterfly individuals generated by the coding and output the butterfly individuals that meet the constraints. The rules are as follows:

[0121] STEP1: Determine whether the current butterfly individual has a bulk material collection hopper capacity exceeding 90%. If yes, insert unloading point 0 after it and go directly to STEP7. If not, go to STEP2.

[0122] STEP2: Determine whether the current butterfly individual has duplicate bulk material collection hoppers. If yes, delete the duplicates and keep only one, then go to STEP3; if no, go directly to STEP3;

[0123] STEP3: Determine whether the current butterfly individual has a bulk material collection hopper with a capacity less than 30%. If yes, delete the bulk material collection hopper and go to STEP4; if no, go directly to STEP4;

[0124] STEP4: Determine whether the current butterfly individual has a bulk material collection hopper whose remaining capacity is lower than the hopper demand. If yes, delete the bulk material collection hopper and go to STEP5; if no, go directly to STEP5;

[0125] STEP5: Determine whether the current butterfly individual has a bulk material collection vehicle with a capacity of 100%. If yes, randomly delete the bulk material collection hopper and go to STEP6; if no, go directly to STEP6;

[0126] STEP6: Determine whether the current butterfly individual has a bulk material collection vehicle with a capacity exceeding 90%. If so, insert unloading point 0 after it and turn to STEP7; if not, directly turn to STEP7;

[0127] STEP7: Decoding is completed and individual butterflies are output.

[0128] Step 3.2 Improve the butterfly optimization algorithm

[0129] The butterfly optimization algorithm is improved by introducing a dynamic switching factor and a crossover operation of a genetic algorithm. The dynamic switching factor p is expressed as follows:

[0130]

[0131] Among them, iter is the current iteration number, iter max is the maximum number of iterations, and the dynamic switching factor p changes with iterations, thus affecting the individual butterfly The update method finally achieves the balance between local and global search. The butterfly individuals formed by coding are updated by introducing the crossover operation of the genetic algorithm. The formula is:

[0132]

[0133] Among them, X gbest is the optimal solution in the current iteration, used to evaluate the butterfly individual corresponding to the minimum value of the function, rand is a random number between [0,1], is the solution corresponding to the mth butterfly in the tth iteration, where m is any number in the population except i, representing a local random search, and cross represents a crossover operation;

[0134] The dynamic switching factor p changes with iteration, which in turn affects the individual butterflies The update method finally achieves the effect of balancing local and global search;

[0135] The step 3.2 improves the butterfly optimization algorithm by introducing a dynamic switching factor and a crossover operation of a genetic algorithm, and specifically includes the following steps:

[0136] 3.2.1 Parameter settings: population size P, maximum number of iterations iter max ;

[0137] 3.2.2 Encode and generate P butterfly individuals to form a population and complete initialization;

[0138] 3.2.3 Calculate the population fitness according to the fitness evaluation function and obtain the current optimal solution;

[0139] 3.2.4 Determine whether to terminate the iteration. If the current number of iterations iter>iter max , then the iteration is terminated and the global optimal solution X of the population obtained in the current iteration is output gbest , redirect to (3.2.6); otherwise execute (3.2.5);

[0140] 3.2.5 Update the dynamic switching factor, generate a random number rand between [0,1] and perform crossover operation to generate new butterfly individuals, perform decoding constraint check and calculate the fitness of each butterfly individual, and update the global optimal solution of the population;

[0141] 3.2.6 The optimal solution output is the optimal solution for dispatching the bulk material collection truck for the ring cooler.

[0142] Step 3.3 Evaluation function based on linear weighting method

[0143] The linear weighted method is used to set the weights, and the running time, total driving distance and number of operations of the bulk material collection vehicle are converted into a single-objective optimization problem with the minimum comprehensive cost. The fitness evaluation function is:

[0144]

[0145] Among them, λ1, λ2, λ3 are weights, λ1+λ2+λ3=1;

[0146] After population initialization and crossover operation, compare the original solution X i The fitness f i And the updated fitness f newi , the butterfly with smaller fitness is the optimal solution. max After iterations, the optimal solution of the butterfly individual obtained by optimization is the optimal solution for scheduling the bulk material collection vehicle for the ring cooler.

[0147] An example is given below to further illustrate the present invention.

[0148] Assume that there are 12 bulk material collection hoppers, the unloading point is defined as 0, and the positive integer (1 to 12) indicates that the bulk material collection vehicle takes materials from the bulk material collection hopper, and each 0 indicates that the bulk material collection vehicle returns to the unloading point to discharge materials. Initialization of butterfly individuals: randomly generate n (1≤n≤12) integer numbers (each number is an integer in the interval [1,12] without repetition) and insert the number 0 at the beginning and end, and insert 0 to n-1 number 0s in the gaps before and after. For example: a certain butterfly individual X1 = [0 2 5 4 0 3 8 0 1 1112 7 0 10 6 9 0] represents that the bulk material collection vehicle starts from the unloading point 0, first takes material from the bulk material collection hopper 2, then takes material from the bulk material collection hoppers 5 and 4, returns to the unloading point 0 to discharge the material; then takes material from the bulk material collection hoppers 3 and 8, returns to the unloading point 0 to discharge the material; takes material from the bulk material collection hoppers 1, 11, 12, and 7, returns to the unloading point 0 to discharge the material; finally, takes material from the bulk material collection hoppers 10, 6, and 9, returns to the unloading point 0 to discharge the material, and stops at the unloading point 0.

[0149] After decoding constraint check, X1'=[0 2 5 4 0 3 8 0 1 11 12 7 0 10 0] means that the bulk material collection vehicle starts from unloading point 0, first takes material from bulk material collection hopper 2, then takes material from bulk material collection hoppers 5 and 4, and returns to unloading point 0 to discharge the material; then takes material from bulk material collection hoppers 3 and 8, and returns to unloading point 0 to discharge the material; takes material from bulk material collection hoppers 1, 11, 12, and 7, and returns to unloading point 0 to discharge the material; finally, takes material from bulk material collection hopper 10, returns to unloading point 0 to discharge the material, and stops at unloading point 0.

[0150] In the standard butterfly optimization algorithm, P butterfly individuals are randomly generated to form a butterfly population. The local and global search processes are controlled by a constant switching probability p = 0.8. This cannot dynamically adjust the algorithm search mode, and the optimization ability is defective. In order to enhance the ability of the butterfly optimization algorithm to solve the scheduling of bulk material collection vehicles for annular coolers, it is improved by introducing a dynamic switching factor (formula (11)) and a crossover operation of the genetic algorithm (formula (12)). Among them, the switching factor p determines whether the butterfly performs a global search or a local search. If the random number rand is greater than p, the current butterfly performs a global search, otherwise it performs a local search. The crossover operation increases the diversity of the population. The dynamic switching factor can better search the global solution space in the early iteration and better search the local solution space in the later iteration.

[0151] The following example demonstrates the position-based crossover operation.

[0152] The encoding and decoding result in two butterfly individuals as shown below:

[0153] Butterfly individual 1: X1 = [0 2 5 4 0 3 8 0 1 11 12 7 0 10 0];

[0154] Butterfly individual 2: X2 = [0 4 6 0 1 7 4 0 12 3 0 2 8 10 0].

[0155] STEP1 First, remove the number 0 in butterfly individuals X1 and X2, randomly select an element e1 at position p1 in butterfly individual X1, then find the element e2 at position p1 in butterfly individual X2, then go back to butterfly individual X1 to find the position p1 where e2 is located, and then find the element e3 at position p2 in butterfly individual X2. Repeat the previous work until a ring is formed, and the positions of all elements in the ring are the last selected positions.

[0156] STEP2: Use the selected elements in butterfly individual X1 to generate butterfly individual X′1 at the next position, and ensure the position correspondence, and then put the remaining elements in butterfly individual X2 into X′1.

[0157] STEP3: Use the selected elements in butterfly individual X2 to generate butterfly individual X'2 at the next position, and ensure the corresponding positions. Then put the remaining elements in butterfly individual X2 into X'2, and finally fill in the number 0 as close as possible before the crossover.

[0158] The two butterfly individuals generated after the above crossover operation are:

[0159] X1′=[0 4 5 1 0 3 8 0 12 11 2 7 0 10 0];

[0160] X2′=[0 2 6 0 4 3 8 0 2 3 0 12 8 10 0].

[0161] The fitness of individual butterflies is calculated by formula (13). After population initialization and crossover operation, the improved butterfly optimization algorithm is compared with the original solution X i The fitness f i And the updated fitness f newi , the butterfly with smaller fitness is the optimal solution. max After iterations, the optimal solution of the butterfly individual obtained by optimization is the optimal solution for dispatching the bulk material collection vehicle for the ring cooler. The iterative process is as follows: Figure 4 shown.

[0162] As mentioned above, the pseudo code of the improved butterfly optimization algorithm for solving the scheduling problem of bulk material collection vehicles for annular coolers is as follows:

[0163]

[0164] The present invention aims to minimize the comprehensive cost of the bulk material collection vehicle's operating time, total driving distance and number of operations, constructs a scheduling problem model for a ring cooler bulk material collection vehicle, and adopts an improved butterfly optimization algorithm to help the algorithm jump out of the local optimum, so as to more quickly solve a more accurate optimal scheduling solution for the bulk material collection vehicle, thereby improving the operating efficiency of the bulk material collection vehicle.

[0165] Other details not described belong to the prior art.

Claims

1. A method for dispatching bulk material collection vehicles for annular coolers based on an improved butterfly optimization algorithm, characterized in that: The following steps are involved: Step 1. Set the constraints of the bulk material collection vehicle for the ring cooler; Step 2. Taking the minimization of the comprehensive cost of the bulk material collection vehicle operation time, total driving distance and operation times as the goal, a scheduling problem model for the bulk material collection vehicle of the ring cooler is constructed; Step 3. Use the improved butterfly optimization algorithm to solve the scheduling problem model of the bulk material collection vehicle for the ring cooler. The optimal solution obtained is the optimal scheduling solution for the bulk material collection vehicle for the ring cooler.

2. The method for dispatching bulk material collection vehicles for annular coolers based on an improved butterfly optimization algorithm according to claim 1 is characterized in that: The step 1 sets the constraints of the bulk material collection vehicle for the ring cooler, specifically including: Step 1.1 Variable definition and symbols 1.1.1N: Bulk material collection hopper set, N = {1, 2, ..., n}; 1.1.2V: Bulk material collection hopper required material volume, V = {v1, v2, ..., v n }; 1.1.3D: Distance from bulk material collection hopper to discharge point 0, d = {d1, d2, ..., d n }; 1.1.4V e : Rated capacity of bulk material collection hopper; 1.1.5V c : Rated capacity of bulk material collection vehicle; 1.1.6V i : The volume of the bulk material collection vehicle after taking materials from the bulk material collection hopper i, i∈N; 1.1.7x ij : Whether the bulk material collection vehicle starts from the bulk material collection hopper i and goes to the next point j (bulk material collection hopper or unloading point, i.e. j∈N∪0). If yes, then x ij =1, otherwise x ij =0; 1.1.8t 0i : The travel time of the bulk material collection vehicle from the unloading point 0 to the bulk material collection hopper i; 1.1.9t i : The time taken by the bulk material collection vehicle to retrieve the bulk material collection hopper i; 1.1.10t ij : The travel time of the bulk material collection vehicle from the bulk material collection hopper i to the next point j; 1.1.11t0: time taken by bulk material collection truck to unload materials; 1.1.12t max : The longest time for a single dispatching operation cycle of a bulk material collection vehicle; 1.1.13c: Bulk material collection hopper collection that has not been picked up during a single dispatch operation cycle of the bulk material collection vehicle; Step 1.2 Constraint Statement 1.2.1 The volume of each bulk material collection hopper is less than 90% of the rated volume, that is: 1.2.2 Bulk material collection vehicles can only take materials from bulk material collection hoppers with a volume that is 30% higher than the rated volume, that is: 1.2.3 The remaining capacity of the bulk material collection vehicle going to any bulk material collection hopper is greater than the required capacity of the hopper, that is: 1.2.4 The total duration limit of a single dispatching operation cycle of a bulk material collection vehicle is: 1.2.5 During a single dispatching operation cycle of a bulk material collection vehicle, materials can only be taken from each bulk material collection hopper once, namely: 1.2.6 Bulk material collection vehicles are operated at less than rated capacity; 1.2.7 If the bulk material collection truck exceeds 90% of the rated capacity, it must return to the unloading point to discharge the material.

3. The method for dispatching bulk material collection vehicles for annular coolers based on an improved butterfly optimization algorithm according to claim 1 is characterized in that: In step 2, the goal is to minimize the comprehensive cost of the bulk material collection vehicle's operating time, total driving distance, and number of operations. The problem model constructed is specifically described as follows: Step 2.1 Bulk material collection vehicle operation time, expressed as: Step 2.2 The total distance traveled by the bulk material collection vehicle is expressed as: Step 2.3 The number of times the bulk material collection vehicle runs is expressed as: nc(10).

4. The method for dispatching bulk material collection vehicles for annular coolers based on an improved butterfly optimization algorithm according to claim 1 is characterized in that: The solution process of the improved butterfly optimization algorithm in step 3 includes: Step 3.1 Encoding and decoding Integer coding is used to express the bulk material collection vehicle dispatching scheme. Each coding scheme is a butterfly individual. Decoding is to check the constraints of the butterfly individuals generated by the coding and output the butterfly individuals that meet the constraints. Step 3.2 Improve the butterfly optimization algorithm The butterfly optimization algorithm is improved by introducing a dynamic switching factor and a crossover operation of a genetic algorithm. The dynamic switching factor p is expressed as follows: Among them, iter is the current iteration number, iter max is the maximum number of iterations, and the dynamic switching factor p changes with iterations, thus affecting the individual butterfly The update method finally achieves the balance between local and global search. The butterfly individuals formed by coding are updated by introducing the crossover operation of the genetic algorithm. The formula is: Among them, X gbest is the optimal solution in the current iteration, used to evaluate the butterfly individual corresponding to the minimum value of the function, rand is a random number between [0,1], is the solution corresponding to the mth butterfly in the tth iteration, where m is any number in the population except i, representing a local random search, and cross represents a crossover operation; The dynamic switching factor p changes with iteration, which in turn affects the individual butterflies The update method finally achieves the effect of balancing local and global search; Step 3.3 Evaluation function based on linear weighting method The linear weighted method is used to set the weights, and the running time, total driving distance and number of operations of the bulk material collection vehicle are converted into a single-objective optimization problem with the minimum comprehensive cost. The fitness evaluation function is: Among them, λ1, λ2, λ3 are weights, λ1+λ2+λ3=1; After population initialization and crossover operation, compare the original solution X i The fitness f i And the updated fitness f newi , the butterfly with smaller fitness is the optimal solution. max After iterations, the optimal solution of the butterfly individual obtained by optimization is the optimal solution for dispatching the bulk material collection vehicle for the ring cooler.

5. The method for dispatching bulk material collection vehicles for annular coolers based on an improved butterfly optimization algorithm according to claim 3 is characterized in that: The step 3.2 improves the butterfly optimization algorithm by introducing a dynamic switching factor and a crossover operation of a genetic algorithm, and specifically includes the following steps: 3.2.1 Parameter settings: population size P, maximum number of iterations iter max ; 3.2.2 Encode and generate P butterfly individuals to form a population and complete initialization; 3.2.3 Calculate the population fitness according to the fitness evaluation function and obtain the current optimal solution; 3.2.4 Determine whether to terminate the iteration. If the current number of iterations iter>iter max , then the iteration is terminated and the global optimal solution X of the population obtained in the current iteration is output gbest , redirect to (3.2.6); otherwise execute (3.2.5); 3.2.5 Update the dynamic switching factor, generate a random number rand between [0,1] and perform crossover operation to generate new butterfly individuals, perform decoding constraint check and calculate the fitness of each butterfly individual, and update the global optimal solution of the population; 3.2.6 The optimal solution output is the optimal solution for dispatching the bulk material collection truck for the ring cooler.

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