Cyclone parameter optimization design method based on iterative optimization

Abstract

The invention discloses a cyclone parameter optimization design method based on iterative optimization, which belongs to the technical field of design optimization, is used for optimizing cyclone parameters, and comprises the steps of initializing parameter setting, designing a coding disc, judging the number of iterations, an ant transfer rule, a pheromone updating rule and chaotic interference, and is characterized in that the cyclone parameters comprise three radiuses and four lengths; the three radiuses comprise shaft neck radiuses and shaft shoulder radiuses at the two ends of the rotating shaft, and the four lengths are the generatrix length of the two shaft radiuses and the generatrix length of the two shaft heads; compared with an existing algorithm, the improved algorithm has better global search capability and convergence speed, and after 25 times of optimization, a global optimal combination of optimized parameters is found, so that the torsional vibration amplitude of a rotating system of a cleaning device of the cyclone is reduced by 17.2%.

Description

Technical Field

[0001] The present invention discloses a method for optimizing the design of cyclone parameters based on iterative optimization, belonging to the technical field of design optimization. Background Art

[0002] The basic principle of a cyclone is to separate a two-phase or multi-phase mixture such as liquid-liquid, liquid-solid, or liquid-gas with a certain density difference under the action of centrifugal force. The mixed liquid enters the cyclone tangentially at a certain pressure, generating a high-speed rotating flow field in the cylindrical cavity. The component with a large density in the mixture moves downward along the axial direction and outward along the radial direction under the action of the swirling flow field. When reaching the cone section, it moves downward along the wall of the device and is discharged from the underflow port, thus forming an outer swirling flow field; the component with a small density moves towards the central axis direction and forms an upward moving inner vortex at the center of the axis, and then is discharged from the overflow port, thus achieving the purpose of two-phase separation. A Chinese patent with the authorization announcement number CN118106139B discloses a cyclone that relies on a self-rotating cleaning mechanism for internal cleaning. It can be seen that the self-rotating cleaning mechanism, as the rotating system in the cyclone, its operating state determines the cleaning ability of the cyclone. Excessive rotational torsional vibration will affect the stability of the rotating system and further reduce the cleaning ability of the cyclone. The current research on the control method of the rotating system mainly focuses on the frequency adjustment method of changing the relevant parameters of the system. For equipment with limited installation space, the frequency adjustment method is a better choice. The parameter optimization algorithms based on the frequency adjustment method mainly include genetic algorithms, simulated annealing algorithms, and particle swarm algorithms, etc. Currently, the ant colony algorithm is widely used in solving combinatorial optimization problems, such as discrete system optimization fields like the traveling salesman problem, pipeline design, wireless sensor networks, and target tracking problems. However, in continuous domain optimization problems such as size parameter optimization, the ant colony algorithm has the defects of difficult solution and low accuracy. The present invention makes targeted improvements to the ant colony algorithm and introduces it into continuous domain optimization problems to solve the engineering problems of optimizing the structure of the rotating system of the equipment. Summary of the Invention

[0003] The purpose of the present invention is to provide a method for optimizing the design of cyclone parameters based on iterative optimization to solve the problem that in the prior art, the rotating system of the cleaning device of the cyclone is affected by excessive torsional vibration and thus affects normal operation, thereby reducing the cleaning ability.

[0004] The method for optimizing the design of cyclone parameters based on iterative optimization includes initial parameter setting, design of the code disk, judgment of the number of iterations, ant transfer rules, pheromone update rules, and chaotic interference. The cyclone parameters include three radii and four lengths. The three radii include the journal radii R1 and R3 at both ends of the rotating shaft and the shoulder radius R2. The four lengths are the bus lengths of the two shaft diameters and the bus lengths of the two shaft ends respectively; The objective of optimizing the parameters of the cyclone rotating shaft is: where f i is the maximum torsional amplitude value at the output end of the rotating shaft generated in the i-th iteration, I1 is the maximum number of iterations for optimizing the parameters of the cyclone rotating shaft, and x i are the optimized cyclone parameters, and are the maximum and minimum values of the optimized cyclone parameters, respectively.

[0005] The initialization parameter settings include: Set the number of ants m, which represents the number of candidate solutions for parallel search in one iteration process; Set the pheromone importance factor α, which represents the relative influence degree of pheromone on the probability of selecting the current path; Set the heuristic function importance factor β, which represents the relative importance degree of ant visibility in guiding the ant colony to search for paths; Set the pheromone evaporation factor ρ, and ρ affects the balance between the global search ability and the convergence speed of the ant colony algorithm; Set the pheromone concentration increment Q, which represents the amount of pheromone released by an ant once. The pheromone concentration increment is obtained by an adaptive method: where e is the natural constant, I is the current iteration number of the pheromone concentration increment, and I max is the maximum iteration number of the pheromone concentration increment; Set the ant transfer judgment factor q0. q0 judges the probability of an ant choosing different transfer rules. An adaptive adjustment method is used to make q0 decrease as the number of iterations increases:

[0006] Design a coding disk, including setting the number of optimized parameters as n, designing a coding space of size 10×nL, where nL is the maximum value of j, and arranging the ten digits 0 to 9 in sequence for each column. Each ant starts from the left side of the coding disk, takes only one coding number for each column. After the ant passes through the coding space, the ant will obtain a 1×nL sequence denoted as d j , where j represents the j-th sequence, and j = 1, 2, 3... nL; The optimized cyclone parameter x i is: x i =(x max (i) - x min (i))s i +x min (i); where s iis the parameter value coefficient, x max (i) is for x i 's maximum value, x min (i) is for x i 's minimum value.

[0007] Judging the number of iterations includes the start of iteration. Determine whether the maximum number of iterations is reached. If the number of iterations is not greater than the maximum number of iterations, initialize the initial value of pheromone and proceed to the next step. Loop through the steps until the maximum number of iterations is reached. If the number of iterations is greater than the maximum number of iterations, directly output the optimization result.

[0008] The ant transfer rule is that the pheromone of each path at the initial moment is a constant. m ants are randomly placed at ten initial positions on the first column of the coding disk. Each ant selects the coding number of the next coding column according to the pheromone and heuristic information on the path. The rule for each ant to select each coding number in the next column is: S pi = find(Pcum(i,j)≥rand); P k (i,j) = [τ ij α [η(i,j)] β In the formula, S j is the node number selected by the ant in the j-th column. arg returns the independent variable when the function is maximum. τ ij is the pheromone concentration at (i, j) on the coding disk. t represents the current iteration time. S pi is the node number selected by the roulette wheel method. rand is a random function that returns a random number in the range (0, 1); find represents the search function, Pcum is the roulette wheel function, and cumsum returns the vector of cumulative element sums; P k (i,j) is the probability function for the ant to select the node in the next column. η(i, j) is the heuristic function, and f(k) is the torsional amplitude value at the output end of the rotating shaft obtained by the path generated after selecting the k node; The unselected nodes after the k node are predicted by the roulette wheel method. At the initial stage of the algorithm execution, a set of parameters are randomly generated for heuristic calculation. After the pheromone converges, heuristic optimization is performed on the current optimal parameters.

[0009] ​The pheromone update rule includes simultaneously adopting local pheromone update and global pheromone update, restricting the maximum and minimum ranges of pheromone, setting the initial pheromone. The local pheromone update rule is that after each ant completes a path search, the pheromone added to the current path is: τ ij (t + 1) = ρτ ij (t) + ρτ max ; In the formula, τ max is the current maximum pheromone value.

[0010] The pheromone update rule includes that after the iteration is completed, pheromone is added to the current optimal path, and the pheromone of all paths is evaporated once: τ ij (t + 1) = (1 - ρ)τ ij (t) + Δτ ij ; In the formula, Δτ ij is the pheromone increment at the encoding disk (i, j) in this iteration, and f best is the current optimal solution.

[0011] The pheromone update rule includes that after each pheromone addition operation, the maximum and minimum ranges of pheromone are restricted: In the formula, τ min is the minimum pheromone value, τ is the pheromone.

[0012] Chaotic interference includes using Tent mapping for pheromone interference: Let the parameter δ = 0.5. When T k = 1, T k+1 takes a random number in the range (0, 1), and T k is the pheromone interference value of node k.

[0013] Chaotic interference includes: Initially set the convergence count S to 0. After each iteration, compare the optimal solution obtained in this iteration with the optimal solution of the previous generation. If they are equal, the convergence count is incremented by 1. If they are not equal, the convergence count is reset to zero; When the convergence count S is equal to 5, which is equivalent to the optimal solutions obtained in 5 iterations being equal, it is determined that the algorithm converges, and chaotic interference is performed on the pheromone: C = reshape(Tent(10nL), [10, nL]); In the formula, is the normalized pheromone after adding chaotic interference, is the normalization parameter of the original pheromone, r is the chaotic interference radius, C is the chaotic variable, reshape() is the command to change the matrix shape in MATLAB; Tent() is a function constructed based on the Tent mapping principle to construct a mapping vector of a custom length. After interference, the convergence count S is reset to zero, and the convergence count is accumulated again.

[0014] Compared with the existing technology, the present invention has the following beneficial effects: The improved algorithm of the present invention has better global search ability and convergence speed than the basic algorithm. After 25 times of optimization, the global optimal combination of the optimized parameters is found, and the torsional vibration amplitude at the output end of the rotating shaft is reduced by 17.2%. Description of the Drawings

[0015] Figure 1 is a schematic diagram of the coding disk of the present invention; Figure 2 is the convergence curve diagram of the basic ant colony algorithm; Figure 3 is the convergence curve diagram of the improved ant colony algorithm. Detailed Embodiments

[0016] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below. Obviously, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present invention without creative efforts shall fall within the protection scope of the present invention.

[0017] A cyclone parameter optimization design method based on iterative optimization includes initial parameter setting, designing a coding disk, judging the number of iterations, ant transfer rules, pheromone update rules and chaotic interference. The cyclone parameters include three radii and four lengths. The three radii include the journal radii R1 and R3 at both ends of the rotating shaft and the shoulder radius R2. The four lengths are the bus lengths of the two shaft diameters and the bus lengths of the two shaft heads respectively; The objective of optimizing the parameters of the cyclone rotating shaft is: In the formula, f i is the maximum torsional vibration amplitude value at the output end of the rotating shaft generated in the i-th iteration, I1 is the maximum number of iterations for optimizing the cyclone rotating shaft parameters, x i are the optimized cyclone parameters, and are the maximum and minimum values of the optimized cyclone parameters respectively.

[0018] The initialization parameter settings include: Set the number of ants m, which represents the number of candidate solutions for parallel search in one iteration process; Set the pheromone importance factor α, which represents the relative influence degree of pheromone on the probability of selecting the current path; Set the heuristic function importance factor β, which represents the relative importance degree of ant visibility in guiding the ant colony to search for paths; Set the pheromone evaporation factor ρ, and ρ affects the balance between the global search ability and the convergence speed of the ant colony algorithm; Set the pheromone concentration increment Q, which represents the amount of pheromone released by an ant at one time, and an adaptive method is used to obtain the pheromone concentration increment: In the formula, e is the natural constant, I is the current iteration number of the pheromone concentration increment, and I max is the maximum iteration number of the pheromone concentration increment; Set the ant transfer judgment factor q0, and q0 judges the probability of ants choosing different transfer rules. An adaptive adjustment method is used to make q0 decrease as the iteration number increases:

[0019] Design a coding disk, including setting the number of optimization parameters as n, designing a coding space of size 10×nL, where nL is the maximum value of j, and arranging the ten numbers 0 to 9 in sequence for each column. Each ant starts from the left side of the coding disk, takes only one coding number for each column. After the ant passes through the coding space, the ant will obtain a 1×nL sequence denoted as d j , j represents the jth sequence, and j = 1, 2, 3...nL; The optimized cyclone parameter x i is: x i =(x max (i)-x min (i))s i +x min (i); In the formula, s i is the parameter value coefficient, x max (i) is the maximum value of x i , and x min (i) is the minimum value of x i .

[0020] The number of iterations is judged, including the start of iteration. It is judged whether the maximum number of iterations is reached. If the number of iterations is not greater than the maximum number of iterations, the initial value of pheromone is initialized and the next step is carried out. The steps are executed in a loop until the maximum number of iterations is reached. If the number of iterations is greater than the maximum number of iterations, the optimized result is directly output.

[0021] The ant transfer rule is that the pheromone of each path at the initial moment is a constant. m ants are randomly placed at ten initial positions on the first column of the coding disk. Each ant selects the coding number of the next coding column according to the pheromone and heuristic information on the path. The rule for an ant to select each coding number in the next column is as follows: S pi = find(Pcum(i,j)≥rand); P k (i,j)=[τ ij α [η(i,j)] β ; In the formula, S j is the node number selected by the ant in the j-th column. arg returns the independent variable when the function is maximum. τ ij is the pheromone concentration at (i,j) on the coding disk. t represents the current iteration time. S pi is the node number selected by using the roulette wheel method. rand is a random function that returns a random number in the range of (0,1); find represents the search function, Pcum is the roulette wheel function, and cumsum returns the vector of cumulative sums of elements; P k (i,j) is the probability function for the ant to select the node in the next column. η(i,j) is the heuristic function, and f(k) is the torsional amplitude value at the output end of the rotating shaft obtained by the path generated after selecting the k node; The unselected nodes after the k node are predicted by using the roulette wheel method. A set of parameters is randomly generated at the initial stage of the algorithm execution for heuristic calculation. After the pheromone converges, the current optimal parameters are selected heuristically.

[0022] The pheromone update rule includes simultaneously using local pheromone update and global pheromone update, restricting the maximum and minimum ranges of the pheromone, setting the initial pheromone. The local pheromone update rule is that after each ant completes a path search, the pheromone added to the current path is: τ ij (t + 1)= ρτ ij (t)+ ρτ max ; In the formula, τ max ​is the current maximum pheromone value.

[0023] The pheromone update rule includes that after the iteration is completed, pheromone is added to the current optimal path, and the pheromone of all paths is evaporated once: τ ij (t + 1)=(1 - ρ)τ ij (t)+Δτ ij ; In the formula, Δτ ij is the pheromone increment at the encoding disk (i, j) in this iteration, and f best is the current optimal solution.

[0024] The pheromone update rule includes that after each pheromone addition operation, the maximum and minimum range limits are imposed on the pheromone: In the formula, τ min is the minimum pheromone value, τ is the pheromone.

[0025] Chaotic interference includes using Tent mapping for pheromone interference: Let the parameter δ = 0.5. When T k = 1, T k+1 takes a random number in the range (0, 1), and T k is the pheromone interference value of the k node.

[0026] Chaotic interference includes: The initial convergence count S is set to 0. After each iteration, compare the optimal solution obtained in this iteration with the optimal solution of the previous generation. If they are equal, the convergence count is incremented by 1. If they are not equal, the convergence count is reset to zero; When the convergence count S is equal to 5, which is equivalent to the optimal solutions obtained in 5 iterations being equal, it is determined that the algorithm converges, and chaotic interference is performed on the pheromone: C = reshape(Tent(10nL), [10, mL]); In the formula, is the normalized pheromone after adding chaotic interference, is the original pheromone normalization parameter, r is the chaotic interference radius, C is the chaotic variable, reshape() is the command in MATLAB to change the shape of the matrix; Tent() is the function constructed based on the Tent mapping principle to construct a mapping vector of a custom length. After interference, the convergence count S is reset to zero, and the convergence count accumulation is restarted.

[0027] In the present invention, m = 30, α = 2, β = 4, ρ = 0.3, I max = 100. By designing the coding disk, the ant colony algorithm is introduced into the optimization of continuous domain parameters to solve the problem of structural size optimization. Aiming at the defects of the ant colony algorithm in continuous domain optimization problems, the coding disk is designed to improve it. The coding disk is as shown in Figure 1 . After coding, the continuous domain optimization problem is transformed into a discrete path optimization problem within the coding disk, which is exactly what the ant colony algorithm is good at. At the initial stage of the algorithm operation, the pheromone differences on each node are not obvious, and at this time, the algorithm has strong global search ability. As the algorithm goes through multiple iterations, since multiple ants pass through the same path, the pheromone will accumulate on one path, while the pheromone on other nodes decreases to τ min due to multiple evaporation operations and has a very small chance of being selected, easily falling into local optimum. In order to enhance the global search ability of the algorithm, it is necessary to interfere with or initialize the pheromone when the algorithm converges. Chaos is a process similar to randomness that appears in deterministic systems. It has initial value sensitivity and uncertainty and can traverse all states within a certain region without repetition. Based on this characteristic of chaos, the present invention selects to use the chaos mapping method to interfere with the pheromone in the convergent state. The current main chaos mapping methods include Logistic mapping, PWLCM mapping, Singer mapping, Gussian mapping, and Tent mapping, etc. The Tent mapping has good traversal characteristics.

[0028] According to the relevant simplification principles of the lumped parameter model, the rotational system is modeled and simplified to obtain the lumped parameter model. The structural size of the rotating shaft directly affects the torsional stiffness and moment of inertia of the system, and thus affects the torsional vibration characteristics of the rotational system. Therefore, optimizing the design of the key structural size can, to a certain extent, optimize the performance of the rotational system and even the whole machine. Since the rotating shaft needs to assemble gears on both sides and the distance between the two gears is fixed, the radius dimensions at the two shaft necks on both sides and the length of the shaft shoulder cannot be changed. Select the two shaft neck radii R1 and R3, the shaft shoulder radius R2, and the lengths L1 to L4 of each step of the middle rotating shaft as the seven design variables, in order to optimize the performance of the rotating shaft by reconfiguring these structural sizes that do not affect the assembly relationship of other components. The value ranges of the design variables are shown in Table 1.

[0029] Table 1 Value ranges of design variables Design variable Initial value (mm) Constraint range (mm) <![CDATA[R1]]> 50 45～55 <![CDATA[R2]]> 60 58～68 <![CDATA[R3]]> 50 45～52 <![CDATA[L1]]> 40 36～44 <![CDATA[L2]]> 137 120～150 <![CDATA[L3]]> 149 134～164 <![CDATA[L4]]> 40 36～44 .

[0030] The basic ant colony algorithm and the improved ant colony algorithm are respectively used to perform 25 optimization calculations on the nonlinear system, and the results are shown in Table 2.

[0031] Table 2 Comparison of algorithm optimization results Comparison item Basic ant colony algorithm Improved ant colony algorithm Average number of convergence 63.8 18.2 Global optimal solution 0.48748° 0.48546° Number of times to find the global optimal solution 6 24 Global convergence rate 24％ 96％ 。

[0032] As can be seen from the table, the improved ant colony algorithm is superior to the basic ant colony algorithm in both global search and convergence speed. The convergence curves obtained by using the basic ant colony algorithm and the improved ant colony algorithm are shown in Figure 2 and Figure 3 respectively. It can be seen from the convergence curve graph that the basic ant colony algorithm enters the convergence state after 80 iterations, and the amplitude of the original system drops by 16.85%. While the improved ant colony algorithm converges after 7 iterations, and the amplitude drops by 17.19% after optimization, indicating that the basic ant colony algorithm falls into local convergence; the final pheromone of the basic ant colony algorithm does not converge to a single path, showing an irregular discrete state, and the difference between the maximum and minimum values of the pheromone is large, making it easy to fall into local optimum; while the final pheromone of the improved ant colony algorithm converges to a single path, and the difference between the maximum and minimum values of the pheromone is small, and it still retains good global search ability in the convergence state.

[0033] Using the improved ant colony algorithm for 25 times of optimization, the specific operating states obtained are shown in Table 3.

[0034] Table 3 Comparison of multiple optimization results Number of optimization Convergence algebra Parameter variable Response amplitude Optimization rate 1 7 [45，58，45，36，120，134，36] 0.48546° 17.199％ 2 2 [45，58，45，36，120，134，36] 0.48546° 17.199％ 3 13 [45，58，45，36，120，134，36] 0.48546° 17.199％ 4 2 [45，58，45，36，120，134，36] 0.48546° 17.199％ 5 8 [45，58，45，36，120，134，36] 0.48546° 17.199％ 6 12 [45，58，45，36，120，134，36] 0.48546° 17.199％ 7 12 [45，58，45，36，120，134，36] 0.48546° 17.199％ 8 10 [45.07，58，45，36，120，134，36] 0.48548° 17.196％ 9 43 [45，58，45，36，120，134，36] 0.48546° 17.199％ 10 7 [45，58，45，36，120，134，36] 0.48546° 17.199％ 11 14 [45，58，45，36，120，134，36] 0.48546° 17.199％ 12 3 [45，58，45，36，120，134，36] 0.48546° 17.199％ 13 4 [45，58，45，36，120，134，36] 0.48546° 17.199％ 14 14 [45，58，45，36，120，134，36] 0.48546° 17.199％ 15 79 [45，58，45，36，120，134，36] 0.48546° 17.199％ 16 2 [45，58，45，36，120，134，36] 0.48546° 17.199％ 17 57 [45，58，45，36，120，134，36] 0.48546° 17.199％ 18 62 [45，58，45，36，120，134，36] 0.48546° 17.199％ 19 17 [45，58，45，36，120，134.36] 0.48546° 17.199％ 20 26 [45，58，45，36，120，134，36] 0.48546° 17.199％ 21 30 [45，58，45，36，120，134，36] 0.48546° 17.199％ 22 6 [45，58，45，36，120，134，36] 0.48546° 17.199％ 23 12 [45，58，45，36，120，134，36] 0.48546° 17.199％ 24 11 [45，58，45，36，120，134，36] 0.48546° 17.199％ 25 3 [45，58，45，36，120，134，36] 0.48546° 17.199％ 。

[0035] From the 25 times of optimization results, it can be seen that the improved ant colony algorithm found the best optimization solution 24 times, and only 1 time of optimization converged midway, and the global convergence rate of the algorithm is 96%. Comparing before and after optimization, the torsional amplitude value of the rotating system dropped by 17.199%. The maximum torsional amplitude value of the optimized rotating system is 0.48546°, which does not exceed the maximum allowable torsional angle of safety, meeting the design requirements. Based on the improved ant colony algorithm for torsional vibration optimization of the equipment rotating system, the main conclusions are as follows: The ant colony algorithm is introduced into the continuous domain optimization problem by adding an encoder disk, and aiming at the disadvantage of the basic ant colony algorithm being prone to premature convergence, its parameters are made adaptive, chaotic interference is introduced and a heuristic function is designed for improvement. Based on the improved ant colony algorithm for torsional amplitude optimization of the rotating system, the torsional vibration amplitude at the output end of the rotating shaft is reduced by 17.199%. Comparing the torsional vibration responses of the system before and after optimization, the optimization result of the improved ant colony algorithm is obvious, and the optimized system meets the usage requirements.

[0036] Through the torsional vibration optimization of the rotating system by the improved ant colony algorithm, the global optimal parameter combination is found, a targeted vibration reduction scheme is proposed, the torsional vibration amplitude at the output end of the rotating shaft is reduced, the vibration phenomenon is reduced, and the reliability of the system is greatly improved.

[0037] Through the above application example analysis and data comparison, it can be verified that the beneficial effects of the improved ant colony algorithm for optimization design include, but are not limited to, the following points: Faster convergence speed: The improved ant colony algorithm can usually converge to the solution of the optimization problem faster, which means that a satisfactory design solution can be found in a shorter time, thus accelerating the design and development process.

[0038] Better solutions: The improved ant colony algorithm can find better design solutions through global search and pheromone guidance to meet the given design goals and constraints. These solutions often have higher efficiency, lower cost, or better performance.

[0039] Better global search ability: Through the cooperation of ants and the spread of pheromones, the improved ant colony algorithm can conduct global search in the solution space, thus avoiding being trapped in local optimal solutions. This enables the algorithm to find better design solutions rather than just local optimal solutions.

[0040] Self - adaptability: The improved ant colony algorithm has a certain degree of self - adaptability and can flexibly adjust the search strategy and parameters in a dynamic environment to adapt to environmental changes. This enables the algorithm to cope with real - time changing design requirements and constraints and maintain the effectiveness and reliability of the optimization results.

[0041] The improved ant colony algorithm is superior to the basic ant colony algorithm in terms of computational time, the quality and efficiency of obtaining dominant solutions, and is one of the effective tools for solving various complex optimization problems.

[0042] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An optimization design method for cyclone parameters based on iterative optimization, characterized in that, It includes initialization parameter setting, design of the coding disk, judgment of the iteration times, ant transfer rules, pheromone update rules, and chaotic interference. The cyclone parameters include three radii and four lengths. The three radii are the journal radii R1 and R3 at both ends of the rotating shaft and the shoulder radius R2. The four lengths are the bus lengths of the two shaft diameters and the bus lengths of the two shaft ends respectively; The goal of optimizing the parameters of the cyclone rotating shaft is: where f i is the maximum torsional amplitude value at the output end of the rotating shaft generated in the i-th iteration, I1 is the maximum number of iterations for optimizing the rotating shaft parameters of the cyclone, x i are the optimized cyclone parameters, and are the maximum and minimum values of the optimized cyclone parameters, respectively.

2. The optimized design method for cyclone parameters based on iterative optimization according to claim 1, characterized in that The initialization parameter setting includes: Set the number of ants m, which represents the number of candidate solutions for parallel search in one iteration process; Set the pheromone importance factor α, which represents the relative influence degree of pheromone on the probability of selecting the current path; Set the heuristic function importance factor β, which represents the relative importance degree of ant visibility in guiding the ant colony to search for paths; Set the pheromone evaporation factor ρ, and ρ affects the balance between the global search ability and the convergence speed of the ant colony algorithm; Set the pheromone concentration increment Q, which represents the amount of pheromone released by an ant once. The pheromone concentration increment is obtained by an adaptive method: where e is the natural constant, I is the current iteration number of the pheromone concentration increment, and I max is the maximum iteration number of the pheromone concentration increment; Set the ant transfer judgment factor q0, and q0 judges the probability of an ant choosing different transfer rules. An adaptive adjustment method is used to make q0 decrease as the iteration times increase:

3. The method for optimizing the design of cyclone parameters based on iterative optimization according to claim 2, wherein The designed coding disk includes optimizing the number of parameters to be n, designing a coding space of size 10×nL, where nL is the maximum value of j, and arranging the ten digits from 0 to 9 in sequence for each column. Each ant starts from the left side of the coding disk, takes only one coding number for each column. After the ant passes through the coding space, the sequence of 1×nL obtained by the ant is denoted as d j , j represents the j-th sequence, and j = 1, 2, 3... nL; Optimized hydrocyclone parameter x i is as follows: x i = (x max (i) - x min (i))s i + x min (i); where s i is the parameter value coefficient, x max (i) is the maximum value of x i , and x min (i) is the minimum value of x i .

4. The optimized design method for cyclone parameters based on iterative optimization according to claim 3, wherein Judging the iteration times includes the start of iteration, judging whether the maximum number of times is reached. If the iteration times is not greater than the maximum number of times, initialize the initial value of pheromone and proceed to the next step. Loop and execute the steps until the maximum iteration times is reached. If the iteration times is greater than the maximum number of times, directly output the optimization result.

5. The optimized design method for cyclone parameters based on iterative optimization according to claim 4, characterized in that, The ant transfer rules include that at the initial moment, the pheromone of each path is a constant. m ants are randomly placed at ten initial positions in the first column of the coding disk. Each ant selects the coding number of the next coding column according to the pheromone and heuristic information on the path. The rules for an ant to select each coding number in the next column are: S pi = find(Pcum(i,j)≥rand; P k (i, j) = [τ ij α [η(i, j)] β ;​ Where S j is the node number selected by the ant in the j-th column, arg returns the independent variable when the function is maximized, and τ ij is the pheromone concentration at (i, j) of the coding disk, t represents the current iteration time, and S pi is the node number selected by the roulette wheel method, rand is a random function that returns a random number in the range (0, 1); find represents the search function, Pcum is the roulette wheel function, and cumsum returns the cumulative sum vector of elements; P k (i, j) is the probability function for the ant to select the next column node, η(i, j) is the heuristic function, and f(k) is the torsional amplitude value at the output end of the rotating shaft obtained by generating a path after selecting node k; The unselected nodes after the k node are predicted by the roulette wheel method. At the initial stage of the algorithm execution, a set of parameters are randomly generated for heuristic calculation. After the pheromone converges, heuristic optimization is performed on the current optimal parameters.

6. The optimized design method for cyclone parameters based on iterative optimization according to claim 5, characterized in that The pheromone update rules include simultaneously adopting local pheromone update and global pheromone update, restricting the maximum and minimum ranges of pheromone, setting the initial pheromone. The local pheromone update rule is that after each ant completes a path search, the pheromone added to the current path is: τ ij (t + 1) = ρτ ij (t) + ρτ max ; where τ max is the current maximum pheromone value.

7. The optimized design method for cyclone parameters based on iterative optimization according to claim 6, characterized in that The pheromone update rules include that when the iteration is completed, pheromone is added to the current optimal path, and the pheromone of all paths is evaporated once; τ ij (t + 1) = (1 - ρ)τ ij (t) + Δτ ij ; where Δτ ij is the pheromone increment at the encoder disk (i, j) in this iteration, and f best is the current optimal solution.

8. The method for optimizing the design of cyclone parameters based on iterative optimization according to claim 7, wherein The pheromone update rules include that after each pheromone addition operation, the maximum and minimum ranges of pheromone are restricted; In the formula, τ min is the minimum pheromone, and τ is the pheromone.

9. The optimized design method for cyclone parameters based on iterative optimization according to claim 8, characterized in that, Chaotic interference includes using Tent mapping for pheromone interference: Let the parameter δ = 0.

5. When T k = 1, T k+1 takes a random number within the range (0, 1). T k is the pheromone interference value of the k-th node.

10. The method for optimizing the design of cyclone parameters based on iterative optimization according to claim 9, wherein, Chaotic interference includes: Initially set the convergence times S to 0. After each iteration, compare the optimal solution obtained in this iteration with the optimal solution of the previous generation. If they are equal, the convergence times is incremented by 1. If they are not equal, the convergence times is reset to zero; When the convergence times S is equal to 5, which is equivalent to the optimal solutions obtained in 5 iterations being equal, it is considered that the algorithm converges, and chaotic interference is performed on the pheromone: C = reshape(Tent(10nL), [10, nL]); Wherein, is the normalized pheromone after adding chaotic interference, is the original pheromone normalization parameter, r is the chaotic interference radius, C is the chaotic variable, reshape( ) is the command to change the matrix shape in MATLAB; Tent( ) is a function constructed based on the Tent mapping principle to construct a mapping vector of a custom length, the number of convergence times S after interference is reset to zero, and the accumulation of the number of convergence times is carried out again.

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