Cyclone parameter optimization design method based on iterative optimization

Abstract

The application discloses an iterative optimization-based cyclone parameter optimization design method, belongs to the technical field of design optimization, and is used for optimizing cyclone parameters and comprises initialization parameter setting, design coding disc, iteration number judgment, ant transfer rule, pheromone update rule and chaotic interference. The cyclone parameters include three radii and four lengths. The three radii include the two end neck radii of a rotating shaft and a shaft shoulder radius. The four lengths are the generatrix lengths of two shaft diameters and the generatrix lengths of two shaft heads. The improved algorithm of the application has better global search capability and convergence speed compared with existing algorithms. After 25 optimization searches, the global optimal combination of the optimization parameters is found, and the torsional vibration amplitude of the rotating system of the cleaning device of the cyclone is reduced by 17.2%.

Description

TECHNICAL FIELD

[0001] The application discloses a cyclone parameter optimization design method based on iterative optimization, and belongs to the technical field of design optimization. BACKGROUND

[0002] The basic principle of the cyclone is to separate liquid-liquid, liquid-solid, liquid-gas and other two-phase or multi-phase mixtures with certain density difference under the action of centrifugal force. The mixed liquid is tangentially introduced into the cyclone at a certain pressure to generate a high-speed rotating flow field in the cylindrical cavity. The components with large density in the mixture move downward along the axial direction and move outward along the radial direction under the action of the cyclone field, move downward along the wall in the cone section, and are discharged from the underflow port, so that an outer vortex flow field is formed. The components with small density move towards the central axis and form an upwardly moving inner vortex at the central axis, and then are discharged from the overflow port, so that the purpose of two-phase separation is achieved. A cyclone is disclosed in Chinese Patent No. CN118106139B, which relies on a self-rotating cleaning mechanism for internal cleaning. It can be seen that the running state of the self-rotating cleaning mechanism as a rotating system in the cyclone determines the cleaning capacity of the cyclone. Excessive rotational torsional vibration will affect the stability of the rotating system and further reduce the cleaning capacity of the cyclone. The current research on the control method of the rotating system mainly focuses on the frequency adjustment method of changing the related parameters of the system, and the frequency adjustment method is a better choice for equipment with limited installation space. The parameter optimization algorithm based on the frequency adjustment method mainly includes genetic algorithm, simulated annealing algorithm and particle swarm algorithm. However, the ant colony algorithm is widely used in solving combinatorial optimization problems, such as the traveling salesman problem, pipeline design, wireless sensor network, target tracking problem and other discrete system optimization fields. However, the ant colony algorithm has defects such as difficulty in solving and low precision in solving continuous domain optimization problems such as size parameter optimization. The application improves the ant colony algorithm and introduces it into the continuous domain optimization problem to solve the engineering problem of structure optimization of the rotating system of the equipment. SUMMARY

[0003] The application aims to provide a cyclone parameter optimization design method based on iterative optimization to solve the problem in the prior art that the rotating system of the cleaning device of the cyclone is affected by excessive torsional vibration, thereby reducing the cleaning capacity.

[0004] The cyclone parameter optimization design method based on iterative optimization comprises initialization parameter setting, design coding disc, judgment iteration number, ant transfer rule, pheromone update rule and chaos interference. The cyclone parameters include three radii and four lengths. The three radii include the two end neck radii R1 and R3 of the rotating shaft and the shaft shoulder radius R2. The four lengths are the generatrix lengths of the two shaft diameters and the generatrix lengths of the two shaft heads.

[0005] The target of the cyclone rotating shaft parameter optimization is:

[0006]

[0007] where f i is the maximum torsional amplitude of the rotating shaft output, I1is the maximum number of iterations for the optimization of the rotating shaft parameters of the cyclone, x i is the optimized cyclone parameter, and are the maximum and minimum values of the optimized cyclone parameter, respectively.

[0008] The initialization parameter settings include:

[0009] The number of ants m is set, which represents the number of candidate solutions searched in parallel in one iteration process;

[0010] The importance factor of pheromone α is set, which represents the relative influence of pheromone on the selection probability of the current path;

[0011] The importance factor of the heuristic function β is set, which represents the relative importance of the visibility of the ants in guiding the search path of the ant colony;

[0012] The pheromone evaporation factor ρ is set, which affects the balance between the global search ability and the convergence speed of the ant colony algorithm;

[0013] The pheromone concentration increment Q is set, which represents the amount of pheromone released by the ants in a single time, and the pheromone concentration increment is obtained by using an adaptive method:

[0014]

[0015] where e is a natural constant, I is the current iteration number of the pheromone concentration increment, I max is the maximum iteration number of the pheromone concentration increment;

[0016] The ant turning judgment factor q0 is set, which judges the probability of the ants selecting different transition rules, and an adaptive adjustment method is used to make q0 decrease with the increase of the iteration number:

[0017]

[0018] The design of the coding disc includes setting the number of optimized parameters as n, designing a coding space with a size of 10×nL, nL is the maximum value of j, and arranging the ten numbers from 0 to 9 in each column in turn, each ant starts from the left side of the coding disc, and only one coding number is taken from each column, after the ant passes through the coding space, the 1×nL number sequence obtained by the ant is recorded as d j , j represents the jth number sequence, j = 1, 2, 3... nL;

[0019] The optimized cyclone parameter xi is:

[0020] x i = (x max (i) - x min (i))s i + x min (i);

[0021]

[0022] where s i is a parameter value coefficient, x max (i) is the maximum value of x i (i), and x min (i) is the minimum value of x i .

[0023] The judgment iteration number includes iteration start, judgment whether to reach the maximum number, if the iteration number is not greater than the maximum number, initialize the pheromone initial value, proceed to the next step, and execute the step in a loop until the maximum iteration number is reached, and if the iteration number is greater than the maximum number, the optimization result is directly output.

[0024] The ant transfer rule includes that the pheromone of each path at the initial moment is a constant, m ants are randomly placed in ten initial positions on the first column of the coding disc, each ant selects the coding number of the next coding column according to the pheromone and heuristic information on the path, and the rule for the ant to select each coding number of the next column is:

[0025]

[0026] S pi = find (Pcum(i,j) ≥ rand);

[0027]

[0028] P k (i,j) = [τ ij ] α [η(i,j)] β

[0029]

[0030] where S j is the node number selected by the ant in the jth column, arg returns the independent variable when the maximum is reached, τ ij is the pheromone concentration of the coding disc at (i,j), t represents the current iteration time, and S piThe node number is selected by roulette method, rand is a random function returning a random number in the range of (0, 1), find indicates a search function, Pcum is a roulette function, cumsum returns an element cumulative sum vector, and P is a probability function. k (i,j) is a probability function for the next column node selected by the ant, η(i,j) is a heuristic function, and f(k) is the output end torsional amplitude value of the rotation axis generated by the path after k nodes are selected;

[0031] The unselected nodes after k nodes are predicted by roulette method, a set of parameters is randomly generated for heuristic calculation at the beginning of algorithm execution, and the optimal parameters are selected by heuristic after pheromone convergence.

[0032] The pheromone update rule includes simultaneous local and global pheromone update, limiting the maximum and minimum range of pheromone, setting the initial pheromone, and the local pheromone update rule is that after each ant completes a path search, the pheromone added to the current path is:

[0033] τ ij (t+1)=ρτ ij (t)+ρτ max ;

[0034] In the formula, τ max is the maximum value of the current pheromone.

[0035] The pheromone update rule includes adding pheromone to the current optimal path after iteration is completed, and evaporating the pheromone of all paths once:

[0036] τ ij (t+1)=(1-ρ)τ ij (t)+Δτ ij ;

[0037]

[0038] In the formula, Δτ ij is the pheromone increment of the current iteration at the encoding disc (i,j), f best is the current optimal solution.

[0039] The pheromone update rule includes limiting the maximum and minimum range of pheromone after each pheromone addition operation:

[0040] In the formula, τ min is the minimum value of the pheromone, τ is the pheromone.

[0041] The chaos interference includes pheromone interference by Tent mapping:

[0042]

[0043] Let parameter delta = 0.5, when T k = 1, T k+1 Take a random number in the range of (0, 1), T k is the pheromone interference value of k nodes.

[0044] The chaotic interference includes:

[0045] The convergence number S is initially set to 0, and after each iteration, the optimal solution obtained this time is compared with the optimal solution of the last generation, if they are equal, the convergence number is added by 1, if they are not equal, the convergence number is reset to zero.

[0046] When the convergence number S is equal to 5, it is equivalent to that the optimal solutions obtained by 5 iterations are equal, the algorithm is considered to be converged, and the pheromone is subjected to chaotic interference:

[0047]

[0048] C = reshape (Tent (10nL), [10, nL]);

[0049] 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 for changing the shape of a matrix in MATLAB, Tent() is a function constructed according to the Tent mapping principle, and a mapping vector of a custom length is constructed, after interference, the convergence number S is reset to zero, and the convergence number accumulation is performed again.

[0050] Compared with the prior art, the improved algorithm has better global search ability and convergence speed, and after 25 optimization, the global optimal combination of the optimization parameters is found, and the torsional vibration amplitude of the output end of the rotating shaft is reduced by 17.2%. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 is a schematic diagram of the code disc of the application;

[0052] Figure 2 is a convergence curve diagram of the basic ant colony algorithm;

[0053] Figure 3 is a convergence curve diagram of the improved ant colony algorithm. DETAILED DESCRIPTION

[0054] In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions in the present application are described clearly and completely below. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0055] The cyclone parameter optimization design method based on iterative optimization comprises initialization parameter setting, design coding disc, judgment iteration number, ant transfer rule, pheromone update rule and chaos interference. The cyclone parameters comprise three radii and four lengths. The three radii comprise two end shaft neck radii R1 and R3 of a rotating shaft and a shaft shoulder radius R2. The four lengths are generatrix lengths of two shaft diameters and generatrix lengths of two shaft heads.

[0056] The target of the cyclone rotating shaft parameter optimization is:

[0057]

[0058] In the formula, f i is a maximum torsional vibration amplitude value of the rotating shaft output end generated in the ith iteration, I1 is a maximum iteration number of the cyclone rotating shaft parameter optimization, x i is an optimized cyclone parameter, and are a maximum value and a minimum value of the optimized cyclone parameter respectively.

[0059] The initialization parameter setting comprises:

[0060] The number of ants m is set, which represents the number of candidate solutions searched in parallel in one iteration process.

[0061] The pheromone importance factor alpha is set, which represents the relative influence degree of pheromone on the current path selection probability.

[0062] The heuristic function importance factor beta is set, which represents the relative importance degree of the ant visibility in guiding the ant colony to search the path.

[0063] The pheromone evaporation factor p is set, which affects the balance between the global search ability and the convergence speed of the ant colony algorithm.

[0064] The pheromone concentration increment Q is set, which represents the amount of pheromone released by the ant once. The adaptive method is used to obtain the pheromone concentration increment:

[0065]

[0066] In the formula, e is a natural constant, I is the current iteration number of the pheromone concentration increment, I maxThe maximum number of iterations for pheromone concentration increment

[0067] An ant transition judgment factor q0 is set, q0 judges the probability of the ant selecting different transition rules, and an adaptive adjustment method is adopted to make q0 decrease with the increase of the number of iterations:

[0068]

[0069] The design of the coding disc includes setting the number of optimization parameters to n, designing a coding space with a size of 10×nL, nL is the maximum value of j, and making each column arrange the ten numbers 0-9 in turn, each ant starts from the left side of the coding disc, and only one coding number is taken from each column, after the ant passes through the coding space, the ant will get a 1×nL number sequence d j , j represents the jth number sequence, j=1, 2, 3...nL;

[0070] Optimized cyclone parameter x i is:

[0071] x i =(x max (i)-x min (i))s i +x min (i);

[0072]

[0073] In the formula, s i is a parameter value coefficient, x max (i) is the maximum value of x i , x min (i) is the minimum value of x i .

[0074] The number of iterations includes the start of iteration, the judgment of 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, the next step is performed, and 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 optimization result is directly output.

[0075] The ant transition rule includes that the pheromone of each path at the initial moment is a constant, m ants are randomly placed in the ten initial positions on the first column of the coding disc, each ant selects the coding number of the next coding column according to the pheromone and heuristic information on the path, and the rule for the ant to select the coding number of the next column is:

[0076]

[0077] S pi =find(Pcum(i,j)≥rand);

[0078]

[0079] P k (i, j) = [τ ij ] α [η(i,j)] β ;

[0080]

[0081] Where S j is the node number selected by the ant in column j, arg returns the independent variable when the function is maximized, τ ij is the pheromone concentration of the coding disk at (i, j), t represents the current iteration time, S pi is the node number selected by roulette, rand is a random function that returns a random number in the range of (0, 1); find represents the search function, Pcum is a roulette function, and cumsum returns the element cumulative sum vector; P k (i, j) is the probability function of the ant selecting the next column node, η(i, j) is the heuristic function, and f(k) is the torsional amplitude of the output end of the rotating shaft obtained by generating the path after selecting the kth node;

[0082] The unselected nodes after k nodes are predicted using a roulette wheel method. At the beginning of the algorithm execution, a set of parameters are randomly generated for heuristic calculation. After the pheromone converges, the current optimal parameters are heuristically selected.

[0083] The pheromone update rule includes the simultaneous use of local pheromone updates and global pheromone updates, limiting the maximum and minimum ranges of pheromones, setting the initial pheromone, and the local pheromone update rule is that after each ant completes a path search, the pheromone added to the current path is:

[0084] τ ij (t+1)=ρτ ij (t)+ρτ max ;

[0085] Where, τ max is the current maximum value of pheromone.

[0086] The pheromone update rule includes adding pheromones to the current optimal path after the iteration is completed, and evaporating the pheromones of all paths:

[0087] τ ij (t+·1)=(1-ρ)τ ij (t)+Δτ ij ;

[0088]

[0089] In the formula, Δτ ij is pheromone increment of the current iteration at the encoding disk (i, j), f best is the current optimal solution.

[0090] The pheromone updating rule comprises limiting the pheromone in a maximum and minimum range after each pheromone adding operation:

[0091] In the formula, τ min is a minimum value of the pheromone, τ is pheromone.

[0092] The chaotic interference comprises adopting Tent mapping to interfere with the pheromone:

[0093]

[0094] Let the parameter δ=0.5, when T k =1, T k+1 takes a random number in the range of (0, 1), and T k is the pheromone interference value of the k node.

[0095] The chaotic interference comprises:

[0096] The convergence number S is initially set as 0, and after each iteration, the optimal solution obtained in the current iteration is compared with the optimal solution of the last generation, if they are equal, the convergence number is added by 1, if they are not equal, the convergence number is reset to zero;

[0097] When the convergence number S is equal to 5, it is equivalent to that the optimal solutions obtained in 5 iterations are equal, the algorithm is considered to be converged, and the pheromone is subjected to chaotic interference:

[0098]

[0099] C=reshape(Tent(10nL),[10,mL]);

[0100] In the formula, is normalized pheromone after adding chaotic interference, is a normalized parameter of the original pheromone, r is a chaotic interference radius, C is a chaotic variable, reshape() is a command for changing the shape of a matrix in MATLAB, Tent() is a function constructed according to the principle of Tent mapping, a mapping vector of a self-defined length is constructed, after interference, the convergence number S is reset to zero, and accumulation of the convergence number is performed again.

[0101] In the application, m=30, α=2, β=4, ρ=0.3, I max=100. By designing a coding disk, the ant colony algorithm is introduced into the continuous domain parameter optimization to solve the structural size optimization problem. In view of the defects of the ant colony algorithm in the continuous domain optimization problem, the coding disk is designed to improve it. Figure 1 As shown in Figure 1, after encoding, the continuous domain optimization problem is transformed into a discrete path optimization problem within the encoding disk, which is exactly what the ant colony algorithm excels at. In the early stages of the algorithm, the pheromone differences between nodes are not obvious, and the algorithm has a strong global search capability. As the algorithm goes through multiple iterations, as multiple ants pass through the same path, pheromones will accumulate on one path, while the pheromones of other nodes will be reduced to τ due to multiple evaporation operations. min , the probability of being selected is very small, and it is easy to fall into the local optimum. In order to enhance the global search capability of the algorithm, it is necessary to interfere with or initialize the pheromone when the algorithm converges. Chaos is a random-like process that occurs in a deterministic system. It has initial value sensitivity and uncertainty, and can traverse all states in a certain area without repeating. Based on this characteristic of chaos, the present invention chooses to use a chaotic mapping method to interfere with the pheromone that is trapped in the convergence state. The current main chaotic mapping methods include Logistic mapping, PWLCM mapping, Singer mapping, Gussian mapping and Tent mapping, etc. Tent mapping has better traversal characteristics.

[0102] The rotating system model is simplified according to the simplification principle of the lumped parameter model to obtain a lumped parameter model. The structural dimensions of the rotating shaft directly affect the torsional stiffness and moment of inertia of the system, and thus affect the torsional vibration characteristics of the rotating system. Therefore, optimizing the design of key structural dimensions can optimize the performance of the rotating system and even the entire machine to a certain extent. Since the rotating shaft needs to be equipped with gears on both sides and the spacing between the two gears is fixed, the radius dimensions and shoulder lengths of the journals on both sides cannot be changed. Seven parameters, namely the journal radii R1 and R3 at both ends of the intermediate rotating shaft, the shoulder radius R2, and the step lengths L1 to L4, are selected as design variables in order to optimize the performance of the rotating shaft by reconfiguring these structural dimensions that do not affect the assembly relationship of other components. The range of design variable values ​​is shown in Table 1.

[0103] Table 1 Design variable value range

[0104] Design variable Initial value (mm) Constraint range (mm) [R1] 50 45～55 [R2] 60 58～68 [R3] 50 45～52 ​ 40 36～44 <L2> 137 120～150 L3 149 134～164 ​ 40 36～44 .

[0105] The basic ant colony algorithm and the improved ant colony algorithm were used to perform 25 optimization calculations on the nonlinear system, and the results are shown in Table 2.

[0106] Table 2 Comparison of algorithm optimization results

[0107] Comparative item Basic ant colony algorithm Improved ant colony algorithm Average convergence times 63.8 18.2 Global optimal solution 0.48748° 0.48546° Number of finding global optimal solution 6 24 Global convergence rate 24％ 96％ .

[0108] It can be seen from the table that the improved ant colony algorithm is superior to the basic ant colony algorithm in terms of global search and convergence speed. The convergence curves obtained by using the basic ant colony algorithm and the improved ant colony algorithm are as follows: Figure 2 and Figure 3 As shown in the figure, it can be seen from the convergence curve that the basic ant colony algorithm enters the convergence state after 80 iterations, and the amplitude decreases by 16.85% compared with the original system, while the improved ant colony algorithm converges after 7 iterations, and the amplitude decreases 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 path, and is in an irregular discrete state, and the maximum and minimum values ​​of the pheromone differ greatly, which makes it easy to fall into the local optimum; while the final pheromone of the improved ant colony algorithm converges to a path, and the maximum and minimum values ​​of the pheromone do not differ much, and it still retains a good global search ability in the convergence state.

[0109] The improved ant colony algorithm was used to perform 25 optimizations, and the specific operating status obtained is shown in Table 3.

[0110] Table 3 Comparison of multiple optimization results

[0111] Optimization times 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％ .

[0112] The improved ant colony algorithm found the optimal solution 24 times out of 25 optimization runs, with only one optimization failing midway, resulting in a global convergence rate of 96%. Comparing the torsional vibration amplitude of the rotating system before and after optimization, the optimized system achieved a 17.199% decrease. The maximum torsional amplitude after optimization was 0.48546°, within the permitted maximum torsional angle and meeting design requirements. The torsional vibration optimization of the equipment's rotating system using the improved ant colony algorithm yielded the following key conclusions: The ant colony algorithm was introduced to the continuous domain optimization problem by adding an encoder disk. To address the inherent drawback of the basic ant colony algorithm, parameter adaptation, chaotic interference, and design heuristic functions were implemented. The improved ant colony algorithm was used to optimize the torsional vibration amplitude of the rotating system, resulting in a 17.199% decrease in the torsional vibration amplitude at the output end of the rotating shaft. Comparing the torsional vibration responses of the system before and after optimization, the improved ant colony algorithm achieved significant results, and the optimized system met operational requirements.

[0113] By improving the ant colony algorithm to optimize the torsional vibration of the rotating system, the global optimal parameter combination was found, and a targeted vibration reduction scheme was proposed to reduce the torsional vibration amplitude at the output end of the rotating shaft, reduce the vibration phenomenon, and greatly improve the reliability of the system.

[0114] The above application example analysis and data comparison can verify the beneficial effects of improving the ant colony algorithm for optimization design, including but not limited to the following points:

[0115] Faster convergence: Improved ant colony algorithms are generally able to converge to the solution of optimization problems more quickly, which means that satisfactory design solutions can be found in a shorter time, thus speeding up the design and development process.

[0116] Better solutions: Improved ant colony algorithms can find better design solutions by global search and pheromone guidance to meet the given design objectives and constraints. These solutions often have higher efficiency, lower cost or better performance.

[0117] Better global search capability: Improved ant colony algorithms can perform global search in the solution space through the cooperation of ants and the spread of pheromones, thus avoiding falling into local optimal solutions. This enables the algorithm to find better design solutions, not just local optimal solutions.

[0118] Adaptability: Improved ant colony algorithms have certain adaptability, which can flexibly adjust search strategies and parameters in dynamic environments to adapt to changes in the environment. This enables the algorithm to cope with real-time changes in design requirements and constraints, maintaining the effectiveness and reliability of the optimization results.

[0119] Improved ant colony algorithm is superior to basic ant colony algorithm in terms of computation time, quality and efficiency of obtaining superior solutions, and is one of the effective tools for solving various complex optimization problems.

[0120] The above examples are only used to illustrate the technical solutions of the present application, but not to limit it. Although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that the technical solutions recorded in the foregoing examples can still be modified, or some or all of the technical features can be replaced by equivalents, without changing the essence of the corresponding technical solutions out of the scope of the technical solutions of the embodiments of the present application.

Claims

1. A cyclone parameter optimization design method based on iterative optimization, characterized in that: Including initialization parameter setting, design of encoder disk, judgment of iteration number, ant transfer rule, pheromone update rule and chaotic interference. The cyclone parameters include three radii and four lengths. The three radii include the journal radius at both ends of the rotating shaft. and and shoulder radius , the four lengths are the generatrix lengths of the two shaft diameters and the generatrix lengths of the two shaft heads; The objectives of cyclone shaft parameter optimization are: ； Where, is the maximum torsional vibration amplitude of the output end of the rotating shaft generated by the i-th iteration, is the maximum number of iterations, To optimize the cyclone parameters, and are the maximum and minimum values ​​of the optimized cyclone parameters respectively; Initialization parameter settings include: Set the number of ants , Indicates the number of candidate solutions searched in parallel during one iteration; Set the pheromone importance factor , Indicates the relative influence of pheromone on the probability of selecting the current path; Set the heuristic function importance factor , Indicates the relative importance of ant visibility in guiding the ant colony's path search process; Setting the pheromone volatility factor , Affects the balance between the global search capability and convergence speed of the ant colony algorithm; Set the pheromone concentration increment , represents the amount of pheromone released by ants in a single time, and the pheromone concentration increment is obtained using an adaptive method: ； Where, is the current iteration number, is the maximum number of iterations; Set the ant transfer judgment factor , Determine the probability of ants choosing different transfer rules and use adaptive adjustment method to make Decreases as the number of iterations increases: ； The ant transfer rule includes: at the initial moment, the pheromone of each path is a constant, m ants are randomly placed at the ten initial positions on the first column of the coding disk, and each ant selects the code number of the next coding column based on the pheromone on the path and the heuristic information. The rule for the ant to select each code number in the next column is: ； ； ； ； ； Where, is the node number selected by the ant in column j, Returns the function's argument at its maximum. For the encoder disk The pheromone concentration at , t represents the current iteration time, is the node number selected by roulette, It is a random function that returns a random number in the range of (0, 1); is the roulette function, Returns the element-wise sum vector; is the probability function for the ant to choose the next column of nodes, is the heuristic function, For choosing The torsional vibration amplitude of the output end of the rotating shaft obtained by generating the path after the node; The unselected nodes after the node are predicted using a roulette wheel method. At the beginning of the algorithm execution, a set of parameters are randomly generated for heuristic calculation. After the pheromone converges, the current optimal parameters are heuristically selected; The pheromone update rule includes using local pheromone update and global pheromone update at the same time, limiting the maximum and minimum range of pheromone, and setting the initial pheromone to , the local pheromone update rule is that after each ant completes a path search, the pheromone added to the current path is: ； Where: For the encoder disk The pheromone concentration at is the current maximum value of pheromone, It is the volatile factor of pheromone; The pheromone update rules include: the global pheromone update rule is: after the iteration is completed, pheromone is added to the current optimal path, and the pheromone of all paths is evaporated: ； ； Where, For this iteration, the encoder disk The pheromone increment at is the current optimal solution; The pheromone update rule includes setting the maximum and minimum range limits for pheromones after each pheromone addition operation.

2. The cyclone parameter optimization design method based on iterative optimization according to claim 1, characterized in that: Designing the encoder disk includes setting the number of optimized parameters to n and designing a size of The coding space is arranged in such a way that the ten numbers 0 to 9 are arranged in sequence in each column. Each ant starts from the left side of the coding disk and takes only one coding number in each column. After the ant passes through the coding space, it will get The sequence of numbers is recorded as ; Optimized cyclone parameters for: ； ； Where, is the parameter value coefficient, for The maximum value of for The minimum value of .

3. The cyclone parameter optimization design method based on iterative optimization according to claim 2, characterized in that: The determination of the number of iterations includes: starting the iteration, determining whether the maximum number of iterations is reached, initializing the initial value of the pheromone if the number of iterations is not greater than the maximum number of iterations, proceeding to the next step, and looping through the steps until the maximum number of iterations is reached. If the number of iterations is greater than the maximum number of iterations, the optimization result is directly output.

4. The cyclone parameter optimization design method based on iterative optimization according to claim 3 is characterized in that: ； Where, is the maximum value of pheromone, ; is the minimum value of pheromone, .

5. The cyclone parameter optimization design method based on iterative optimization according to claim 4 is characterized in that: Chaos interference includes pheromone interference using tent mapping: ； make ,when hour, Get a random number in the range (0, 1).

6. The cyclone parameter optimization design method based on iterative optimization according to claim 5, characterized in that: Chaos Interference includes: Set the initial convergence number 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 number is increased by 1. If they are not equal, the convergence number is reset to zero. When the number of convergences S is equal to 5, that is, the optimal solutions obtained after 5 iterations are equal, the algorithm is considered to have converged, and chaotic interference is performed on the pheromone: + ； ； ； ； ； Where, To add the normalized pheromone after chaotic interference, is the original pheromone normalization parameter, is the chaos interference radius, is a chaotic variable, This is a command to change the shape of a matrix in MATLAB; The name of the function constructed based on the Tent mapping principle, the mapping vector of custom length, the number of convergences after interference Reset to zero and re-accumulate the number of convergences.

Images