CN106372270A

CN106372270A - Life cycle group search optimization algorithm-based optimization design method for pressure container

Info

Publication number: CN106372270A
Application number: CN201510444042.9A
Authority: CN
Inventors: 朱云龙; 申海; 张丁; 张丁一; 张�浩
Original assignee: Shenyang Institute of Automation of CAS
Current assignee: Shenyang Institute of Automation of CAS
Priority date: 2015-07-23
Filing date: 2015-07-23
Publication date: 2017-02-01

Abstract

The invention relates to a life cycle group search optimization algorithm-based optimization design method for a pressure container. The method comprises the following steps of initializing parameters; assessing a fitness value; updating data; performing iteration: if a preset stop condition is not met, returning to the step of assessing the fitness value; and if the iterative stop condition is met, stopping calculation and finally outputting a result. The design method of the invention is easy to realize, has the advantages of relatively good global search capability, high convergence speed, high optimization precision and the like, and has a very effective optimization effect for the problem on the weight of the pressure container.

Description

Optimal Design Method of Pressure Vessel Based on Life Cycle Group Search Optimization Algorithm

技术领域technical field

本发明涉及一种基于生命周期群搜索优化算法的压力容器优化设计方法，属于化工机械领域，同时涉及群体智能算法领域。The invention relates to a pressure vessel optimization design method based on a life cycle group search optimization algorithm, which belongs to the field of chemical machinery and also relates to the field of group intelligence algorithms.

背景技术Background technique

压力容器是化工设备的重要组成部分，对化工原料及产品的开发和完善起着巨大的关键的作用。压力容器主要包括薄壁压力容器、压力储罐、外压容器、多层压力容器、高压容器和超高压容器等。压力容器在生产和使用过程中，尤其是内置易燃、易爆或腐蚀性物料时，存在相当的危险性，须十分注意其安全问题。另一方面，压力容器的制造消耗大量的金属材料。因而，压力容器的设计与优化日益引起人们的关注。Pressure vessels are an important part of chemical equipment, and play a huge key role in the development and improvement of chemical raw materials and products. Pressure vessels mainly include thin-wall pressure vessels, pressure storage tanks, external pressure vessels, multi-layer pressure vessels, high pressure vessels and ultra-high pressure vessels, etc. During the production and use of pressure vessels, especially when they contain flammable, explosive or corrosive materials, there are considerable dangers, and great attention must be paid to their safety. On the other hand, the manufacture of pressure vessels consumes a large amount of metal materials. Therefore, the design and optimization of pressure vessels has attracted people's attention day by day.

针对压力容器优化设计这种复杂优化问题，如果采用传统的数学规划方法解决,则无法在模型的求解精度和求解效率两个方面同时达到理想的结果。近年来，广泛模拟生物行为的基于生物启发计算的智能优化算法得到了学者们的广泛关注，用其来求解此类问题取得了较好的结果，显示出了基于生物启发计算的智能优化算法在解决复杂优化问题中的独特优势。但是较早提出的遗传算法、蚁群算法优化实现复杂度较高，鲁棒性较差，得到的结果随机性大；粒子群算法虽然优化速度较快，但是容易陷入局部最优，尤其是对高维优化问题的求解精度不高；经典的细菌觅食算法侧重于对细菌行为的描述和模拟。这些已存在的算法在求解相对复杂的优化问题时，其寻优性能还不能达到满意的精度和稳定性要求。为了更好解决这些问题，借鉴生物生命周期理论，发明了基于种群正态分布的生命周期群搜索优化算法，该算法实现了自适应搜索，具有全局搜索能力强，收敛速度快，优化精度高等优点。For the complex optimization problem of pressure vessel optimal design, if the traditional mathematical programming method is used to solve it, it is impossible to achieve ideal results in both the solution accuracy and solution efficiency of the model. In recent years, the intelligent optimization algorithm based on bio-inspired computing, which widely simulates biological behavior, has attracted widespread attention from scholars. Unique advantage in solving complex optimization problems. However, the genetic algorithm and ant colony algorithm proposed earlier have high complexity and poor robustness, and the randomness of the obtained results is large; although the particle swarm algorithm has a fast optimization speed, it is easy to fall into local optimum, especially for The solution accuracy of high-dimensional optimization problems is not high; the classic bacterial foraging algorithm focuses on the description and simulation of bacterial behavior. When these existing algorithms solve relatively complex optimization problems, their optimization performance cannot meet the requirements of satisfactory accuracy and stability. In order to better solve these problems, a life cycle group search optimization algorithm based on the normal distribution of the population was invented by referring to the biological life cycle theory. This algorithm realizes adaptive search, has the advantages of strong global search ability, fast convergence speed and high optimization accuracy .

发明内容Contents of the invention

针对现有技术中存在的上述不足之处，本发明借鉴生物生命周期理论，提供一种基于生命周期群搜索优化算法的压力容器优化设计方法，实现了自适应搜索，具有全局搜索能力强，收敛速度快，优化精度高等优点。Aiming at the above-mentioned deficiencies in the prior art, the present invention draws on the biological life cycle theory to provide a pressure vessel optimization design method based on the life cycle group search optimization algorithm, which realizes self-adaptive search, has strong global search ability, and converges It has the advantages of fast speed and high optimization precision.

本发明为实现上述目的所采用的技术方案是：一种基于生命周期群搜索优化算法的压力容器优化设计方法，包括以下步骤：The technical solution adopted by the present invention to achieve the above object is: a pressure vessel optimization design method based on life cycle group search optimization algorithm, comprising the following steps:

参数初始化；parameter initialization;

评估适应度值；Evaluate the fitness value;

数据更新；Data Update;

迭代：如果未达到预先设定的终止条件，则返回评估适应度值步骤；若达到迭代终止条件，则停止计算，最后输出结果。Iteration: If the preset termination condition is not met, return to the step of evaluating the fitness value; if the iteration termination condition is met, stop the calculation, and finally output the result.

所述参数初始化包括：初始化种群规模；初始化搜索空间上下限，最大迭代次数和收敛精度；初始化觅食方式选择概率，交叉概率和变异概率；初始化混沌变量，正态分布平均数和正态分布标准差。The parameter initialization includes: initializing population size; initializing upper and lower limits of search space, maximum number of iterations and convergence precision; initializing foraging mode selection probability, crossover probability and mutation probability; initializing chaotic variables, normal distribution mean number and normal distribution standard Difference.

所述评估适应度值的计算方法为：The calculation method of the evaluation fitness value is:

更新全局极值：将初始种群中的最优个体p_g设置为全局初始极值；Update the global extremum: set the optimal individual p _g in the initial population as the global initial extremum;

根据压力容器设计标准：在相同环境下，以压力容器重量越小越有为准则制定种群的适应度评价标准。According to the pressure vessel design standard: under the same environment, the smaller the weight of the pressure vessel, the better the fitness evaluation standard of the population is formulated.

所述数据更新包括以下步骤：The data update includes the following steps:

执行生长发育操作：群中最优个体执行混沌趋化操作，其他个体根据觅食方式选择概率选择执行同化操作或换位操作；Perform growth and development operations: the optimal individual in the group performs chaotic chemotaxis operations, and other individuals choose to perform assimilation operations or transposition operations according to the selection probability of foraging methods;

执行繁殖操作：将群中的个体进行两两顺序配对，执行单点交叉操作；Execute the breeding operation: pair the individuals in the group in pairs, and perform a single-point crossover operation;

执行死亡操作：对群中个体按适应值进行线性排列，调整适应度值，采用轮盘赌法方法选择个体；Execute the death operation: linearly arrange the individuals in the group according to the fitness value, adjust the fitness value, and select the individual by the roulette method;

执行变异操作：群中的个体执行方向变异操作；Execute mutation operation: Individuals in the group perform direction mutation operation;

更新全局极值：计算当前群中所有个体适应度，设置当前群中的最优个体。Update the global extremum: calculate the fitness of all individuals in the current group, and set the optimal individual in the current group.

本发明具有以下优点及有益效果：设计实现容易，具有较强的全局搜索能力，收敛速度快，优化精度高等优点，对于压力容器重量问题，有非常有效的优化效果。The invention has the following advantages and beneficial effects: easy design and realization, strong global search ability, fast convergence speed, high optimization precision, etc., and has very effective optimization effect on the weight problem of the pressure vessel.

附图说明Description of drawings

图1是生命周期群搜索优化算法的执行流程图；Fig. 1 is the execution flowchart of the life cycle group search optimization algorithm;

图2是压力容器结构图；Fig. 2 is a structural diagram of a pressure vessel;

图3是压力容器设计的收敛曲线比较图。Figure 3 is a comparative graph of the convergence curves for pressure vessel design.

具体实施方式detailed description

下面结合附图及实施例对本发明做进一步的详细说明。The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

工程上常见的半球形封头压力容器设计简单，如图1所示，广泛应用于石油及化学等工业。在力求满足强度等要求的前提下，以压力容器重量为目标函数。此问题共4个约束条件和4个优化变量。The common hemispherical head pressure vessel in engineering is simple in design, as shown in Figure 1, and is widely used in petroleum and chemical industries. On the premise of striving to meet the requirements such as strength, the weight of the pressure vessel is taken as the objective function. There are 4 constraints and 4 optimization variables in this problem.

目标函数： $f (X) = 0.6224 X_{1} X_{3} X_{4} + 1.7781 X_{2} X_{3}^{2} + 3.1661 X_{1}^{2} X_{4} + 19.84 X_{1}^{2} X_{3}$ Objective function: $f (x) = 0.6224 x_{1} x_{3} x_{4} + 1.7781 x_{2} x_{3}^{2} + 3.1661 x_{1}^{2} x_{4} + 19.84 x_{1}^{2} x_{3}$

约束条件：g₁(X)＝0.0193X₃-X₁≤0Constraints: g ₁ (X)＝0.0193X ₃ -X ₁ ≤0

g₂(X)＝0.00954X₃-X₂≤0g ₂ (X)＝0.00954X ₃ -X ₂ ≤0

${g g}_{33} ((X x)) = = 11,, 296296,, 000000 - - {πX πX}_{33}^{22} {X x}_{44} - - 44 / / 33 {πX πX}_{33}^{33} \leq \leq 00$

g₄(X)＝X₄-240≤0g ₄ (X)＝X ₄ -240≤0

其中，X₁和X₂分别为封头(Th)和筒体壁厚(Ts)，0.0625≤X₁，X₂≤6.1875；X₃为筒体及封头底面半径(R)，X₄为筒体长度(L)，10≤X₃，X₄≤200。4个变量中，X₁和X₂是间隔为0.0625的均匀离散变量，X₃和X₄是连续变量。Among them, X ₁ and X ₂ are the wall thickness of the head (Th) and the shell (Ts) respectively, 0.0625≤X ₁ , X ₂ ≤6.1875; X ₃ is the radius of the bottom surface of the shell and the head (R), and X ₄ is Cylinder length (L), 10≤X ₃ , X ₄ ≤200. Among the 4 variables, X ₁ and X ₂ are uniform discrete variables with an interval of 0.0625, and X ₃ and X ₄ are continuous variables.

针对传统数学规划方法在求解压力容器优化设计这种大规模、多维的复杂优化问题时所暴露出来的缺陷，以及传统智能优化算法寻优性能不能达到满意精度和稳定性要求，发明了一种借鉴生物生命周期理论，并基于种群正态分布的智能优化算法的压力容器优化设计方法。Aiming at the defects exposed by the traditional mathematical programming method in solving the large-scale, multi-dimensional and complex optimization problem of pressure vessel optimization design, and the optimization performance of the traditional intelligent optimization algorithm can not meet the requirements of satisfactory accuracy and stability, a reference is invented. The theory of biological life cycle, and the optimal design method of pressure vessel based on the intelligent optimization algorithm of population normal distribution.

本发明用于在满足强度等要求的前提下，设计压力容器的最小重量，包括以下步骤：The present invention is used to design the minimum weight of the pressure vessel on the premise of meeting the requirements of strength and the like, including the following steps:

1)参数初始化1) Parameter initialization

根据压力容器设计准则，设计X₁和X₂分别为封头(Th)和筒体壁厚(Ts)，0.0625≤X₁，X₂≤6.1875；X₃为筒体及封头底面半径(R)，X₄为筒体长度(L)，10≤X₃，X₄≤200。4个变量中，X₁和X₂是间隔为0.0625的均匀离散变量，X₃和X₄是连续变量。然后根据决策变量范围，确定初始种群规模，一般情况下，选择50～100个，产生群体规模为并满足正态分布的初始群体；初始化搜索空间上下限，最大迭代次数和收敛精度；初始化觅食方式选择概率，交叉概率和变异概率；初始化混沌变量，正态分布平均数和正态分布标准差。算法所涉及的部分参数可通过压力容器重量目标函数进行测定。According to the design criteria of pressure vessels, design X ₁ and X ₂ are the wall thickness of the head (Th) and the shell (Ts), respectively, 0.0625≤X ₁ , X ₂ ≤6.1875; X ₃ is the radius of the bottom surface of the shell and the head (R ), X ₄ is the cylinder length (L), 10≤X ₃ , X ₄ ≤200. Among the four variables, X ₁ and X ₂ are uniform discrete variables with an interval of 0.0625, and X ₃ and X ₄ are continuous variables. Then, according to the range of decision variables, determine the initial population size, in general, select 50 to 100, and generate an initial population with a population size of 0 and satisfy a normal distribution; initialize the upper and lower limits of the search space, the maximum number of iterations and the convergence accuracy; initialize the foraging Mode selection probability, crossover probability and mutation probability; initialize chaotic variables, normal distribution mean and normal distribution standard deviation. Some parameters involved in the algorithm can be determined by the pressure vessel weight objective function.

2)评估适应度值2) Evaluate the fitness value

g₂(X)＝0.00954X₃-X₂≤0g ₂ (X)＝0.00954X ₃ -X ₂ ≤0

g₄(X)＝X₄-240≤0g ₄ (X)＝X ₄ -240≤0

针对上述带有约束的压力容器设计优化问题，可以利用自适应罚函数的方法，将约束条件转换成为适应度值。然后制定评判准则，可定义重量越小越优或者越大越优。更新全局极值。将初始种群中的最优个体设置为全局初始极值。For the above-mentioned optimization problem of pressure vessel design with constraints, the method of adaptive penalty function can be used to convert the constraints into fitness values. Then formulate the judging criteria, which can be defined as the smaller the weight, the better or the bigger the better. Update global extrema. Set the optimal individual in the initial population as the global initial extreme value.

3)数据更新3) Data update

执行生长发育操作：群中最优个体执行混沌趋化操作，其他个体根据觅食方式选择概率选择执行同化操作或换位操作；执行繁殖操作：将群中的个体进行两两顺序配对，执行单点交叉操作；执行死亡操作：对群中个体按适应值进行线性排列，调整适应度值，采用轮盘赌法方法选择个体；执行变异操作：群中的个体执行方向变异操作；更新全局极值，计算当前群中所有个体适应度，设置当前群中的最优个体。Perform growth and development operations: the optimal individual in the group performs chaotic chemotaxis operations, and other individuals choose to perform assimilation operations or transposition operations according to the selection probability of foraging methods; perform reproduction operations: pair the individuals in the group in pairs, perform single Point crossover operation; execute death operation: linearly arrange the individuals in the group according to the fitness value, adjust the fitness value, and use the roulette method to select individuals; perform mutation operation: perform direction mutation operation on the individuals in the group; update the global extremum , calculate the fitness of all individuals in the current group, and set the optimal individual in the current group.

4)迭代4) iteration

如果未达到预先设定的终止条件，则返回步骤2)，若达到迭代终止条件，则停止计算，最后输出结果。If the preset termination condition is not met, return to step 2), if the iteration termination condition is met, stop the calculation, and finally output the result.

算法实现步骤如图2所示。The algorithm implementation steps are shown in Figure 2.

所述步骤1)具体包括以下步骤：Described step 1) specifically comprises the following steps:

1.1)确定初始种群规模n，一般情况下，选择50～100个，产生群体规模为并满足正态分布的初始群体；其变量维度为4，决策变量X₁和X₂是间隔为0.0625的均匀离散变量，X₃和X₄是连续变量。1.1) Determine the initial population size n. In general, select 50 to 100 individuals to generate an initial population with a population size of 1 and satisfying a normal distribution; its variable dimension is 4, and the decision variables X ₁ and X ₂ are uniform Discrete variables, _X3 and _X4 are continuous variables.

1.2)初始化搜索空间上下限B_up和B_lo，最大迭代次数T_max和收敛精度ξ；初始化觅食方式选择概率P_f，交叉概率P_c和变异概率P_m；初始化混沌变量Sc，正态分布平均数μ和正态分布标准差σ。1.2) Initialize the upper and lower limits of the search space B _up and B _lo , the maximum number of iterations T _max and the convergence accuracy ξ; initialize the foraging mode selection probability P _f , crossover probability P _c and mutation probability P _m ; initialize the chaotic variable Sc, normal distribution The mean μ and the standard deviation σ of a normal distribution.

所述步骤2)具体包括以下步骤：Described step 2) specifically comprises the following steps:

2.1)更新全局极值。将初始种群中的最优个体p_g设置为全局初始极值。2.1) Update the global extremum. Set the optimal individual p _g in the initial population as the global initial extreme value.

2.2)根据压力容器设计标准，在相同环境下，以压力容器重量越小越有为准则制定种群的适应度评价标准。2.2) According to the pressure vessel design standard, under the same environment, the smaller the weight of the pressure vessel, the better the fitness evaluation standard of the population is formulated.

所述步骤3)具体包括以下步骤：Described step 3) specifically comprises the following steps:

3.1)生长发育3.1) Growth and development

群中最优个体执行混沌趋化操作。趋化规则采取基于当前位置进行混沌搜索方式，试图在全局范围内找到比当前更优的位置并移动。Logistic方程是一个典型的混沌系统：The optimal individual in the group performs the chaotic chemotaxis operation. The chemotaxis rule adopts a chaotic search method based on the current position, trying to find a better position than the current one in the global scope and move. The Logistic equation is a typical chaotic system:

S_n+1＝uS_n(1-S_n),n＝0,1,2,...S _n+1 ＝uS _n (1-S _n ),n＝0,1,2,...

其中，u为控制参量，当u＝4，当0≤S₀≤1时，系统的动力学特征完全不同，系统的初始信息已全部丧失，处于混沌状态。由任意初值S₀∈[0,1]，可迭代出一个确定的混沌序列S₁，S₂，S₃，…。这个输出实际上相当于一个0～1之间的随机输出。系统的输出在0～1之间具有遍历性，且其中的任一状态不会重复出现。Among them, u is the control parameter, when u=4, when 0≤S ₀ ≤1, the dynamic characteristics of the system are completely different, the initial information of the system has been completely lost, and it is in a chaotic state. From any initial value S ₀ ∈ [0,1], a certain chaotic sequence S ₁ , S ₂ , S ₃ , . . . can be iterated out. This output is actually equivalent to a random output between 0 and 1. The output of the system is ergodic between 0 and 1, and any state will not appear repeatedly.

群中最优个体觅食策略采用类似载波的方法将Logistic映射产生的混沌变量引入到优化变量中，同时将混沌运动的遍历范围转换到优化变量的定义域，然后利用混沌变量进行搜索。实现步骤如下：The optimal individual foraging strategy in the group adopts a carrier-like method to introduce the chaotic variables generated by the Logistic mapping into the optimal variables, and at the same time convert the traversal range of the chaotic motion to the definition domain of the optimal variables, and then use the chaotic variables to search. The implementation steps are as follows:

步骤1：当前优化变量记为X₀，它的性能函数值f(X₀)。Step 1: The current optimization variable is denoted as X ₀ , and its performance function value f(X ₀ ).

步骤2：利用Logistic映射产生n个混沌变量(X₁,X₂,…,X_n)Step 2: Use Logistic mapping to generate n chaotic variables (X ₁ ,X ₂ ,…,X _n )

X_i+1＝4X_i(1-X_i),i＝0,1,2,...,n-1X _i+1 ＝4X _i (1-X _i ), i=0,1,2,...,n-1

步骤3：将混沌运动的遍历范围转换到优化变量的定义域。Step 3: Transform the ergodic range of chaotic motion into the domain of optimization variables.

X_i＝B_lo+(B_up-B_lo)X_i,i＝1,2,...,nX _i ＝B _lo +(B _up -B _lo )X _i , i=1,2,...,n

其中，B_up和B_lo是搜索空间的上下限。Among them, B _up and B _lo are the upper and lower bounds of the search space.

步骤4：计算n个混沌变量的性能函数值。(f(X₁),f(X₂),…,f(X_n))Step 4: Calculate the performance function values of n chaotic variables. (f(X ₁ ),f(X ₂ ),…,f(X _n ))

步骤5：如果存在f(X_i)优于f(X₀)，则Step 5: If f(X _i ) is better than f(X ₀ ), then

${X x}_{00} &DoubleLeftArrow; &DoubleLeftArrow; {X x}_{i i},, f f (({X x}_{00})) &DoubleLeftArrow; &DoubleLeftArrow; f f (({X x}_{i i}))$

其他个体根据觅食方式选择概率选择执行同化操作或换位操作。Other individuals choose to perform assimilation operation or transposition operation according to the probability of foraging mode selection.

群内采取社会觅食方式个体的觅食路径被最优个体同化，追随群内最优个体进行搜索。The foraging path of individuals adopting the social foraging method in the group is assimilated by the optimal individual, and follows the optimal individual in the group to search.

${X x}_{i i}^{k k + + 11} = = {X x}_{i i}^{k k} + + r r a a n no d d (()) (({X x}_{p p}^{k k} - - {X x}_{i i}^{k k}))$

上式表示在第k次迭代第i个体的位置追随群内当前最优个体进行搜索；r₁∈Rⁿ是介于(0,1)之间均匀分布的随机数。The above formula represents the position of the i-th individual in the k-th iteration Follow the current best individual in the group Search; r ₁ ∈ R ⁿ is a random number uniformly distributed between (0,1).

群内除最优个体外，采取独立觅食方式的个体其觅食执行方法采取换位规则，个体在其自身具备的能量范围内进行搜索。In addition to the optimal individual in the group, the foraging execution method of the individual who adopts the independent foraging method adopts the transposition rule, and the individual searches within its own energy range.

${ub ub}_{i i}^{k k} = = \frac{{X x}_{p p}^{k k}}{{X x}_{i i}^{k k}} \cdot \cdot Δ Δ$

${lb lb}_{i i}^{k k} = = - - {ub ub}_{i i}^{k k}$

其中，是个体的换位步长；r₂∈Rⁿ是介于(0,1)之间均匀分布的随机数；和是个体i在第k代搜索最大范围；Δ是整个搜索空间范围。in, is an individual The transposition step size; r ₂ ∈ R ⁿ is a random number uniformly distributed between (0,1); and is the maximum search range of individual i in the kth generation; Δ is the range of the entire search space.

3.2)繁殖3.2) Breeding

将群中的个体进行两两顺序配对，执行单点交叉操作。The individuals in the group are paired sequentially, and a single-point crossover operation is performed.

${X x}_{i i}^{k k + + 11} [[m m : : p p]] = = {X x}_{j j}^{k k} [[m m : : p p]]$

${X x}_{j j}^{k k + + 11} [[m m : : p p]] = = {X x}_{i i}^{k k} [[m m : : p p]]$

其中，m和p分别代表基因段开始和结束的索引。Among them, m and p represent the index of the start and end of the gene segment, respectively.

3.3)死亡3.3) Death

选择规则先采用线性排序的方法对群中个体适应值进行调整，对调整后的目标函数值进行降序排序，然后采用轮盘赌选择策略执行个体选择操作。The selection rule adopts the method of linear sorting to adjust the fitness value of individuals in the group, sorts the adjusted objective function values in descending order, and then uses the roulette selection strategy to perform the individual selection operation.

${f f}^{* *} (({x x}_{i i})) = = 22 - - s the s p p + + 22 \times \times ((s the s p p - - 11)) \times \times \frac{p p (({x x}_{i i})) - - 11}{S S - - 11}$

其中，f^*(x_i)(i＝1,2,…S)是调整后个体的适应度；S是种群中个体的数量；sp是选择压差，sp＝2；p(x_i)是个体i的适应度值f(x_i)在种群中的排序位置。Among them, f ^* ( _xi )(i=1,2,...S) is the fitness of the adjusted individual; S is the number of individuals in the population; sp is the selection pressure difference, sp=2; p( _xi ) is The ranking position of individual i's fitness value f( _xi ) in the population.

3.4)变异3.4) Variation

群中的个体执行方向变异操作。在n维搜索空间中，每个个体X_i∈Rⁿ，X_i＝(x_i1,x_i2,…,x_in)的每一维j(j＝1,2,…,n)是代表它的一个移动方向，每一维的值x_ij则表示此个体在此方向上的移动步长。方向变异是指个体在其选定方向上的移动步长发生随机改变。Individuals in the population perform a directional mutation operation. In the n-dimensional search space, each individual X _i ∈ R ⁿ , each dimension j ( _j =1,2,…,n) of Xi = ( _xi1 , _xi2 ,…,x _in ) represents it A moving direction of , and the value x _ij of each dimension represents the moving step of the individual in this direction. Directional variation refers to the random change in the step size of an individual's movement in its chosen direction.

${X x}_{i i}^{k k + + 11} ((j j)) = = r r a a n no d d (()) (({B B}_{u u p p} - - {B B}_{l l o o})) + + {B B}_{l l o o}$

3.5)更新全局极值3.5) Update the global extremum

计算当前群中所有个体适应度f(X)，当前群中的最优个体设置为X_g。Calculate the fitness f(X) of all individuals in the current group, and set the optimal individual in the current group as X _g .

所述步骤4)具体包括以下步骤：Described step 4) specifically comprises the following steps:

4.1)判断是否终止：根据预先制定的迭代终止条件，一般是预先设定收敛精度或是达到最大函数评估次数；若满足终止条件，则终止迭代；否则继续重复上述步骤，直到满足终止条件为止；4.1) Judging whether to terminate: According to the pre-established iteration termination conditions, generally the convergence accuracy is preset or the maximum number of function evaluations is reached; if the termination conditions are met, the iteration is terminated; otherwise, the above steps are continued until the termination conditions are met;

4.2)优化结束，输出各变量参数的最终优化结果以及压力容器重量的最终优化值，如图3所示。4.2) After the optimization is completed, the final optimization results of each variable parameter and the final optimized value of the pressure vessel weight are output, as shown in FIG. 3 .

Claims

1. A pressure container optimization design method based on a life cycle group search optimization algorithm is characterized by comprising the following steps:

initializing parameters;

evaluating the fitness value;

updating data;

iteration: if the preset termination condition is not reached, returning to the step of evaluating the fitness value; and if the iteration termination condition is reached, stopping the calculation, and finally outputting the result.

2. The method of claim 1, wherein the initializing parameters comprises: initializing the population scale; initializing upper and lower limits of a search space, maximum iteration times and convergence precision; initializing foraging mode selection probability, cross probability and mutation probability; initializing a chaotic variable, a normal distribution mean and a normal distribution standard deviation.

3. The pressure vessel optimization design method based on the life cycle group search optimization algorithm as claimed in claim 1, wherein the calculation method for the evaluation fitness value is as follows:

and updating the global extremum: the optimal individual p in the initial population_gSetting the initial extreme value as a global initial extreme value;

according to the design standard of the pressure container: under the same environment, the fitness evaluation standard of the population is established by taking the criterion that the smaller the weight of the pressure container is, the more the population is.

4. The method for optimally designing a pressure vessel based on the life cycle group search optimization algorithm as claimed in claim 1, wherein the data updating comprises the following steps:

executing growth and development operation: the optimal individual in the group executes the chaotic chemotaxis operation, and other individuals select the probability to execute the assimilation operation or the transposition operation according to the foraging mode;

and (3) executing reproduction operation: pairing individuals in the group in sequence in pairs, and executing single-point cross operation;

and (3) executing death operation: linearly arranging the individuals in the group according to the adaptive value, adjusting the adaptive value, and selecting the individuals by adopting a roulette method;

performing mutation operation: the individuals in the group perform a direction mutation operation;

and updating the global extremum: and calculating the fitness of all individuals in the current group, and setting the optimal individual in the current group.