共查询到18条相似文献,搜索用时 78 毫秒
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基于神经网络建模和遗传算法的重油脱盐系统优化研究 总被引:2,自引:1,他引:2
概述了重油脱盐系统的BP神经网络建模以及基于遗传算法的系统优化过程,将遗传算法与惩罚函数法相结合应用于约束优化的问题,改善了遗传算法的局限性。同时为了将不等式约束优化问题转化为单目标优化问题,对惩罚函数法进行了改进。结果表明:此方法可以有效解决静电脱盐问题。 相似文献
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约束处理是约束优化的关键问题,特别是非线性约束的处理一直缺少特别有效的解决方法,将惩罚函数法与修复策略结合使用,可以有效地避免迭代过程中大量非可行解的产生,使得约束优化问题在惩罚函数和修复算子的协同作用下收敛于全局最优,较好地解决了在遗传算法约束优化问题中单独使用惩罚和修复方法时一些难以解决的问题。基于随机方向法构造的修复算子作用效果显著,采用多个测试函数对算法进行检验,均能较好地收敛于可行域中的最优解,验证了算法的可靠性。 相似文献
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装备系统可靠性分配的本质之一是有约束条件的非线性规划问题,从内罚函数角度出发,通过构造惩罚函数,将问题转化为无约束的优化问题;构造出基于装备系统复杂度和平均故障间隔时间的费用函数,将系统可靠度与费用关联起来,给出了在费用约束条件下的可靠性分配数学模型;在实例计算分析中,利用Matlab中的ode15s功能函数对分配模型进行计算,得到了不同惩罚因子下可靠性分配的最优解,计算结果具有较好的收敛性和稳定性,说明本分配方法具有良好的可行性。 相似文献
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目标层解分析是一种层次化、多层系统设计优化方法.为了确保求解多学科设计优化各子问题的可行性,提高求解效率,应用增广拉格朗日惩罚函数松弛化方法,对目标层解分析的内外层嵌套式求解策略进行改进,通过对内层循环的惩罚函数松弛化来减少内层循环病态子问题的求解计算时间,当内层循环获得收敛之后,外层循环更新惩罚权重来获得可行解.并置设计次数由10到1000的具体实例来对比分析各种惩罚函数对求解效率的影响.由实验可得,应用增广拉格朗日惩罚函数松弛化方法求解,计算权重得到减小.迭代次数减少到二次惩罚函数法的2%. 相似文献
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SQP法是求解非线性规划问题最有效的方法之一,在求解过程中,一般需要对惩罚函数进行线性搜索.惩罚因子的选择会带来一些问题,filter-SQP是Roger Fletcher和Sven Leyffer提出的一种不用惩罚函数的算法.本文红模块环境下应用改进的filter-SQP对化工过程优化进行了研究,提出了相应的算法.采用的优化策略是不可行路径法,filter中的约束目标是由断裂流方程、设计规定及不满足的不等式约束线性组合得到.使用filter检验是否接受QP步长作为下次迭代的出发点,避免了对惩罚函数进行线性搜索带来的弊端.当filter搜索失败时,提出了相应的处理策略,提高了算法的稳定性.用于判断优化是否收敛的判据不冉是K-T误差,而是目标函数和约束条件地同时收敛.提出了一个逐步规格化策略,提高了计算效率.计算实例表明,filter-SQP法优于传统的SQP法,本文提出的策略提高了算法的效率和稳定性. 相似文献
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针对步行双足机器人实时步态规划问题,提出了一种改进的非线性模型预测控制(NMPC)方法.采用扩展的关节坐标,将单腿支撑相(SSP)和双腿支撑相(DSP)统一表示为一个非线性动力学模型.通过对SSP和DSP的3个阶段设定运动学和动力学虚拟约束,将复杂实时步态规划问题转化为4个以预测时域内控制量二次型为代价函数的NMPC问题.采用直接法将连续优化问题参数化为有限维优化问题,并采用惩罚函数法将状态变量约束转化为代价函数中的惩罚项,从而得到能够用渐进二次规划(SQP)求解的有限维静态优化问题.仿真结果表明,应用该方法对BIP机器人模型进行实时步态规划,实现了包含足部转动的动态步行,且机器人满足稳定性条件,不发生侧滑,从而证明了该方法的有效性和可实现性. 相似文献
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提出了用于解决约束优化问题的新的类电磁机制算法,针对约束优化问题,利用惩罚函数法,构造了新的适应度函数,将约束问题转化为无约束问题求解,结合类电磁机制算法的寻优特点,设计了适合于问题特点的局部搜索过程,并重新定义了粒子的电荷和力的计算公式,以使在算法实现过程中,引导不可行点逐步向可行点转化,并最终找到问题的最优解。数值模拟结果验证了算法的有效性。 相似文献
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有等式约束优化问题的粒子群优化算法 总被引:3,自引:5,他引:3
目前大多数粒子群优化算法针对无约束优化问题或不等式约束优化问题,求解有等式约束优化问题的方法是把每个等式约束变成两个不等式约束,这种方法的缺点是在进化过程中粒子位置很难满足等式约束条件,影响了收敛速度和解的精度。提出了求解有等式约束优化问题的两种新粒子群优化算法,数值试验结果表明,算法是有效的。 相似文献
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本文提出了一种求解非线性约束优化的全局最优的新方法—它是基于利用非线性互补函数和不断增加新的约束来重复解库恩-塔克条件的非线性方程组的新方法。因为库恩-塔克条件是非线性约束优化的必要条件,得到的解未必是非线性约束优化的全局最优解,为此,本文首次给出了通过利用该优化问题的先验知识,不断地增加约束来限制全局最优解范围的方法,一些仿真例子表明提出的方法和理论有效的,并且可行的。 相似文献
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Rituparna Datta Kalyanmoy Deb 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2016,20(6):2367-2382
The holy grail of constrained optimization is the development of an efficient, scale invariant and generic constraint handling procedure. To address these, the present paper proposes a unified approach of constraint handling, which is capable of handling all inequality, equality and hybrid constraints in a coherent manner. The proposed method also automatically resolves the issue of constraint scaling which is critical in real world and engineering optimization problems. The proposed unified approach converts the single-objective constrained optimization problem into a multi-objective problem. Evolutionary multi-objective optimization is used to solve the modified bi-objective problem and to estimate the penalty parameter automatically. The constrained optimum is further improved using classical optimization. The efficiency of the proposed method is validated on a set of well-studied constrained test problems and compared against without using normalization technique to show the necessity of normalization. The results establish the importance of scaling , especially in constrained optimization and call for further investigation into its use in constrained optimization research. 相似文献
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Constrained optimization problems are very important and frequently appear in the real world. The /spl alpha/ constrained method is a new transformation method for constrained optimization. In this method, a satisfaction level for the constraints is introduced, which indicates how well a search point satisfies the constraints. The /spl alpha/ level comparison, which compares search points based on their level of satisfaction of the constraints, is also introduced. The /spl alpha/ constrained method can convert an algorithm for unconstrained problems into an algorithm for constrained problems by replacing ordinary comparisons with the /spl alpha/ level comparisons. In this paper, we introduce some improvements including mutations to the nonlinear simplex method to search around the boundary of the feasible region and to control the convergence speed of the method, we apply the /spl alpha/ constrained method and we propose the improved /spl alpha/ constrained simplex method for constrained optimization problems. The effectiveness of the /spl alpha/ constrained simplex method is shown by comparing its performance with that of the stochastic ranking method on various constrained problems. 相似文献
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In this paper, we propose a novel hybrid global optimization method to solve constrained optimization problems. An exact penalty function is first applied to approximate the original constrained optimization problem by a sequence of optimization problems with bound constraints. To solve each of these box constrained optimization problems, two hybrid methods are introduced, where two different strategies are used to combine limited memory BFGS (L-BFGS) with Greedy Diffusion Search (GDS). The convergence issue of the two hybrid methods is addressed. To evaluate the effectiveness of the proposed algorithm, 18 box constrained and 4 general constrained problems from the literature are tested. Numerical results obtained show that our proposed hybrid algorithm is more effective in obtaining more accurate solutions than those compared to. 相似文献
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求解约束优化问题的改进灰狼优化算法 总被引:3,自引:0,他引:3
针对基本灰狼优化(GWO)算法存在求解精度低、收敛速度慢、局部搜索能力差的问题,提出一种改进灰狼优化(IGWO)算法用于求解约束优化问题。该算法采用非固定多段映射罚函数法处理约束条件,将原约束优化问题转化为无约束优化问题,然后利用IGWO算法对转换后的无约束优化问题进行求解。在IGWO算法中,引入佳点集理论生成初始种群,为算法全局搜索奠定基础;为了提高局部搜索能力和加快收敛,对当前最优灰狼个体执行Powell局部搜索。采用几个标准约束优化测试问题进行仿真实验,结果表明该算法不仅克服了基本GWO的缺点,而且性能优于差分进化和粒子群优化算法。 相似文献
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满意控制作为预测控制的实用发展,是基于模型在线实现有约束多目标多自由度优化(CMMO),并由操作者参与决策的一种实用控制方法。针对有约束多目标多自由度优化中输出变量逼进期望值时存在的优先级结构,将一般的用二次规划表示的满意优化控制问题转化为一个分两层进行的直接优先级优化的满意优化控制问题,使用宽容分层优化方法进行分层,使用变可行域优化方法进行每层的优化,最终决策者得到满意解。 相似文献
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A directed searching optimization algorithm (DSO) is proposed to solve constrained optimization problems in this paper. The proposed algorithm includes two important operations — position updating and genetic mutation. Position updating enables the non-best solution vectors to mimic the best one, which is beneficial to the convergence of the DSO; genetic mutation can increase the diversity of individuals, which is beneficial to preventing the premature convergence of the DSO. In addition, we adopt the penalty function method to balance objective and constraint violations. We can obtain satisfactory solutions for constrained optimization problems by combining the DSO and the penalty function method. Experimental results indicate that the proposed algorithm can be an efficient alternative on solving constrained optimization problems. 相似文献