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非线性规划问题的极大熵多目标粒子群算法 总被引:1,自引:0,他引:1
刘淳安 《计算机工程与设计》2008,29(4):914-916
结合非线性规划的约束条件构造了一个新的极大熵函数,利用该函数将问题转化成了两个目标的多目标优化问题.通过对违反约束动态的进行惩罚,提出了一种新的极大熵多目标粒子群算法.该方法能有效的保持群体中不可行解的一定比例,从而增加了群体的多样性,而且避免了传统的过度惩罚缺陷,使群体更好地向最优解逼近.计算机仿真表明,该算法对非线性规划问题求解是非常有效的. 相似文献
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对于多杂质的用水和水处理集成优化问题,建立了以总费用最小为目标的混合整数非线性规划(MINLP)模型,并提出了一种将列队竞争算法(Line-up competition algorithm,LCA)和序列二次规划(Sequential Quadratic Programming,SQP)法相结合的求解策略。其中,用LCA优化整数变量,而用SQP法优化连续变量,通过这两种方法的交替求解来逼近最优解。将所提出的计算方法对文献中的2个典型实例进行了求解,求解结果优于文献。实例计算表明,本文所提出的计算方法是有效的。 相似文献
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一种新的非线性规划神经网络模型 总被引:1,自引:0,他引:1
提出一种新型的求解非线性规划问题的神经网络模型.该模型由变量神经元、Lagrange
乘子神经元和Kuhn-Tucker乘子神经元相互连接构成.通过将Kuhn-Tucker乘子神经元限
制在单边饱和工作方式,使得在处理非线性规划问题中不等式约束时不需要引入松弛变量,避
免了由于引入松弛变量而造成神经元数目的增加,有利于神经网络的硬件实现和提高神经网
络的收敛速度.可以证明,在适当的条件下,文中提出的神经网络模型的状态轨迹收敛到与非
线性规划问题的最优解相对应的平衡点. 相似文献
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一个通用的混合非线性规划问题的演化算法 总被引:8,自引:0,他引:8
提出了一种新的求解非线性规划问题的演化算法,它是在郭涛算法的基础上提出的,新算法的主要特点是引入了变维子空间,加入了子空间搜索过程和规范化约束条件以及增加了处理带等式约束的实数规划,整数规划,0-1规划和混合整数规划问题的功能,使之成为一种求解非线性规划(NLP)问题的通用算法,数值实验表明,新算法不仅是一种通用的算法,而且与已有算法的计算结果相比,其解的精确度也最好。 相似文献
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Zvi Drezner Pawel Kalczynski 《International Transactions in Operational Research》2020,27(3):1320-1342
The sequential linear programming (SLP) method for solving nonlinear problems was introduced in the 1960s. Many papers that attempted to use SLP reported poor performance and convergence issues. We found that nonlinear programs with reverse convex constraints, which are the most difficult nonlinear programs with many local optima, are solved (heuristically) very well by SLP. We proved that for this type of problems, the solutions to the sequence of the linear programming problems converge to a local optimum. Since the final solution depends on the starting solution, we propose to apply SLP in a multistart approach starting from randomly generated solutions. This multistart SLP is very easy to implement. We recommend that the research community reconsiders the application of SLP for this type of problems. 相似文献
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介绍一种用于解决带有模糊目标和资源约束的传感器系统的模糊非线性规划问题的非精确方法。提出一种沿加权梯度方向进行变异的特殊遗传算法,在遗传算子中运用模糊控制的思想,寻找最优解所在的邻域,而不是发现精确最优解。从而实现模糊非线性规划传感器系统的优化。 相似文献
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A significant amount of research has been done on bilevel optimization problems both in the realm of classical and evolutionary optimization. However, the multiobjective extensions of bilevel programming have received relatively little attention from researchers in both the domains. The existing algorithms are mostly brute-force nested strategies, and therefore computationally demanding. In this paper, we develop insights into multiobjective bilevel optimization through theoretical progress made in the direction of parametric multiobjective programming. We introduce an approximated set-valued mapping procedure that would be helpful in the development of efficient evolutionary approaches for solving these problems. The utility of the procedure has been emphasized by incorporating it in a hierarchical evolutionary framework and assessing the improvements. Test problems with varying levels of complexity have been used in the experiments. 相似文献
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V. K. SrivastavaA. Fahim 《Computers & Mathematics with Applications》2001,42(12):1585-1595
This paper presents a simple two-phase method for optimizing integer programming problems with a linear or nonlinear objective function subject to multiple linear or nonlinear constraints. The primary phase is based on a variation of the method of steepest descent in the feasible region, and a hem-stitching approach when a constraint is violated. The secondary phase zeros on the optimum solution by exploring the neighborhood of the suboptimum found in the first phase of the optimization process. The effectiveness of this method is illustrated through the optimization of several examples. The results from the proposed optimization approach are compared to those from methods developed specially for dealing with integer problems. The proposed method is simple, easy to implement yet very effective in dealing with a wide class of integer problems such as spare allocation, reliability optimization, and transportation problems. 相似文献
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Many problems in scientific research and engineering applications can be decomposed into the constrained optimization problems. Most of them are the nonlinear programming problems which are very hard to be solved by the traditional methods. In this paper, an electromagnetism-like mechanism (EM) algorithm, which is a meta-heuristic algorithm, has been improved for these problems. Firstly, some modifications are made for improving the performance of EM algorithm. The process of calculating the total force is simplified and an improved total force formula is adopted to accelerate the searching for optimal solution. In order to improve the accuracy of EM algorithm, a parameter called as move probability is introduced into the move formula where an elitist strategy is also adopted. And then, to handle the constraints, the feasibility and dominance rules are introduced and the corresponding charge formula is used for biasing feasible solutions over infeasible ones. Finally, 13 classical functions, three engineering design problems and 22 benchmark functions in CEC’06 are tested to illustrate the performance of proposed algorithm. Numerical results show that, compared with other versions of EM algorithm and other state-of-art algorithms, the improved EM algorithm has the advantage of higher accuracy and efficiency for constrained optimization problems. 相似文献
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In this paper, a new algorithm for solving constrained nonlinear programming problems is presented. The basis of our proposed algorithm is none other than the necessary and sufficient conditions that one deals within a discrete constrained local optimum in the context of the discrete Lagrange multipliers theory. We adopt a revised particle swarm optimization algorithm and extend it toward solving nonlinear programming problems with continuous decision variables. To measure the merits of our algorithm, we provide numerical experiments for several renowned benchmark problems and compare the outcome against the best results reported in the literature. The empirical assessments demonstrate that our algorithm is efficient and robust. 相似文献
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为研究连续函数优化问题,基于图解的蚁群系统,提出二进制蚁群算法,并实现与遗传算法混合编程,以提高求解效率。算例表明,蚁群-遗传算法混合编程求解连续优化问题,收敛速度快,计算精度高,可用于求解实际工程问题。 相似文献
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穷举法是程序设计中经常用到的一种算法,用来解决一些用常规的数学方法无法解决的问题.文章通过两个典型的例子对穷举法的思路和有关注意事项进行了分析,供编程学习者参考. 相似文献
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P. S. V. Nataraj M. Arounassalame 《国际自动化与计算杂志》2007,4(4):342-352
In this paper,an improved algorithm is proposed for unconstrained global optimization to tackle non-convex nonlinear multivariate polynomial programming problems.The proposed algorithm is based on the Bernstein polynomial approach.Novel features of the proposed algorithm are that it uses a new rule for the selection of the subdivision point,modified rules for the selection of the subdivision direction,and a new acceleration device to avoid some unnecessary subdivisions.The performance of the proposed algorithm is numerically tested on a collection of 16 test problems.The results of the tests show the proposed algorithm to be superior to the existing Bernstein algorithm in terms of the chosen performance metrics. 相似文献
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Viviana Cocco Mariani Leandro dos Santos Coelho 《Mathematics and computers in simulation》2011,81(9):1901-1909
The difficulties associated with using classical mathematical programming methods on complex optimization problems have contributed to the development of alternative and efficient numerical approaches. Recently, to overcome the limitations of classical optimization methods, researchers have proposed a wide variety of meta-heuristics for searching near-optimum solutions to problems. Among the existing meta-heuristic algorithms, a relatively new optimization paradigm is the Shuffled Complex Evolution at the University of Arizona (SCE-UA) which is a global optimization strategy that combines concepts of the competition evolution theory, downhill simplex procedure of Nelder-Mead, controlled random search and complex shuffling. In an attempt to reduce processing time and improve the quality of solutions, particularly to avoid being trapped in local optima, in this paper is proposed a hybrid SCE-UA approach. The proposed hybrid algorithm is the combination of SCE-UA (without Nelder-Mead downhill simplex procedure) and a pattern search approach, called SCE-PS, for unconstrained optimization. Pattern search methods are derivative-free, meaning that they do not use explicit or approximate derivatives. Moreover, pattern search algorithms are direct search methods well suitable for the global optimization of highly nonlinear, multiparameter, and multimodal objective functions. The proposed SCE-PS method is tested with six benchmark optimization problems. Simulation results show that the proposed SCE-PS improves the searching performance when compared with the classical SCE-UA and a genetic algorithm with floating-point representation for all the tested problems. As evidenced by the performance indices based on the mean performance of objective function in 30 runs and mean of computational time, the SCE-PS algorithm has demonstrated to be effective and efficient at locating best-practice optimal solutions for unconstrained optimization. 相似文献