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解非线性规划的多目标遗传算法及其收敛性 总被引:1,自引:0,他引:1
刘淳安 《计算机工程与应用》2006,42(25):27-29,79
给出非线性约束规划问题的一种新解法。它既不需用传统的惩罚函数,又不需区分可行解和不可行解,新方法把带约束的非线性规划问题转化成为两个目标函数优化问题,其中一个是原约束问题的目标函数,另一个是违反约束的度函数,并利用多目标优化中的Pareto优劣关系设计了一种新的选择算子,通过对搜索操作和参数的合理设计给出了一种新型遗传算法,且给出了算法的收敛性证明,最后数据实验表明该算法对带约束的非线性规划问题求解是非常有效的。 相似文献
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本文推荐一种新的图像边界检测的快速算法,通过CANNY的边界检测函数理论,我们证明了这种新的边界检测函数在信噪比(SIGNALTONOISERATIO)边界检测精度(LOCALIZATION)和伪边界平均距离(MULTIPLERESPONSE)这三个性能指标上都优于目前已知的任何一种边界检测函数,本文首先给出一维信号的递推公式,然后再应用到三维图像处理中,该检测函数的特点是计算简单,对于一维信号的 相似文献
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吴力 《数值计算与计算机应用》1996,(2)
块角型约束线性规划问题的内点分解算法吴力(中国科学院计算数学与科学工程计算研究所)ADECOMPOSITIONALGORITHMFORLINEARPROGRAMMINGPROBLEMSWITHBLOCKANGULARCONSTRAINTS¥WuLi(... 相似文献
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GESA方法是一种并行算法,它以一种新颖的方式综合了遗传算法,模拟退火(simulatedannealing)模拟进化(sinulatedevolution)的思想,特别是GESA方法中实施了区域引导了(regionalguidance),用GESA方法求解任务安排问题,结果表明GESA方法性能优越。 相似文献
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组合非确定推理的通用诊断工程葛彤,邓建华(西北工业大学飞机系西安710072)GENERALDIAGNOSTICENGINEWITHUNCERTAINREASONING¥GETong;DENGJianhua(DepartmentofAircraftE... 相似文献
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《Computers & Operations Research》2002,29(3):261-274
As an extension of the hybrid Genetic Algorithm-HGA proposed by Tang et al. (Comput. Math. Appl. 36 (1998) 11), this paper focuses on the critical techniques in the application of the GA to nonlinear programming (NLP) problems with equality and inequality constraints. Taking into account the equality constraints and embedding the information of infeasible points/chromosomes into the evaluation function, an extended fuzzy-based methodology and three new evaluation functions are proposed to formulate and evaluate the infeasible chromosomes. The extended version of concepts of dominated semi-feasible direction (DSFD), feasibility degree (FD1) of semi-feasible direction, feasibility degree (FD2) of infeasible points ‘belonging to’ feasible domain are introduced. Combining the new evaluation functions and weighted gradient direction search into the Genetic Algorithm, an extended hybrid Genetic Algorithm (EHGA) is developed to solve nonlinear programming (NLP) problems with equality and inequality constraints. Simulation shows that this new algorithm is efficient.Scope and purposeNon-linear Programming (NLP) problems with equality and inequality constraints is an important type of constrained optimization problems. Genetic Algorithm (GA) is one of the well known evolutionary computation techniques. In the application of GA to NLP problems, chromosomes randomly generated at the beginning and/or generated by genetic operators during the evolutionary process usually violate the constraints, resulting in infeasible chromosomes. Therefore, the handling of system constraints, particularly the nonlinear equation constraints, and the measurement and evaluation of infeasible chromosomes, are major concerns in GA. Penalty strategy in the construction of fitness function is commonly used to evaluate the infeasible chromosomes in some traditional AG methods. However, this approach essentially narrows down the search space by eliminating all infeasible chromosomes from the evolutionary process, and it may reduce the chances of finding better candidates for the global optimization. In particular, it absolutely ignores the information carried by the infeasible chromosomes itself. Therefore, formulating the infeasible chromosomes by embedding the relevant information into the evaluation function are important when applying GA to NLP.As an extension of the Hybrid Genetic Algorithm-HGA proposed by Tang et al. (1998), this paper focuses on the critical techniques in the application of GA to NLP problems with equality and inequality constraints. Taking into account the equality constraints and embedding the information of infeasible chromosomes into the evaluation function, an extended fuzzy-based methodology and three new evaluation functions are designed to formulate and evaluate the infeasible chromosomes. By introducing an extended version of the concepts of dominated semi-feasible direction (DSFD), feasibility degree (FD1) of semi-feasible direction, feasibility degree (FD2) of infeasible points ‘belonging to’ feasible domain, an extended hybrid Genetic Algorithm (EHGA) is developed for solving NLP problems with equality and inequality constraints. 相似文献
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Co-evolutionary particle swarm optimization to solve constrained optimization problems 总被引:1,自引:0,他引:1
Xiaoli Kou Sanyang Liu Jianke Zhang Wei Zheng 《Computers & Mathematics with Applications》2009,57(11-12):1776
This paper presents a co-evolutionary particle swarm optimization (CPSO) algorithm to solve global nonlinear optimization problems. A new co-evolutionary PSO (CPSO) is constructed. In the algorithm, a deterministic selection strategy is proposed to ensure the diversity of population. Meanwhile, based on the theory of extrapolation, the induction of evolving direction is enhanced by adding a co-evolutionary strategy, in which the particles make full use of the information each other by using gene-adjusting and adaptive focus-varied tuning operator. Infeasible degree selection mechanism is used to handle the constraints. A new selection criterion is adopted as tournament rules to select individuals. Also, the infeasible solution is properly accepted as the feasible solution based on a defined threshold of the infeasible degree. This diversity mechanism is helpful to guide the search direction towards the feasible region. Our approach was tested on six problems commonly used in the literature. The results obtained are repeatedly closer to the true optimum solution than the other techniques. 相似文献
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When solving constrained multi-objective optimization problems (CMOPs), keeping infeasible individuals with good objective values and small constraint violations in the population can improve the performance of the algorithms, since they provide the information about the optimal direction towards Pareto front. By taking the constraint violation as an objective, we propose a novel constraint-handling technique based on directed weights to deal with CMOPs. This paper adopts two types of weights, i.e. feasible and infeasible weights distributing on feasible and infeasible regions respectively, to guide the search to the promising region. To utilize the useful information contained in infeasible individuals, this paper uses infeasible weights to maintain a number of well-diversified infeasible individuals. Meanwhile, they are dynamically changed along with the evolution to prefer infeasible individuals with better objective values and smaller constraint violations. Furthermore, 18 test instances and 2 engineering design problems are used to evaluate the effectiveness of the proposed algorithm. Several numerical experiments indicate that the proposed algorithm outperforms four compared algorithms in terms of finding a set of well-distributed non-domination solutions. 相似文献
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差分进化(differential evolution,简称DE)算法解决约束优化问题(constrained optimization problems,简称COPs)时通常采用可行解优先的比较规则,但是该方法不能利用种群中不可行解的信息.设计了可以利用不可行解信息的ε-DE算法.该算法通过构造一种比较准则,使得进化过程可以充分利用种群中优秀不可行解的信息.该准则通过引入种群约束允许放松程度的概念,在进化初始阶段使可行域边界上且拥有较优目标函数的不可行解进入种群;随着进化代数增加,种群约束允许放松程度不断减小,使得种群中不可行解数量减少,直到种群约束允许放松程度为0,种群完全由可行解组成.此外,还选择了一种改进的DE算法作为搜索算法,使得进化过程具有较快的收敛性.13个标准Benchmark函数实验仿真的结果表明:ε-DE算法是目前利用DE算法解决COPs问题中效果最好的. 相似文献
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Genetic algorithm (GA) is a branch of evolutionary algorithm, has proved its effectiveness in solving constrain based complex real world problems in variety of dimensions. The individual phases of GA are the mimic of the basic biological processes and hence the self-adaptability of GA varied in accordance to the adjustable natural processes. In some instances, self-adaptability in GA fails in identifying adaptable genes to form a solution set after recombination, which leads converge toward infeasible solution, sometimes, this, infeasible solution could not be converted into feasible form by means of any of the repairing techniques. In this perspective, Gene Suppressor (GS), a bio-inspired process is being proposed as a new phase after recombination in the classical GA life cycle. This phase works on new individuals generated after recombination to attain self-adaptability by adapting best genes in the environment to regulate chromosomes expression for achieving desired phenotype expression. Repairing in this phase converts infeasible solution into feasible solution by suppressing conflicting gene from the environment. Further, the solution vector expression is improved by inducing best genes expression in the environment within the set of intended constrains. Multiobjective Multiple Knapsack Problems (MMKP), one of the popular NP hard combinatorial problems is being considered as the test-bed for proving the competence of the proposed new phase of GA. The standard MMKP benchmark instances obtained from OR-library [22] are used for the experiments reported in this paper. The outcomes of the proposed method is compared with the existing repairing techniques, where the analyses proved the proficiency of the proposed GS model in terms of better error and convergence rates for all instances. 相似文献
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基于内部罚函数的进化算法求解约束优化问题 总被引:1,自引:0,他引:1
为解决现有约束处理方法可行解的适应度函数不包含约束条件的问题,提出了一种内部罚函数候选解筛选规则.该候选解筛选规则分别对可行解和不可行解采用内部罚函数和约束违反度进行筛选,从而达到平衡最小化目标函数和满足约束条件的目的.以进化策略算法为基础,给出了基于内部罚函数候选解筛选规则的进化算法的一个实现.进一步地,从理论和实验角度分别验证了内部罚函数候选解筛选规则的有效性:以(1+1)进化算法为例,从进化成功率方面验证了内部罚函数候选解筛选规则的理论有效性;通过13个测试问题的数值实验,从进化成功率、候选解后代是可行解的比例、进化步长和收敛速度方面验证了内部罚函数候选解筛选规则的实验有效性. 相似文献
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针对传统蚁群算法在机器人路径规划时存在收敛速度慢、易陷入局部最优等问题,提出了一种基于自适应归档更新的蚁群算法。根据路径性能指标建立多目标性能评估模型,对最优路径进行多指标优化;采用路径方案归档更新策略进行路径方案的更新和筛选,提高算法的收敛速度;当搜索路径进入不可行区域时,采用自适应路径补偿策略转移不可行路径节点,构造可行路径,减少死锁蚂蚁数量;若算法无法避开障碍或者进入停滞状态,则进行种群重新初始化,增加物种多样性,避免算法陷入局部最优。仿真实验表明,改进后的算法收敛速度更快、收敛精度更高、稳定性更好。 相似文献
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A. D. Belegundu L. Berke S. N. Patnaik 《Structural and Multidisciplinary Optimization》1995,9(2):83-88
The theory and implementation of an optimization algorithm code based on the method of feasible directions are presented. Although the method of feasible directions was developed during the 1960's, the present implementation of the algorithm includes several modifications to improve its robustness. In particular, the search direction is generated by solving a quadratic program which uses an interior method based on a variation of Karmarkar's algorithm. The constraint thickness parameter is dynamically adjusted to yield usable-feasible directions. The theory is discussed with emphasis on the important and often overlooked role played by the various parameters guiding the iterations within the program. Also discussed is a robust approach for handling infeasible starting points. The code was validated by solving a variety of structural optimization test problems that have known solutions (obtained by other optimization codes). A variety of problems from different infeasible starting points has been solved successfully. It is observed that this code is robust and accurate. Further research is required to improve its numerical efficiency while retaining its robustness. 相似文献
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Xing K Han L Zhou M Wang F 《IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics》2012,42(3):603-615
Deadlock-free control and scheduling are vital for optimizing the performance of automated manufacturing systems (AMSs) with shared resources and route flexibility. Based on the Petri net models of AMSs, this paper embeds the optimal deadlock avoidance policy into the genetic algorithm and develops a novel deadlock-free genetic scheduling algorithm for AMSs. A possible solution of the scheduling problem is coded as a chromosome representation that is a permutation with repetition of parts. By using the one-step look-ahead method in the optimal deadlock control policy, the feasibility of a chromosome is checked, and infeasible chromosomes are amended into feasible ones, which can be easily decoded into a feasible deadlock-free schedule. The chromosome representation and polynomial complexity of checking and amending procedures together support the cooperative aspect of genetic search for scheduling problems strongly. 相似文献
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《Computers & Mathematics with Applications》2005,49(2-3):223-238
In this paper, a kind of nonlinear optimization problems with nonlinear inequality constraints are discussed, and a new SQP feasible descent algorithm for solving the problems is presented. At each iteration of the new algorithm, a convex quadratic program (QP) which always has feasible solution is solved and a master direction is obtained, then, an improved (feasible descent) direction is yielded by updating the master direction with an explicit formula, and in order to avoid the Maratos effect, a height-order correction direction is computed by another explicit formula of the master direction and the improved direction. The new algorithm is proved to be globally convergent and superlinearly convergent under mild conditions without the strict complementarity. Furthermore, the quadratic convergence rate of the algorithm is obtained when the twice derivatives of the objective function and constrained functions are adopted. Finally, some numerical tests are reported. 相似文献