共查询到20条相似文献,搜索用时 156 毫秒
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多背包问题的遗传算法求解 总被引:13,自引:0,他引:13
本文提出了一种新的组合优化问题—多背包问题,并给出了它的基于0/1规划的数学模型;提出了解决多背包问题的遗传算法。该算法以目标函数加约束惩罚函数作为适应值函数,交叉算子选用了一致交叉的方法,仿真的结果表明该遗传算法在求解多背包问题上的表现是良好的。 相似文献
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动态多目标约束优化问题是一类NP-Hard问题,定义了动态环境下进化种群中个体的序值和个体的约束度,结合这两个定义给出了一种选择算子.在一种环境变化判断算子下给出了求解环境变量取值于正整数集Z+的一类带约束动态多目标优化问题的进化算法.通过几个典型的Benchmark函数对算法的性能进行了测试,其结果表明新算法能够较好地求出带约束动态多目标优化问题在不同环境下质量较好、分布较均匀的Pareto最优解集. 相似文献
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解约束最优化问题的一个新的多目标进化算法 总被引:1,自引:2,他引:1
把约束函数作为目标函数,将约束优化问题转化为多目标规划问题。对这个多目标规划,根据带权极小极大策略构造了一个同进化代数有关的变适应值函数。利用广义球面坐标变换和均匀设计法来选择权重,使得由此权重确定的适应值函数能使种群中的容许解逐渐增加并且保持其多样性。用均匀设计法构造的带有自适应性的变异算子增强了算法的局部搜索能力。该方法能有效处理约束,特别是紧约束。计算机仿真显示了该方法是有效的。 相似文献
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一种基于输运理论的多目标演化算法 总被引:2,自引:1,他引:2
提出了一种根据输运理论中的粒子输运方程、相空间能量定律和熵增法则构造的一种能够准确、高效地求解多目标优化问题的多目标演化算法(MOPEA).由于该算法使用了粒子系统从非平衡达到平衡的理论来定义求解多目标问题的Rank函数和Niche适应值函数,使得种群中的所有个体都有机会参与演化操作,以达到快速、均匀地求出多目标优化问题的Pareto最优解.数据实验显示,利用该算法求解多目标优化问题不仅能够使算法快速地收敛到全局Pareto前沿,同时由于该算法要求所有的粒子都要参与杂交和变异等演化操作,从而避免问题早熟现象的出现,并通过与传统演化算法的性能指标分析比较说明,使用该算法求解多目标优化问题具有明显的优越性. 相似文献
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张建科 《计算机工程与应用》2009,45(27):43-45
针对非线性互补问题求解的困难,利用粒子群算法并结合极大熵函数法给出了该类问题的一种新的有效算法。该算法首先利用极大熵函数将非线性互补问题转化为一个无约束最优化问题,将该函数作为粒子群算法的适应值函数;然后应用粒子群算法来优化该问题。数值结果表明,该算法收敛快、数值稳定性较好,是求解非线性互补问题的一种有效算法。 相似文献
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In this paper, we present a novel immune multiobjective optimization algorithm based on micro-population, which adopts a novel adaptive mutation operator for local search and an efficient fine-grained selection operator for archive update. With the external archive for storing nondominated individuals, the population diversity can be well preserved using an efficient fine-grained selection procedure performed on the micro-population. The adaptive mutation operator is executed according to the fitness values, which promotes to use relatively large steps for boundary and less-crowded individuals in high probability. Therefore, the exploratory capabilities are enhanced. When comparing the proposed algorithm with a recently proposed immune multiobjective algorithm and a scatter search multiobjective algorithm in various benchmark functions, simulations show that the proposed algorithm not only improves convergence ability but also preserves population diversity adequately in most cases. 相似文献
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一种基于免疫原理的多目标优化方法 总被引:1,自引:0,他引:1
借鉴生物免疫原理中抗体多样性产生及保持的机理,建立了一种多目标优化方法.该方法定义了多目标选择熵和浓度调节选择概率的概念,采用了抗体克隆选择策略和高度变异策略.最后采用四种典型的多目标优化函数,将本方法同几种常用的多目标遗传算法进行了比较研究,证明了所建立的基于免疫原理的多目标优化方法能有效解决多目标优化问题且具有一定的优越性. 相似文献
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Shinn-Ying Ho Li-Sun Shu Jian-Hung Chen 《Evolutionary Computation, IEEE Transactions on》2004,8(6):522-541
This work proposes two intelligent evolutionary algorithms IEA and IMOEA using a novel intelligent gene collector (IGC) to solve single and multiobjective large parameter optimization problems, respectively. IGC is the main phase in an intelligent recombination operator of IEA and IMOEA. Based on orthogonal experimental design, IGC uses a divide-and-conquer approach, which consists of adaptively dividing two individuals of parents into N pairs of gene segments, economically identifying the potentially better one of two gene segments of each pair, and systematically obtaining a potentially good approximation to the best one of all combinations using at most 2N fitness evaluations. IMOEA utilizes a novel generalized Pareto-based scale-independent fitness function for efficiently finding a set of Pareto-optimal solutions to a multiobjective optimization problem. The advantages of IEA and IMOEA are their simplicity, efficiency, and flexibility. It is shown empirically that IEA and IMOEA have high performance in solving benchmark functions comprising many parameters, as compared with some existing EAs. 相似文献
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Herrero J.G. Berlanga A. Lopez J.M.M. 《Evolutionary Computation, IEEE Transactions on》2009,13(1):151-168
This paper addresses a real-world engineering design requiring the application of effective and global optimization techniques. The problem it deals with is the design of nonlinear tracking filters under up to several hundreds of performance specifications. The suitability of different evolutionary computation techniques for solving multiobjective problems is explored, contrasting the performance achieved with recent multiobjective evolutionary algorithm (MOEAs) proposals and different aggregation schemes. In particular, a new scheme is proposed to build a fitness function based on an operator that selects worst cases of multiple specifications in different situations. They have been evaluated in the design of an air traffic control (ATC) tracking filter that should accomplish a specific normative with 264 specifications. Results show their performance in terms of effectiveness and computational load, comparing their capability to scale the problem with respect to problem size. 相似文献
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The potential and effectiveness of the newly developed Pareto-based multiobjective evolutionary algorithms (MOEA) for solving a real-world power system multiobjective nonlinear optimization problem are comprehensively discussed and evaluated in this paper. Specifically, nondominated sorting genetic algorithm, niched Pareto genetic algorithm, and strength Pareto evolutionary algorithm (SPEA) have been developed and successfully applied to an environmental/economic electric power dispatch problem. A new procedure for quality measure is proposed in this paper in order to evaluate different techniques. A feasibility check procedure has been developed and superimposed on MOEA to restrict the search to the feasible region of the problem space. A hierarchical clustering algorithm is also imposed to provide the power system operator with a representative and manageable Pareto-optimal set. Moreover, an approach based on fuzzy set theory is developed to extract one of the Pareto-optimal solutions as the best compromise one. These multiobjective evolutionary algorithms have been individually examined and applied to the standard IEEE 30-bus six-generator test system. Several optimization runs have been carried out on different cases of problem complexity. The results of MOEA have been compared to those reported in the literature. The results confirm the potential and effectiveness of MOEA compared to the traditional multiobjective optimization techniques. In addition, the results demonstrate the superiority of the SPEA as a promising multiobjective evolutionary algorithm to solve different power system multiobjective optimization problems. 相似文献
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Local search with quadratic approximations into memetic algorithms for optimization with multiple criteria 总被引:2,自引:0,他引:2
This paper proposes a local search optimizer that, employed as an additional operator in multiobjective evolutionary techniques, can help to find more precise estimates of the Pareto-optimal surface with a smaller cost of function evaluation. The new operator employs quadratic approximations of the objective functions and constraints, which are built using only the function samples already produced by the usual evolutionary algorithm function evaluations. The local search phase consists of solving the auxiliary multiobjective quadratic optimization problem defined from the quadratic approximations, scalarized via a goal attainment formulation using an LMI solver. As the determination of the new approximated solutions is performed without the need of any additional function evaluation, the proposed methodology is suitable for costly black-box optimization problems. 相似文献
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Local selection is a simple selection scheme in evolutionary computation. Individual fitnesses are accumulated over time and compared to a fixed threshold, rather than to each other, to decide who gets to reproduce. Local selection, coupled with fitness functions stemming from the consumption of finite shared environmental resources, maintains diversity in a way similar to fitness sharing. However, it is more efficient than fitness sharing and lends itself to parallel implementations for distributed tasks. While local selection is not prone to premature convergence, it applies minimal selection pressure to the population. Local selection is, therefore, particularly suited to Pareto optimization or problem classes where diverse solutions must be covered. This paper introduces ELSA, an evolutionary algorithm employing local selection and outlines three experiments in which ELSA is applied to multiobjective problems: a multimodal graph search problem, and two Pareto optimization problems. In all these experiments, ELSA significantly outperforms other well-known evolutionary algorithms. The paper also discusses scalability, parameter dependence, and the potential distributed applications of the algorithm. 相似文献
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Nanoscale crossbar architectures have received steadily growing interests as a result of their great potential to be main building blocks in nanoelectronic circuits. However, due to the extremely small size of nanodevices and the bottom-up self-assembly nanofabrication process, considerable process variation will be an inherent vice for crossbar nanoarchitectures. In this paper, the variation tolerant logical mapping problem is treated as a bilevel multiobjective optimization problem. Since variation mapping is an NP-complete problem, a hybrid multiobjective evolutionary algorithm is designed to solve the problem adhering to a bilevel optimization framework. The lower level optimization problem, most frequently tackled, is modeled as the min–max-weight and min-weight-gap bipartite matching (MMBM) problem, and a Hungarian-based linear programming (HLP) method is proposed to solve MMBM in polynomial time. The upper level optimization problem is solved by evolutionary multiobjective optimization algorithms, where a greedy reassignment local search operator, capable of exploiting the domain knowledge and information from problem instances, is introduced to improve the efficiency of the algorithm. The numerical experiment results show the effectiveness and efficiency of proposed techniques for the variation tolerant logical mapping problem. 相似文献
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In recent years, a general-purpose local-search heuristic method called Extremal Optimization (EO) has been successfully applied
in some NP-hard combinatorial optimization problems. In this paper, we present a novel Pareto-based algorithm, which can be
regarded as an extension of EO, to solve multiobjective optimization problems. The proposed method, called Multiobjective
Population-based Extremal Optimization (MOPEO), is validated by using five benchmark functions and metrics taken from the
standard literature on multiobjective evolutionary optimization. The experimental results demonstrate that MOPEO is competitive
with the state-of-the-art multiobjective evolutionary algorithms. Thus MOPEO can be considered as a viable alternative to
solve multiobjective optimization problems. 相似文献
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Handling multiple objectives with particle swarm optimization 总被引:35,自引:0,他引:35
Coello C.A.C. Pulido G.T. Lechuga M.S. 《Evolutionary Computation, IEEE Transactions on》2004,8(3):256-279
This paper presents an approach in which Pareto dominance is incorporated into particle swarm optimization (PSO) in order to allow this heuristic to handle problems with several objective functions. Unlike other current proposals to extend PSO to solve multiobjective optimization problems, our algorithm uses a secondary (i.e., external) repository of particles that is later used by other particles to guide their own flight. We also incorporate a special mutation operator that enriches the exploratory capabilities of our algorithm. The proposed approach is validated using several test functions and metrics taken from the standard literature on evolutionary multiobjective optimization. Results indicate that the approach is highly competitive and that can be considered a viable alternative to solve multiobjective optimization problems. 相似文献