共查询到20条相似文献,搜索用时 62 毫秒
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本文介绍了粒子群优化算法PSO中的多目标优化的粒子群算法及其应用,并将其运用在防守对方多个前锋球员的进攻威胁,以粒子群算法随机性来适应不断变化的形势。 相似文献
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解多目标优化问题的新粒子群优化算法 总被引:3,自引:0,他引:3
刘淳安 《计算机工程与应用》2006,42(2):30-32,72
通过定义的粒子序值方差和U-度量方差,把对任意多个目标函数的优化问题转化成为两个目标函数的优化问题。继而把Pareto最优与粒子群优化(PSO)算法相结合,对转化后的优化问题提出了一种新的多目标粒子群优化算法,并证明了其收敛性。新方法用较少计算量便可以求出一组在最优解集合中分布均匀且数量充足的最优解。计算机仿真表明该算法对不同的试验函数均可用较少计算量求出在最优解集合中分布均匀且数量充足的最优解。 相似文献
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基于粒子群优化的军事物流配送中心选址 总被引:2,自引:0,他引:2
针对当前军事物流配送改革中配送中心选址问题,在成本最小的基础上,构建了一个混合整数规划模型,并将粒子群优化算法(PSO)引入到模型的求解中,采用离散PSO解决物流配送中心选择问题,用基本PSO解决货物运输分配问题,通过嵌套调用离散PSO和基本PSO,得到模型最优解.该方法降低了计算复杂度,有效选择了物流配送中心,优化了军事物流网络.实例表明了方法的可行性和有效性. 相似文献
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基于混沌搜索的粒子群优化算法 总被引:28,自引:6,他引:28
粒子群优化算法(PSO)是一种有效的随机全局优化技术。文章把混沌优化搜索技术引入到PSO算法中,提出了基于混沌搜索的粒子群优化算法。该算法保持了PSO算法结构简单的特点,改善了PSO算法的全局寻优能力,提高的算法的收敛速度和计算精度。仿真计算表明,该算法的性能优于基本PSO算法。 相似文献
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基于多轮PSO算法的中长期动态优化配矿研究* 总被引:1,自引:0,他引:1
中长期动态优化配矿是研究如何制定在一个较长时期内采矿顺序与配矿的问题,是一个高度非线性受限条件下的多目标优化问题。采用多轮粒子群算法(particle swarm optimization algorithm, PSO)来求解矿山企业动态配矿问题。首先,依据开采条件圈定出可开采的矿块,并给出预测的产品价格趋势结果作为算法的输入条件;然后,通过对PSO算法轮的划分以确定每轮的动态配矿方案,经过多轮PSO算法优化后的最终目标为中长期动态配矿的优化结果。在PSO算法中用粒子的一位来代表矿块,并用0或1来代表选择 相似文献
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粒子群算法在投影寻踪模型优化求解中的应用 总被引:5,自引:0,他引:5
粒子群优化(Particle Swarm Optimization,PSO)算法是一种新兴的优化技术,其思想来源于人工生命和进化计算理论.PSO算法通过粒子追随自己找到的最好解和整个群体的最好解完成问题的优化.针对投影寻踪模型中的最佳投影方向优化问题.运用PSO算法和惩罚函数法相结合对该优化问题进行了计算.仿真实验结果表明:PSO算法对于求解有复杂约束的非线性目标函数优化问题是可行的,且算法的收敛速度快,编程结构简单,易于实现,从而为各领域运用投影寻踪模型评价方法提供了强有力的寻优方法,具有较广的应用前景. 相似文献
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Evolutionary multi-objective optimization: some current research trends and topics that remain to be explored 总被引:3,自引:0,他引:3
Carlos A. Coello Coello 《Frontiers of Computer Science in China》2009,3(1):18-30
This paper provides a short review of some of the main topics in which the current research in evolutionary multi-objective
optimization is being focused. The topics discussed include new algorithms, efficiency, relaxed forms of dominance, scalability,
and alternative metaheuristics. This discussion motivates some further topics which, from the author’s perspective, constitute
good potential areas for future research, namely, constraint-handling techniques, incorporation of user’s preferences and
parameter control. This information is expected to be useful for those interested in pursuing research in this area. 相似文献
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提出了一种基于密度熵的多目标粒子群算法(EMOPSO)。采用一个外部集保存所发现的Pareto最优解(精英),并将外部集作为粒子的全局极值。为保证种群的多样性,当精英大于外部集的大小时采用一种基于密度熵的策略进行分布度保持,从而使所得到的解集保持良好的分布性。最后与经典的多目标进化算法(MOEAs)进行了对比实验,实验结果表明了该算法的有效性。 相似文献
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This paper provides a short review of some of the main topics in which the current research in evolutionary multi-objective optimization is being focused. The topics discussed include new algorithms, efficiency, relaxed forms of dominance, scalability, and alternative metaheuristics. This discussion motivates some further topics which, from the author’s perspective, constitute good potential areas for future research, namely, constraint-handling techniques, incorporation of user’s preferences and parameter control. This information is expected to be useful for those interested in pursuing research in this area. 相似文献
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This paper studies the strategies for multi-objective optimization in a dynamic environment. In particular, we focus on problems with objective replacement, where some objectives may be replaced with new objectives during evolution. It is shown that the Pareto-optimal sets before and after the objective replacement share some common members. Based on this observation, we suggest the inheritance strategy. When objective replacement occurs, this strategy selects good chromosomes according to the new objective set from the solutions found before objective replacement, and then continues to optimize them via evolution for the new objective set. The experiment results showed that this strategy can help MOGAs achieve better performance than MOGAs without using the inheritance strategy, where the evolution is restarted when objective replacement occurs. More solutions with better quality are found during the same time span. 相似文献
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S. C. Chiam K. C. Tan A. Al Mamum 《国际自动化与计算杂志》2008,5(1):67-80
This paper addresses evolutionary multi-objective portfolio optimization in the practical context by incorporating realistic constraints into the problem model and preference criterion into the optimization search process. The former is essential to enhance the realism of the classical mean-variance model proposed by Harry Markowitz, since portfolio managers often face a number of realistic constraints arising from business and industry regulations, while the latter reflects the fact that portfolio managers are ultimately interested in specific regions or points along the efficient frontier during the actual execution of their investment orders. For the former, this paper proposes an order-based representation that can be easily extended to handle various realistic constraints like floor and ceiling constraints and cardinality constraint. An experimental study, based on benchmark problems obtained from the OR-library, demonstrates its capability to attain a better approximation of the efficient frontier in terms of proximity and diversity with respect to other conventional representations. The experimental results also illustrated its viability and practicality in handling the various realistic constraints. A simple strategy to incorporate preferences into the multi-objective optimization process is highlighted and the experimental study demonstrates its capability in driving the evolutionary search towards specific regions of the efficient frontier. 相似文献
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基于聚类的快速多目标遗传算法 总被引:8,自引:1,他引:8
多目标遗传算法非常适合于求解多目标优化问题.讨论了进化个体之间的支配关系及有关性质,论证了可以用快速排序的方法对进化群体中的个体进行分类,同时探讨了用聚类方法来保持群体的多样性,具体讨论了基于层次凝聚距离的聚类,在此基础上提出了用分类和聚类的方法构造新的进化群体.理论分析与实验结果表明,所讨论的方法比较国际上已有的方法具有更快的收敛速度. 相似文献
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一个能够同时优化生物能源的生产供应链,包括环境、经济和社会方面影响的多目标优化模型,模拟和优化了综合的生物能生产系统,即用数种不同类的生物质原料以生产电能、热能和可燃气的系统。该系统包含可供用户选择的多种技术,以模块的形式体现在单元过程中。通过模型生命周期的评价(LCA),最终优化目标确定为解决最小化能量生产成本、最大化节能潜力、最小化环境负担、根据用户选择最大或最小化工人数和最大化生物能系统总效率。通过平衡环境和社会责任,本研究结果可帮助计划和生产人员有效地提高生物质系统的经济竞争力。 相似文献
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一种基于快速排序的快速多目标遗传算法 总被引:2,自引:2,他引:2
多目标遗传算法的一个重要步骤就是构造非支配集,本文提出了一种基于快速排序的非支配集构造方法,提高了非支配集构造效率,并且在Deb提出的NSGAⅡ的基础上,改进了其种群构造策略,设计了一类新的多目标遗传算法。实验表明,这种方法比NSGAⅡ具有更快的收敛速度且保持了良好的分布性。 相似文献