共查询到18条相似文献,搜索用时 109 毫秒
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将粒子群算法与局部优化方法相结合,提出了一种混合粒子群多目标优化算法(HMOPSO)。该算法针对粒子群局部优化性能较差的缺点,引入多目标线搜索与粒子群算法相结合的策略,以增强粒子群算法的局部搜索能力。HMOPSO首先运行PSO算法,得到近似的Pareto最优解;然后启动多目标线搜索,发挥传统数值优化算法的优势,对其进行进一步的优化。数值实验表明,HMOPSO具有良好的全局优化性能和较强的局部搜索能力,同时HMOPSO所得的非劣解集在分散性、错误率和逼近程度等量化指标上优于MOPSO。 相似文献
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为提高多目标粒子群算法(MOPSO)的收敛性与解集多样性,提出一种基于侧步爬山策略的混合多目标粒子群算法(H-MOPSO).通过建立局部搜索与粒子群优化的混合模型,在该模型中后期引入基于侧步爬山策略的局部搜索,周期性代替粒子群搜索并优化混合参数,使粒子根据距离前沿的远近朝下降或非支配方向搜索,加快粒子群收敛并改善其分布.同时采用非均匀变异算子和线性递减的惯性权重策略,避免算法早熟.通过标准测试函数的对比实验表明,该算法整体上比MOPSO、NSGA-II和MOEA/D具有更好的多样性与收敛性. 相似文献
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将粒子群优化算法应用于求解多目标优化问题,提出一种双向搜索机制,指导粒子向着搜索空间中非劣目标区域以及粒子分布最为稀疏的区域这两个方向进行寻优,进而提出了求解多目标优化问题的基于粒子群优化算法的双向搜索法,该算法对粒子全局最优经验的选择策略以及粒子群的状态更新机制进行了改进。实验研究表明,该算法不仅能快速有效地获得多目标优化问题的非劣最优解集,而且求出的解集具有良好的分布性。 相似文献
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针对多目标优化问题提出了一种基于最大最小适应度函数(F_maximin)的粒子群算法,将此算法简称为IMPSO。它在求解多目标问题的非劣解前沿(Pareto Front)时表现出很好的性能。通过经典测试函数计算表明该算法保证收敛到多目标优化问题的Pareto最优前沿;同时,使用两个性能指标(GD和Diversity)验证了此算法优于其他的多目标粒子群优化算法。 相似文献
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提出一种改进的多目标粒子群优化算法,该算法采用精英归档策略,由档案库中的非劣解提供粒子速度更新时的全局最优位置,根据Pareto支配关系来更新粒子的个体最优位置。使用非劣解目标的线密度度量非劣解前端的均匀性,通过删除小密度的非劣解提高非劣解前端的均匀性。针对多目标进化算法理论型指标的不足,设计了应用型评价指标。标准函数的仿真实验结果表明,所提算法能够获得大量的非劣解,快速地收敛于Pareto最优解前端,且分布比较均匀。 相似文献
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基于混沌的多目标粒子群优化算法 总被引:1,自引:0,他引:1
针对多目标优化问题,提出了一种改进的粒子群算法.该算法为了寻找新解,引入了混沌搜索技术,同时采用了一种新的方法--拥挤距离法定义解的适应度.并采取了精英保留策略,在提高非劣解集多样性的同时,使解集更加趋近于Pareto集.最后,把算法应用到4个典型的多目标测试函数.数值结果表明,该算法能够有效的收敛到Pareto非劣最优目标域,并沿着Pareto非劣目标域有很好的分散性. 相似文献
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This article proposes an algorithm to search for solutions which are robust against small perturbations in design variables.
The proposed algorithm formulates robust optimization as a bi-objective optimization problem, and fi nds solutions by multi-objective
particle swarm optimization (MOPSO). Experimental results have shown that MOPSO has a better performance at fi nding multiple
robust solutions than a previous method using a multi-objective genetic algorithm. 相似文献
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This paper presents a new multi-objective optimization algorithm in which multi-swarm cooperative strategy is incorporated into particle swarm optimization algorithm, called multi-swarm cooperative multi-objective particle swarm optimizer (MC-MOPSO). This algorithm consists of multiple slave swarms and one master swarm. Each slave swarm is designed to optimize one objective function of the multi-objective problem in order to find out all the non-dominated optima of this objective function. In order to produce a well distributed Pareto front, the master swarm is developed to cover gaps among non-dominated optima by using a local MOPSO algorithm. Moreover, in order to strengthen the capability locating multiple optima of the PSO, several improved techniques such as the Pareto dominance-based species technique and the escape strategy of mature species are introduced. The simulation results indicate that our algorithm is highly competitive to solving the multi-objective optimization problems. 相似文献
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为了提高多目标优化算法解集的分布性和收敛性,提出一种基于分解和差分进化的多目标粒子群优化算法(dMOPSO-DE).该算法通过提出方向角产生一组均匀的方向向量,确保粒子分布的均匀性;引入隐式精英保持策略和差分进化修正机制选择全局最优粒子,避免种群陷入局部最优Pareto前沿;采用粒子重置策略保证群体的多样性.与非支配排序(NSGA-II)算法、多目标粒子群优化(MOPSO)算法、分解多目标粒子群优化(dMOPSO)算法和分解多目标进化-差分进化(MOEA/D-DE)算法进行比较,实验结果表明,所提出算法在求解多目标优化问题时具有良好的收敛性和多样性. 相似文献
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Xiaoyan Sun Yang Chen Yiping Liu Dunwei Gong 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2016,20(6):2219-2232
Multi-objective particle swarm optimization (MOPSO) has been well studied in recent years. However, existing MOPSO methods are not powerful enough when tackling optimization problems with more than three objectives, termed as many-objective optimization problems (MaOPs). In this study, an improved set evolution multi-objective particle swarm optimization (S-MOPSO, for short) is proposed for solving many-objective problems. According to the proposed framework of set evolution MOPSO (S-MOPSO), including quality indicators-based objective transformation, the Pareto dominance on sets, and the particle swarm operators for set evolution, an enhanced S-MOPSO method is developed by updating particles hierarchically, i.e., a set of solutions is first regarded as a particle to be updated and then the solutions in a selected set are further evolved by a modified PSO. In the set evolutionary stage, the strategy for efficiently updating the set particle is proposed. When further evolving a single solution in the initial decision space of the optimized MaOP, the global and local best particles are dynamically determined based on those ideal reference points. The performance of the proposed algorithm is empirically demonstrated by applying it to several scalable benchmark many-objective problems. 相似文献
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针对多目标粒子群优化算法在求解约束优化问题时存在难以兼顾收敛性能和求解质量这一问题,提出一种基于免疫网络的改进多目标粒子群优化算法.该算法通过免疫网络互通种群最优信息达到粒子群算法与人工免疫网络算法的协同搜索,同时给出了速度迁移策略、自适应方差变异策略和基于聚类的免疫网络策略.最后将所提出的方法应用于求解电弧炉供电优化模型,达到了减少电量消耗、缩短冶炼时间、延长炉衬使用寿命的目的,同时表明了该算法的有效性. 相似文献
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This paper presents the Multi-Objective Vortex Particle Swarm Optimization MOVPSO as a strategy based on the behavior of a particle swarm using rotational and translational motions. The MOVPSO strategy is based upon the emulation of the emerging property performed by a swarm (flock), achieving a successful motion with diversity control, via collaborative, using linear and circular movements.The proposed algorithm is tested through several multi-objective optimization functions and is compared with standard Multi-Objective Particle Swarm Optimization (MOPSO).The qualitative results show that particle swarms behave as expected. Finally, statistical analysis allows to appreciate that the MOVPSO algorithm has a favorable performance compared to traditional MOPSO algorithm. 相似文献