Description: 这文档介绍的是一种改进的粒子群优化算法及其仿真-This document is the introduction of an improved particle swarm optimization algorithm and simulation Platform: |
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Author:Jesse Chen |
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Description: 一种基于混合策略的改进粒子群优化算法，包括执行程序hpso.m和参数设定hPSOoptions.m两个源文件。代码清晰，易于改进实现。结果显示有效的提高了SPSO的性能-A hybrid strategy based on improved particle swarm optimization algorithm, including the implementation of procedures and parameters set hpso.m two hPSOoptions.m source file. Code clear and easy to improve the achievement. The results showed that the improvement of effective performance of the SPSO Platform: |
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Author:楚湘华 |
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Description: 这是一个经过改进的PSO算法，是用FORTRAN语言编写的，因为此语言计算速度快，适合PSO的应用，收敛速度明显加快，并且这是改优化后的PSO程序，比标准的速度快。-This is an improved PSO algorithm, are used FORTRAN language, because this language computing speed, suitable PSO applications, significantly speeding up the convergence rate, and change This is the optimized PSO procedures, faster than the standard. Platform: |
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Author:xin |
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Description: 基于改进的粒子群算法，可以用来求解函数优化问题-Based on Improved Particle Swarm algorithm, can be used to solve the function optimization problem Platform: |
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Author:qinming |
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Description: ：目前，粒子群算法在连续问题优化上的应用已经很广泛，然而在离散问题优化方面仍处在尝试阶段。提出了一种改
进粒子群算法来解决矩形件排样优化问题(离散优化问题)。该算法融合了遗传算法中的交叉和变异思想，采用了信息交流
策略，使其达到快速优化目的。算法也对“最低水平线法”解码方式进行了改进。实验结果表明，该算法具有快速、高效特点，
与现有同类算法比较，在解决矩形件排样问题方面的优势明显。-: At present, the particle swarm optimization algorithm in a row on the issue has very broad applications, but in the discrete optimization problem is still trying to stage. An improved particle swarm optimization to solve the optimization of the rectangular piece nesting problems (discrete optimization problem). The algorithm combines the genetic algorithm crossover and mutation in the thinking of information-sharing strategies used to achieve the purpose of rapid optimization. Algorithm of the " minimum horizontal method" approach to the improvement of decoding. Experimental results show that the algorithm is fast, high-performance characteristics compared with existing similar algorithm, in solving rectangular piece nesting problems have obvious advantages. Platform: |
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Author:sdkj |
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Description: 提出一种改进的粒子群优化算法———基于全局劣汰策略的混合粒子群优化算法(GTPSO) 。GTPSO在
保持PSO算法快速收敛的基础上,以郭涛算法(GuoA)的寻优机制确保种群的多样性和算法的坚韧性。数值计
算结果表明,对于高维(维数≥10)复杂非凸多峰函数的数值优化问题, GTPSO算法的计算结果均优于GuoA算
法和粒子群优化算法。-An improved particle swarm optimization algorithm--- poor overall survival strategy based on a mixture of particle swarm optimization (GTPSO). GTPSO while maintaining fast convergence PSO algorithm based on the algorithm to Guo Tao (GuoA) optimization of the mechanism in place to ensure the diversity of the population of the tenacity and algorithms. The numerical results show that for high-dimensional (dimension ≥ 10) the complexity of multi-peak function non-convex numerical optimization problems, GTPSO algorithm results are superior to GuoA algorithms and particle swarm optimization algorithm. Platform: |
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Author:zhao |
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Description: 最新SD期刊上关于改进型PSO算法，我是通过学校内部数据库在SD下载的哦！包括《A dynamic inertia weight particle swarm optimization algorithm》、《Adaptive Particle Swarm Optimization》、《Cyber Swarm Algorithms – Improving particle swarm optimization using adaptive memory strategies》。这三篇都是比较有研究价值的学术文章，识货的请下载！-Particle swarm optimization (PSO) algorithm has been developing rapidly and has been applied widely since it was introduced, as it is easily understood and realized. This paper presents an improved particle swarm optimization algo-rithm (IPSO) to improve the performance of standard PSO, which uses the dynamic inertia weight that decreases according to iterative generation increasing. It is tested with a set of 6 benchmark functions with 30, 50 and 150 diff erent
dimensions and compared with standard PSO. Experimental results indicate that the IPSO improves the search perfor-mance on the benchmark functions signifi cantly. Platform: |
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Author:asdwe |
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Description: An Improved PSO Algorithm to Optimize BP Neural Network
Abstract
This paper presents a new BP neural network
algorithm which is based on an improved particle swarm
optimization (PSO) algorithm. The improved PSO (which
is called IPSO) algorithm adopts adaptive inertia weight
and acceleration coefficients to significantly improve the
performance of the original PSO algorithm in global
search and fine-tuning of the solutions. This study uses the
IPSO algorithm to optimize authority value and threshold
value of BP nerve network and IPSO-BP neural network
algorithm model has been established. The results
demonstrate that this model has significant advantages
inspect of fast convergence speed, good generalization
ability and not easy to yield minimal local results Platform: |
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Author:dasu |
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Description: 提出一种基于改进粒子群动态搜索算法的网络重构方法，算法把初始粒子群按照适应度的大小分为两个互不交叉，且具有不同分工的子群，并进行动态搜索。通过引入了交叉和禁忌思想，减少了解陷入局部最优的可能性．与遗传、禁忌搜索算法重构的结果进行比较，表明本文算法具有更高的搜索效率，更容易找到全局最优解．-：An improved method based on improved particle swarm optimization dynamic search algodthm networkrcconfiguration
is presented in this paper．The initial particle swarm is divided into two different subgroups according to the size of fitness，with a division of labour between the different subgroups of dynamic search．Cross
thinking and ideological tabu ale ledinto this algorithm to reduce the possibility of local Optimum．Compared with genetic and tabu search algorithm,the results show that the proposed method possesses a hi．gher search efficiency andis easier tO find the global optimum solutions． Platform: |
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Author:yirufang |
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Description: 一种改进的粒子群算法，在保持基本粒子群算法有点的同时，提高了算法效率-An improved particle swarm optimization, while the particle swarm algorithm a bit to maintain and improve the efficiency of the algorithm Platform: |
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Author:段誉 |
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Description: 这是一篇关于利用改进的粒子群优化算法来构造多层人工神经网络的文章，可用于预测风力和天气。-This is an article on the use of improved particle swarm optimization algorithm to construct a multilayer artificial neural network of the article, can be used to predict the wind and weather. Platform: |
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Author:棉花糖 |
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Description: 本程序介绍了一种改进的粒子群寻优算法；该算法可以更好的实现粒子群寻优过程中的全局搜索与局部搜索值间的平衡。从而寻得最优结果。-This program introduces an improved particle swarm optimization algorithm This algorithm can better realize the particle swarm optimization in the process of the balance between global search and local search value.To find out the optimal results. Platform: |
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Author:王义 |
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Description: 一种改进的基于模拟退火的粒子群算法(SimPSO)。将模拟退火算法的思想引入粒子群算法中,并对更新公式进行优化-An improved particle swarm optimization algorithm based on simulated annealing (SimPSO). The simulated annealing algorithm is introduced into the particle swarm optimization algorithm, and the updating formula is optimized. Platform: |
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Author:涂超 |
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Description: 航迹规划是无人机执行侦察和作战任务中的关键技术，规划算法的性能直接影响着航迹规划的质量。针对航迹规划最优性和实时性问题，提出一种惯性权值" 阶梯式" 调整策略与跳出局部最优解策略相结合的改进粒子群无人机航迹规划算法。(Route planning is the key technology for UAV to perform reconnaissance and combat tasks, and the performance of planning algorithm directly affects the quality of route planning. Aiming at the optimization and real-time performance of trajectory planning, an improved particle swarm optimization (UAV) trajectory planning algorithm is proposed, which combines the inertia weight "ladder" adjustment strategy with the strategy of jumping out of the local optimal solution.) Platform: |
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Author:龙龙121381235
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Search - An Improved Particle Swarm Optimization Algorithm