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精通MATLAB最优化计算 配套光盘
本书的主要内容是应用MATLAB来解决最优化问题，通过将“最优化问题”、“MATLAB优化工具箱”和“MATLAB编程”这三方面有机结合进行讲述，即一方面是使用工具箱来快速解决最优化问题，另一方面是通过算法编程深入解决最优化问题。主要包括MATLAB优化工具箱、无约束多维极值问题、约束优化问题、非线性最小二乘优化问题、线性规划、整数规划、二次规划、粒子群优化算法、遗传算法。
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SecVibratPSO用二阶振荡粒子群优化算法求解无约束优化问题-SecVibratPSO order oscillating particle swarm optimization algorithm for constrained optimization problems
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用于求解约束优化问题的算法，算法为差分进化/遗传算法/微粒群算法的融合。-Algorithm for solving constrained optimization problems, algorithms, differential evolution/genetic algorithm/particle swarm algorithm fusion.
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Research on multi-mode resource constrained project scheduling based on particle swarm optimization algorithm.rar
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多目标非线性约束的粒子群算法，采用灰度理论，混沌理论，动态惩罚函数，可针对任何复杂函数进行优化，效果很好-Nonlinear constrained multi-objective particle swarm algorithm, using gray theory, chaos theory, dynamic penalty function can be optimized for any complex function, the effect is very good
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Parsopoulos等采用罚函数法，利用非固定多段映射函数对约束优化问题进行转化，再利用PSO算法求解转化后问题，仿真结果显示PSO算法相对遗传算法更具有优越性，但其罚函数的设计过于复杂，不利于求解；Hu等采用可行解保留政策处理约束，即一方面更新存储中所有粒子时仅保留可行解，另一方面在初始化阶段所有粒子均从可行解空间取值，然而初始可行解空间对于许多问题是很难确定的；Ray等提出了具有多层信息共享策略的粒子群原理来处理约束，根据约束矩阵采用多层Pareto排序机制来产生优良粒子，进而用一些优良的
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一种改进的粒子群优化算法求解约束优化复杂问题-An improved particle swarm optimization algorithm for solving constrained optimization of complex problems!!
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各种粒子群或改进型粒子群算法
1）粒子群优化算法(求解无约束优化问题)
1>PSO(基本粒子群算法)
2>YSPSO(待压缩因子的粒子群算法)
3>LinWPSO(线性递减权重粒子群优化算法)
4>SAPSO(自适应权重粒子群优化算法)
5>RandWSPO(随机权重粒子群优化算法)
6>LnCPSO(同步变化的学习因子)
7>AsyLnCPSO(异步变化的学习因子)(算法还有bug)
8>SecPSO(用二阶粒
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粒子群算法求解多维约束函数极值，并与遗传算法比较。结果发现，粒子群有很好的精度。-A particle swarm optimization algorithm for solving the extreme value of multi dimensional constrained function, and compared with genetic algorithm. The results show that the particle swarm has a good accurac
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A hybrid multi-swarm particle swarm optimization to solve constrained optimization
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在典型的pso粒子群算法应用中，解决有约束的问题的pso算法(In the application of typical PSO particle swarm optimization, the PSO algorithm for solving constrained problems is presented)
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MATLAB中解决约束问题的算法，罚函数的粒子群算法的精度高，速度快(The algorithm of solving constrained problems in MATLAB, the particle swarm algorithm with penalty function has high precision and fast speed)
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DEPSO, or called DEPS, is an algorithm for (constrained) numerical optimization problem (NOP). DEPSO combines the advantages of Particle Swarm Optimization (PSO) and Differential Evolution (DE). It is incorporated into cooperative group optimization
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用粒子群算法处理带约束问题，处理约束条件使用的是罚函数法，代码中附有中文注释，txstA.txt文件是约束函数的系数组成的矩阵(Particle swarm algorithm is applied to deal with the constrained problem. The penalty function is applied to deal with the constraint condition. The code is accompanied by Chinese annota
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在求解装备的资源约束型装配线一类平衡问题时，借助遗传算法和粒子群算法进行优化求解，并对遗传算法和粒子群算法进行改进，利用matlab程序进行优化求解。(The type 1 assembling line balancing problem with resource constrained for certain equipment is sovled with modified genetic particle-swarm algorithm by using matlab languge
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在求解装备的资源约束型装配线一类平衡问题时，借助遗传粒子群算法进行优化求解，并对遗传粒子群算法进行改进，利用matlab程序进行优化求解。本次结果是进行仿真的结果。(When solving the resource-constrained assembly line balance problem of equipment, the genetic particle swarm optimization algorithm is used to optimize the solution, a
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带有不等式/等式约束的加速粒子群算法（apso），主要通过罚函数来进行约束，速度较快，可处理带约束问题(Accelerated particle swarm optimization (APSO) with inequality / equality constraints, which is mainly constrained by penalty function, is fast and can deal with constrained problems)
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粒子群算法求解约束多目标优化万能matlab代码。本代码是基于有约束条件的寻优问题，采用APSO可以加快寻优的速度，修改参数比较简单，目标函数和上下限根据自身的需要进行改动，，可以实现目标函数的寻优(Particle swarm algorithm for solving constrained multi-objective optimization universal matlab code. This code is based on the optimization problem wi
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