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无约束一维、多维极值，约束优化问题，粒子群优化算法，遗传优化算法-Unconstrained one-dimensional, multi-dimensional extremal constrained optimization problem, particle swarm optimization, genetic optimization algorithm
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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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多目标粒子群的基本源代码，两个目标的源代码-Multi-objective particle swarm basic source code, source code in two target
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云多目标粒子群算法，基于matlab环境，可供学习者参考。-Cloud multi-objective particle swarm algorithm, based on the Matlab environment available to learners.
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模糊多目标粒子群算法，基于matlab环境，可供学习者参考。-Fuzzy multi-objective particle swarm algorithm, based on the Matlab environment for learners Reference.
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运行数据是吸纳粒子群算法的精华，是先多目标优化。-Run data to absorb the essence of the particle swarm algorithm is the first multi-objective optimization.
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粒子群算法，实现多维参数寻找最优解，含有代码说明-Particle swarm optimization, multi-dimensional parameters to find the optimal solution containing code Descr iption
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基于扩展粒子群算法的群机器人多目标搜索，引入动态闭环任务分工，能够并行搜索，提高搜索效率。-Particle swarm optimization algorithm based on extended multi-target search robot group, introducing dynamic closed-loop division of tasks in parallel searches, improve search efficiency.
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Recently more research works are focused on multi-objective particle swarm optimization
algorithm (MOPSO) due to its ability of global and local search for solving multi-objective
optimization problems (MOOPs) however, most of existing MOPSOs can
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多目标粒子群算法，在基本粒子群算法的基础上进行了改进，程序简单明了。-Multi-objective particle swarm algorithm based on particle swarm algorithm has been improved, the program is simple and straightforward.
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提出了一种新的多目标粒子群优化(MOPSO)算法，该算法采用自适应网格方法来估计非劣解集中粒子的密度信息、平衡全局和局
部搜索能力的 Pareto 最优解的搜索机制、删除品质差的多余粒子的 Archive 集的修剪技术。通过对三峡梯级多目标优化调度问题的计算，
表明该算法是求解大规模复杂多目标优化问题的一种有效手段。-A new multi-objective particle swarm optimization(MOPSO) is proposed. The proposed alg
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采用多目标粒子群算法求解多目标背包问题 问题：假设存在五类物品，每类物品又包含四种具体物品，要求从五类物品中分别选择一种放入背包，使得背包总价值最大，总体积最小，总质量不超过92kg(The problem is solved by multi objective particle swarm optimization algorithm, multi-objective knapsack problem: suppose there are five categories of goods,
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