An Improved Hybrid Multi-objective Particle Swarm Optimization Algorithm

机译：一种改进的混合多目标粒子群优化算法

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Particle Swarm Optimization is a promising evolutionary optimization algorithm. In this paper, an improved hybrid multi-objective particle swarm optimization algorithm (IHMOPSO) is proposed. IHMOPSO uses orthogonal design to initialize population, selects global optimal position from Pareto set. Apply mutation, cross operation and evolutionary selection, and uses two ways to update the position and velocity of particles. Experimental results on many well-known benchmark optimization problems have shown that IHMOPSO is effective and efficient.

机译：粒子群优化是一个有前途的进化优化算法。本文提出了一种改进的混合多目标粒子群优化算法（IHMOPSO）。 IHMOPSO使用正交设计来初始化群体，从Pareto集中选择全局最佳位置。应用突变，交叉操作和进化选择，并使用两种方法来更新粒子的位置和速度。许多着名的基准优化问题的实验结果表明，IHMOPSO是有效且有效的。

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来源
《International Symposium on Intelligence Computation and Applications》|2008年||共8页
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Zuan Zhou; Guangming Dai; Pan Fang; Fangjie Chen; Yi Tan;
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中图分类 TP3-53;
关键词
Particle swarm optimization; Multi-objective optimization; Orthogonal initialization; Cauchy mutation;

机译：粒子群优化;多目标优化;正交初始化;Cauchy突变;

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An Improved Hybrid Multi-objective Particle Swarm Optimization Algorithm

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