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A self-adaptive multi-objective optimization algorithm based on the Pareto's non-dominated sets

机译：基于帕累托非支配集的自适应多目标优化算法

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摘要

In order to improve the optimization efficiency in the multi-objective fault optimization, the self-adaptive multi-objective optimization algorithm based on the Pareto's non-dominated sets by binary tree (SMOS) are proposed in the paper. Firstly, the Self-adaptive adjustment of inertia weight is put forward to adjust the fitness function based on niche sharing mechanism. Secondly, the Pareto non-dominated sets are constructed by the binary tree to improve the optimization efficiency. Then, the SMOS algorithm is present reduce the optimized time complexity of constructed Pareto non-dominated sets when the optimized object number are larger. Meanwhile that the constructed non-dominated sets belongs the Pareto sets is proved. Finally the simulation results show when the numbers of non-dominated population are more than 5, the non-dominated efficiency can improve approximately 50%.
机译：为了提高多目标故障优化的优化效率，提出了一种基于帕累托二叉树非支配集的自适应多目标优化算法。首先提出了惯性权重的自适应调整，以利基共享机制为基础来调整适应度函数。其次，通过二叉树构造帕累托非支配集，以提高优化效率。然后，当优化对象数较大时，提出的SMOS算法降低了构造的Pareto非控制集的优化时间复杂度。同时证明了构造的非支配集属于帕累托集。最后的仿真结果表明，当非支配人口数量大于5时，非支配效率可以提高大约50％。

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