An efficient multi-objective optimization approach based on the micro genetic algorithm and its application
An efficient multi-objective optimization approach based on the micro genetic algorithm and its application
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DOI：
10.1007/s10999-011-9174-2
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发表时间：
2011-11
影响因子：
3.7
通讯作者：
G. P. Liu;X. Han-;C. Jiang
中科院分区：
文献类型：
--
作者：
G. P. Liu;X. Han-;C. Jiang
针对多目标优化问题，提出了一种基于微遗传算法的多目标优化方法。一个外部的精英档案是用来存储在进化过程中发现的帕累托最优解。采用非支配排序将进化种群和外部精英种群的组合种群划分为若干个不同的非支配水平。一旦进化种群收敛，将执行一个探索性算子来探索更多的非支配解，并随后采用重启策略。对几个困难测试函数的仿真结果表明，与NSGAII相比，该方法具有更高的效率和更好的全局Pareto最优解集附近的收敛性，并对某些测试函数具有更好的解的传播性.最后，将该方法应用于复合材料层合板的结构优化，以获得最大厚度方向刚度和最小质量。
In this paper, an efficient multi-objective optimization approach based on the micro genetic algorithm is suggested to solving the multi-objective optimization problems. An external elite archive is used to store Pareto-optimal solutions found in the evolutionary process. A non-dominated sorting is employed to classify the combinational population of the evolutionary population and the external elite population into several different non-dominated levels. Once the evolutionary population converges, an exploratory operator will be performed to explore more non-dominated solutions, and a restart strategy will be subsequently adopted. Simulation results for several difficult test functions indicate that the present method has higher efficiency and better convergence near the globally Pareto-optimal set for all test functions, and a better spread of solutions for some test functions compared to NSGAII. Eventually, this approach is applied to the structural optimization of a composite laminated plate for maximum stiffness in thickness direction and minimum mass.