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针对在解决某些复杂多目标优化问题过程中,所得到的Pareto最优解易受设计参数或环境参数扰动的影响.引入了鲁棒的概念并提出一种改进的鲁棒多目标优化方法,它利用了经典的基于适应度函数期望和方差方法各白的优势,有效地将两种方法结合在一起.为了实现该方法,给出一种基于粒子群优化算法的多目标优化算法.仿真实例结果表明,所给出的方法能够得到更为鲁棒的Pareto最优解.
In order to solve the problems of some complex multi-objective optimization problems, the Pareto optimal solution is easily influenced by the disturbance of the design parameters or environmental parameters.A robust concept is introduced and an improved robust multi-objective optimization method is proposed, It takes advantage of the classical white balance based on fitness function expectation and variance and effectively combines the two methods.In order to implement this method, a multi-objective optimization algorithm based on particle swarm optimization is given. The results show that the proposed method can obtain a more robust Pareto optimal solution.