基于粒子群优化算法的不确定性多目标优化问题研究及其应用

项目名称： 基于粒子群优化算法的不确定性多目标优化问题研究及其应用

项目编号： No.61203372

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 自动化学科

项目作者： 魏静萱

作者单位： 西安电子科技大学

项目金额： 24万元

中文摘要： 动态多目标优化问题是指其目标函数和约束条件不仅与决策变量有关，而且与时间(环境)有关的一类优化问题，是不确定优化领域的难点和热点问题。现有算法的大部分并不能快速而准确的追踪到随时间动态变化的Pareto最优解，而且没有考虑到如何处理动态变化的约束条件。本项目以粒子群算法为搜索引擎，首先研究上一时刻(环境)所获得的Pareto最优解与下一时刻(环境)Pareto最优解之间的关系，建立预测模型，通过该模型近似下一时刻最优解的位置；为了处理随时间(环境)动态变化的约束条件，给出基于多样性设计的粒子个体极值和全局极值扰动的新方法。另外，噪声多目标优化问题是不确定优化领域的另一难点问题，现有大部分算法的计算量都非常巨大，为了克服此缺陷，本项目拟通过建立一个局部优化模型来过滤噪音，减少算法的计算量。最后将上述的动态多目标粒子群算法应用到网格安全任务调度问题中去，体现了动态多目标优化的应用价值。

中文关键词： 多目标粒子群；预测技术；多样化扰动；网格任务调动；

英文摘要： Dynamic multi-objective optimization problems (DMOPs) usually involve objective functions, constraints which change with time. This kind of problem is a main branch of uncertain optimization. Most of the existing algorithms can not track varying Pareto fronts effectively and do not consider how to deal with the constraints in dynamic environments. In order to address these limitations, firstly, we should consider the relationship between the Pareto optimal solutions from the last time and that of the current time. Based on this, a prediction model will be proposed to predict the position of the Pareto optimal solutions at the current time; secondly,in order to handle the dynamically changed constraints, a new particle swarm algorithm will be proposed based on the diversity design. The personal best position and global best position of a particle will be perturbed based on the diversity design. Noisy multi-objective optimization problem is another main branch of uncertain optimization. To date, most common method to deal with noise is re-sampling. This kind of method is effective, but costly. Thus, it is hard to be used in practice. To address this issue, a local model in the context of noisy particle swarm multi-objective optimization will be proposed. We expect it to filter noise effectively and increase th

英文关键词： multi-objective particle swarm optimization；hyper-rectangle search；perturbation based on universal law；grid scheduling problem；