多目标进化算法中选择策略的研究

在多目标进化算法(multiobjective evolutiorlsry algorithms,MOEAs)的文献中,对算法的选择策略进行系统研究的还很少,而MOEAs的选择策略不仅引导算法的搜索过程、决定搜索的方向而且对算法的收敛性有重要的影响,它是算法能否成功求解多目标优化问题的关键因素之一.在统一的框架下,首先讨论了多目标优化问题中适应度函数的构造问题,然后根据MOEAs的选择机制和原理将它们的选择策略重新分成了6种类型.一般文献中很少对多目标进化算法的操作算子采用符号化描述,这样不利于对算子的深层次理解,符号化描述了各类选择策略的操作机制和原理,并分析了各类策略的优劣性.最后,从理论上证明了具备一定特征的多目标进化算法的收敛性,证明的过程表明了将算法运行终止时得到的P known作为多目标优化问题的Pareto最优解集或近似最优解集的合理性.

Study on Selection Strategies of Multiobjective Evolutionary Algorithms

It is scarce for literatures devoted to the multiobjective evolutionary algorithms(MOEAs) to systematically research on selection strategies,however,these strategies are crucial to MOEAs for solving some multiobjective optimization problems successfully,as they not only guide the process of search and determine the search directions,but also exert great effect on the convergence of MOEAs.With the unified framework,the paper first discussed how to construct an appropriate fitness function in multiobjective o...