一种基于偏好的多目标调和遗传算法

最近涌现了各种进化方法来解决多目标优化问题,多数方法使用Pareto优胜关系作为选择策略而没有采用偏好信息.这些算法不能有效处理目标数目许多时的优化问题.通过在不同准则之间引入偏好来解决该问题,提出一种多目标调和遗传算法MOCGA(multi-objective concordance genetic algorithm).当同时待优化的目标数目增加时,根据决策者提供的信息使用弱优胜关系进行个体优劣的比较.这种算法被证明为能收敛至全局最优.对于目标数目为很多的优化问题,测试实验结果表明了这种新算法的有效性.

A Preference-Based Multi-Objective Concordance Genetic Algorithm

Recently various evolutionary approaches have been developed for multi-objective optimization. Most of them take Pareto dominance as their selection strategy and do not require any preference information. However these algorithms cannot perform well on problems involving many objectives. By introducing preferences among different criteria, a multi-objective concordance genetic algorithm (MOCGA) is proposed to deal with the problems in the paper. As the number of objectives to be simultaneously optimized increases, the weak dominance is used to compare among the individuals with decision-maker's information. It is proven that the algorithm can guarantee the convergence towards the global optimum. Experimental results of the multi-objective optimization benchmark problems demonstrate the validity of the new algorithm.