Abstract:
Interval multi-objective optimization problems are very popular and
important. There exist few evolutionary optimization methods for directly solving
them, and these existing methods aim at finding a set of well-converged and evenly-
distributed Paretooptimal solutions. Three preference-based interval multi-objective
evolutionary algorithms are surveyed to obtain the most preferred solution fitted the
decision maker’s preferences. Additionally, the above algorithms are applied in robot
path planning problems under a special environment, and are compared about their
performance. The research enriches the methods of solving robot path planning
problems under a special environment, and improves the optimization performance of
the problems.
区间参数多目标优化问题是普遍存在且非常重要的。目前直接求解该
类问题的进化优化方法非常少,且已有方法的目的是找到收敛性好且分布均匀
的 Pareto 最优解集。为得到符合决策者偏好的最满意解,本文综述 3 种基于偏
好的区间多目标进化算法,并将其应用于特定环境下机器人路径规划问题,比
较 3 种算法的性能。研究结果可丰富特定环境下机器人路径规划的求解方法,
提高机器人路径优化效果。