论文标题
SKPNSGA-II：基于膝关点的MOEA，具有自适应角度用于任务计划问题
sKPNSGA-II: Knee point based MOEA with self-adaptive angle for Mission Planning Problems
论文作者
论文摘要
现实世界和复杂问题通常具有许多目标功能，必须一次全部优化。在过去的几十年中，多目标进化算法（MOEAS）旨在解决此类问题。然而，有些问题具有许多目标，这些目标导致了通过优化算法获得的大量非主导解决方案。大量非主导的解决方案阻碍了决策者最合适的解决方案。本文提出了一种新算法，该算法旨在从帕累托最佳边界（POF）获得最重要的解决方案。这种方法基于适用于MOEA的锥体分数，该锥体可以找到膝关点溶液。为了获得最佳的圆锥角，我们提出了一个高量分布度量，该度量用于在不断发展的过程中自适应角度。该新算法已用于无人驾驶飞机（UAV）任务计划问题的现实世界应用。实验结果表明，算法性能在超量，溶液的数量以及所需的世代收敛数量方面有显着改善。
Real-world and complex problems have usually many objective functions that have to be optimized all at once. Over the last decades, Multi-Objective Evolutionary Algorithms (MOEAs) are designed to solve this kind of problems. Nevertheless, some problems have many objectives which lead to a large number of non-dominated solutions obtained by the optimization algorithms. The large set of non-dominated solutions hinders the selection of the most appropriate solution by the decision maker. This paper presents a new algorithm that has been designed to obtain the most significant solutions from the Pareto Optimal Frontier (POF). This approach is based on the cone-domination applied to MOEA, which can find the knee point solutions. In order to obtain the best cone angle, we propose a hypervolume-distribution metric, which is used to self-adapt the angle during the evolving process. This new algorithm has been applied to the real world application in Unmanned Air Vehicle (UAV) Mission Planning Problem. The experimental results show a significant improvement of the algorithm performance in terms of hypervolume, number of solutions, and also the required number of generations to converge.
