论文标题
在多目标图像错误隐藏中应用折衷进化的应用
Application of Compromising Evolution in Multi-objective Image Error Concealment
论文作者
论文摘要
遇到许多多个健身函数的多个多目标优化问题，同时优化了它们的相互偏好是固有的。由于缺乏潜在的生成模型，现有的凸优化方法可能无法为复杂领域（例如图像增强）中的这些问题提供帕累托最佳解决方案。为了消除此类缺点，本报告中提出了折衷的进化方法，以通过利用妥协的概念来修改简单的遗传算法。模拟结果显示了在图像误差隐藏的案例研究中，提出的方法解决多目标优化的功能。
Numerous multi-objective optimization problems encounter with a number of fitness functions to be simultaneously optimized of which their mutual preferences are not inherently known. Suffering from the lack of underlying generative models, the existing convex optimization approaches may fail to derive the Pareto optimal solution for those problems in complicated domains such as image enhancement. In order to obviate such shortcomings, the Compromising Evolution Method is proposed in this report to modify the Simple Genetic Algorithm by utilizing the notion of compromise. The simulation results show the power of the proposed method solving multi-objective optimizations in a case study of image error concealment.
