演化优化的自适应约束处理机理及在生化过程中的应用

项目名称： 演化优化的自适应约束处理机理及在生化过程中的应用

项目编号： No.61503087

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 自动化技术、计算机技术

项目作者： 吴昱

作者单位： 广州大学

项目金额： 20万元

中文摘要： 现实世界的大多数优化问题由于物理限制或功能需求往往都涉及到约束，演化约束优化的传统惩罚函数法和有前景的随机排序法中分别存在惩罚因子和惩罚概率难以选定合适的参数值，存在欠惩罚或过惩罚风险。本项目分别借鉴二目标优化和退火过程研究演化约束优化中可动态控制惩罚因子与惩罚概率的两种自适应约束处理机理。本项目一方面拟建立非劣拟合信息抽取模型有效挖掘二目标非劣前沿蕴含的自适应惩罚因子等关键信息，进而设计自适应非劣拟合约束处理机制利用非劣前沿自适应地控制惩罚因子引导算法搜索到最优的可行解。另一方面，借鉴退火过程中Metropolis接受准则设计自适应随机排序约束处理机制，使算法能根据进化进程与约束违反量自适应地合理确定惩罚概率, 克服随机排序在宏观和微观上的盲目性。最后将采用两种自适应约束处理机制的演化算法应用于生化学中ThreeStep过程和HIV过程等参数估计问题，使其可高效求得这些问题的最优可行解。

中文关键词： 演化算法；约束优化；二目标优化；惩罚函数；随机排序

英文摘要： Most real-world optimization problems involve constraints mainly due to physical limitations or functional requirements. It is difficult to choose a reasonable value for the penalty factor of the traditional penalty function method or the penalty probability of the stochastic ranking method in evolutionary constrained optimization because of the potential risks of over-penalization and under-penalization. In this project, two adaptive mechanisms of handling constraints which can dynamically control the penalty factor and the penalty probability respectively in evolutionary constrained optimization are studied by borrowing ideas from bi-objective optimization and annealing. On one hand, a non-dominated fitting model is built so that the critical information such as the adaptive penalty factor can be mined from the non-dominated front in the bi-objective space in this project. Further, an adaptive constraint-handling mechanism of non-dominated fitting is designed to guide search processes towards the optimal feasible solutions by taking advantage of the non-dominated front to adaptively control the penalty factor. On the other hand, an adaptive constraint-handling mechanism of stochastic ranking is also designed by introducing the metropolis acceptance criteria in annealing. This mechanism can adaptively choose an appropriate penalty probability for stochastic ranking according to the evolutionary process and the overall constraint violation in order to overcome the blindness at a microscopic as well as a macroscopic level. As a result, evolutionary algorithms with two adaptive constraint-handling mechanisms are applied to efficiently solve the constrained parameter estimation problems in biochemical pathways such as Threestep model and HIV model.

英文关键词： evolutionary algorthms;constrained optimization;bi-objective optimization;penalty function;stochastic ranking