区间参数多目标优化问题是普遍存在的，尽管采用进化优化方法，能够解决上述问题，但是，已有方法需要大量的目标函数评价，且生成的Pareto前沿具有很大不确定性，上述缺陷使其难以应用到实际的复杂优化问题中。本项目研究Memetic区间参数多目标进化优化理论及其在太阳能海水淡化中的应用。通过研究，拟揭示全局搜索与局部代理模型构建的内在联系，建立进化个体适应度函数的代理模型，提出基于代理模型估计个体适应值的Memetic区间参数多目标进化优化方法，并应用于小型太阳能海水淡化系统的优化中。本项目是自动化、计算机、数学，以及新能源等多学科交叉、新颖且富有挑战性的研究方向，有非常明确的行业需求，产生的成果能够丰富不确定进化优化理论，提高太阳能海水淡化系统的性能，因此，具有重要理论意义和实际应用价值。

Multi-objective optimization problems with interval parameters are ubiquitous. Evolutionary optimization methods are among the best multi-objective optimizers for the above problems. However, they need a large number of function evaluations, and the Pareto front generated by them involves a big uncertainty. The above shortcomings cause these methods difficult to be applied in real-world applications. This project studies memetic evolutionary optimization theory for multi-objective optimization problems with interval parameters and their applications in solar desalination, and expects to obtain the following achievements: (1) revealing the internal connection between global search and construction of a local surrogate model; (2) constructing surrogate models of fitness functions of an evolutionary individual; (3) presenting memetic evolutionary optimization methods for multi-objective optimization problems with interval parameters based on utilizing the constructed model to estimate the fitness of an individual; (4) applying the proposed methods to optimize a small-size solar desalination system. The project, at the cross edge of such disciplines as automation, computer, mathematics, and new energy, is novel and challenging, and is urgently demanded by industry. The achievements of this project can enrich the uncertain evolutionary optimization theory, and improve the performance of a solar desalination system. Therefore, the project is considerable significance in theory and application.