Oppostition-based evolutionary algorithms: toward solving high-dimensional optimization problems efficiently

对科学或工程问题进行建模通常会导致优化问题。优化为各种应用中的决策提供了正式的基础，从工程设计到医疗保健、金融和交通等面向服务的应用。在这个方案中，进化算法(EA)将被用来解决那些用经典方法很难或不可能解决的优化问题。解决混合类型变量、动态环境、多目标约束函数和非解析函数的问题都是突出EAS突出能力的例子。但目前，由于进化的本质，进化算法在计算上是昂贵的。此外，区域经济实体还受到维度问题的困扰。这意味着它们的性能随着搜索空间维度的增加而迅速恶化。在研究和设计过程中，这些问题的快速解决是非常必要的。因此，加速环境影响分析是科学界在较短的时间间隔内解决大规模问题的一个重要问题。这项研究开发了新的EA加速方案，并开发了智能采样方法，这是高维问题特别需要的。将这些方案和采样方法结合起来，将产生更高效、更健壮的方法来加速著名的进化算法，并有效地解决高维问题。对于耗时的优化问题，减少函数的赋值要求很高。求解高维昂贵的优化问题是一个极具挑战性的研究领域。这项提议旨在为这一领域做出重大贡献。它将为高素质的人才提供一个非常好的培训环境，他们可以成为加拿大未来在科学和工程应用的优化技术方面的领导者和开拓性的研究人员。这些应用依赖于在产品设计或解决科学问题的过程中成功地找到一百个参数的最佳值。

Modeling of a scientific or engineering problem often leads to an optimization problem. Optimization provides a formal basis for decision making in a wide variety of applications, ranging from engineering design to service oriented applications such as healthcare, finance, and transportation. In this proposal, Evolutionary Algorithms (EAs) will be investigated to solve those optimization problems, which are otherwise difficult or impossible to solve by classical methods. Solving problems with mixed-type variables, dynamic environments, multi-objective constrained functions, and non-analytical functions are examples that highlight the outstanding capabilities of EAs. But currently, EAs are computationally expensive because of their evolutionary nature. Furthermore, EAs suffer from the problem of dimensionality. This means that their performance deteriorates quickly as the dimensionality of the search space increases. Rapid solutions of the problems are highly desirable during research and design processes. As a consequence, acceleration of EAs is a significant issue of importance for the scientific community to solve large-scale problems in a smaller time interval. This research develops new acceleration schemes for EAs, and also smart sampling methods, which are needed particularly for high-dimensional problems. Combining these proposed schemes and sampling methods would result in more efficient and robust approaches to accelerate well-known evolutionary algorithms and effectively solve high-dimensional problems. Reduction of function evaluations for time-consuming optimization problems is highly demanding. Solving high-dimensional expensive optimization problems is a challenging research area. This proposal aims to make significant contributions to this field. It would provide a very good training environment for highly qualified personnel, who can become Canada's future leaders and pioneering researchers in optimization techniques for applications in science and engineering. These applications depend on successfully finding optimum values for a hundred parameters during design of a product or solving a scientific problem.