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

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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.