Evolutionary Algorithm For Multi-objective Optimization And Decision-making Methods

Many, if not most, optimization problem have multiple objectives. Historically, multiple objectives ( i. e. ,attributes or criteria ) have been combined to form a scalar objective function, usually through a linear combination ( weighted sum ) of the multiple attributes, or by turning objectives into constrains. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands the user to have knowledge about the underlying problem. Moreover, the optimal solutions of multiple objectives is a set of Pareto optimal points , instead of a single point.Evolutionary Computation gets inspirations and ideas from natural evolutionary processes. Due to its intrinsic parallelism, self-organizing, adaptation and self-learning intelligent properties, evolutionary computation has large potential to solve multiple objectives optimal solutions. The multiple objectives optimization and decision-making has become an important research area of evolutionary computation in recent years.In this paper, we summarize some classical multiple objectives optimization and decision making methods, and then introduce the research progress of 'his area in evolutionary field. Evolutionary computation and theory are separately study, in these base using evolutionary methed research multiple optimization problems. The main work of this paper is the investigation of follow multiple objectives optimization algorithms: MSGA, VEGA, NSGA and NPGA. Spread niche share technology to multiple optimization problem and then study the desigh of niche size. The properties of the algorithms such as convergence, effective, parameter selection, distribution of the population also has studied by experiments. In order to improve the performance of algorithms' some modify has tried.The study on multiple objectives optimization based evolutionary computation in this paper has some apply value. It also has important use in the optimization and decesion making problems of engineering, economy, management, military field, system engineering and operations.