Evolutionary Computation for Multiobjective Optimization Using Taguchi MethodEvolutionary Computation for Multiobjective Optimization Using Taguchi Method

• Main Authors: Chien-Chin Peng, 彭乾欽
• Other Authors: Cheng-Yuan Tang
• Format: Others
• Language: zh-TW
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/94593058197290522271

碩士 === 華梵大學 === 資訊管理學系碩士班 === 94 === Abstract Evolutionary computation, such as evolutionary programming, evolution strategy, genetic algorithm, and genetic programming etc., has already been widely used for solving the optimization problems. Evolutionary algorithms can be divided into two categories: single objective and multiple objectives. With single objective, we introduce two modified evolutionary algorithms: the orthogonal genetic algorithm with quantization (OGA/Q), which utilizes the orthogonal design and quantization technique, and the Hybrid Taguchi-Genetic Algorithm (HTGA), which utilized the Taguchi method. With multiple objectives, because of the multiple objective functions, the design of multi-objective genetic algorithms focuses on fitness assignment, diversity preservation, and the addition of elite set. In this paper, we compare two multi-objective evolutionary algorithms: Strength Pareto Evolutionary Algorithm 2(SPEA2) and Intelligent Multi-objective Evolutionary Algorithms (IMOEA). In this paper, we propose to include an additional random population besides the original initial population, and the proposed method can expand the searching space to identify better solutions. In each generation we replace the random population and select only the non-dominated individuals into the elite set. The proposed method can explore more general solution space and locate better solutions. We then apply the Taguchi method to generate better individuals in the random population, so individuals in the random population are more representative. In the experiments, we show that the proposed method that includes random population can lead to better solutions.