Modular Feedback Assistance Hybrid Evolution Algorithm Based on Cloud Environment for Job Shop Scheduling Problem Optimization

• Main Authors: Yi-Chen Jhou, 周奕辰
• Other Authors: 簡銘伸
• Format: Others
• Language: en_US
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/x8s9jy

碩士 === 國立虎尾科技大學 === 資訊工程研究所 === 103 === This thesis develops Modular Feedback Assistance Hybrid Evolution Algorithm Based on Cloud Environment to find the optimal solution of NP-complete problems such as job shop scheduling problems. In this research, the different steps and types of the evolution algorithm can be established via individual thread procedures based on various virtual machines in cloud. After the evolution steps, methods, or procedures of the genetic algorithm, the fitness evaluation result and survival ratio of different crossover methods in the current generation can be used for the proposed feedback assistance method. The feedback assistance method can be added into the evolution procedure and dynamically emphasize the corresponding methods or procedures with better performance in optimal solution searching. All the steps or methods in genetic algorithm are created independently (or modular). Furthermore, via using the feedback assistance, the convergence time of the optimal solution can be enhanced.