Applying Memetic Algorithm for Multi-objective Scheduling Problems

• Main Authors: Yu-Ling Liu, 劉育伶
• Other Authors: 張百棧
• Format: Others
• Language: zh-TW
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/30810029186930856069

碩士 === 元智大學 === 工業工程與管理學系 === 96 === Memetic Algorithm (MA) has been a new approach proposed for years; it is not a uniform method but is generally called as a philosophy. For those algorithms with neighboring search (Local Search) are classified to MA. The approach proposed in this paper is named as Sub-population with Memetic Algorithm (SPMA), which is applied for solving multi-objective Flowshop Scheduling Problems. This paper consists of four phases which firstly apply NEH to be initial solution to search effectively better solution; the second phase is to apply weighted-approach to cluster solved problems for searching more widespread in time constraint; the third phase is to combine with neighboring search to shift the sequence for local search to find out the possible unsearchable feasible solutions; in the final phase, the Artificial Chromosome (AC) with probability matrix will be introduced when the algorithm evolves to certain iteration for injecting to individual to search better combination of chromosomes, this mechanism will make faster convergent time for evolving. For the first test instance, SPMA compares with three algorithms which is MGISPGA, NSGA-II and SPEA2, the measuring index is D1r. In the second instance, the proportion of Pareto Optimal solutions is applied to be the index for evaluating SPMA and MOSA. The experiments result shows that SPMA has the two important characteristics of Pareto solutions simultaneously which is convergence and spread for solving multi-objective Flowshop Scheduling Problems in test instances.