A Novel Approach for Resolving Multi-Objective Optimization Problems- Immune Algorithms

• Main Authors: Cheng-Wei Ma, 馬誠韋
• Other Authors: Yun-Kung Chung
• Format: Others
• Language: zh-TW
• Published: 2002
• Online Access: http://ndltd.ncl.edu.tw/handle/60694306771310700934

碩士 === 元智大學 === 工業工程與管理學系 === 90 === It is well known that an optimal technique in the form of biological swarm behavior, e.g., ants, genes, neurons and cells, is an universal method in which the solution procedure is achieved by the nature cooperative behavior. This is the metaphor that an optimization problem should be suitably decomposed into several constituent sub-problems that can be solved in the way of the cooperation one another. In this thesis, an artificial immune system (AIS), one of the bio-computation methods, is presented to be a novel approach for resolving multi-objective optimization problems. The structure of a multi-objective optimization is very suitable for the biological cooperation’s nature. Two search procedures are used to cooperatively perform the multi-objective optimization. The local AIS search is to find the optimal solution of an individual (or decomposed) objective with all of constraints; the global one is to cooperatively minimize the conflict among the all objectives. Emphatically, the cooperative behavior of the proposed AIS distinguishes from that of the original one. Also, the optimizing ability of the proposed AIS algorithm is identified by comparing it with that of the classical multi-objective optimization techniques appeared in the published articles. The compared results show that the proposed AIS algorithm can significantly reduce the convergence time and potentially increase the solution accuracy.