Memetic immune algorithm for multiobjective optimization

作者：Qi Yu Tao^*; Liu Fang; Chang Wei Yuan; Ma Xiao Liang; Jiao Li Cheng

来源：Journal of Software, 2013, 24(7): 1529-1544.

DOI：10.3724/SP.J.1001.2013.04282

• Artificial immune algorithms
• Differential Evolution
• Immune algorithms
• Local search operators
• Local searching
• Memetic algorithms
• Pareto dominance
• Spatial relations

A Memetic immune algorithm for multiobjective optimization (MIAMO) is proposed by introducing two types of local search operators. These operators are the Pareto dominance based descent operator and the differential evolution based operator. In MIAMO, the position and spatial relations between antibodies in the decision space are used to design the two heuristic local searching strategies with the assistance of which the efficiency of the immune multiobjective optimization algorithm can be improved. Experimental results indicate that, comparing with the other four efficient multiobjective optimization algorithms, the MIAMO performs better in approximation, uniformity, and coverage. It converges significantly faster than the immune multiobjective optimization algorithm.

• 出版日期2013
• 单位西安电子科技大学