Progress of Research on Multi-Objective Evolutionary Algorithms
Zheng Xiang · 2007
Evolutionary Algorithms (EAs have become popular in multi-objective optimization problems, which are parallel in nature and don’t require differentiability of objective functions and constraints, and also which deal with a set of possible solutions in a single run. Many Multi-Objective Evolutionary Algorithms (MOEAs are proposed at present. Firstly, this paper reviews the origin of MOEAs; secondly, the first generation MOEAs are analyzed, which are characterized by simplicity, such as NSGA, NPGA, MOGA and so on, and the achievements and shortage during the first generation are also discussed. Thirdly, the MOEAs developed during the second generation, including SPEA、PAES、NSGA II、NPGA2、PESA、Micro-GA and etc., are detailed and compared, which use elitism to improve the efficiency. At last, some important research areas of MOEA are addressed.