Supported by the National Natural Science Foundation of China under Grant No.60703107 (国家自然科学基金); the National High-Tech Research and Development Plan of China under Grant No.2006AA01Z107 (国家高技术研究发展计划(863)); the National Basic Research Program of China under Grant No.2006CB705700 (国家重点基础研究发展计划(973)); the Program for Cheung Kong Scholars and Innovative Research Team in University of China under Grant No.IRT0645 (长江学者和创新团队支持计划)
Evolutionary multi-objective optimization (EMO), whose main task is to deal with multi-objective optimization problems by evolutionary computation, has become a hot topic in evolutionary computation community. After summarizing the EMO algorithms before 2003 briefly, the recent advances in EMO are discussed in details. The current research directions are concluded. On the one hand, more new evolutionary paradigms have been introduced into EMO community, such as particle swarm optimization, artificial immune systems, and estimation distribution algorithms. On the other hand, in order to deal with many-objective optimization problems, many new dominance schemes different from traditional Pareto-dominance come forth. Furthermore, the essential characteristics of multi-objective optimization problems are deeply investigated. This paper also gives experimental comparison of several representative algorithms. Finally, several viewpoints for the future research of EMO are proposed.