An enhanced preference-inspired co-evolutionary algorithm using orthogonal design and an epsilon-dominance archiving strategy

作者：Zhang, Tao; Wang, Rui^*; Liu, Yajie; Guo, Bo

来源：Engineering Optimization, 2016, 48(3): 415-436.

DOI：10.1080/0305215X.2015.1012078

• multi-objective optimization
• evolutionary computation
• PICEA orthogonal design
• adaptive epsilon-dominance

The concept of co-evolution of preferences and candidate solutions has proven effective for many-objective optimization. One realization of this concept, namely preference-inspired co-evolutionary algorithms using goal vectors (PICEA-g), is found to outperform many state-of-the-art multi-objective evolutionary algorithms for many-objective problems. However, PICEA-g is susceptible to unevenness in the solution distribution. This study seeks to tackle this issue and to improve the performance of PICEA-g further. Two established strategies are incorporated into PICEA-g: (i) an adaptive epsilon-dominance archiving strategy which is applied to obtain a set of well spread solutions online; and (ii) the orthogonal design method which is used to initialize candidate solutions. The improved algorithm, denoted as a epsilon-ODPICEA-g, shows a better performance than PICEA-g on both 2- and 7-objective benchmark problems as well as a real-world problem.

• 出版日期2016-3-3
• 单位中国人民解放军国防科学技术大学