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

Abstract

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 ε-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 aε-ODPICEA-g, shows a better performance than PICEA-g on both 2- and 7-objective benchmark problems as well as a real-world problem.

Keywords:

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

Notes

1 New parents (of size μ) are selected from a combined set of parents (of size μ) and offspring (of size λ).

2 The data are obtained from Dufo-López et al. (2011).

Additional information

Funding

This work is supported by the National Natural Science Foundation of China [Nos. 61403404, 71371181] and the National University of Defense Technology [No. JC 14-05-01].