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摘要
Evolutionary algorithms are successfully used for many-objective optimization. However, solutions are prone to become nondominated from each other with the increase in the number of objectives, which reduces the efficiency of Pareto dominance-based algorithms. In this paper, a new hypervolume-based differential evolution algorithm (MODEhv) is proposed for many-objective optimization problems (MaOPs). In MODEhv, a modified differential evolution paradigm with automatic parameter configuration strategy is used to balance exploration and exploitation of the algorithm. Besides, the hypervolume indicator is incorporated for the selection of solutions to be varied and solutions to be kept in archive respectively. Finally, a threshold technique is employed to improve diversity of solutions in archive. MODEhv is investigated on a set of widely used benchmark problems and compared with five state-of-the-art algorithms. The experimental results show the efficiency of MODEhv for solving MaOPs.
- 出版日期2017-12
- 单位北京工业大学
