1. A Fast Objective Reduction Algorithm Based on Dominance Structure for Many Objective Optimization
- Author
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Yiu-ming Cheung, Hai-Lin Liu, and Fangqing Gu
- Subjects
021103 operations research ,Optimization problem ,Computational complexity theory ,Computer science ,0211 other engineering and technologies ,Evolutionary algorithm ,Pareto principle ,02 engineering and technology ,Multi-objective optimization ,Reduction (complexity) ,Set (abstract data type) ,0202 electrical engineering, electronic engineering, information engineering ,Redundancy (engineering) ,020201 artificial intelligence & image processing ,Algorithm - Abstract
The performance of the most existing classical evolutionary multiobjective optimization (EMO) algorithms, especially for Pareto-based EMO algorithms, generally deteriorates over the number of objectives in solving many-objective optimization problems (MaOPs), in which the number of objectives is greater than three. Objective reduction methods that transform an MaOP into the one with few objectives, are a promising way for solving MaOPs. The dominance-based objective reduction methods, e.g. k-EMOSS and \(\delta \)-MOSS, omitting an objective while preserving the dominant structure of the individuals as much as possible, can achieve good performance. However, these algorithms have higher computational complexity. Therefore, this paper presents a novel measure for measuring the capacity of preserving the dominance structure of an objective set, i.e., the redundancy of an objective to an objective set. Subsequently, we propose a fast algorithm to find a minimum set of objectives preserving the dominance structure as much as possible. We compare the proposed algorithm with its counterparts on eleven test instances. Numerical studies show the effectiveness of the proposed algorithm.
- Published
- 2017
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