A Fast Objective Reduction Algorithm Based on Dominance Structure for Many Objective Optimization

  • Fangqing Gu
  • Hai-Lin Liu
  • Yiu-ming CheungEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10593)


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.


Many-objective optimization Evolutionary algorithm Objective reduction 


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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Guangdong University of TechnologyGuangdongChina
  2. 2.Department of Computer ScienceHong Kong Baptist University (HKBU)Hong Kong SARChina
  3. 3.HKBU Institute of Research and Continuing EducationShenzhenChina

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