A Survey of Evolutionary Algorithms for Multi-objective Optimization Problems with Irregular Pareto Fronts

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A Survey of Evolutionary Algorithms for Multi-objective Optimization Problems with Irregular Pareto Fronts

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[1]HUA Yicun,LIU Qiqi,HAO Kuangrong,et al.A Survey of Evolutionary Algorithms for Multi-objective Optimization Problems with Irregular Pareto Fronts[J].Journal of Zhengzhou University (Engineering Science),2021,42(01):1-8.[doi:10.13705/j.issn.1671-6833.2021.01.001]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 42 Number of periods: 2021 01 Page number: 1-8 Column: Public date: 2021-03-14

Title:: A Survey of Evolutionary Algorithms for Multi-objective Optimization Problems with Irregular Pareto Fronts

Author(s):: HUA Yicun1; LIU Qiqi2; HAO Kuangrong1; JIN Yaochu1; 2; 1.College of Information Science and Technology, Donghua University, Shanghai 201620, China; 2.Department of Computer Science, University of Surrey, Surrey GU2 7XH, U.K.

Keywords:: multi-objective optimization; evolutionary algorithm; irregular Pareto front; survey

CLC:: TP301

DOI:: 10.13705/j.issn.1671-6833.2021.01.001

Abstract:: In reality, the Pareto fronts of multi-objective optimization problems are often irregular. Evolutionary algorithms for such problems have gradually become a hot topic. This paper provides a survey of the existing evolutionary algorithms for the multi-objective optimization problems with irregular Pareto fronts, gives a general mathematical description of the multi-objective optimization problems, and introduces the relevant definitions in the research field such as dominated and non-dominated solutions. It suggests a taxonomy of the typical multi-objective optimization test problems with irregular Pareto fronts, as well as the actual multi-objective optimization test problems with irregular Pareto fronts such as car crash test problem. The existing evolutionary algorithms for multi-objective optimization problems with irregular Pareto fronts are divided into four categories: the methods of adjusting the reference vectors according to the population distribution, the fixed reference vectors merging other auxiliary methods, the methods of reference points, and the methods of clustering and partitioning. Their strengths and weaknesses are discussed. Although evolutionary algorithms for multi-objective optimization problems with irregular Pareto fronts have achieved certain success, existing algorithms generally perform well only on some irregular Pareto front problems. Algorithms that can work efficiently on all kinds of irregular Pareto front problems are yet to be developed. High dimensional, dynamic and the data-driven multi-objective problems with irregular Pareto fronts remain to be solved. More intelligent evolutionary algorithms that can identify and handle multiple types of multi-objective optimization problems with irregular Pareto fronts are the focus of future research. Hybrid approaches, transfer learning or multi-task learning and optimization combined with evolutionary computation are possible solutions.

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Last Update: 2021-03-15