Proceedings of the
The Nineteenth International Conference on Computational Intelligence and Security (CIS 2023)
December 1 – 4, 2023, Haikou, China
A Short Survey of Large-scale Multi-objective Optimization Algorithms
School of Information Engineering, Ningxia University, Yinchuan, China and Ningxia Key Laboratory of Artificial Intelligence and Information Security for Channeling Computing Resources from the East to the West, Yinchuan, China.
ABSTRACT
Large-scale multi-objective optimization problems (LSMOPs) are prevalent in scientific and engineering applications, and solving these problems holds significant practical significance. In the past few years, many large-scale multi-objective optimization evolutionary algorithms(LSMOEAs) have been proposed and proven to be effective for solving LSMOPs. This paper investigates and gives a short survey on the progress of state-of-the-art LSMOEAs. Firstly, we give a categorization of the existing LSMOEAs: divide and conquer-based algorithms, non-grouping dimension reduction-based algorithms, effective offspring generation-based algorithms, and learning model-based algorithms. Then, the classical algorithms proposed belonging to each category are thoroughly analyzed. Finally, we outline the major challenges faced by LSMOEAs and future research directions.
Keywords: Divide and conquer, Non-grouping dimension reduction, Effective offspring generation, Learning model, LSMOPs.
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School of Information Engineering, Ningxia University, Yinchuan, China and Ningxia Key Laboratory of Artificial Intelligence and Information Security for Channeling Computing Resources from the East to the West, Yinchuan, China.
ABSTRACT
Large-scale multi-objective optimization problems (LSMOPs) are prevalent in scientific and engineering applications, and solving these problems holds significant practical significance. In the past few years, many large-scale multi-objective optimization evolutionary algorithms(LSMOEAs) have been proposed and proven to be effective for solving LSMOPs. This paper investigates and gives a short survey on the progress of state-of-the-art LSMOEAs. Firstly, we give a categorization of the existing LSMOEAs: divide and conquer-based algorithms, non-grouping dimension reduction-based algorithms, effective offspring generation-based algorithms, and learning model-based algorithms. Then, the classical algorithms proposed belonging to each category are thoroughly analyzed. Finally, we outline the major challenges faced by LSMOEAs and future research directions.
Keywords: Divide and conquer, Non-grouping dimension reduction, Effective offspring generation, Learning model, LSMOPs.
Download PDF