Large‐Scale Multi‐Objective Optimization Algorithms: A Decade Survey

ABSTRACT Large‐scale multi‐objective optimization problems (LSMOPs) are characterised by concurrent optimization of multiple conflicting objectives and no fewer than 100 decision variables. They widely exist in the fields of practical engineering and scientific research. Over the past decade, many large‐scale multi‐objective evolutionary algorithms (LSMOEAs) have emerged to address LSMOPs. This paper systematically reviews and comprehensively analyzes the ideas, advantages, disadvantages, and latest developments of these LSMOEAs. Firstly, it introduces the relevant concepts of LSMOEAs. Then classify them into four categories: decision variable grouping‐based LSMOEAs, non‐grouping dimensionality reduction‐based LSMOEAs, effective offspring generation‐based LSMOEAs, and learning models‐based LSMOEAs. It analyzes representative algorithms in each category, elaborating on their core strategies, advantages, and disadvantages. Finally, it explores the applications of LSMOEAs in computer vision, like tackling pixel‐level correlation, high‐resolution feature redundancy, dynamic target tracking, and complex visual modelling. This paper provides readers with a comprehensive and systematic overview of LSMOEAs, serving as a valuable reference for both researchers entering this field and practitioners seeking to select appropriate algorithms for practical problems.