An Jaccard similarity coefficient-based evolutionary algorithm for sparse large-scale multi-objective optimization problems

Due to the high-dimensional decision variables and the sparse nature of solutions in sparse large-scale multi-objective optimization problems (SLMOPs), traditional multi-objective evolutionary algorithms (MOEAs) encounter substantial challenges. Researchers have proposed various sparse evolutionary algorithms (SEAs) to address these challenges. However, most existing SEAs focus on accurately identifying non-zero variable positions while neglecting changes in objective function values during optimization. This paper introduces a Jaccard similarity coefficient-based evolutionary algorithm (JSCEA) designed to search for sparse distributions that optimize objective values rapidly and efficiently. Leveraging the Jaccard similarity coefficient (JSC) to identify critical sparse patterns among promising solutions and propagate them to subsequent generations enhances the algorithm’s computing efficiency, particularly under limited computational resources. Experimental results on three real-world problems and eight benchmark tests demonstrate that JSCEA performs competitively on problem sizes of up to 10,000 variables.