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Opposition-based learning memetic algorithm for the maximum intersection of k-subsets problem
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DOI：10.1016/j.engappai.2025.113271.png)
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Abstract
En 中文
Given m elements and n subsets of elements, the maximum intersection of k-subsets (kMIS) problem is to select k subsets of elements to maximize the number of elements simultaneously covered by all of the selected subsets. As a general model, kMIS can be used to formulate some practical problems including data privacy control, community detection, and deoxyribonucleic acid microarray technology. This paper presents an opposition-based learning memetic algorithm that integrates opposition-based learning initialization, adaptive crossover, and solution-based tabu search. Experimental results on 608 instances show that the algorithm competes favorably with the state-of-the-art methods. The importance of the algorithmic components is experimentally validated.
Keywords:
Opposition-based learning
Solution-based tabu search
Maximum intersection
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