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A master-apprentice evolutionary algorithm for maximum weighted set K-covering problem
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AI
DOI：10.1007/s10489-022-03531-2.png)
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Abstract
En 中文
The maximum weighted set k-covering problem (MWKCP) is a fundamental optimization problem, which depicts the application scenario of resource-constrained environment and user preference selection. In this paper, the mathematical formulation of MWKCP is given for the first time. Then, a novel master-apprentice evolutionary algorithm (MAE) is proposed for solving this NP-hard optimization problem. In order to make MAE applicable to MWKCP, a path re-linking operator is designed as the mutual learning process of two individuals, and a bare bones fireworks algorithm with explosion amplitude adaptation is adopted as the self-learning stage. Experimental results on 150 classical instances show that the proposed algorithm performs best among all competitors including an exact solver and three heuristic algorithms.
Keywords:
Maximum weighted set k-covering problem
Master-apprentice evolutionary algorithm
Firework algorithm
Path re-linking
Local search
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