期刊
PROCEEDINGS OF THE 2023 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, GECCO 2023
卷 -, 期 -, 页码 339-347出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3583131.3590399
关键词
Adaptive operator selection; local search; metaheuristics; experiencebased optimisation; capacitated vehicle routing problem; combinatorial optimisation
This paper proposes an empirical analysis method to study the relationship between search operators in combinatorial optimization problems. The correlation between their local optima is measured to quantify their relationship. The results show a consistent pattern in the correlation between commonly used operators. Based on this, a novel approach for adaptively selecting operators is proposed, which outperforms commonly used methods.
For solving combinatorial optimisation problems with metaheuristics, different search operators are applied for sampling new solutions in the neighbourhood of a given solution. It is important to understand the relationship between operators for various purposes, e.g., adaptively deciding when to use which operator to find optimal solutions efficiently. However, it is difficult to theoretically analyse this relationship, especially in the complex solution space of combinatorial optimisation problems. In this paper, we propose to empirically analyse the relationship between operators in terms of the correlation between their local optima and develop a measure for quantifying their relationship. The comprehensive analyses on a wide range of capacitated vehicle routing problem benchmark instances show that there is a consistent pattern in the correlation between commonly used operators. Based on this newly proposed local optima correlation metric, we propose a novel approach for adaptively selecting among the operators during the search process. The core intention is to improve search efficiency by preventing wasting computational resources on exploring neighbourhoods where the local optima have already been reached. Experiments on randomly generated instances and commonly used benchmark datasets are conducted. Results show that the proposed approach outperforms commonly used adaptive operator selection methods.
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