A hyper-heuristic algorithm is a general solution framework that adaptively selects the optimizer to address complex problems.A classical hyper-heuristic framework consists of two levels,including the high-level heuri...A hyper-heuristic algorithm is a general solution framework that adaptively selects the optimizer to address complex problems.A classical hyper-heuristic framework consists of two levels,including the high-level heuristic and a set of low-level heuristics.The low-level heuristics to be used in the optimization process are chosen by the high-level tactics in the hyper-heuristic.In this study,a Cooperative Multi-Stage Hyper-Heuristic(CMS-HH)algorithm is proposed to address certain combinatorial optimization problems.In the CMS-HH,a genetic algorithm is introduced to perturb the initial solution to increase the diversity of the solution.In the search phase,an online learning mechanism based on the multi-armed bandits and relay hybridization technology are proposed to improve the quality of the solution.In addition,a multi-point search is introduced to cooperatively search with a single-point search when the state of the solution does not change in continuous time.The performance of the CMS-HH algorithm is assessed in six specific combinatorial optimization problems,including Boolean satisfiability problems,one-dimensional packing problems,permutation flow-shop scheduling problems,personnel scheduling problems,traveling salesman problems,and vehicle routing problems.The experimental results demonstrate the efficiency and significance of the proposed CMS-HH algorithm.展开更多
基金supported by the National Key Research and Development Plan(No.2020YFB1713600)the National Natural Science Foundation of China(No.62063021)+2 种基金the Lanzhou Science Bureau Project(No.2018-rc-98)Public Welfare Project of Zhejiang Natural Science Foundation(No.LGJ19E050001)Project of Zhejiang Natural Science Foundation(No.LQ20F020011).
文摘A hyper-heuristic algorithm is a general solution framework that adaptively selects the optimizer to address complex problems.A classical hyper-heuristic framework consists of two levels,including the high-level heuristic and a set of low-level heuristics.The low-level heuristics to be used in the optimization process are chosen by the high-level tactics in the hyper-heuristic.In this study,a Cooperative Multi-Stage Hyper-Heuristic(CMS-HH)algorithm is proposed to address certain combinatorial optimization problems.In the CMS-HH,a genetic algorithm is introduced to perturb the initial solution to increase the diversity of the solution.In the search phase,an online learning mechanism based on the multi-armed bandits and relay hybridization technology are proposed to improve the quality of the solution.In addition,a multi-point search is introduced to cooperatively search with a single-point search when the state of the solution does not change in continuous time.The performance of the CMS-HH algorithm is assessed in six specific combinatorial optimization problems,including Boolean satisfiability problems,one-dimensional packing problems,permutation flow-shop scheduling problems,personnel scheduling problems,traveling salesman problems,and vehicle routing problems.The experimental results demonstrate the efficiency and significance of the proposed CMS-HH algorithm.
