A Novel Cooperative Multi-Stage Hyper-Heuristic for Combination Optimization Problems
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 th...
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| Vydáno v: | Complex System Modeling and Simulation Ročník 1; číslo 2; s. 91 - 108 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Tsinghua University Press
01.06.2021
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| ISSN: | 2096-9929, 2096-9929 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Zhao, Fuqing Cao, Jie Jonrinaldi Tang, Jianxin Di, Shilu |
| Author_xml | – sequence: 1 givenname: Fuqing surname: Zhao fullname: Zhao, Fuqing – sequence: 2 givenname: Shilu surname: Di fullname: Di, Shilu – sequence: 3 givenname: Jie surname: Cao fullname: Cao, Jie – sequence: 4 givenname: Jianxin surname: Tang fullname: Tang, Jianxin – sequence: 5 surname: Jonrinaldi fullname: Jonrinaldi |
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| Title | A Novel Cooperative Multi-Stage Hyper-Heuristic for Combination Optimization Problems |
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