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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Bibliographic Details
Published in:Complex System Modeling and Simulation Vol. 1; no. 2; pp. 91 - 108
Main Authors: Zhao, Fuqing, Di, Shilu, Cao, Jie, Tang, Jianxin, Jonrinaldi
Format: Journal Article
Language:English
Published: Tsinghua University Press 01.06.2021
Subjects:
combinatorial optimization
hyper-heuristic algorithm
multi-armed bandits (mab)
relay hybridization technology
ISSN:2096-9929, 2096-9929
Online Access:Get full text
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Summary: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.
ISSN:2096-9929
2096-9929
DOI:10.23919/CSMS.2021.0010