Abstract
While meta-heuristics are usually designed for the optimization problems of the same domain and can achieve superior performance compared with heuristics, their performances suffer severely when dealing with cross-domain problems. Recently, many-objective optimization methods have been proposed to handle the increase of the objectives, and this further renders the cross-domain problems more challenging. A technique known as hyper-heuristic (HH) has been proposed to effectively handle cross-domain optimization problems, without the need to alter the HH extensively. However, existing HHs focus mainly on single- or multi-objective optimization problems, and little work has been done on the many-objective optimization problems (MaOOPs) and lack delta evaluation. Inspired by the sport of cricket, we propose a novel many-objective selection hyper-heuristic technique named cricket-based selection hyper-heuristic (CB-SHH) in this paper, to produce well-diverse and converged optimal solutions for MaOOPs. To the best of our knowledge, we are the first to propose a sports-inspired HH. The proposed technique computes the objective value based on the most recent modification to address one of the problems with HHs, namely the lack of using delta evaluation, which is another contribution of the paper. In CB-SHH, the exploitation and the exploration have been handled using the greedy and randomization mechanism, respectively. Moreover, many-objective meta-heuristics have been used as low-level heuristics to drive the CB-SHH search. CB-SHH has been tested against the benchmark and real-life datasets and has performed significantly better or equal when compared with other meta-heuristics and HHs on 196 out of 200 instances based on Hypervolume (HV) values. Moreover, CB-SHH has the best cross-domain performance measured by \(\mu \) norm mean values i.e. producing 234.8% and 76.4% better results than state-of-the-art HH across HV and IGD respectively.
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Acknowledgement
Adeem Ali Anwar is the recipient of an iMQRES funded by Macquarie University, NSW (allocation No. 20213183) and Dr. Xuyun Zhang is the recipient of an ARC DECRA (project No. DE210101458) funded by the Australian Government.
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Anwar, A.A., Younas, I., Liu, G., Beheshti, A., Zhang, X. (2022). A Cricket-Based Selection Hyper-Heuristic for Many-Objective Optimization Problems. In: Chen, W., Yao, L., Cai, T., Pan, S., Shen, T., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2022. Lecture Notes in Computer Science(), vol 13726. Springer, Cham. https://doi.org/10.1007/978-3-031-22137-8_23
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