Abstract
Hyper-heuristic is a crucial answer to how problem-specific heuristics can be selected or generated automatically for solving different optimization problems. However, selecting a low-level heuristics algorithm has complicated designing for the efficient hyper-heuristic algorithm. This paper presents a new design of a hyper-heuristic (NHH) algorithm to address this limitation. In NHH, the population is divided into two different types of individuals. The first group is explorative, while the second group is exploitative. The NHH incorporates three components, which are a hybrid vector-based operator, an adaptive selection operator and a behavioral schema. In the hybrid vector-based operator, four low-level operators with different characteristics are incorporated which are divided into two groups explorative or exploitative operators. It enables NHH to apply different search strategies during the optimization process. An adaptive selection operator which is called an adaptive selection based on the individual situation (ASIS), assigns one of the four operators of hybrid vector-based operator to each individual based on their role (explorative and exploitative) and simplified Q-learning. The NHH’s behavioral schema is developed inspired by the human social behavioral schema. This supportive operator includes progress, hard-working, adventure, and changing. Individuals adopt them during the search process which helps the algorithm to better execute different problems. Extensive experiments have been conducted on 35 well-known benchmark functions and CEC 2017 test suite. The performance comparison with the other comparative 11 algorithms verified the reliability of the proposed algorithm.
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Bozorgi, S.M., Yazdani, S., Golsorkhtabaramiri, M. et al. A hyper-heuristic approach based on adaptive selection operator and behavioral schema for global optimization. Soft Comput 27, 16759–16808 (2023). https://doi.org/10.1007/s00500-023-09018-7
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DOI: https://doi.org/10.1007/s00500-023-09018-7