Abstract
It is acknowledged that no single heuristic can outperform all the others in every optimization problem. This has given rise to hyper-heuristic methods for providing solutions to a wider range of problems. In this work, a set of five non-competing low-level heuristics is proposed in a hyper-heuristic framework. The multi-armed bandit problem analogy is efficiently leveraged and Thompson Sampling is used to actively select the best heuristic for online optimization. The proposed method is compared against ten population-based metaheuristic algorithms on the well-known CEC’05 optimizing benchmark consisting of 23 functions of various landscapes. The results show that the proposed algorithm is the only one able to find the global minimum of all functions with remarkable consistency.
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