Quantum Inspired Hazelnut Tree Search Algorithm

In this paper, a quantum-inspired optimization algorithm, Quantum Inspired Hazelnut Tree Search (QIHTS), is proposed to represent a modification of the classical Hazelnut Tree Search (HTS) approach. The promising nature of the hybrid optimization technique has been demonstrated through experiments in which the classical heuristic is mixed with quantum-inspired concepts to achieve an advantageous and scalable optimization technique applicable in real-life circumstances. One of the main objectives of QIHTS is to find a sufficiently good balance between exploration and exploitation by transforming the classical HTS concept into a quantum-inspired approach that employs superposition-based exploration, controlled chaotic dynamics, and probabilistic blending. We compare the HTS with the proposed QIHTS method using a range of benchmark functions, including unimodal, multimodal, composite, and shifted functions. All population settings, the number of generations, as well as the chaos function for the structured noise, can be guaranteed to have consistent characteristics due to the control of the experimental design.Through a range of benchmark tests, it has been observed that QIHTS outdoes its classical counterpart in terms of the rate at which it arrives at an optimal location and the quality of the solution. By integrating quantum-inspired methods, the proposed algorithm can cope with complex search spaces and at the same time bypass local optima much more effectively.