# Development of crow search algorithm using the characteristics of qubits and application of engineering problems

## TL;DR

This paper introduces a new quantum-based crow search algorithm that improves optimization performance for engineering problems.

## Contribution

The novel QbCSA algorithm combines quantum systems with crow search to enhance search efficiency and stability in optimization.

## Key findings

- QbCSA outperforms QbHSA in convergence stability and solution diversity for multimodal problems.
- The algorithm achieves comparable performance to CSA while using qubit-based encoding.
- Engineering examples confirm the practical applicability of QbCSA.

## Abstract

Recently, researchers have attempted to develop a new algorithm by combining quantum systems and metaheuristics algorithms and are confirming its applicability in engineering optimization problems. This paper proposes a new QbCSA (quantum-based crow search algorithm) combining quantum systems and CSA (crow search algorithm). Unlike CSA, the initial matrix of QbCSA consists of qubits and performs operations through spin and measurement processes. Six benchmark functions were used to compare the convergence performance according to the parameter change used in the developed QbCSA, and the optimal parameter range is suggested. In addition, the CEC2019 benchmark functions and four engineering example problems were solved and compared with the results of previous studies. QbCSA demonstrated comparable performance to CSA, which uses decimal-based design variables, while achieving lower variance and more stable convergence than QbHSA. In particular, for multimodal optimization problems, QbCSA exhibited superior search efficiency and solution diversity. Furthermore, the four engineering examples confirmed the practical applicability of QbCSA, and these results indicate that qubit-based encoding can enhance the search efficiency of CSA and suggest broader applicability to engineering optimization problems.