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

Area of Science:

• Computational Intelligence
• Quantum Computing
• Optimization Algorithms

Background:

• Metaheuristics algorithms are increasingly combined with quantum systems for engineering optimization.
• Existing algorithms like Crow Search Algorithm (CSA) have limitations in certain optimization scenarios.

Purpose of the Study:

• To propose a novel quantum-based crow search algorithm (QbCSA) by integrating quantum systems with CSA.
• To evaluate the performance and applicability of QbCSA in engineering optimization problems.

Main Methods:

• Developed QbCSA utilizing qubits, spin, and measurement processes for its initial matrix and operations.
• Tested QbCSA on six benchmark functions to analyze convergence performance and suggest optimal parameters.
• Validated QbCSA on CEC2019 benchmark functions and four engineering problems, comparing results with existing studies.

Main Results:

• QbCSA showed comparable performance to traditional CSA but with lower variance and more stable convergence.
• The algorithm demonstrated superior search efficiency and solution diversity, especially for multimodal optimization problems.
• Practical applicability was confirmed through four engineering example problems.

Conclusions:

• Qubit-based encoding enhances the search efficiency of CSA.
• QbCSA offers a promising approach for broader applicability in engineering optimization problems.