Dual-Chain Quantum Genetic Algorithm Source Code Implementation

The dual-chain quantum genetic algorithm is an advanced intelligent optimization method that integrates quantum computing principles with genetic algorithms, demonstrating superior global search capability and faster convergence speed compared to conventional genetic algorithms. This algorithm employs quantum bit encoding, utilizing a dual-chain structure to simultaneously maintain two potential solutions, thereby expanding the search space through quantum superposition properties. The core methodology encompasses several critical phases: Quantum Chromosome Encoding: Individuals are represented using quantum bit probability amplitudes, enabling simultaneous expression of multiple state superpositions. Dual-Chain Structure Design: Each chain represents a solution space, with inter-chain interactions enhancing algorithm diversity. Quantum Rotation Gate Update: This constitutes the core operational mechanism, evolving solutions through adjustments to quantum bit phases. Measurement Operation: Quantum states are converted to classical solutions for fitness evaluation. Adaptive Mutation Mechanism: Mutation probability is dynamically adjusted based on population diversity. In MATLAB implementations, key components typically include quantum population initialization functions, fitness evaluation modules, and quantum gate update functions. The algorithm iteratively optimizes rotation angles of quantum gates to guide population evolution toward optimal solutions. Critical implementation aspects involve defining qubit representation matrices, designing quantum rotation gate operations using angle adjustment formulas, and implementing collapse operations through probability-based state selection. This algorithm is particularly effective for solving complex multimodal function optimization problems, with broad application potential in engineering optimization and machine learning parameter tuning. Compared to traditional optimization algorithms, it demonstrates enhanced capability in preventing premature convergence issues through its quantum-inspired exploration mechanisms. Implementation typically requires careful parameter tuning of rotation angles and collapse probabilities to balance exploration and exploitation phases.