Quantum-Inspired Acromyrmex Evolutionary Algorithm.

Area of Science:

• Computational Science
• Artificial Intelligence
• Optimization Algorithms

Background:

• Classical computers struggle with complex optimization problems common in machine learning and traffic management.
• Quantum computing offers a potential solution but remains in early development stages.
• Quantum-inspired algorithms (QIAs) bridge the gap by simulating quantum phenomena on classical computers.

Purpose of the Study:

• To propose a novel and highly efficient global optimization method for complex systems.
• To introduce the quantum-inspired Acromyrmex evolutionary algorithm (QIAEA).
• To evaluate the performance of QIAEA against existing state-of-the-art algorithms.

Main Methods:

• Development of the quantum-inspired Acromyrmex evolutionary algorithm (QIAEA).
• Simulation of quantum phenomena like superposition and entanglement on classical hardware.
• Comparative statistical analysis using benchmark functions.

Main Results:

• QIAEA demonstrated high efficiency as a global optimization method.
• Statistical analyses confirmed QIAEA's superior performance compared to existing quantum-inspired evolutionary algorithms.
• The proposed algorithm effectively addresses complex system optimization challenges.

Conclusions:

• QIAEA represents a significant advancement in quantum-inspired optimization techniques.
• The algorithm offers a practical and efficient solution for complex optimization problems.
• This work highlights the potential of nature-inspired algorithms enhanced by quantum principles.