Artificial infectious disease optimization: A SEIQR epidemic dynamic model-based function optimization algorithm

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Epidemic Modeling

Background:

• Complex function optimization problems require advanced computational methods.
• Existing algorithms may lack efficiency in handling high-dimensional or intricate problems.
• Epidemic models offer a unique framework for developing novel optimization strategies.

Purpose of the Study:

• To introduce the SEIQRA algorithm, an artificial infectious disease optimization method.
• To leverage the SEIQR epidemic model for enhanced function optimization.
• To demonstrate the algorithm's effectiveness in solving complicated and high-dimensional optimization tasks.

Main Methods:

• The SEIQRA algorithm models individuals and disease spread using the SEIQR (Susceptible, Exposure, Infection, Quarantine, Recovery) states.
• State transitions are defined as 13 distinct operators, including average, differential, and crossover operations.
• The Part Variables Iteration (PVI) strategy is integrated for computational efficiency.

Main Results:

• SEIQRA exhibits strong search capabilities and global convergence properties.
• The algorithm demonstrates a high convergence speed for complex function optimization.
• The PVI strategy contributes to high computational performance, particularly for high-dimensional problems.

Conclusions:

• The SEIQRA algorithm offers a powerful and efficient approach to complex function optimization.
• Its foundation in epidemic modeling provides a novel perspective on computational intelligence.
• SEIQRA is well-suited for high-dimensional optimization tasks, showcasing robust performance.