Coronavirus herd immunity optimizer (CHIO).

Area of Science:

• Computational Intelligence
• Nature-Inspired Optimization Algorithms
• Epidemiological Modeling

Background:

• The COVID-19 pandemic highlighted the importance of herd immunity and social distancing.
• Existing optimization algorithms may not fully capture complex population dynamics.
• Developing novel algorithms inspired by biological and social phenomena is crucial for advancing computational intelligence.

Purpose of the Study:

• To propose a new nature-inspired human-based optimization algorithm named Coronavirus Herd Immunity Optimizer (CHIO).
• To model herd immunity and social distancing concepts for optimization problem-solving.
• To evaluate the efficacy and competitiveness of CHIO against existing state-of-the-art methods.

Main Methods:

• CHIO algorithm development inspired by herd immunity and social distancing principles.
• Modeling three population states: susceptible, infected, and immune, to guide solution updates.
• Utilizing 23 benchmark functions and three real-world engineering optimization problems for evaluation.

Main Results:

• CHIO exhibited high performance and competitiveness when compared to seven state-of-the-art optimization methods.
• Sensitivity analysis confirmed CHIO's robustness to parameter variations.
• Validation on real-world engineering problems demonstrated CHIO's practical efficiency.

Conclusions:

• CHIO is a powerful and effective optimization algorithm.
• The algorithm shows significant potential for addressing a wide range of optimization problems.
• Nature-inspired algorithms, like CHIO, offer promising solutions in computational intelligence.