Coronavirus Mask Protection Algorithm: A New Bio-inspired Optimization Algorithm and Its Applications.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Bio-inspired Computing

Background:

• Meta-heuristic algorithms are crucial for solving high-dimensional nonlinear optimization problems.
• Existing algorithms often lack efficiency in complex, real-world scenarios.
• The COVID-19 pandemic highlighted the importance of public health measures and inspired novel approaches.

Purpose of the Study:

• To propose a novel meta-heuristic algorithm, the Coronavirus Mask Protection Algorithm (CMPA), inspired by COVID-19 prevention strategies.
• To mathematically model human self-protection behaviors (masking, social distancing) for optimization.
• To evaluate CMPA's performance against state-of-the-art algorithms on benchmark and engineering problems.

Main Methods:

• Developed the Coronavirus Mask Protection Algorithm (CMPA) based on COVID-19 transmission dynamics and human self-protection.
• Modeled infection, diffusion, and immune stages, incorporating exploration (masking) and exploitation (social distancing).
• Tested CMPA on benchmark functions (CEC2020) and truss design problems, comparing its efficacy.

Main Results:

• CMPA demonstrated competitive performance compared to other advanced metaheuristic optimizers.
• The algorithm was successfully applied to identify parameters for a gantry crane's main girder.
• Significant improvements were achieved: 16.44% reduction in mass and 7.49% reduction in deflection for the main girder.

Conclusions:

• The Coronavirus Mask Protection Algorithm (CMPA) is a promising and effective meta-heuristic optimizer.
• CMPA's bio-inspired approach, simulating COVID-19 self-protection, yields superior results in complex optimization tasks.
• The algorithm shows practical applicability and potential for engineering design optimization, as evidenced by the gantry crane application.