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Engineering Antiviral Agents via Surface Plasmon Resonance

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Anti-coronavirus optimization algorithm.

¹Department of Computer Engineering, University of Bonab, Bonab, Iran.

|March 21, 2022

Summary

This summary is machine-generated.

A new anti-coronavirus optimization (ACVO) algorithm uses social distancing, quarantine, and isolation to simulate public health strategies. This swarm intelligence approach effectively optimizes solutions for complex problems, demonstrating superiority over existing methods.

Keywords:

Algorithms Anti-coronavirus optimization algorithm Coronavirus Numerical optimization Swarm intelligence

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Area of Science:

Computational Intelligence
Epidemiology Modeling
Optimization Algorithms

Background:

The COVID-19 pandemic highlighted the need for effective strategies to slow disease spread.
Swarm intelligence offers a multi-agent approach to complex problem-solving.
Simulating public health interventions is crucial for understanding disease dynamics.

Purpose of the Study:

To introduce a novel swarm intelligence algorithm, the anti-coronavirus optimization (ACVO) algorithm.
To model public health containment protocols (social distancing, quarantine, isolation) within an optimization framework.
To evaluate the effectiveness of ACVO in solving optimization problems.

Main Methods:

The ACVO algorithm employs a multi-agent system where each agent represents an individual.
Agents interact through simulated social distancing, quarantine of suspected cases, and isolation of infected individuals.
The algorithm iteratively applies these operators to optimize for the healthiest population state.

Main Results:

ACVO was tested on standard multi-variable single-objective optimization problems.
Performance was compared against several established counterpart algorithms.
The proposed ACVO algorithm demonstrated superior performance on most test problems.

Conclusions:

The anti-coronavirus optimization (ACVO) algorithm is a viable and effective swarm intelligence strategy.
ACVO successfully models and optimizes public health containment protocols.
The algorithm shows significant advantages over existing methods in solving optimization tasks.