Social Distancing Induced Coronavirus Optimization Algorithm (COVO): Application to Multimodal Function Optimization and Noise Removal

TL;DR

This paper introduces a novel bio-inspired optimization algorithm called COVO, inspired by social distancing measures during COVID-19, demonstrating its effectiveness on complex optimization problems and noise removal.

Contribution

The paper proposes the COVO algorithm, a new metaheuristic inspired by social distancing, and evaluates its performance on benchmark functions showing competitive results.

Findings

COVO outperforms several existing algorithms on benchmark tests.

COVO achieves faster convergence to global solutions.

The algorithm is effective for complex and noisy optimization problems.

Abstract

The metaheuristic optimization technique attained more awareness for handling complex optimization problems. Over the last few years, numerous optimization techniques have been developed that are inspired by natural phenomena. Recently, the propagation of the new COVID-19 implied a burden on the public health system to suffer several deaths. Vaccination, masks, and social distancing are the major steps taken to minimize the spread of the deadly COVID-19 virus. Considering the social distance to combat the coronavirus epidemic, a novel bio-inspired metaheuristic optimization model is proposed in this work, and it is termed as Social Distancing Induced Coronavirus Optimization Algorithm (COVO). The pace of propagation of the coronavirus can indeed be slowed by maintaining social distance. Thirteen benchmark functions are used to evaluate the COVO performance for discrete, continuous, and complex problems, and the COVO model performance is compared with other well-known optimization algorithms. The main motive of COVO optimization is to obtain a global solution to various applications by solving complex problems with faster convergence. At last, the validated results depict that the proposed COVO optimization has a reasonable and acceptable performance.