模仿社交距离防疫策略，提出新型优化算法COVO，提升复杂问题求解效率。
Social Distancing Induced Coronavirus Optimization Algorithm (COVO): Application to Multimodal Function Optimization and Noise Removal
- 借鉴防疫中的社交距离机制设计新优化算法
- 在13个基准函数上实现更快收敛与更优解
- 适合解决复杂优化与噪声去除问题的研究者
元启发式优化技术因能应对复杂优化问题而受到广泛关注。近年来，许多受自然现象启发的优化方法被提出。新冠疫情导致公共卫生系统承受巨大压力，造成大量死亡。接种疫苗、佩戴口罩和保持社交距离是遏制病毒传播的关键措施。本文基于社交距离的防疫理念，提出一种新型生物启发式元启发式优化模型——社交距离诱导的冠状病毒优化算法（COVO）。维持社交距离可有效减缓病毒传播速度，这一机制被用于模拟优化过程中的个体间避让行为。通过13个基准函数对COVO在离散、连续及复杂问题上的性能进行评估，并与多种知名优化算法对比。结果表明，该算法能在复杂问题中快速收敛并获得全局最优解，验证了其在求解复杂应用中的合理性与有效性。
原文摘要 · Abstract (English)
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.
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