Problem
Research questions and friction points this paper is trying to address.
global optimization
box-constrained
evolutionary optimizer
adaptive
Coronavirus Optimization Algorithm: A Success-History Adaptive Evolutionary Framework with Archive-Assisted Search and Stagnation Recovery for Global Optimization
🤖 AI Summary
本文提出了一种基于新冠病毒机制映射的冠状病毒优化算法（COA），用于解决全局优化问题，通过多种搜索操作如精英引导吸引、试向量生成等方法提高性能。
📝 Abstract
This paper proposes the Coronavirus Optimization Algorithm (COA), a SARS-CoV-2-inspired success-history adaptive evolutionary optimizer for box-constrained continuous global optimization. COA does not model disease transmission; instead, it maps selected coronavirus mechanisms to explicit search operators, including elite-guided attraction, trial-vector generation, adaptive parameter variation, stagnation recovery, and population-size scheduling. The algorithm combines opposition-based initialization, current-to-pbest mutation, binomial crossover, an external archive, success-history adaptation, population reduction, and partial restart. COA is evaluated on 29 CEC 2017 benchmark functions at 10, 30, and 50 dimensions against 15 competitive optimizers. Results show that COA achieves the best overall Friedman rank across all dimensions, with particularly strong performance on composition functions. The findings demonstrate that COA is a compact, transparent, and competitive adaptive evolutionary optimizer, while also highlighting limitations on some hybrid functions and the need for further high-dimensional validation.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Coronavirus Optimization Algorithm
success-history adaptation
archive-assisted search
stagnation recovery
adaptive parameter variation
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