Coronavirus Optimization Algorithm: A Success-History Adaptive Evolutionary Framework with Archive-Assisted Search and Stagnation Recovery for Global Optimization

arXiv:2608.23847 · cs.NE, cs.AI, cs.CC · Submitted 2026-08-24 · Read on arXiv

cs.NE, cs.AI, cs.CC

Submitted: 2026-08-24

Updated: 2026-08-24

License: http://creativecommons.org/licenses/by-nc-sa/4.0/

The gist: This paper proposes the Coronavirus Optimization Algorithm (COA), a SARS-CoV-2-inspired success-history adaptive evolutionary optimizer for box-constrained continuous global optimization.

Terminology

Adaptation — 29× in this paper · explained in 1 episode
Differential Evolution — 17× in this paper
Benchmark — 16× in this paper · explained in 2 episodes
Coronavirus Optimization Algorithm — 7× in this paper
Ablation — 6× in this paper · explained in 1 episode
Objective Function — 6× in this paper · explained in 2 episodes
Robustness — 6× in this paper · explained in 5 episodes
Congress on Evolutionary Computation — 4× in this paper
Particle Swarm Optimization — 4× in this paper
Whale Optimization Algorithm — 4× in this paper
Ablation study — 3× in this paper · explained in 1 episode
Abstraction — 3× in this paper · explained in 1 episode
Aquila Optimizer — 3× in this paper
Crested Porcupine Optimizer — 3× in this paper
Grey Wolf Optimizer — 3× in this paper
Harris Hawks Optimization — 3× in this paper
Salp Swarm Algorithm — 3× in this paper
Benchmarking — 2× in this paper · explained in 4 episodes
Feature Selection — 2× in this paper · explained in 4 episodes
Joint Adaptive Differential Evolution — 2× in this paper
Core — 1× in this paper · explained in 2 episodes
Fidelity — 1× in this paper · explained in 2 episodes
Grounding — 1× in this paper · explained in 3 episodes
Pruning — 1× in this paper · explained in 4 episodes

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.

Terminology

Abstract

Related papers