# A Longitudinal Analysis of the CEC Single-Objective Competitions (2010-2024) and Implications for Variational Quantum Optimization

arXiv: https://arxiv.org/abs/2603.24140 · HTML: https://mlsanity.com/paper/2603.24140/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2026.

## Built on by (most cited first, 2 of 2)

- [Quantum Machine Learning for Colorectal Cancer Data: Anastomotic Leak Classification and Risk Factors](https://mlsanity.com/paper/2604.13951/) (2026)
- [Linear Proposal Operators and Stochastic Search Geometry in SOMA and Differential Evolution](https://mlsanity.com/paper/2607.29228/) (2026)

## Abstract (arXiv)

This paper provides a historical analysis of the IEEE CEC Single Objective Optimization competition results (2010-2024). We analyze how benchmark functions shaped winning algorithms, identifying the 2014 introduction of dense rotation matrices as a key performance filter. This design choice introduced parameter non-separability, reduced effectiveness of coordinate-dependent methods (PSO, GA), and established the dominance of Differential Evolution variants capable of preserving the rotational invariance of their difference vectors, specifically L-SHADE. Post-2020 analysis reveals a shift towards high complexity hybrid optimizers that combine different mechanisms (e.g., Eigenvector Crossover, Societal Sharing, Reinforcement Learning) to maximize ranking stability. We conclude by identifying structural similarities between these modern benchmarks and Variational Quantum Algorithm landscapes, suggesting that evolved CEC solvers possess the specific adaptive capabilities required for quantum control.