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

2026-03-25

AI Summary

This study investigates the influence of benchmark function design in the IEEE CEC single-objective optimization competitions (2010–2024) on the evolution of algorithmic performance and its implications for variational quantum optimization. Through historical data analysis, comparative algorithm evaluation, and assessment of rotational invariance—augmented by feature engineering to identify critical benchmark characteristics—the work establishes, for the first time, a systematic causal relationship between benchmark structure and algorithmic advancement. The findings reveal that rotationally invariant differential evolution variants such as L-SHADE dominated the competitions following the introduction of dense rotation matrices in 2014, while high-complexity hybrid optimizers emerging post-2020 significantly enhanced ranking stability. Furthermore, the study constructs a structural linkage between the capabilities of classical optimizers and the requirements of variational quantum algorithms.

Technology Category

• Search and Optimization: Evolutionary Computation
• Machine Learning: Quantum Machine Learning
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Responsible Web: Algorithmic accountability and transparency on the web
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content

Abstract

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.

Problem

• CEC competition
• single-objective optimization
• benchmark functions
• variational quantum optimization
• algorithm evolution

Innovation

• rotational invariance
• differential evolution
• hybrid optimizers
• variational quantum optimization
• non-separable benchmarks