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

TL;DR

This paper analyzes a decade of IEEE CEC optimization competitions, revealing how benchmark design influences algorithm success and drawing parallels to quantum algorithm landscapes, highlighting the evolution of optimizer strategies.

Contribution

It provides a comprehensive historical analysis of competition results, identifies key design impacts on algorithm performance, and links classical optimization benchmarks to quantum control landscapes.

Findings

Introduction of dense rotation matrices affected algorithm effectiveness.

Differential Evolution variants like L-SHADE dominate due to rotational invariance.

Recent trends favor hybrid optimizers combining multiple mechanisms.

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.