SEvoBench : A C++ Framework For Evolutionary Single-Objective Optimization Benchmarking

TL;DR

SEvoBench is a modern C++ framework that enables efficient, modular, and parallel benchmarking of evolutionary single-objective optimization algorithms like PSO and DE, facilitating algorithm comparison and analysis.

Contribution

The paper introduces SEvoBench, a novel C++ framework with modular design, parallel execution, and SIMD vectorization for benchmarking evolutionary algorithms.

Findings

Demonstrates superior benchmarking performance and efficiency.

Validates capabilities in algorithm hybridization and parameter analysis.

Shows accelerated benchmarking through parallel execution.

Abstract

We present SEvoBench, a modern C++ framework for evolutionary computation (EC), specifically designed to systematically benchmark evolutionary single-objective optimization algorithms. The framework features modular implementations of Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms, organized around three core components: (1) algorithm construction with reusable modules, (2) efficient benchmark problem suites, and (3) parallel experimental analysis. Experimental evaluations demonstrate the framework's superior performance in benchmark testing and algorithm comparison. Case studies further validate its capabilities in algorithm hybridization and parameter analysis. Compared to existing frameworks, SEvoBench demonstrates three key advantages: (i) highly efficient and reusable modular implementations of PSO and DE algorithms, (ii) accelerated benchmarking through parallel execution, and (iii) enhanced computational efficiency via SIMD (Single Instruction Multiple Data) vectorization for large-scale problems.