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.

翻译：本文提出SEvoBench，一个用于进化计算（EC）的现代C++框架，专门为系统化基准测试进化单目标优化算法而设计。该框架以模块化方式实现了粒子群优化（PSO）和差分进化（DE）算法，围绕三个核心组件构建：(1) 采用可复用模块的算法构建，(2) 高效的基准测试问题集，以及(3) 并行实验分析。实验评估表明该框架在基准测试和算法比较中具有优越性能。案例研究进一步验证了其在算法混合与参数分析方面的能力。与现有框架相比，SEvoBench展现出三大关键优势：(i) 提供高效且可复用的PSO与DE算法模块化实现，(ii) 通过并行执行加速基准测试过程，(iii) 针对大规模问题，借助SIMD（单指令多数据）向量化技术显著提升计算效率。