SEvoBench高效评测进化优化算法，支持并行与向量化加速。
SEvoBench : A C++ Framework For Evolutionary Single-Objective Optimization Benchmarking
- 模块化设计，复用PSO和DE算法组件
- 并行执行与SIMD向量化，提升大规模问题效率
- 适合算法对比、参数分析与混合优化研究
我们提出SEvoBench，一个面向进化计算（EC）的现代C++框架，专为系统性评测单目标进化优化算法而设计。框架包含粒子群优化（PSO）和差分进化（DE）的模块化实现，围绕三大核心组件：(1) 可复用的算法构建模块，(2) 高效的基准测试问题集，(3) 并行实验分析能力。实验评估显示其在算法比较与基准测试中表现优异。案例研究进一步验证其在算法混合与参数分析中的有效性。相比现有框架，SEvoBench具有三项优势：(i) PSO与DE算法的高效可复用实现，(ii) 通过并行执行加速基准测试，(iii) 利用SIMD向量化提升大规模问题的计算效率。
原文摘要 · Abstract (English)
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.
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