SEvoBench : A C++ Framework For Evolutionary Single-Objective Optimization Benchmarking
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| Format: | Preprint |
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2025
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| _version_ | 1866918031947464704 |
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| author | Yang, Yongkang Zhao, Jian Yang, Tengfei |
| author_facet | Yang, Yongkang Zhao, Jian Yang, Tengfei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_17430 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SEvoBench : A C++ Framework For Evolutionary Single-Objective Optimization Benchmarking Yang, Yongkang Zhao, Jian Yang, Tengfei Neural and Evolutionary Computing Artificial Intelligence Mathematical Software Optimization and Control 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. |
| title | SEvoBench : A C++ Framework For Evolutionary Single-Objective Optimization Benchmarking |
| topic | Neural and Evolutionary Computing Artificial Intelligence Mathematical Software Optimization and Control |
| url | https://arxiv.org/abs/2505.17430 |