Ten New Benchmarks for Optimization
arXiv:2309.00644v13 citationsh-index: 3
Originality Synthesis-oriented
AI Analysis
This work addresses the need for more varied benchmarks in optimization research, though it is incremental as it adds to existing benchmark sets.
The authors tackled the lack of diverse benchmarks for optimization algorithms by introducing ten new benchmarks with properties like noise and discontinuity, providing a broader test set for evaluation.
Benchmarks are used for testing new optimization algorithms and their variants to evaluate their performance. Most existing benchmarks are smooth functions. This chapter introduces ten new benchmarks with different properties, including noise, discontinuity, parameter estimation and unknown paths.