CEC2022 Benchmark Suite

该数据集是一个针对单目标黑盒优化的函数集合，包括基础函数、混合函数和组合函数，这些函数能够满足各种优化特性的需求。该集合包含了具有单峰/多峰以及可分/不可分等特性的函数，用于评估不同差分进化算法变体以及所提出的MetaDE方法的表现。该数据集包含了多个函数（F1-F12），并在各种优化问题上进行了评估。其任务是对优化算法进行基准测试。

This dataset is a collection of functions tailored for single-objective black-box optimization, encompassing basic, hybrid, and composite functions that meet the requirements of various optimization characteristics. It includes functions with properties such as unimodal/multimodal, separable/non-separable, etc., and is used to evaluate the performance of different differential evolution algorithm variants and the proposed MetaDE method. Comprising multiple functions (F1-F12), this dataset has been tested across various optimization problems, with its core task being benchmarking optimization algorithms.

搜集汇总

背景与挑战

背景概述

CEC2022 Benchmark Suite是一个用于评估优化算法性能的数据集，包含多种测试函数，适用于不同类型的优化问题。该数据集支持多种编程语言和框架，便于研究者进行算法比较和性能测试。