基于集群的混合优化算法:详尽的分析及其对电力负载和价格预测的应用
Rahul Kottath1,2, Priyanka Singh3, Anirban Bhowmick2
1Digital Tower, Bentley Systems India Private Limited, Pune, India.
Soft computing
|June 26, 2023
概括
本研究介绍了混合优化算法,结合了Cuckoo Search Algorithm (CSA),灰狼优化 (GWO),哈里斯霍克斯优化 (HHO) 和鱼优化算法 (WOA). 在预测应用中,CSA-GWO混合算法表现出卓越的性能.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 人工智能的人工智能
背景情况:
- 在解决复杂的优化问题时,Metaheuristic算法至关重要.
- 算法的混合化可以通过利用各种搜索策略来提高性能.
- 预测电力负载和价格需要强大而准确的预测模型.
研究的目的:
- 通过结合CSA,GWO,HHO和WOA提出和评估新的混合优化算法.
- 评估这些混合算法在基准函数和现实世界预测任务上的性能.
- 为优化和预测应用程序确定最有效的混合算法.
主要方法:
- 通过结合CSA,GWO,HHO和WOA在各种组合中开发混合算法.
- 在24个单模和多模函数上对基准对应的混合算法进行基准测试.
- 与人工神经网络 (ANN) 集成的混合算法,用于使用ISO新英格兰数据集进行短期电力负载和价格预测.
主要成果:
- 混合优化算法在各种测试案例中通常优于它们的基础算法.
- 与其他测试过的算法相比,CSA-GWO混合算法显示出明显优异的性能.
- 混合算法与ANN相结合,为电力负载和价格预测提供了有效的解决方案.
结论:
- 混合化是一种有效的策略,可以提高元启发式优化算法的性能.
- CSA-GWO混合算法为复杂的优化和预测问题提供了一个有希望的方法.
- 开发的混合算法为提高能源市场预测准确度提供了有价值的工具.
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