Vận hành hồ chứa quy mô lớn thông qua tiếp cận meta-heuristic tích hợp

Memetic Computing - Tập 13 - Trang 359-382 - 2021
Bilal1, Millie Pant1, Deepti Rani2
1Department of Applied Science and Engineering, Indian Institute of Technology, Roorkee, India
2National Institute of Hydrology, Roorkee, India

Tóm tắt

Các mô hình tối ưu hóa hồ chứa thường có quy mô lớn, với cấu trúc phức tạp, phi tuyến tính và đa chiều, điều này đặt ra thách thức cho các phương pháp cổ điển trong việc giải quyết chúng. Điều này đã khuyến khích các nhà nghiên cứu tập trung vào Meta-heuristic, vì tính linh hoạt và khả năng thích ứng của chúng đã mang lại thành công trong việc giải quyết nhiều vấn đề tối ưu hóa trong đời sống thực. Nghiên cứu này đưa ra việc triển khai và so sánh sáu phương pháp Meta-heuristic nổi tiếng, bao gồm: Làm mát giả, Thuật toán di truyền, Tối ưu hóa đàn mẫu, Tiến hóa vi phân, Đàn ong nhân tạo, và Tìm kiếm Cuckoo, cũng như một phiên bản tích hợp của những thuật toán này với lập trình động để tối ưu hóa chính sách vận hành hồ chứa. Ngoài ra, hai biến thể thích ứng của DE với tên gọi: FCADE2 và SaDE cũng được xem xét để so sánh. Nghiên cứu tình huống được xem xét cho hồ chứa Mula cung cấp nước cho Dự án Tưới tiêu lớn trên sông Mula (lưu vực Godavari), huyện Ahmednagar, tiểu bang Maharashtra, Ấn Độ. Mục tiêu là xác định chính sách xả nước tối ưu cho hồ chứa Mula. Hiệu suất của các thuật toán được phân tích trên hai tập dữ liệu (1) một năm và (2) 30 năm.

Từ khóa

#Tối ưu hóa hồ chứa #Meta-heuristic #Thuật toán di truyền #Làm mát giả #Tối ưu hóa đàn mẫu #Tiến hóa vi phân #Đàn ong nhân tạo #Tìm kiếm Cuckoo

Tài liệu tham khảo

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