Nội dung được dịch bởi AI, chỉ mang tính chất tham khảo
Đo lường sự không chắc chắn với các tập hợp thay thế cho tối ưu hóa blackbox
Tóm tắt
Tối ưu hóa blackbox giải quyết các vấn đề mà ở đó các hàm khó đánh giá và không có thông tin phân tích sẵn có. Trong bối cảnh này, một kỹ thuật đã được thử nghiệm và chứng minh là xây dựng các thay thế cho mục tiêu và các ràng buộc nhằm thực hiện tối ưu hóa với chi phí tính toán thấp hơn. Công trình này giới thiệu một mở rộng cho một loại thay thế cụ thể: tập hợp các thay thế, cho phép chúng định lượng sự không chắc chắn cho các dự đoán mà chúng đưa ra. Các tập hợp thay thế mở rộng thu được hoạt động như các mô hình ngẫu nhiên và cho phép sử dụng các công cụ tối ưu hóa Bayesian hiệu quả. Phương pháp này được tích hợp vào bước tìm kiếm của thuật toán tìm kiếm trực tiếp thích ứng lưới (MADS) nhằm cải thiện việc khám phá không gian tìm kiếm. Các thí nghiệm tính toán được thực hiện trên bảy vấn đề phân tích, hai vấn đề tối ưu hóa đa ngành và hai vấn đề mô phỏng. Kết quả cho thấy phương pháp đề xuất giải quyết các vấn đề dựa trên mô phỏng tốn kém với độ chính xác cao hơn và với ít công sức tính toán hơn so với các mô hình ngẫu nhiên.
Từ khóa
#tối ưu hóa blackbox #mô hình ngẫu nhiên #phương pháp thay thế #tối ưu hóa Bayesian #thuật toán MADSTài liệu tham khảo
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