A Review: Machine Learning for Combinatorial Optimization Problems in Energy Areas

Statistical Machine Learning and Combinatorial Optimization

Machine Learning Meliorates Computing and Robustness in Discrete Combinatorial Optimization Problems

Discrete combinatorial optimization problems in the real world are typically defined via an ensemble of potentially high dimensional measurements pertaining to all subjects of a system under study. We point out that such a data ensemble in fact embeds with system’s information content that is not directly used in defining the combinatorial optimization problems. Can machine learning algorithms ...

economic optimization and energy consumption in tray dryers

دراین پروژه به بررسی مدل سازی خشک کردن مواد غذایی با استفاده از هوای خشک در خشک کن آزمایشگاهی نوع سینی دار پرداخته شده است. برای آنالیز انتقال رطوبت در طی خشک شدن به طریق جابجایی، یک مدل لایه نازک برای انتقال رطوبت، مبتنی بر معادله نفوذ فیک در نظر گفته شده است که شامل انتقال همزمان جرم و انرژی بین فاز جامد و گاز می باشد. پروفایل دما و رطوبت برای سه نوع ماده غذایی شامل سیب زمینی، سیب و موز در طی...

Machine learning problems from optimization perspective

Both optimization and learning play important roles in a system for intelligent tasks. On one hand, we introduce three types of optimization tasks studied in the machine learning literature, corresponding to the three levels of inverse problems in an intelligent system. Also, we discuss three major roles of convexity in machine learning, either directly towards a convex programming or approxima...

A New Optimization Algorithm For Combinatorial Problems

Combinatorial optimization problems are those problems that have a finite set of possible solutions. The best way to solve a combinatorial optimization problem is to check all the feasible solutions in the search space. However, checking all the feasible solutions is not always possible, especially when the search space is large. Thus, many meta-heuristic algorithms have been devised and modifi...