Simulated Annealing Method-Based Flight Schedule Optimization in Multiairport Systems
نویسندگان
چکیده
منابع مشابه
A Simulated Annealing-based Immune Optimization Method
This paper proposes a hybrid optimization method based on the fusion of the Simulated Annealing (SA) and Clonal Selection Algorithm (CSA), in which the SA is embedded in the CSA to enhance its search capability. The novel optimization algorithm is also employed to deal with several nonlinear benchmark functions as well as a practical engineering design problem. Simulation results demonstrate th...
متن کاملAn Efficient Simulated Annealing Schedule
The popularity of simulated annealing comes from its ability to find close to optimal solutions for NPhard combinatorial optimization problems. Unfortunately, the method has a major drawback: its massive requirement of computation time. In this dissertation, we present an efficient annealing schedule which speeds up simulated annealing by a factor of up to twenty-four when compared with general...
متن کاملAn Efficient Simulated Annealing Schedule: Derivation
The popularity of simulated annealing comes from its ability to find close to optimal solutions for NP-hard combinatorial optimization problems. Unfortunately, current implementations of the method usually require massive computation time. This paper presents a general annealing schedule which speeds up simulated annealing significantly when compared with general schedules available in the lite...
متن کاملOptimization by simulated annealing.
There is a deep and useful connection between statistical mechanics (the behavior of systems with many degrees of freedom in thermal equilibrium at a finite temperature) and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters). A detailed analogy with annealing in solids provides a framework for optimization of the properties of very ...
متن کاملSimulated Annealing and Global Optimization
Nelder-Mead (when you don’t know ∇f ) and steepest descent/conjugate gradient (when you do). Both of these methods are based on attempting to generate a sequence of positions xk with monotonically decreasing f(xk) in the hopes that the xk → x∗, the global minimum for f . If f is a convex function (this happens surprisingly often), and has only one local minimum, these methods are exactly the ri...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Mathematical Problems in Engineering
سال: 2020
ISSN: 1024-123X,1563-5147
DOI: 10.1155/2020/4731918