Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Simulated annealing <s> 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 large and complex systems. This connection to statistical mechanics exposes new information and provides an unfamiliar perspective on traditional optimization problems and methods. <s> BIB001 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Simulated annealing <s> Metaheuristics are widely recognized as efficient approaches for many hard optimization problems. This paper provides a survey of some of the main metaheuristics. It outlines the components and concepts that are used in various metaheuristics in order to analyze their similarities and differences. The classification adopted in this paper differentiates between single solution based metaheuristics and population based metaheuristics. The literature survey is accompanied by the presentation of references for further details, including applications. Recent trends are also briefly discussed. <s> BIB002
Simulated annealing (SA) BIB001 is a single-solution based metaheuristic algorithm inspired by the annealing technique used to obtain a well ordered solid state of minimal energy. The objective function of a problem in SA is then minimized by introducing the temperature parameter T, which represents the main parameter of the algorithm BIB002 . At each iteration, SA selects a random solution from the neighbourhood of the current solution. The new solution is accepted based on the value of the objective function and the value of the parameter T, which decreases during the search process BIB002 .