метод імітації відпалу

Simulated annealing algorithm is one of the most popular metaheuristics that has been successfully applied to many optimization problems.  The main advantage of SA is its ability to escape from local optima by allowing hill-climbing moves and exploring new solutions at the beginning of the search process.  One of its drawbacks is its slow convergence, requiring high computational time with a good set of parameter values to find a reasonable solution.  In this work, a new improved SA is proposed to solve the well-known travelling salesman problem.  In order to improve SA

National Renal Registry Malaysia has reported that the dialysis treatment demand among chronic kidney and end-stage kidney disease patients rises yearly.  However, available haemodialysis (HD) units have limited facilities to meet the current and increasing demand.  This leads to congestion, long waiting times, and an increase in the duration of treatment (DOT) among HD patients during their treatment sessions.  Two essential factors in providing optimal treatment plans are outpatient scheduling and nurse assignment.  Therefore, the objectives of this study are to minim

In this work, we propose a new estimate algorithm for the parameters of a $\mathrm{GARCH}(p,q)$ model.  This algorithm turns out to be very reliable in estimating the true parameter’s values of a given model.  It combines maximum likelihood method, Kalman filter algorithm and the simulated annealing (SA) method, without any assumptions about initial values.  Simulation results demonstrate that the algorithm is liable and promising.