simulated annealing

A new improved simulated annealing for traveling salesman problem

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

Simulated annealing approach for outpatient scheduling in a haemodialysis unit

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

Numerical optimization of the likelihood function based on Kalman filter in the GARCH models

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.

Complex Optimization Method of Routing Information Flows in Self-organized Networks

Modified routing algorithms are presented based on basic meta-heuristic algorithms: ant colony optimization, genetic and simulated annealing to determine the best route for information flows in self-organized networks. An ant colony optimization is based on the use of the probability parameter for the transition between the nodes located between the source node and the receiving node. To solve the problem of optimization of routing in a simulated annealing, its modification is proposed by adding or removing a transit node based on the coverage of the reaching range of neighboring nodes.