Visualization of Solution Space Explored in Simulated Annealing

Visualization and Interactive Steering of Simulated Annealing

Combinatorial optimization problems involve the selection of an arrangement of discrete objects (a state) from a discrete, nite space of mutually exclusive states. The goal is to select the particular state that represents the extremization (maximization or minimization) of an objective function. Often, these problems are simple to state, but quite diicult to solve. Exact solution methods, whil...

Comparison of Simulated Annealing and Electromagnetic Algorithms for Solution of Extended Portfolio Model

This paper presents two meta-heuristic algorithms to solve an extended portfolio selection model. The extended model is based on the Markowitz's Model, aiming to minimize investment risk in a specified level of return. In order to get the Markowitz model close to the real conditions, different constraints were embedded on the model which resulted in a discrete and non-convex solution space. ...

A simulated annealing approach to the solution of minlp problems

An algorithm (M-SIMPSA) suitable for the optimization of mixed integer non-linear programming (MINLP) problems is presented. A recently proposed continuous non-linear solver (SIMPSA) is used to update the continuous parameters, and the Metropolis algorithm is used to update the complete solution vector of decision variables. The M-SIMPSA algorithm, which does not require feasible initial points...

Visualization with Nurbs Using Simulated Annealing Optimization Technique

ACKNOWLEDGEMENTS In the name of Allah, Most Gracious, Most Merciful All praise and glory to Almighty Allah (Subhanahu Wa Taalaa) who gave me courage and patience to carry out this work. Peace and blessing of Allah be upon last Prophet Muhammad (Peace Be Upon Him). Acknowledgment is due to King Fahd University of Petroleum & Minerals for supporting this research. My unrestrained appreciation goe...

Simulated Annealing of Two Electron Density Solution Systems