In this paper we discuss the solution of the clustering problem usually solved by the K-means algorithm. The problem is known to have local minimum solutions.A simulated annealing algorithm for the clustering problem. The solution of the clustering problem usually solved by the K-means algorithm.In this paper, we explore the applicability of simulated annealing. Clustering problem is investigat...

This paper proposes a multi-objective memetic algorithm based on NSGA-II and Simulated Annealing (SA), NSGA-II-SA, for calibration of microscopic vehicular traffic flow simulation models. The NSGA-II algorithm performs a scan in the search space and obtains the Pareto front which is optimized locally with SA. The best solution of the obtained front is selected. Two CORSIM models were calibrated...

Bus network design is an important problem in public transportation. A main step to this design is determining the number of required terminals and their locations. This is a special type of facility location problem, which is a time-consuming, large scale, combinatorial problem. In a previous attempt by the authors, this problem had been solved by GAMS, based on a branch and bound algorithm.&...

Projects scheduling by the project portfolio selection, something that has its own complexity and its flexibility, can create different composition of the project portfolio. An integer programming model is formulated for the project portfolio selection and scheduling.Two heuristic algorithms, genetic algorithm (GA) and simulated annealing (SA), are presented to solve the problem. Results of cal...

In this paper, an improved floorplanning algorithm, named the floorplanning algorithm based on particle swarm optimization algorithm nesting simulated annealing to optimize the floorplans (PSO-SA-NoC), has been proposed with simulations conducted to verify this algorithm. The simulation results are compared with the original Simulated Annealing-NoC. The results show that the CPU’s process time ...

Many significant engineering and scientific problems involve optimization of some criteria over a combinatorial configuration space. The two methods most often used to solve these problems effectively—simulated annealing (SA) and genetic algorithms (GA)—do not easily lend themselves to massive parallel implementations. Simulated annealing is a naturally serial algorithm, while GA involves a sel...

It is a long time that the Simulated Annealing (SA) procedure has been introduced as a model-free optimization for solving NP-hard problems. Improvements from the standard SA in the recent decade mostly concentrate on combining its original algorithm with some heuristic methods. These modifications are rarely happened to the initial condition selection methods from which the annealing schedules...

This article explains how to apply the deterministic annealing (DA) and simulated annealing (SA) methods to fuzzy entropy based fuzzy c-means clustering. By regularizing the fuzzy c-means method with fuzzy entropy, a membership function similar to the Fermi-Dirac distribution function, well known in statistical mechanics, is obtained, and, while optimizing its parameters by SA, the minimum of t...

Solving a discrete optimization problem consists in finding a solution which maximizes (or minimizes) an objective function. The function is often called the fitness and the corresponding landscape the fitness landscape. We are concerned with statistical measures of a fitness landscape in the context of the vehicle routing problem with time windows (VRPTW). The measures are determined by using ...