Stochastic optimization using simulated annealing has found wide applications in combinatorial optimization of NP-complete problems in recent years, especially in VLSI related problems. The Grid Connection Problem is a combinatorial optimization problem that is proven to be NP-complete. In this paper the simulated annealing algorithm has been applied to the Grid Connection Problem. We also prop...

The Methodology to Parallelize Simulated Annealing (MPSA) leads to massive parallelization by executing each temperature cycle of the Simulated Annealing (SA) algorithm in parallel. The initial solution for each internal cycle is set through a Monte Carlo random sampling to adjust the Boltzmann distribution at the cycle beginning. MPSA uses an asynchronous communication scheme and any implement...

Population annealing Monte Carlo is an efficient sequential algorithm for simulating k-local Boolean Hamiltonians. Because of its structure, the algorithm is inherently parallel and therefore well-suited for large-scale simulations of computationally hard problems. Here we present various ways of optimizing population annealing Monte Carlo using 2-local spin-glass Hamiltonians as a case study. ...

The paper focuses on a parallel implementation of a simulated annealing algorithm. In order to take advantage of the properties of modern clustered SMP architectures a hybrid method using a combination of OpenMP nested in MPI is advocated. The development of the reference implementation is proposed. Furthermore, a few load balancing strategies are introduced: time scheduling at the annealing pr...

This paper presents a simulated annealing algorithm to minimize makespan of single machine scheduling problem with uniform parallel machines. The single machine scheduling problem with uniform parallel machines consists of n jobs, each with single operation, which are to be scheduled on m parallel machines with different speeds. Since, this scheduling problem is a combinatorial problem; usage o...

In this communication Simulated Annealing and Genetic Algorithms, are applied to the graph partitioning problem. These techniques mimic processes in statistical mechanics and biology, respectively, and are the most popular meta-heuristics or general-purpose optimization strategies. A hybrid algorithm for circuit partitioning, which uses tabu search to improve the simulated annealing meta-heuris...

A genetic algorithm and a simulated annealing approach is presented for the guidance of a cellular automaton toward optimal configurations. The algorithm is applied to a problem of groundwater allocation in a rectangular area consisting of adjacent land blocks and modeled as a cellular automaton. The new algorithm is compared to a more conventional genetic algorithm and its efficiency is clearl...

In this paper a Simulated Annealing algorithm (SA) for solving the Protein Folding Problem (PFP) is presented. This algorithm has two phases: quenching and annealing. The first phase is applied at very high temperatures and the annealing phase is applied at high and low temperatures. The temperature during the quenching phase is decreased by an exponential function. We run through an efficient ...

This paper investigates the problem of selecting and scheduling a set of projects among available projects. Each project consists of several tasks and to perform each one some resource is required. The objective is to maximize total benefit. The paper constructs a mathematical formulation in form of mixed integer linear programming model. Three effective metaheuristics in form of the imperialis...

The purpose of this article is to review the literature on the topic of deterministic vehicle routing problem (VRP) and to give a review on the exact and approximate solution techniques. More specifically the approximate (meta-heuristic) solution techniques are classified into: tabu search, simulated annealing, genetic algorithm, evolutionary algorithm, hybrid algorithm, and Ant Colony Opti...