The quadratic assignment problem (QAP) is one of the most difficult combinatorial optimization problems. An effective heuristic for obtaining approximate solutions to the QAP is simulated annealing (SA). Here we describe an SA implementation for the QAP which runs on a graphics processing unit (GPU). GPUs are composed of low cost commodity graphics chips which in combination provide a powerful ...

This paper proposes a new algorithm using Simulated Annealing (SA) for stochastic scheduling. Stochastic effects are added with nondeterministic activity durations. Simple simulated annealing can not properly handle such a complex problem so an improved version is produced and utilized within the algorithm. Generally, the availability of resources is not enough to complete all the current activ...

Quadratic Assignment Problem (QAP) is an NPhard combinatorial optimization problem, therefore, solving the QAP requires applying one or more of the meta-heuristic algorithms. This paper presents a comparative study between Meta-heuristic algorithms: Genetic Algorithm, Tabu Search, and Simulated annealing for solving a real-life (QAP) and analyze their performance in terms of both runtime effici...

This paper explores the use of simulated annealing (SA) for solving arbitrary combinatorial optimisation problems. It reviews an existing code called GPSIMAN for solving 0-1 problems, and evaluates it against a commercial branch-and-bound code, OSL. The problems tested include travelling salesman, graph colouring, bin packing, quadratic assignment and generalised assignment. The paper then desc...

In this paper, we present a parallel simulated annealing (SA) on distributed computing resources. The parallel SA include two types of cooperation mechanism: inner group and inter group cooperations. In the inner group cooperation, SA processes start from the same initial point, however, move to different direction. That is, two concepts: concentration and diversity are included. On the other h...

In this paper, an unequal area Cyclic Facility Layout Problem (CFLP) is studied. Dynamic and seasonal nature of the product demands results in the necessity for considering the CFLP where product demands as well as the departmental area requirements are changing from one period to the next one. Since the CFLP is NP-hard, we propose a Simulated Annealing (SA) metaheuristic with a dynamic tempera...

The paper describes existing applications of probabilistic methods, namely simulated annealing and genetic algorithms, in solving problems concerning triangulated models. Most of applications are related to global optimization triangulation criteria, improvement of shapes of triangles or tetrahedra and positions of vertices, sometimes in the context of digital images. The paper also sums up typ...

The authors propose a new data dimensionality reduction method that is formulated as an optimization problem solved in two stages. In the first stage, Generalized Principal Component Analysis (GPCA) is used to find a solution with local maximum (local solution) whereas the algorithm Simulated Annealing (SA) is performed, in the second stage, to converge the local solution to the optimal solutio...

Flexible job shop scheduling problem (FJSP) is one of the hardest combinatorial optimization problems known to be NP-hard. This paper proposes a novel hybrid imperialist competitive algorithm with simulated annealing (HICASA) for solving the FJSP. HICASA explores the search space by using imperial competitive algorithm (ICA) and use a simulated annealing (SA) algorithm for exploitation in the s...