Multidisciplinary Design Optimization (MDO) is the most active fields in the current complex engineering system design. Forcing to the defects of traditional Collaborative Optimization, such as unable to convergence or falling into local optimum, we propose a Collaborate Optimization based on Simulated Annealing and Artificial Neural Networks, (SA-ANN-CO). The SA-ANN-CO algorithm inherit the pa...

The 0-1 Multidimensional Knapsack Problem (MKP) is a widely-studied problem in combinatorial optimization domaine which has been proven as NP-hard. Various approximate heuristics have been developed and applied effectively to this problem, such as local search and evolutionary methods. This paper proposes the Stochastic Local Search-Simulated Annealing (SLSA) approach that combines the stochast...

A general problem in model selection is to obtain the right parameters that make a model t observed data. If the model selected is a Multilayer Perceptron (MLP) trained with Backpropagation (BP), it is necessary to nd appropriate initial weights and learning parameters. This paper proposes a method that combines Simulated Annealing (SimAnn) and BP to train MLPs with a single hidden layer, terme...

In this study, the performance of supplementary stabilizer for static synchronous compensator (STATCOM) tuned based on simulated annealing in order to improvement of dynamic stability is studied. Since STATCOM is usually used for damping Low Frequency Oscillations (LFO), a supplementary stabilizer is incorporated with STATCOM to reach the mentioned purpose. Simulated Annealing (SA) as a heurist...

In this paper we consider a simplified version of the stock cutting (two-dimensional bin packing) problem. We compare three meta-heuristic algorithms (genetic algorithm (GA), tabu search (TS) and simulated annealing (SA)) when applied to this problem. The results show that tabu search and simulated annealing produce good quality results. This is not the case with the genetic algorithm. The prob...

in today’s dynamic market, organizations must be adaptive to market fluctuations. in addition, studies show that material-handling cost makes up between 20 and 50 percent of the total operating cost. therefore, this paper considers the problem of arranging and rearranging, when there are changes in product mix and demand, manufacturing facilities such that the sum of material handling and rearr...

This paper presents an educational tool which can be used for teaching the Simulated Annealing (SA) algorithm. The SA is applied to solve integrated circuit placement. SA is an effective method to solve NPcomplete problems as circuit placement. The interface developed in this work provides a visualization of the execution steps of the SA, which makes the tool more iterative and didactic. The to...

In this paper, we propose a new hybrid scheme of parallel tempering and simulated annealing (hybrid PT/SA). Within the hybrid PT/SA scheme, a composite system with multiple conformations is evolving in parallel on a temperature ladder with various transition step sizes. The simulated annealing (SA) process uses a cooling scheme to decrease the temperature values in the temperature ladder to the...

In this paper, we propose to mimic some well-known methods of control theory to automatically fix the parameters of a multi-objective Simulated Annealing (SA) method. Our objective is to allow a decision maker to efficiently use advanced operation research techniques without a deep knowledge of this domain. Classical SA controls the probability of acceptance using an a priori temperature schedu...

The quadratic assignment problem (QAP) is one of the most difficult combinatorial optimization problems. One of the most powerful and commonly used heuristics to obtain approximations to the optimal solution of the QAP is simulated annealing (SA). We present an efficient implementation of the SA heuristic which performs more than 100 times faster then existing implementations for large problem ...