For many optimization algorithms the time to solution depends not only on the problem size but also on the specific problem instance and may vary by many orders of magnitude. It is then necessary to investigate the full distribution and especially its tail. Here, we analyze the distributions of annealing times for simulated annealing and simulated quantum annealing (by path integral quantum Mon...

this paper considers the economic lot scheduling problem, that is, the problem of scheduling several products on a single facility so as to minimize holding and setup costs. combination of frequency and timing as well as production quantity make this problem np-hard. a heuristic is developed to obtain a good solution to elsp. the proposed heuristic makes use of the simulated annealing technique...

This paper presents a simulation optimization of a real scheduling problem in industry, simulated annealing is introduced for this purpose. Investigation is performed into the practicality of using simulated annealing to produce high quality schedules. Results on the solution quality and computational effort show the inherent properties of the simulated annealing. It is shown that when using th...

In this paper, the author proposes the application of a genetic algorithm and simulated annealing to solve the network planning problem. Compared with other optimisation methods, genetic algorithm and simulated annealing are suitable for traversing large search spaces since they can do this relatively rapidly and because the use of mutation diverts the method away from local minima, which will ...

The Hopfield neural network is extensively applied to obtaining an optimal/feasible solution in many different applications such as the traveling salesman problem (TSP), a typical discrete combinatorial problem. Although providing rapid convergence to the solution, TSP frequently converges to a local minimum. Stochastic simulated annealing is a highly effective means of obtaining an optimal sol...

Usually lifeline systems, such as water distribution network and gas supply network, cover large areas. For these systems, network topology design based on seismic reliability provides a good way to design new systems and update old ones. In this paper, a topology optimization model for lifeline networks is established. The aim of the model is to find the least-cost network topology while its s...

Simulated annealing is an established method for global optimization. Perhaps its most salient feature is the statistical promise to deliver a globally optimal solution. In this work, we propose a technique which attempts to combine the robustness of annealing in rugged terrain with the efficiency of local optimization methods in simple search spaces. On a variety of benchmark functions, the pr...

Stochastic combinatorial optimization techniques, such as simulated annealing and genetic algorithms, have become increasingly important in design automation as the size of design problems have grown and the design objectives have become increasingly complex. However, stochastic algorithms are often slow since a large number of random design perturbations are required to achieve an acceptable r...