Simulated annealing

About: Simulated annealing is a research topic. Over the lifetime, 21436 publications have been published within this topic receiving 563467 citations.

A Two-Fold Simulated Annealing to Reconstruct Household Composition from Statistics

Abstract: In this paper, we propose a two-fold simulated annealing (a 2-fold SA) to reconstruct household compositions from real statistics. A social simulation has received attention in recent years, which is one of research topics to see the real world in terms of social sciences. We need individual data such as age, gender, work etc. To construct a realistic simulation model. However, it is difficult to use an individual data because the government restricts the secondary use of their individual data. Therefore, we should reconstruct artificial data from the real statistics using a reconstructing method. We have improved Ikeda's method to minimize the error between reconstructed households and the real statistics using an evolutionary computation (EC). However, the method takes a longer computation time than Ikeda's method due to time consuming processes of EC. We should try to reduce the computation time in view of future applications. In this paper, we propose a 2-fold SA in order to reduce the computation time. Our simulation results indicate that the proposed 2-fold SA is better in the accuracy and the computation time.

An improved nested partitions algorithm based on simulated annealing in complex decision problem optimization

Abstract: This paper introduces the main ideas of the nested partitions (NP) method, analyses its efficiency theoretically and proposes the way to improve the optimization efficiency of the algorithm. Then the paper introduces the simulated annealing (SA) algorithm and incorporates the ideas of SA into two of the arithmetic operators of NP algorithm to form the combined NP/SA algorithm. Moreover, the paper presents the explicit optimization procedure of the combined algorithm NP/SA and explains the feasibility and superiority of it. The NP/SA algorithm adopts the global optimization ability of NP algorithm and the local search ability of SA algorithm so that it improves the optimization efficiency and the convergence rate. This paper also illustrates the NP/SA algorithm through an optimization example.

Simulated annealing based placement algorithms and research challenges: a survey

Abstract: In this survey paper, we have done a comprehensive study of VLSI placement algorithms in the literature and classified them from different perspectives. After an extensive review, we have performed a chronological analytical study of the simulated annealing based placement algorithm. We have explored the pros and cons of simulated annealing based placement along with its applications. To the best of our knowledge, there is no state of the art survey on the simulated annealing based placement algorithms in the literature till date. This motivated us to make a survey on simulated annealing based placement algorithms. We have explored some of the research issues and challenges to open up future directions of research work in this area. We have also addressed some research issues and suggested few directions to fill up the research gap.

Finding Hadamard Matrices by a Quantum Annealing Machine

Abstract: Finding a Hadamard matrix (H-matrix) among the set of all binary matrices of corresponding order is a hard problem, which potentially can be solved by quantum computing. We propose a method to formulate the Hamiltonian of finding H-matrix problem and address its implementation limitation on existing quantum annealing machine (QAM) that allows up to quadratic terms, whereas the problem naturally introduces higher order ones. For an M-order H-matrix, such a limitation increases the number of variables from M2 to (M3 + M2 − M)/2, which makes the formulation of the Hamiltonian too exhaustive to do by hand. We use symbolic computing techniques to manage this problem. Three related cases are discussed: (1) finding N < M orthogonal binary vectors, (2) finding M-orthogonal binary vectors, which is equivalent to finding a H-matrix, and (3) finding N-deleted vectors of an M-order H-matrix. Solutions of the problems by a 2-body simulated annealing software and by an actual quantum annealing hardware are also discussed.