This paper describes a near-optimal allocation method for web-based multi-player online role-playing games (MORPGs), which must be able to cope with a large number of users and high frequency of user requests. Our previous work introduced a dynamic data reallocation method. It uses multiple web servers and divides the entire game world into small blocks. Each ownership of block is allocated to a web server. Additionally, the ownership is reallocated to the other web server according to the user's requests. Furthermore, this block allocation was formulated as a combinational optimization problem. And a simulation based experiment with an exact algorithm showed that our system could achieve 31% better than an ad-hoc approach. However, the exact algorithm takes too much time to solve a problem when the problem size is large. This paper proposes a meta-heuristic approach based on a tabu search to solve a problem quickly. A simulation result shows that our tabu search algorithm can generate solutions, whose average correctness is only 1% different from that of the exact algorithm. In addition, the average calculation time for 50 users on a system with five web servers is about 25.67 msec while the exact algorithm takes about 162 msec. An evaluation for a web-based MORPG system with our tabu search shows that it could achieve 420 users capacity while 320 for our previous system.

We present an Interactive Tabu Search (ITS) algorithm to reduce the evaluation load of Interactive Evolutionary Computation (IEC) users. Most previous IEC studies used an evaluation interface that required users to provide evaluation values for all candidate solutions. However, user's burden with such an evaluation interface is large. Therefore, we propose ITS where users choose the favorite candidate solution from the presented candidate solutions. Tabu Search (TS) is recognized as an optimization technique. ITS evaluation is simpler than Interactive Genetic Algorithm (IGA) evaluation, in which users provide evaluation values for all candidate solutions. Therefore, ITS is effective for reducing user evaluation load. We evaluated the performance of our proposed ITS and a Normal IGA (NIGA), which is a conventional 10-stage evaluation, using a numerical simulation with an evaluation agent that imitates human preferences (Kansei). In addition, we implemented an ITS evaluation for a running-shoes-design system and examined its effectiveness through an experiment with real users. The simulation results showed that the evolution performance of ITS is better than that of NIGA. In addition, we conducted an evaluation experiment with 21 subjects in their 20 s to assess the effectiveness of these methods. The results showed that the satisfaction levels for the candidates generated by ITS and NIGA were approximately equal. Moreover, it was easier for test subjects to evaluate candidate solutions with ITS than with NIGA.

Despite the growing research effort in formal verification, industrial verification often relies on the constrained random simulation methodology, which is supported by constraint solvers as the stimulus generator integrated within simulator, especially for the large design with complex constraints nowadays. These stimulus generators need to be fast and well-distributed to maintain simulation performance. In this paper, we propose a dynamic method to guide stimulus generation by SAT solvers. An adjusting strategy named Tabu Search with Memory (TSwM) is integrated in the stimulus generator for the search and prune processes along with the constraint solver. Experimental results show that the method proposed in this paper could generate well-distributed stimuli with good performance.

An improved algorithm of Guided Local Search called objective function adjustment algorithm is proposed for combinatorial optimization problems. The performance of Guided Local Search is improved by objective function adjustment algorithm using multipliers which can be adjusted during the search process. Moreover, the idea of Tabu Search is introduced into the objective function adjustment algorithm to further improve the performance. The simulation results based on some TSPLIB benchmark problems showed that the objective function adjustment algorithm could find better solutions than Local Search, Guided Local Search and Tabu Search.

This paper proposes a low power scan test scheme and formulates a problem based on this scheme. In this scheme the flip-flops are grouped into N scan chains. At any time, only one scan chain is active during scan test. Therefore, both average power and peak power are reduced compared with conventional full scan test methodology. This paper also proposes a tabu search-based approach to minimize test application time. In this approach we handle the information during deterministic test efficiently. Experimental results demonstrate that this approach drastically reduces both average power and peak power dissipation at a little longer test application time on various benchmark circuits.

A novel rough neural network (RNN) structure and its application are proposed in this paper. We principally introduce its architecture and training algorithms: the genetic training algorithm (GA) and the tabu search training algorithm (TSA). We first compare RNN with the conventional NN trained by the BP algorithm in two-dimensional data classification. Then we compare RNN with NN by the same training algorithm (TSA) in functional approximation. Experiment results show that the proposed RNN is more effective than NN, not only in computation time but also in performance.

The problem of detecting straight lines arises frequently in image processing and computer vision. Here we consider the problem of extracting lines from range images and more generally from sets of three-dimensional (3D) points. The problem is stated as follows. Given a set Γ of points in 3D space and a non-negative constant , determine the line that is at a distance at most ε from the maximal number of points of . The extraction of multiple lines is achieved iteratively by performing this best line detection and removing at each iteration the points that are close to the line found. We consider two approaches to solve the problem. The first is a simple approach that selects the best line among a randomly chosen subset of lines each defined by a pair of edge points. The second approach, based on tabu search, explores a larger set of candidate lines thus obtaining a better fit of the lines to the points. We present experimental results on different types of three-dimensional data (i) range images of polyhedral objects (ii) secondary structures (helices and strands) of large molecules.

This paper proposes aspiration controls which restrains aspiration branches and keeps the original tabu-based searching by considering past and/or (predicted) future searching profiles. For implementation of the aspiration control we employ not only the short-term and long-term memory but also future memory which is first introduced in this paper as a new concept in the tabu search field. The tabu search with the aspiration control is also parallelized. Moreover two types of parallel cooperative searching scheme are proposed. Through computational experiment, we observe efficiency of our approach comparing to the traditional ones. Especially, we find that cooperative searching has possibility to improve the solution quality very well.