Search

Abstract: The drawbacks of SVD-based image watermarking are false positive, robust and transparency. The former can be overcome by embedding the principal components of the watermark into the host image, the latter is dependent on how much the quantity (i.e., scaling factor) of the principal components is embedded. For the existing methods, the scaling factor is a fixed value; actually, it is image-dependent. Different watermarks need the different scaling factors, although they are embedded in the same host image. In this paper, two methods are proposed to improve the reliability and robustness. To improve the reliability, for the first method, the principal components of the watermark are embedded into the host image in discrete cosine transform (DCT); and for the second method, those are embedded into the host image in discrete wavelets transform (DWT). To improve the robustness, the particle swarm optimization (PSO) is used for finding the suitable scaling factors. The experimental results demonstrate that the performance of the proposed methods outperforms than those of the existing methods. [Copyright &y& Elsevier]

Abstract: In this paper, a new forecasting model based on two computational methods, fuzzy time series and particle swarm optimization, is presented for academic enrollments. Most of fuzzy time series forecasting methods are based on modeling the global nature of the series behavior in the past data. To improve forecasting accuracy of fuzzy time series, the global information of fuzzy logical relationships is aggregated with the local information of latest fuzzy fluctuation to find the forecasting value in fuzzy time series. After that, a new forecasting model based on fuzzy time series and particle swarm optimization is developed to adjust the lengths of intervals in the universe of discourse. From the empirical study of forecasting enrollments of students of the University of Alabama, the experimental results show that the proposed model gets lower forecasting errors than those of other existing models including both training and testing phases. [Copyright &y& Elsevier]

Abstract: Mutual funds have become the most popular products for diversity of investment, since they are able to disperse investment risks to the smallest degree. In selecting mutual funds, the past performance of funds plays a central role in the expectations of the future performance of funds. In 2008, the U.S. sub-prime broke out; numerous investors lost more than half of the capitals donated. Therefore, a good trading strategy is necessary. In this paper, a new funds trading strategy that combines turbulent particle swarm optimization (named TPSO) and mixed moving average techniques is presented and used to find the proper content of technical indicator parameters to achieve high profit and low risk on a mutual fund. The time interval of moving average of the proposed method is adjustable and the trading model could avoid and reduce loss by providing several good buy and sell points. We tested the proposed model using the historical prices of last 10years and the experimental results show that the performance of the proposed model is far better than the best original performance. [Copyright &y& Elsevier]

Abstract: In this paper, we proposed a modified turbulent particle swarm optimization (named MTPSO) model for solving planar graph coloring problem based on particle swarm optimization. The proposed model is consisting of the walking one strategy, assessment strategy and turbulent strategy. The proposed MTPSO model can solve the planar graph coloring problem using four-colors more efficiently and accurately. Compared to the results shown in , not only the experimental results of the proposed model can get smaller average iterations but can get higher correction coloring rate when the number of nodes is greater than 30. [ABSTRACT FROM AUTHOR]

Abstract: A hybrid particle swarm optimization (HPSO) algorithm which utilizes random-key (RK) encoding scheme, individual enhancement (IE) scheme, and particle swarm optimization (PSO) for solving a bi-objective personnel assignment problem (BOPAP) is presented. The HPSO algorithm which was proposed by is used to solve the flow-shop scheduling problem (FSSP). In the research of BOPAP, the main contribution of the work is to improve the f 1_f 2 heuristic algorithm which was proposed by . The objective of the f 1_f 2 heuristic algorithm is to get the satisfaction level (SL) value which is satisfied the bi-objective values f 1, and f 2 for the personnel assignment problem. In this paper, PSO is used to search the solution of the input problem in the BOPAP space. Then, with the RK encoding scheme in the virtual space, we can exploit the global search ability of PSO thoroughly. Based on the IE scheme, we can enhance the local search ability of particles. The experimental results show that the solution quality of BOPAP based on the proposed HPSO algorithm for the first objective f 1 (i.e., total score), the second objective f 2 (i.e., standard deviation), the coefficient of variance (CV), and the time cost is far better than that of the f 1_f 2 heuristic algorithm. To the best our knowledge, this presented result of the BOPAP is the best bi-objective algorithm known. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we proposed a modified turbulent particle swarm optimization (named MTPSO) method for the temperature prediction and the Taiwan Futures Exchange (TAIFEX) forecasting, based on the two-factor fuzzy time series and particle swarm optimization. The MTPSO model can be dealt with two main factors easily and accurately, which are the lengths of intervals and the content of forecast rules. The experimental results of the temperature prediction and the TAIFEX forecasting show that the proposed model is better than any existing models and it can get better quality solutions based on the high-order fuzzy time series, respectively. [Copyright &y& Elsevier]

Abstract: The job-shop scheduling problem has attracted many researchers’ attention in the past few decades, and many algorithms based on heuristic algorithms, genetic algorithms, and particle swarm optimization algorithms have been presented to solve it, respectively. Unfortunately, their results have not been satisfied at all yet. In this paper, a new hybrid swarm intelligence algorithm consists of particle swarm optimization, simulated annealing technique and multi-type individual enhancement scheme is presented to solve the job-shop scheduling problem. The experimental results show that the new proposed job-shop scheduling algorithm is more robust and efficient than the existing algorithms. [Copyright &y& Elsevier]

Abstract: The TAIFEX (Taiwan Stock Index Futures) forecasting problem has attracted some researchers’ attention in the past decades. Several forecast methods for the TAIFEX forecasting based either on the statistic theorems have been proposed, but their results are not satisfied. Fuzzy time series is used to doing forecasting but the forecasted accuracy still needs to be improved. In this paper we present a new hybrid forecast method to solve the TAIFEX forecasting problem based on fuzzy time series and particle swarm optimization. The experimental results show that the new proposed forecast model is better than any existing fuzzy forecast models and is more precise than four famous statistic forecast models. [Copyright &y& Elsevier]

Abstract: In this paper, a new hybrid particle swarm optimization model named HPSO that combines random-key (RK) encoding scheme, individual enhancement (IE) scheme, and particle swarm optimization (PSO) is presented and used to solve the flow-shop scheduling problem (FSSP). The objective of FSSP is to find an appropriate sequence of jobs in order to minimize makespan. Makespan means the maximum completion time of a sequence of jobs running on the same machines in flow-shops. By the RK encoding scheme, we can exploit the global search ability of PSO thoroughly. By the IE scheme, we can enhance the local search ability of particles. The experimental results show that the solution quality of FSSP based on the proposed HPSO is far better than those based on GA [Lian, Z., Gu, X., & Jiao, B. (2008). A novel particle swarm optimization algorithm for permutation flow-shop scheduling to minimize makespan. Chaos, Solitons and Fractals, 35, 851–861.] and NPSO [Lian, Z., Gu, X., & Jiao, B. (2008). A novel particle swarm optimization algorithm for permutation flow-shop scheduling to minimize makespan. Chaos, Solitons and Fractals, 35, 851–861.], respectively. [Copyright &y& Elsevier]

Abstract: Many forecasting models based on the concept of fuzzy time series have been proposed in the past decades. Two main factors, which are the lengths of intervals and the content of forecast rules, impact the forecasted accuracy of the models. How to find the proper content of the main factors to improve the forecasted accuracy has become an interesting research topic. Some forecasting models, which combined heuristic methods or evolutionary algorithms (such as genetic algorithms and simulated annealing) with the fuzzy time series, have been proposed but their results are not satisfied. In this paper, we use the particle swarm optimization to find the proper content of the main factors. A new hybrid forecasting model which combined particle swarm optimization with fuzzy time series is proposed to improve the forecasted accuracy. The experimental results of forecasting enrollments of students of the University of Alabama show that the new model is better than any existing models, and it can get better quality solutions based on the first-order and the high-order fuzzy time series, respectively. [Copyright &y& Elsevier]