Nasser Tairan

Fourier transform-based windowed adaptive switching minimum filter for reducing periodic noise from digital images

This paper presents a windowed adaptive switching minimum filter in frequency domain to restore images corrupted by periodic noise. Periodic noise frequencies that spread throughout the spatial domain image concentrate in frequency domain image as star-shaped peak regions. The proposed algorithm incorporates distinct stages of noisy frequency detection and correction. The noisy frequency detection stage has peak detection and noise map generation sub-stages to effectively identify noisy peak areas into a binary flag image from the directional image of the origin shifted Fourier transformed corrupted image. The proposed noise correction scheme restores the detected noisy areas of the corrupted frequency domain image with the minimum of nearest possible uncorrupted frequencies. Finally, inverse shifting and inverse Fourier transform operations generates the restored image. Experimental results in terms of subjective and objective metrics demarcate that the proposed periodic noise reduction filter is more effective in restoring images corrupted with periodic noise than other filters used in the comparative study.

Threshold Based Dynamic and Adaptive Penalty Functions for Constrained Multiobjective Optimization

Penalty functions are frequently used for dealing with constraints in constrained optimization. Among different types of penalty functions, dynamic and adaptive penalty functions seem effective, since the penalty coefficients in them are adjusted based on the current generation number (or number of solutions searched) and feedback from the search. In this paper, we propose dynamic and adaptive versions of our recently proposed threshold based penalty function. They are then implemented in the multiobjective evolutionary algorithm based on decomposition (MOEA/D) framework to solve constrained multi objective optimization problems (CMOPs). This led to a new algorithm, denoted by CMOEA/D-DE-TDA. The performance of CMOEA/D-DE-TDA is tested on CTP-series test instances in terms of the HV-metric and SC-metric. The experimental results are compared with IDEA and NSGA-II, which show the effectiveness of the proposed algorithm.

A New Threshold Based Penalty Function Embedded MOEA/D

Recently, we proposed a new threshold based penalty function. The threshold dynamically controls the penalty to infeasible solutions. This paper implants the two different forms of the proposed penalty function in the multiobjective evo-lutionary algorithm based on decomposition (MOEA/D) frame-work to solve constrained multiobjective optimization problems. This led to a new algorithm, denoted by CMOEA/D-DE-ATP. The performance of CMOEA/D-DE-ATP is tested on hard CF-series test instances in terms of the values of IGD-metric and SC-metric. The experimental results are compared with the three best performers of CEC 2009 MOEA competition. Experimental results show that the proposed penalty function is very promising, and it works well in the MOEA/D framework.

Reflected Adaptive Differential Evolution with Two External Archives for Large-Scale Global Optimization

JADE is an adaptive scheme of nature inspired algorithm, Differential Evolution (DE). It performed considerably improved on a set of well-studied benchmark test problems. In this paper, we evaluate the performance of new JADE with two external archives to deal with unconstrained continuous large-scale global optimization problems labeled as Reflected Adaptive Differential Evolution with Two External Archives (RJADE/TA). The only archive of JADE stores failed solutions. In contrast, the proposed second archive stores superior solutions at regular intervals of the optimization process to avoid premature convergence towards local optima. The superior solutions which are sent to the archive are reflected by new potential solutions. At the end of the search process, the best solution is selected from the second archive and the current population. The performance of RJADE/TA algorithm is then extensively evaluated on two test beds. At first on 28 latest benchmark functions constructed for the 2013 Congress on Evolutionary Computation special session. Secondly on ten benchmark problems from CEC2010 Special Session and Competition on Large-Scale Global Optimization. Experimental results demonstrated a very competitive perfor-mance of the algorithm.

Laplacian-Based Frequency Domain Filter for the Restoration of Digital Images Corrupted by Periodic Noise

This paper presents a new Laplacian-based frequency domain filter for the effective restoration of images corrupted by periodic noise. In the noise detection phase of the proposed algorithm, the Laplacian high-pass filter eases the thresholding-based noise detection process by highlighting noisy peak areas from uncorrupted areas of the origin-shifted Fourier-transformed corrupted image. The filtering phase of the proposed algorithm restores the detected noisy frequencies with nearest uncorrupted frequencies to ensure high fidelity in the restored image. Experimental analysis on different images reveals that the proposed algorithm produces better outputs in terms of subjective and objective metrics than other prominent algorithms.

Hybridization of Adaptive Differential Evolution with an Expensive Local Search Method

Differential evolution (DE) is an effective and efficient heuristic for global optimization problems. However, it faces difficulty in exploiting the local region around the approximate solution. To handle this issue, local search (LS) techniques could be hybridized with DE to improve its local search capability. In this work, we hybridize an updated version of DE, adaptive differential evolution with optional external archive (JADE) with an expensive LS method, Broydon-Fletcher-Goldfarb-Shano (BFGS) for solving continuous unconstrained global optimization problems. The new hybrid algorithm is denoted by DEELS. To validate the performance of DEELS, we carried out extensive experiments on well known test problems suits, CEC2005 and CEC2010. The experimental results, in terms of function error values, success rate, and some other statistics, are compared with some of the state-of-the-art algorithms, self-adaptive control parameters in differential evolution (jDE), sequential DE enhanced by neighborhood search for large-scale global optimization (SDENS), and differential ant-stigmergy algorithm (DASA). These comparisons reveal that DEELS outperforms jDE and SDENS except DASA on the majority of test instances.

Threshold Based Penalty Functions for Constrained Multiobjective Optimization

This paper compares the performance of our re-cently proposed threshold based penalty function against its dynamic and adaptive variants. These penalty functions are incorporated in the update and replacement scheme of the multiobjective evolutionary algorithm based on decomposition (MOEA/D) framework to solve constrained multiobjective op-timization problems (CMOPs). As a result, the capability of MOEA/D is extended to handle constraints, and a new algorithm, denoted by CMOEA/D-DE-TDA is proposed. The performance of CMOEA/D-DE-TDA is tested, in terms of the values of IGD-metric and SC-metric, on the well known CF-series test instances. The experimental results are also compared with the three best performers of CEC 2009 MOEA competition. Empirical results show the pitfalls of the proposed penalty functions.

Performance of a Constrained Version of MOEA/D on CTP-series Test Instances

Constrained multiobjective optimization arises in many real-life applications, and is therefore gaining a constantly growing attention of the researchers. Constraint handling techniques differ in the way infeasible solutions are evolved in the evolutionary process along with their feasible counterparts. Our recently proposed threshold based penalty function gives a chance of evolution to infeasible solutions whose constraint violation is less than a specified threshold value. This paper embeds the threshold based penalty function in the update and replacement scheme of multi-objective evolutionary algorithm based on decomposition (MOEA/D) to find tradeoff solutions for constrained multiobjective optimization problems (CMOPs). The modified algorithm is tested on CTP-series test instances in terms of the hypervolume metric (HV-metric). The experimental results are compared with the two well-known algorithms, NSGA-II and IDEA. The sensitivity of algorithm to the adopted parameters is also checked. Empirical results demonstrate the effectiveness of the proposed penalty function in the MOEA/D framework for CMOPs

Feature Selection and Term Weighting

Term-based approaches can extract many features in text documents, but most include noise. Many popular text-mining techniques have been adapted to reduce noisy information from extracted features but still contains some noises features. However, the noise features are extracted from the same training documents that good features extracted from. Therefore, the main problem is that some training documents contain large a mount of noises data. If we can reduce the noises data in the training documents that would help to reduce noises in extracted features. Moreover, we believe that remove some of training documents (documents that contains noises data more than useful data) can help to improve the effectiveness of the classifier. Using the advantages of clustering method can help to reduce the affect of noises data. The main problem of clustering is defined to be that of finding groups of similar projects in the data. In this paper we introduce the methodology that using clustering algorithm to group training data before use it. Also we tested our theory that not all training documents are useful to train the classifier.

A Quick Gbest Guided Artificial Bee Colony Algorithm for Stock Market Prices Prediction

The objective of this work is to present a Quick Gbest Guided artificial bee colony (ABC) learning algorithm to train the feedforward neural network (QGGABC-FFNN) model for the prediction of the trends in the stock markets. As it is quite important to know that nowadays, stock market prediction of trends is a significant financial global issue. The scientists, finance administration, companies, and leadership of a given country struggle towards developing a strong financial position. Several technical, industrial, fundamental, scientific, and statistical tools have been proposed and used with varying results. Still, predicting an exact or near-to-exact trend of the Stock Market values behavior is an open problem. In this respect, in the present manuscript, we propose an algorithm based on ABC to minimize the error in the trend and actual values by using the hybrid technique based on neural network and artificial intelligence. The presented approach has been verified and tested to predict the accurate trend of Saudi Stock Market (SSM) values. The proposed QGGABC-ANN based on bio-inspired learning algorithm with its high degree of accuracy could be used as an investment advisor for the investors and traders in the future of SSM. The proposed approach is based mainly on SSM historical data covering a large span of time. From the simulation findings, the proposed QGGABC-FFNN outperformed compared with other typical computational algorithms for prediction of SSM values.