Showing papers presented at "Soft Computing in 2015"
1 Feb 2015
TL;DR: The machine learning techniques have the ability for predicting software fault proneness and can be used by software practitioners and researchers, however, the application of theMachine learning techniques in software fault prediction is still limited and more number of studies should be carried out in order to obtain well formed and generalizable results.
Abstract: Reviews studies from 1991-2013 to assess application of ML techniques for SFP.Identifies seven categories of the ML techniques.Identifies 64 studies to answer the established research questions.Selects primary studies according to the quality assessment of the studies.Systematic literature review performs the following:Summarize ML techniques for SFP models.Assess performance accuracy and capability of ML techniques for constructing SFP models.Provide comparison between the ML and statistical techniques.Provide comparison of performance accuracy of different ML techniques.Summarize the strength and weakness of the ML techniques.Provides future guidelines to software practitioners and researchers. BackgroundSoftware fault prediction is the process of developing models that can be used by the software practitioners in the early phases of software development life cycle for detecting faulty constructs such as modules or classes. There are various machine learning techniques used in the past for predicting faults. MethodIn this study we perform a systematic review of studies from January 1991 to October 2013 in the literature that use the machine learning techniques for software fault prediction. We assess the performance capability of the machine learning techniques in existing research for software fault prediction. We also compare the performance of the machine learning techniques with the statistical techniques and other machine learning techniques. Further the strengths and weaknesses of machine learning techniques are summarized. ResultsIn this paper we have identified 64 primary studies and seven categories of the machine learning techniques. The results prove the prediction capability of the machine learning techniques for classifying module/class as fault prone or not fault prone. The models using the machine learning techniques for estimating software fault proneness outperform the traditional statistical models. ConclusionBased on the results obtained from the systematic review, we conclude that the machine learning techniques have the ability for predicting software fault proneness and can be used by software practitioners and researchers. However, the application of the machine learning techniques in software fault prediction is still limited and more number of studies should be carried out in order to obtain well formed and generalizable results. We provide future guidelines to practitioners and researchers based on the results obtained in this work.
637 citations
1 Feb 2015
TL;DR: A general overview of research in the fuzzy sciences using bibliometric indicators provides a general picture, identifying some of the most influential research in this area.
Abstract: Number of annual publications in fuzzy research (articles+reviews) since 1965. Bibliometrics in fuzzy research.List of most cited papers on fuzzy topics of all time.An overview of influential authors, institutions and countries.Journal analysis in fuzzy research. Bibliometrics is a discipline that analyzes bibliographic material from a quantitative perspective. It is very useful for classifying information according to different variables, including journals, institutions and countries. This paper presents a general overview of research in the fuzzy sciences using bibliometric indicators. The main advantage is that these indicators provide a general picture, identifying some of the most influential research in this area. The analysis is divided into key sections focused on relevant journals, papers, authors, institutions and countries. Most of the results are in accordance with our common knowledge, although some unexpected results are also found. Note that the aim of this paper is to be informative, and these indicators identify most of the fundamental research in this field. However, some very influential issues may be omitted if they are not included in the Web of Science database, which is used for carrying out the bibliometric analysis.
536 citations
1 Sep 2015
TL;DR: A comprehensive survey of the state-of-the-art distributed evolutionary algorithms and models, which have been classified into two groups according to their task division mechanism, and insights into the models are presented and discussed.
Abstract: Graphical abstractDisplay Omitted HighlightsProvide an updated and systematic review of distributed evolutionary algorithms.Classify the models into population and dimension-distributed groups semantically.Analyze the parallelism, search behaviors, communication costs, scalability, etc.Highlight recent research hotspots in this field.Discuss challenges and potential research directions in this field. The increasing complexity of real-world optimization problems raises new challenges to evolutionary computation. Responding to these challenges, distributed evolutionary computation has received considerable attention over the past decade. This article provides a comprehensive survey of the state-of-the-art distributed evolutionary algorithms and models, which have been classified into two groups according to their task division mechanism. Population-distributed models are presented with master-slave, island, cellular, hierarchical, and pool architectures, which parallelize an evolution task at population, individual, or operation levels. Dimension-distributed models include coevolution and multi-agent models, which focus on dimension reduction. Insights into the models, such as synchronization, homogeneity, communication, topology, speedup, advantages and disadvantages are also presented and discussed. The study of these models helps guide future development of different and/or improved algorithms. Also highlighted are recent hotspots in this area, including the cloud and MapReduce-based implementations, GPU and CUDA-based implementations, distributed evolutionary multiobjective optimization, and real-world applications. Further, a number of future research directions have been discussed, with a conclusion that the development of distributed evolutionary computation will continue to flourish.
397 citations
1 May 2015
TL;DR: Zhang et al. as discussed by the authors proposed a new nature-inspired social-spider-based swarm intelligence algorithm, which is mainly based on the foraging strategy of social spiders, utilizing the vibrations on the spider web to determine the positions of preys.
Abstract: Graphical abstractDisplay Omitted HighlightsWe propose a new nature-inspired social-spider-based swarm intelligence algorithm.We introduce a new social animal foraging model into meta-heuristic design.We introduce the design of information loss to handle pre-mature convergence.We perform a series of benchmark simulations to demonstrate the performance.We investigate the impact of control parameters on optimization results. The growing complexity of real-world problems has motivated computer scientists to search for efficient problem-solving methods. Metaheuristics based on evolutionary computation and swarm intelligence are outstanding examples of nature-inspired solution techniques. Inspired by the social spiders, we propose a novel social spider algorithm to solve global optimization problems. This algorithm is mainly based on the foraging strategy of social spiders, utilizing the vibrations on the spider web to determine the positions of preys. Different from the previously proposed swarm intelligence algorithms, we introduce a new social animal foraging strategy model to solve optimization problems. In addition, we perform preliminary parameter sensitivity analysis for our proposed algorithm, developing guidelines for choosing the parameter values. The social spider algorithm is evaluated by a series of widely used benchmark functions, and our proposed algorithm has superior performance compared with other state-of-the-art metaheuristics.
394 citations
1 Oct 2015
TL;DR: A trust based estimation and aggregation methods as part of a visual consensus model for multiple criteria group decision making with incomplete linguistic information and it is proved that the implementation of the visual feedback mechanism guarantees the convergence of the consensus reaching process.
Abstract: Graphical abstract(A) Trust propagating aggregation and visual consensus model for MCGDM under incomplete information. (B) Visual feedback simulation: consensus levels before and after recommendations implemented by experts. Display Omitted HighlightsA theoretical framework to build consensus within a networked social group is presented.A novel trust propagation method is proposed to derive trust relationship from an incomplete connected trust network.A visual feedback process including a recommendation mechanism to provide individualised advice is implemented.The implementation of the visual feedback mechanism guarantees the convergence of the consensus reaching process. A theoretical framework to consensus building within a networked social group is put forward. This article investigates a trust based estimation and aggregation methods as part of a visual consensus model for multiple criteria group decision making with incomplete linguistic information. A novel trust propagation method is proposed to derive trust relationship from an incomplete connected trust network and the trust score induced order weighted averaging operator is presented to aggregate the orthopairs of trust/distrust values obtained from different trust paths. Then, the concept of relative trust score is defined, whose use is twofold: (1) to estimate the unknown preference values and (2) as a reliable source to determine experts' weights. A visual feedback process is developed to provide experts with graphical representations of their consensus status within the group as well as to identify the alternatives and preference values that should be reconsidered for changing in the subsequent consensus round. The feedback process also includes a recommendation mechanism to provide advice to those experts that are identified as contributing less to consensus on how to change their identified preference values. It is proved that the implementation of the visual feedback mechanism guarantees the convergence of the consensus reaching process.
392 citations
1 May 2015
TL;DR: The performance of proposed hybrid method by using fewer ants than the number of cities for the TSPs is better than the performance of compared methods in most cases in terms of solution quality and robustness.
Abstract: The Traveling Salesman Problem (TSP) is one of the standard test problems used in performance analysis of discrete optimization algorithms. The Ant Colony Optimization (ACO) algorithm appears among heuristic algorithms used for solving discrete optimization problems. In this study, a new hybrid method is proposed to optimize parameters that affect performance of the ACO algorithm using Particle Swarm Optimization (PSO). In addition, 3-Opt heuristic method is added to proposed method in order to improve local solutions. The PSO algorithm is used for detecting optimum values of parameters α and β which are used for city selection operations in the ACO algorithm and determines significance of inter-city pheromone and distances. The 3-Opt algorithm is used for the purpose of improving city selection operations, which could not be improved due to falling in local minimums by the ACO algorithm. The performance of proposed hybrid method is investigated on ten different benchmark problems taken from literature and it is compared to the performance of some well-known algorithms. Experimental results show that the performance of proposed method by using fewer ants than the number of cities for the TSPs is better than the performance of compared methods in most cases in terms of solution quality and robustness.
391 citations
1 Nov 2015
TL;DR: A novel metaheuristic optimization method called the lightning search algorithm (LSA) to solve constraint optimization problems based on the natural phenomenon of lightning and the mechanism of step leader propagation using the concept of fast particles known as projectiles.
Abstract: Totally new optimization method inspired by lightning phenomena is introduced.The concept of projectiles and channel forking is adopted for movement of step leaders.Three types of projectiles are modeled as search mechanisms of LSA.The result of the LSA provides better results compared with the other tested methods. This paper introduces a novel metaheuristic optimization method called the lightning search algorithm (LSA) to solve constraint optimization problems. It is based on the natural phenomenon of lightning and the mechanism of step leader propagation using the concept of fast particles known as projectiles. Three projectile types are developed to represent the transition projectiles that create the first step leader population, the space projectiles that attempt to become the leader, and the lead projectile that represent the projectile fired from best positioned step leader. In contrast to that of the counterparts of the LSA, the major exploration feature of the proposed algorithm is modeled using the exponential random behavior of space projectile and the concurrent formation of two leader tips at fork points using opposition theory. To evaluate the reliability and efficiency of the proposed algorithm, the LSA is tested using a well-utilized set of 24 benchmark functions with various characteristics necessary to evaluate a new algorithm. An extensive comparative study with four other well-known methods is conducted to validate and compare the performance of the LSA. The result demonstrates that the LSA generally provides better results compared with the other tested methods with a high convergence rate.
390 citations
Proceedings Article•
1 Jan 2015TL;DR: The advancement of machine learning techniques would mostly benefit the big data computing in addressing the class imbalance problem which is inevitably presented in many real world applications especially in medicine and social media.
Abstract: Most existing classification approaches assume the underlying training set is evenly distributed. In class imbalanced classification, the training set for one class (majority) far surpassed the training set of the other class (minority), in which, the minority class is often the more interesting class. In this paper, we review the issues that come with learning from imbalanced class data sets and various problems in class imbalance classification. A survey on existing approaches for handling classification with imbalanced datasets is also presented. Finally, we discuss current trends and advancements which potentially could shape the future direction in class imbalance learning and classification. We also found out that the advancement of machine learning techniques would mostly benefit the big data computing in addressing the class imbalance problem which is inevitably presented in many real world applications especially in medicine and social media.
386 citations
1 Mar 2015
TL;DR: A novel framework for teaching performance evaluation based on the combination of fuzzy AHP and fuzzy comprehensive evaluation method is presented and it is expected that this work may serve as an assistance tool for managers of higher education institutions in improving the educational quality level.
Abstract: Proposing a novel framework for evaluating teaching performance based on the combination of fuzzy AHP and fuzzy comprehensive evaluation method.Determining the factors and sub-factors in the evaluation index system, and then calculating the factor and sub-factor weights by the extent analysis fuzzy AHP method.On the basis of the constructed system, evaluating teaching performance can be conducted by the fuzzy comprehensive evaluation method.The approach can provide an effective, reasonable and accurate results of the evaluation. Evaluating teaching performance is a main means to improve teaching quality and can plays an important role in strengthening the management of higher education institutions. In this paper, we present a novel framework for teaching performance evaluation based on the combination of fuzzy AHP and fuzzy comprehensive evaluation method. Specifically, after determining the factors and sub-factors, the teaching performance index system was established. In the index system, the factor and sub-factor weights were then estimated by the extent analysis fuzzy AHP method. Employing the fuzzy AHP method in group decision-making can facilitate a consensus of decision-makers and reduce uncertainty. On the basis of the system, the fuzzy comprehensive evaluation method was employed to evaluate teaching performance. A case application was also used to illustrate the proposed framework. The application of this framework can make the evaluation results more scientific, accurate, and objective. It is expected that this work may serve as an assistance tool for managers of higher education institutions in improving the educational quality level.
373 citations
1 Mar 2015
TL;DR: A novel FMEA approach is proposed using combination weighting and fuzzy VIKOR method, which is used for analyzing the risk of general anesthesia process and integration of fuzzy analytic hierarchy process and entropy method is applied for risk factor weighting.
Abstract: A novel FMEA approach is proposed using combination weighting and fuzzy VIKOR.Combination of fuzzy AHP and entropy method is applied for risk factor weighting.Fuzzy VIKOR method is used to determine the risk priorities of failure modes.An empirical example is offered to illustrate the effectiveness of the new method. Failure mode and effects analysis (FMEA) is one of the most popular reliability analysis tools for identifying, assessing and eliminating potential failure modes in a wide range of industries. In general, failure modes in FMEA are evaluated and ranked through the risk priority number (RPN), which is obtained by the multiplication of crisp values of the risk factors, such as the occurrence (O), severity (S), and detection (D) of each failure mode. However, the conventional RPN method has been considerably criticized for various reasons. To deal with the uncertainty and vagueness from humans' subjective perception and experience in risk evaluation process, this paper presents a novel approach for FMEA based on combination weighting and fuzzy VIKOR method. Integration of fuzzy analytic hierarchy process (AHP) and entropy method is applied for risk factor weighting in this proposed approach. The risk priorities of the identified failure modes are obtained through next steps based on fuzzy VIKOR method. To demonstrate its potential applications, the new fuzzy FMEA is used for analyzing the risk of general anesthesia process. Finally, a sensitivity analysis is carried out to verify the robustness of the risk ranking and a comparison analysis is conducted to show the advantages of the proposed FMEA approach.
359 citations
1 Jul 2015
TL;DR: The results of this research show that GWO is able to achieve less power loss and voltage deviation than those determined by other techniques.
Abstract: Gray wolf optimizer (GWO) is employed in solving the optimal reactive power dispatch (ORPD) problems.Three case studies have been utilized to show the effectiveness of GWO.GWO able to find minimum loss and voltage deviation solution than those determined by other techniques. This paper presents the use of a new meta-heuristic technique namely gray wolf optimizer (GWO) which is inspired from gray wolves' leadership and hunting behaviors to solve optimal reactive power dispatch (ORPD) problem. ORPD problem is a well-known nonlinear optimization problem in power system. GWO is utilized to find the best combination of control variables such as generator voltages, tap changing transformers' ratios as well as the amount of reactive compensation devices so that the loss and voltage deviation minimizations can be achieved. In this paper, two case studies of IEEE 30-bus system and IEEE 118-bus system are used to show the effectiveness of GWO technique compared to other techniques available in literature. The results of this research show that GWO is able to achieve less power loss and voltage deviation than those determined by other techniques.
1 Dec 2015
TL;DR: Experimental results and security analysis show that the proposed encryption scheme for color images based on Deoxyribonucleic acid sequence operations and multiple improved one-dimensional chaotic systems has a good encryption effect and high security.
Abstract: A DNA-based color image encryption method is proposed by using three 1D chaotic systems with excellent performance and easy implementation.The key streams used for encryption are related to both the secret keys and the plain-image.To improve the security and sensitivity, a division-shuffling process is introduced.Transforming the plain-image and the key streams into the DNA matrices randomly can further enhance the security of the cryptosystem.The presented scheme has a good robustness for some common image processing operations and geometric attack. This paper proposes a new encryption scheme for color images based on Deoxyribonucleic acid (DNA) sequence operations and multiple improved one-dimensional (1D) chaotic systems with excellent performance. Firstly, the key streams are generated from three improved 1D chaotic systems by using the secret keys and the plain-image. Transform randomly the key streams and the plain-image into the DNA matrices by the DNA encoding rules, respectively. Secondly, perform the DNA complementary and XOR operations on the DNA matrices to get the scrambled DNA matrices. Thirdly, decompose equally the scrambled DNA matrices into blocks and shuffle these blocks randomly. Finally, implement the DNA XOR and addition operations on the DNA matrices obtained from the previous step and the key streams, and then convert the encrypted DNA matrices into the cipher-image by the DNA decoding rules. Experimental results and security analysis show that the proposed encryption scheme has a good encryption effect and high security. Moreover, it has a strong robustness for the common image processing operations and geometric attack.
1 Jul 2015
TL;DR: A novel artificial bee colony based maximum power point tracking algorithm (MPPT) that does not allow only overcoming the common drawback of the conventional MPPT methods, but it gives a simple and a robust MPPT scheme.
Abstract: An artificial bee colony based MPPT under partially shaded conditions is proposed.Photovoltaic systems are considered.A co-simulation methodology combining Simulink and Pspice has been adopted.Excellent efficiency and tracking performance compared to the PSO-based MPPT.The effectiveness of the proposed method has been confirmed experimentally. Artificial bee colony (ABC) algorithm has several characteristics that make it more attractive than other bio-inspired methods. Particularly, it is simple, it uses fewer control parameters and its convergence is independent of the initial conditions. In this paper, a novel artificial bee colony based maximum power point tracking algorithm (MPPT) is proposed. The developed algorithm, does not allow only overcoming the common drawback of the conventional MPPT methods, but it gives a simple and a robust MPPT scheme. A co-simulation methodology, combining Matlab/Simulink? and Cadence/Pspice?, is used to verify the effectiveness of the proposed method and compare its performance, under dynamic weather conditions, with that of the Particle Swarm Optimization (PSO) based MPPT algorithm. Moreover, a laboratory setup has been realized and used to experimentally validate the proposed ABC-based MPPT algorithm. Simulation and experimental results have shown the satisfactory performance of the proposed approach.
1 Feb 2015
TL;DR: A survey of genetic algorithms that are designed for solving multi depot vehicle routing problem, and the efficiency of different existing genetic methods on standard benchmark problems in detail are presented.
Abstract: We reviewed the use of genetic algorithms on the MDVRP (multi depot vehicle routing problem).Survey was made on every operator and setting of genetic algorithm for this problem.We tested different genetic operators and compared the results.We compared the genetic algorithms to other metaheuristic algorithms on MDVRP based on the results on standard benchmarks. This article presents a survey of genetic algorithms that are designed for solving multi depot vehicle routing problem. In this context, most of the articles focus on different genetic approaches, methods and operators, commonly used in practical applications to solve this well-known and researched problem. Besides providing an up-to-date overview of the research in the field, the results of a thorough experiment are presented and discussed, which evaluated the efficiency of different existing genetic methods on standard benchmark problems in detail. In this manner, the insights into strengths and weaknesses of specific methods, operators and settings are presented, which should help researchers and practitioners to optimize their solutions in further studies done with the similar type of the problem in mind. Finally, genetic algorithm based solutions are compared with other existing approaches, both exact and heuristic, for solving this same problem.
1 Oct 2015
TL;DR: This paper will help researchers in selecting appropriate crossover operator for better results and contains description about classical standard crossover operators, binary crossover operator, and application dependant crossover operators.
Abstract: The performance of Genetic Algorithm (GA) depends on various operators. Crossover operator is one of them. Crossover operators are mainly classified as application dependent crossover operators and application independent crossover operators. Effect of crossover operators in GA is application as well as encoding dependent. This paper will help researchers in selecting appropriate crossover operator for better results. The paper contains description about classical standard crossover operators, binary crossover operators, and application dependant crossover operators. Each crossover operator has its own advantages and disadvantages under various circumstances. This paper reviews the crossover operators proposed and experimented by various researchers.
1 May 2015
TL;DR: An evolutionary approach to solve the mobile robot path planning problem is proposed that combines the artificial bee colony algorithm as a local search procedure and the evolutionary programming algorithm to refine the feasible path found by a set of local procedures.
Abstract: Graphical abstractDisplay Omitted HighlightsWe solve the path planning problem using the combination of two evolutionary methods.First, an artificial bee colony (ABC) finds a feasible path in the free space.Second, evolutionary programming (EP) optimizes the path length and smoothness.The proposed approach was compared to a probabilistic roadmap (PRM) method.The ABC-EP approach outperforms the PRM approach on problems of varying complexity. In this paper, an evolutionary approach to solve the mobile robot path planning problem is proposed. The proposed approach combines the artificial bee colony algorithm as a local search procedure and the evolutionary programming algorithm to refine the feasible path found by a set of local procedures. The proposed method is compared to a classical probabilistic roadmap method (PRM) with respect to their planning performances on a set of benchmark problems and it exhibits a better performance. Criteria used to measure planning effectiveness include the path length, the smoothness of planned paths, the computation time and the success rate in planning. Experiments to demonstrate the statistical significance of the improvements achieved by the proposed method are also shown.
1 Feb 2015
TL;DR: Qualitative analysis reveals that the proposed method significantly enhances the image contrast, reduces the blue-green effect, and minimizes under- and over-enhanced areas in the output image.
Abstract: Method to increase the contrast and reduce the noise of underwater image.Applied histogram modification of integrated RGB and HSV color models.Mapping the image histogram according to Rayleigh distribution.Limiting the dynamic range of color models to reduce under- and over-enhanced areas.Outperforms other state-of-the-art methods in term of contrast and noise reduction. The physical properties of water cause light-induced degradation of underwater images. Light rapidly loses intensity as it travels in water, depending on the color spectrum wavelength. Visible light is absorbed at the longest wavelength first. Red and blue are the most and least absorbed, respectively. Underwater images with low contrast are captured due to the degradation effects of light spectrum. Therefore, the valuable information from these images cannot be fully extracted for further processing. The current study proposes a new method to improve the contrast and reduce the noise of underwater images. The proposed method integrates the modification of image histogram into two main color models, Red-Green-Blue (RGB) and Hue-Saturation-Value (HSV). In the RGB color model, the histogram of the dominant color channel (i.e., blue channel) is stretched toward the lower level, with a maximum limit of 95%, whereas the inferior color channel (i.e., red channel) is stretched toward the upper level, with a minimum limit of 5%. The color channel between the dominant and inferior color channels (i.e., green channel) is stretched to both directions within the whole dynamic range. All stretching processes in the RGB color model are shaped to follow the Rayleigh distribution. The image is converted into the HSV color model, wherein the S and V components are modified within the limit of 1% from the minimum and maximum values. Qualitative analysis reveals that the proposed method significantly enhances the image contrast, reduces the blue-green effect, and minimizes under- and over-enhanced areas in the output image. For quantitative analysis, the test with 300 underwater images shows that the proposed method produces average mean square error (MSE) and peak signal to noise ratio (PSNR) of 76.76 and 31.13, respectively, which outperform six state-of-the-art methods.
1 Feb 2015
TL;DR: This paper first reviews different methods of designing thermal error models, before concentrating on employing an adaptive neuro fuzzy inference system (ANFIS) to design two thermal prediction models that show that the ANFIS-FCM model is superior in terms of the accuracy of its predictive ability with the benefit of fewer rules.
Abstract: This paper first reviews different methods of designing thermal error models, before concentrating on employing ANFIS models.The GM(1, N) model and fuzzy c-means clustering are used for variable selection, which is capable of simplifying the system prediction model.The results of the study show that the ANFIS-FCM model is superior in terms of the accuracy of its predictive ability with the benefit of fewer rules. Thermal errors can have significant effects on CNC machine tool accuracy. The errors come from thermal deformations of the machine elements caused by heat sources within the machine structure or from ambient temperature change. The effect of temperature can be reduced by error avoidance or numerical compensation. The performance of a thermal error compensation system essentially depends upon the accuracy and robustness of the thermal error model and its input measurements. This paper first reviews different methods of designing thermal error models, before concentrating on employing an adaptive neuro fuzzy inference system (ANFIS) to design two thermal prediction models: ANFIS by dividing the data space into rectangular sub-spaces (ANFIS-Grid model) and ANFIS by using the fuzzy c-means clustering method (ANFIS-FCM model). Grey system theory is used to obtain the influence ranking of all possible temperature sensors on the thermal response of the machine structure. All the influence weightings of the thermal sensors are clustered into groups using the fuzzy c-means (FCM) clustering method, the groups then being further reduced by correlation analysis.A study of a small CNC milling machine is used to provide training data for the proposed models and then to provide independent testing data sets. The results of the study show that the ANFIS-FCM model is superior in terms of the accuracy of its predictive ability with the benefit of fewer rules. The residual value of the proposed model is smaller than ?4µm. This combined methodology can provide improved accuracy and robustness of a thermal error compensation system.
1 Jan 2015
TL;DR: The experimental results indicate that the AGA-SSVR model is an effective approach with more accuracy than the other alternative models including AGA-SVR and back-propagation neural network (BPNN).
Abstract: The model of support vector regression with adaptive genetic algorithm and the seasonal mechanism is proposed.Parameters selection and seasonal adjustment should be carefully selected.We focus on latest and representative holiday daily data in China.Two experiments are used to prove the effect of the model.The AGASSVR is superior to AGA-SVR and BPNN. Accurate holiday daily tourist flow forecasting is always the most important issue in tourism industry. However, it is found that holiday daily tourist flow demonstrates a complex nonlinear characteristic and obvious seasonal tendency from different periods of holidays as well as the seasonal nature of climates. Support vector regression (SVR) has been widely applied to deal with nonlinear time series forecasting problems, but it suffers from the critical parameters selection and the influence of seasonal tendency. This article proposes an approach which hybridizes SVR model with adaptive genetic algorithm (AGA) and the seasonal index adjustment, namely AGA-SSVR, to forecast holiday daily tourist flow. In addition, holiday daily tourist flow data from 2008 to 2012 for Mountain Huangshan in China are employed as numerical examples to validate the performance of the proposed model. The experimental results indicate that the AGA-SSVR model is an effective approach with more accuracy than the other alternative models including AGA-SVR and back-propagation neural network (BPNN).
1 Jul 2015
TL;DR: This study looks into the recent applications of Big Data technologies in education and presents a review of literature available on Educational Data Mining and Learning Analytics.
Abstract: The usage of learning management systems in education has been increasing in the last few years. Students have started using mobile phones, primarily smart phones that have become a part of their daily life, to access online content. Student's online activities generate enormous amount of unused data that are wasted as traditional learning analytics are not capable of processing them. This has resulted in the penetration of Big Data technologies and tools into education, to process the large amount of data involved. This study looks into the recent applications of Big Data technologies in education and presents a review of literature available on Educational Data Mining and Learning Analytics.
1 May 2015
TL;DR: A multi-objective fuzzy clustering algorithm (MOFCA) is introduced that addresses both hotspots and energy hole problems in stationary and evolving networks and outperforms the existing algorithms in the same set up in terms of efficiency metrics.
Abstract: This study introduces a new clustering approach which is not only energy-efficient but also distribution-independent for wireless sensor networks (WSNs). Clustering is used as a means of efficient data gathering technique in terms of energy consumption. In clustered networks, each node transmits acquired data to a cluster-head which the nodes belong to. After a cluster-head collects all the data from all member nodes, it transmits the data to the base station (sink) either in a compressed or uncompressed manner. This data transmission occurs via other cluster-heads in a multi-hop network environment. As a result of this situation, cluster-heads close to the sink tend to die earlier because of the heavy inter-cluster relay. This problem is named as the hotspots problem. To solve this problem, some unequal clustering approaches have already been introduced in the literature. Unequal clustering techniques generate clusters in smaller sizes when approaching the sink in order to decrease intra-cluster relay. In addition to the hotspots problem, the energy hole problem may also occur because of the changes in the node deployment locations. Although a number of previous studies have focused on energy-efficiency in clustering, to the best of our knowledge, none considers both problems in uniformly and non-uniformly distributed networks. Therefore, we propose a multi-objective solution for these problems. In this study, we introduce a multi-objective fuzzy clustering algorithm (MOFCA) that addresses both hotspots and energy hole problems in stationary and evolving networks. Performance analysis and evaluations are done with popular clustering algorithms and obtained experimental results show that MOFCA outperforms the existing algorithms in the same set up in terms of efficiency metrics, which are First Node Dies (FND), Half of the Nodes Alive (HNA), and Total Remaining Energy (TRE) used for estimating the lifetime of the WSNs and efficiency of protocols.
1 Dec 2015
TL;DR: The power of ensemble of classifiers approach for accelerometer-based activity recognition is explored and a novel activity prediction model based on machine learning classifiers is built and provides better performance than MLP-based recognition approach suggested in previous study.
Abstract: Proposed activity recognition approach. We propose and validate a novel activity recognition model.We examine the power of ensemble of classifiers approach experimentally.The model uses J48, Logistic Regression, and MLP.Proposed recognition model is superior to MLP-based recognition model suggested in a previous study.We suggest researchers to focus on ensemble of classifiers approach for activity recognition. Activity recognition aims to detect the physical activities such as walking, sitting, and jogging performed by humans. With the widespread adoption and usage of mobile devices in daily life, several advanced applications of activity recognition were implemented and distributed all over the world. In this study, we explored the power of ensemble of classifiers approach for accelerometer-based activity recognition and built a novel activity prediction model based on machine learning classifiers. Our approach utilizes from J48 decision tree, Multi-Layer Perceptrons (MLP) and Logistic Regression techniques and combines these classifiers with the average of probabilities combination rule. Publicly available activity recognition dataset known as WISDM (Wireless Sensor Data Mining) which includes information from thirty six users was used during the experiments. According to the experimental results, our model provides better performance than MLP-based recognition approach suggested in previous study. These results strongly suggest researchers applying ensemble of classifiers approach for activity recognition problem.
1 Feb 2015
TL;DR: A set of non-dominated solutions obtained by the proposed algorithm is kept in an archive to be used to display the exploratory capability of the MOWCA as compared to other efficient methods in the literature.
Abstract: Multi-objective water cycle algorithm (MOWCA) is proposed for solving constrained and engineering multi-objective problems.Generational distance, metric of spread, and Δ metric are used as performance criteria.Optimal Pareto fronts are finely covered by the MOWCA with a good distribution of the non-dominated solutions.MOWCA is able to approach a full optimal Pareto front and provide a superior quality of solutions.MOWCA is better able to find a wider range of solutions compared with the other optimizers in this paper. In this paper, a metaheuristic optimizer, the multi-objective water cycle algorithm (MOWCA), is presented for solving constrained multi-objective problems. The MOWCA is based on emulation of the water cycle process in nature. In this study, a set of non-dominated solutions obtained by the proposed algorithm is kept in an archive to be used to display the exploratory capability of the MOWCA as compared to other efficient methods in the literature. Moreover, to make a comprehensive assessment about the robustness and efficiency of the proposed algorithm, the obtained optimization results are also compared with other widely used optimizers for constrained and engineering design problems. The comparisons are carried out using tabular, descriptive, and graphical presentations.
1 Jan 2015
TL;DR: A novel optimisation algorithm, named enhanced leader PSO (ELPSO), is introduced, which mitigates premature convergence problem of conventional PSO and confirms the outperformance of ELPSO over other compared algorithms.
Abstract: A novel optimisation algorithm, named enhanced leader PSO (ELPSO), is introduced.ELPSO mitigates premature convergence problem of conventional PSO.ELPSO is mainly based on a five-staged successive mutation strategy.At each iteration, the successive mutation strategy is applied to swarm leader.The results confirm the outperformance of ELPSO over other compared algorithms. Particle swarm optimisation (PSO) is a well-established optimisation algorithm inspired from flocking behaviour of birds. The big problem in PSO is that it suffers from premature convergence, that is, in complex optimisation problems, it may easily get trapped in local optima. In this paper, a new PSO variant, named as enhanced leader PSO (ELPSO), is proposed for mitigating premature convergence problem. ELPSO is mainly based on a five-staged successive mutation strategy which is applied to swarm leader at each iteration. The experimental results confirm that in all terms of accuracy, scalability and convergence rate, ELPSO performs well.
1 Aug 2015
TL;DR: The experimental results showed that a cost-sensitive neural network can be created successfully by using the ABC optimization algorithm for the purpose of software defect prediction, and a different classification approach for this problem is proposed.
Abstract: Software defect prediction model was built by Artificial Neural Network (ANN).ANN connection weights were optimized by Artificial Bee Colony (ABC).Parametric cost-sensitivity feature was added to ANN by using a new error function.Model was applied to five publicly available datasets from the NASA repository.Results were compared with other cost-sensitive and non-cost-sensitive studies. The software development life cycle generally includes analysis, design, implementation, test and release phases. The testing phase should be operated effectively in order to release bug-free software to end users. In the last two decades, academicians have taken an increasing interest in the software defect prediction problem, several machine learning techniques have been applied for more robust prediction. A different classification approach for this problem is proposed in this paper. A combination of traditional Artificial Neural Network (ANN) and the novel Artificial Bee Colony (ABC) algorithm are used in this study. Training the neural network is performed by ABC algorithm in order to find optimal weights. The False Positive Rate (FPR) and False Negative Rate (FNR) multiplied by parametric cost coefficients are the optimization task of the ABC algorithm. Software defect data in nature have a class imbalance because of the skewed distribution of defective and non-defective modules, so that conventional error functions of the neural network produce unbalanced FPR and FNR results. The proposed approach was applied to five publicly available datasets from the NASA Metrics Data Program repository. Accuracy, probability of detection, probability of false alarm, balance, Area Under Curve (AUC), and Normalized Expected Cost of Misclassification (NECM) are the main performance indicators of our classification approach. In order to prevent random results, the dataset was shuffled and the algorithm was executed 10 times with the use of n-fold cross-validation in each iteration. Our experimental results showed that a cost-sensitive neural network can be created successfully by using the ABC optimization algorithm for the purpose of software defect prediction.
1 Mar 2015
TL;DR: The method proposed in this study is compared with recently published studies in the literature on real-world problems and it is proven that this method is more effective than the studies belonging to other literature on this sort of problems.
Abstract: Optimization can be defined as an effort of generating solutions to a problem under bounded circumstances. Optimization methods have arisen from a desire to utilize existing resources in the best possible way. An important class of optimization methods is heuristic algorithms. Heuristic algorithms have generally been proposed by inspiration from the nature. For instance, Particle Swarm Optimization has been inspired by social behavior patterns of fish schooling or bird flocking. Bat algorithm is a heuristic algorithm proposed by Yang in 2010 and has been inspired by a property, named as echolocation, which guides the bats' movements during their flight and hunting even in complete darkness. In this work, local and global search characteristics of bat algorithm have been enhanced through three different methods. To validate the performance of the Enhanced Bat Algorithm (EBA), standard test functions and constrained real-world problems have been employed. The results obtained by these test sets have proven EBA superior to the standard one. Furthermore, the method proposed in this study is compared with recently published studies in the literature on real-world problems and it is proven that this method is more effective than the studies belonging to other literature on this sort of problems.
1 Feb 2015
TL;DR: It is observed that TLBO optimized fuzzy-PID controller gives better dynamic performance in terms of settling time, overshoot and undershoot in frequency and tie-line power deviation as compared to LCOA, GA, PS and SA based PID controllers.
Abstract: Fuzzy-PID controller is proposed for AGC of multi-area power system.TLBO algorithm is applied to optimize the parameters of fuzzy-PID controller.The superiority of proposed approach over LCOA, GA, PS and SA based PID controller is shown.Robustness analysis is performed under wide changes in system parameters and disturbance. This paper deals with the design of a novel fuzzy proportional-integral-derivative (PID) controller for automatic generation control (AGC) of a two unequal area interconnected thermal system. For the first time teaching-learning based optimization (TLBO) algorithm is applied in this area to obtain the parameters of the proposed fuzzy-PID controller. The design problem is formulated as an optimization problem and TLBO is employed to optimize the parameters of the fuzzy-PID controller. The superiority of proposed approach is demonstrated by comparing the results with some of the recently published approaches such as Lozi map based chaotic optimization algorithm (LCOA), genetic algorithm (GA), pattern search (PS) and simulated algorithm (SA) based PID controller for the same system under study employing the same objective function. It is observed that TLBO optimized fuzzy-PID controller gives better dynamic performance in terms of settling time, overshoot and undershoot in frequency and tie-line power deviation as compared to LCOA, GA, PS and SA based PID controllers. Further, robustness of the system is studied by varying all the system parameters from -50% to +50% in step of 25%. Analysis also reveals that TLBO optimized fuzzy-PID controller gains are quite robust and need not be reset for wide variation in system parameters.
1 Mar 2015
TL;DR: This work first defines intuitionistic fuzzy parameterized soft sets (intuitionistic FP-soft sets) and study some of their properties, and introduces an adjustable approaches to intuitionistic FP -soft sets based decision making.
Abstract: HighlightsWe define a intuitionistic fuzzy parameterized soft sets for dealing with uncertainties that is based on both soft sets and intuitionistic fuzzy sets.We investigated their operations and properties.We introduce a decision making method based on intuitionistic FP-soft sets. In this work, we first define intuitionistic fuzzy parameterized soft sets (intuitionistic FP-soft sets) and study some of their properties. We then introduce an adjustable approaches to intuitionistic FP-soft sets based decision making. Finally, we give a numerical example which shows that this method successfully works.
1 Jan 2015
TL;DR: This chapter provides an overview of possibility theory, emphasizing its historical roots, recent developments, and close connections with random set theory and confidence intervals.
Abstract: This chapter provides an overview of possibility theory, emphasizing its historical roots and its recent developments. Possibility theory lies at the crossroads between fuzzy sets, probability, and nonmonotonic reasoning. Possibility theory can be cast either in an ordinal or in a numerical setting. Qualitative possibility theory is closely related to belief revision theory, and commonsense reasoning with exception-tainted knowledge in artificial intelligence. Possibilistic logic provides a rich representation setting, which enables the handling of lower bounds of possibility theory measures, while remaining close to classical logic. Qualitative possibility theory has been axiomatically justified in a decision-theoretic framework in the style of Savage, thus providing a foundation for qualitative decision theory. Quantitative possibility theory is the simplest framework for statistical reasoning with imprecise probabilities. As such, it has close connections with random set theory and confidence intervals, and can provide a tool for uncertainty propagation with limited statistical or subjective information.
1 Jul 2015
TL;DR: The proposed algorithm mimics the attraction and repulsion of anions and cations to perform optimization and is designed in such a way to have the least tuning parameters, low computational complexity, fast convergence, and high local optima avoidance.
Abstract: A new meta-heuristic called IMO inspired by ions motion is proposed.The IMO algorithm is benchmarked on well-known test functions.The results show the superiority and potential of IMO. This paper proposes a novel optimization algorithm inspired by the ions motion in nature. In fact, the proposed algorithm mimics the attraction and repulsion of anions and cations to perform optimization. The proposed algorithm is designed in such a way to have the least tuning parameters, low computational complexity, fast convergence, and high local optima avoidance. The performance of this algorithm is benchmarked on 10 standard test functions and compared to four well-known algorithms in the literature. The results demonstrate that the proposed algorithm is able to show very competitive results and has merits in solving challenging optimization problems.