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Performance measurement for a unit or an organization is a concrete step in maintaining the quality of services provided by the organization and should be done periodically to assess the strengths and weaknesses of an organization or to assess its efficiency. The assessment of performance depends on several criteria and can therefore be modeled as a multi-criterion decision-making (MCDM) problem. In the present study, the objective is to measure the performance of hostels and mess facilities of a Higher Educational Institute (HEI) through MCDM methods. The study was initiated by conducting a survey, on the basis of which a total of 13 criteria, which affect the performance of the hostel and mess, were identified and were used for qualitative as well as quantitative analysis. While analytic hierarchy process (AHP) is used for qualitative analysis, data envelopment analysis (DEA) is used for quantitative analysis. Further, the relevant performing factors for hostels and mess are identified through AHP and DEA-sensitivity analysis (DEA-SA). [ABSTRACT FROM AUTHOR]

Clustering is an important tool for data mining and knowledge discovery that helps in revealing hidden structures and "clusters" found in large data sets. Fuzzy C-means (FCM) is considered to be popular data clustering method due to its capability of clustering the datasets that are uncertain, vague and/or are otherwise difficult to cluster. Although, noted both for its simplicity of implementation and its output validity, performance of FCM usually gets affected in case of poor initialization resulting in the algorithm getting trapped into a local optimum. To overcome this shortcoming, the present study proposes a novel clustering algorithm called fuzzy magnetic optimization clustering (Fuzzy-MOC) which embeds the concept of fuzzy clustering into magnetic optimization algorithm. In Fuzzy-MOC, the data points apply force directly to the magnetic particles due to which the particles change their positions in the feature space. Magnetic particles are attracted by their neighbours assumed to be in a lattice like structure. The proposed algorithm is evaluated on a set of 16 benchmark datasets taken from the UCI Machine Learning Repository including high dimensional gene expression dataset. Experimental results demonstrate that Fuzzy-MOC outperforms the other state-of-the-art algorithms in terms of different performance metrics like F1, accuracy, purity and RI measure. [ABSTRACT FROM AUTHOR]

Web Service Location-Allocation Problem (WSLAP) is an important and challenging NP-hard combinatorial optimization problem where multiple objectives affecting the quality of service (QoS) are to be optimized simultaneously. Most of the existing research articles mainly focus on selecting web service locations that minimize two QoS: total cost and total response time. However, another QoS factor throughput also needs attention when selecting sites for allocating web services. Therefore, we have constructed a new WSLAP model that minimizes the total cost and the total negative quality factor. This negative quality factor considers throughput along with latency to strengthen the QoS of the located web services. Further, three NSGA-II variants, NSGA-II.V1, NSGA-II.V2, and NSGA-II.V3, with a novel matrix-based chromosome representation, are developed to deal with the proposed bi-objective WSLAP model. These algorithms involve matrix-based genetic operations for better exploration and a repair mechanism for tackling infeasibility. The proposed model and algorithms are validated on 24 test instances, out of which 14 are existing, and 10 are newly created with increased web services, user centres and service locations. The experimental results claim that the proposed WSLAP model succeeded in improving the throughput with a comparatively lower total cost. Further, the proposed algorithms outperformed the compared algorithms in terms of hypervolume, inverted generational distance and computational time on 89% of instances. [ABSTRACT FROM AUTHOR]

Prediction of financial stock market is a challenging task because of its volatile and non- linear nature. The presence of different factors like psychological, sentimental state, rational or irrational behaviour of investors make the stock market more dynamic. With the inculcation of algorithms based on artificial intelligence, deep learning algorithms, the prediction of movement of financial stock market is revolutionized in the recent years. The purpose of using these algorithms is to help the investors for taking decisions related to the Stock Pricing. A model has been proposed to predict the direction of movement of Indian stock market in the near future. This model makes use of historical Indian stock data of companies in nifty 50 since they came existence along with some financial and social indicators like financial news and tweets related to stocks. After pre-processing and normalization various machine learning algorithms like LSTM, support vector machines, KNearest neighbour, random forest, gradient boosting regressor are applied on this time series data to produce better accuracy and to minimize the RMSE error. This model has the ability to reduce major losses to the investors who invest in stock market. The social indicators will give an insight for predicting the direction of stock market. The LSTM network will make use of historical closing prices, tweets and trading volume. [ABSTRACT FROM AUTHOR]

DEA, incepted in 80s, has emerged as a popular decision-making technique, for determining the efficiency of similar units. Due to its simplicity and applicability, DEA has gained the attention of scientists and researchers working in diverse areas, which has contributed towards a rich literature both in terms of theoretical development as well as different applications. This paper tries to bring together the near 40 years of existence of DEA in a concise format by discussing the popular DEA models, their advantages and shortcomings, and different applications of DEA. It also provides a brief bibliometric analysis to highlight the development of DEA over the years in terms of publication trends, highly cited papers, journal citation, etc. [ABSTRACT FROM AUTHOR]

Multiple instance boosting (MILBoost) is a framework which uses multiple instance learning (MIL) with boosting technique to solve the problems regarding weakly labeled inexact data. This paper proposes an enhanced multiple boosting framework—evolutionary MILBoost (EMILBoost) which utilizes differential evolution (DE) to optimize the combination of weak classifier or weak estimator weights in the framework. A standard MIL dataset MUSK and a binary classification dataset Hastie_10_2 are used to evaluate the results. Results are presented in terms of bag and instance classification error and also confusion matrix of test data. [ABSTRACT FROM AUTHOR]

Electro-optical industries worldwide are producing thousands of thermal imagers based on transfer of technology from design agencies. Evaluation of a thermal imager is necessary prior to their effective field deployment. Traditionally, subjective minimum resolvable temperature difference (MRTD) is a parameter which is employed to evaluate the quality of an IR image. But due to human intervention, the usage of this parameter becomes time-consuming and slows down the production process. In the presented work, an accurate auto-MRTD method is proposed, which increases the efficiency of testing while minimizing human dependency cost. This is also a case study of how theory of objective or auto-MRTD can successfully be implemented in production facilities to increase their efficiency. Auto-MRTD method is described by measuring the modulation transfer function and noise-equivalent temperature difference of the thermal imager. Thereafter, calibration constant c(f) is computed, which can be used for faster MRTD computation. Proper experimentation was carried out to compute both auto-MRTD and subjective MRTD. Quantitative analysis and percentage error were carried out to evaluate the effectiveness of this method. It is shown that percentage error using auto-MRTD is within 5% of that of the conventional subjective MRTD method. [ABSTRACT FROM AUTHOR]

Particle Swarm Optimization, a potential swarm intelligence heuristic, has been recognized as a global optimizer for solving various continuous as well as discrete optimization problems. Encourged by the performance of Gompertz PSO on a set of continuous problems, this works extends the application of Gompertz PSO for solving binary optimization problems. Moreover, a new chaotic variant of Gompertz PSO namely Chaotic Gompertz Binary Particle Swarm Optimization (CGBPSO) has also been proposed. The new variant is further analysed for solving binary optimization problems. The new chaotic variant embeds the notion of chaos into GBPSO in later stages of searching process to avoid stagnation phenomena. The efficiency of both the Binary PSO variants has been tested on different sets of Knapsack Problems (KPs): 0–1 Knapsack Problem (0-1 KP) and Multidimensional Knapsack Problems (MKP). The concluding remarks have made on the basis of detailed analysis of results, which comprises the comparison of results for Knapsack and Multidimensional Knapsack problems obtained using BPSO, GBPSO and CGBPSO. [ABSTRACT FROM AUTHOR]

Reservoir optimization models are often large-scale, having a complex, nonlinear, multi-dimensional structure, which poses a challenge for classical methods for their solution. This has encouraged the researchers to focus on Meta-heuristic which due to their flexible and adaptive nature have been successful in solving a plethora of real-life optimization problems. This study brings forward an implementation and comparison of six well-known Meta-heuristics namely: Simulated Annealing, Genetic Algorithms, Particle Swarm Optimization, Differential Evolution, Artificial Bee Colony, and Cuckoo Search and an integrated version of these algorithms with dynamic programming for optimizing the reservoir operations policy. In addition, two adaptive variants of DE named: FCADE2 and SaDE are also considered for the comparison. The case study considered for Mula reservoir supplying water to Major Irrigation Project on River Mula (Godavari basin), Ahmednagar district, Maharashtra, India. The objective is to determine the optimum release policy for Mula reservoir. Performance of the algorithms is analysed on two data sets (1) single year and (2) 30-years. [ABSTRACT FROM AUTHOR]

The Decision-making is undeniable and is an integral part of in almost all the processes either in the complex form or as a simple procedure. It often refers to the prioritizing (ranking) the alternatives based on several conflicting criteria. To ensure that the process of decision making runs smoothly with minimum errors, multiple criteria decision-making abbreviated as "MCDM" is used for obtaining the solution. The present work emphasized on the weighing methods, an important aspect in MCDM methods, that accurately determines the relative importance of each criterion. The relative importance of each criterion is determined by a set of preferences, called weights, represented between 0 and 1. The weights of criteria influence the outcome of any decision-making process, so it is essential to highlight the significance of weighing methods in determining the criteria preference. In literature, researchers have reported various weighing methods for calculating the relative weights of criteria used for ranking the alternatives. The present study, provides an overview of the some popular weighing methods applicable to the MCDM process and also shows the performance of these methods through a case study. [ABSTRACT FROM AUTHOR]

The growth of Internet has produced a high volume of natural language textual data. Such data can be sparse and may contain uninformative features which increase the dimensions of the data. This high dimensionality in turn, decreases the efficiency of text mining tasks such as clustering. Transforming the high dimensional data into a lower dimension is an important pre-processing step before applying clustering. In this paper, dimensionality reduction method based on deep Autoencoder neural network named as DRDAE, is proposed to provide optimized and robust features for text clustering. DRDAE selects less correlated and salient feature space from the high dimensional feature space. To evaluate proposed algorithm, k-means is used to cluster text documents. The proposed method is tested on five benchmark text datasets. Simulation results demonstrate that the proposed algorithm clearly outperforms other conventional dimensionality reduction methods in the literature in terms of RI measure. [ABSTRACT FROM AUTHOR]

The present study suggests an integrated approach for determining the optimal release policy for the Mula reservoir situated at the Godavari basin, India. The proposed integrated algorithm named DP-PSO is a hybridization of Dynamic Programming (DP) and Particle Swarm Optimization (PSO). The reservoir operation problem is demonstrated in the form of a nonlinear optimization model subject to various constraints. Two case studies are considered. In the first case the efficiency of the proposed algorithm is tested on a small data set of 1 year and in the second case, data set taken is for 10 years. The results obtained are compared in terms of objective function value as well CPU time for performance evaluation of the integrated methods. [ABSTRACT FROM AUTHOR]

The role of a healthcare practitioner is to diagnose a disease and find an optimum means for suitable treatment. This has been the most challenging task over the years. The researchers have been developing intelligent tools for providing support in taking medical decision. The application of AI in different health scenario strengthen the mechanism for finding a better treatment plan. The authors share some recent advancements in this domain. The role of artificial intelligence in Indian healthcare system has also been discussed. The paper analyzes the use of different AI techniques like fuzzy logic, Artificial Neural Networks, Particle Swarm Optimization and Fuzzy Neural in critical health scenario. A detail literature review has been provided in this context. The disease taken into consideration are cancer, TB, diabetes, malaria, orthopedics, obesity and disease specific to elderly people. The purpose of this article is to find the relevance of various techniques of AI in different critical health scenarios. A comparative analysis is done based on the publications since 1995. The challenges and risks associated with the usage of AI in healthcare has been analysed and suggestions made for making the analysis in the health domain more accurate and effective. Further the concept of deep learning has also been explained and its inculcation with the medical domain is discussed. [ABSTRACT FROM AUTHOR]

In this paper, a Modified variant of Particle Swarm Optimization named MPSOPR is proposed. The velocity update in MPSOPR follows a neighbourhood-based learning strategy based on PageRank (PR) algorithm and a scale-free network is proposed for the interaction among particles in the population. This is in contrast to the basic PSO which has a fully connected topology or regular topology. The inclusion of these two modifications helps in enhancing the diversity and the information dissemination ability of the algorithm. Performance of MPSOPR is validated on a set of 17 benchmark problems divided into 3 groups, on basis of level of difficulties. Comparative analysis of the results obtained through MPSOPR with 9 other variants of PSO, indicate that the proposed scheme can help in improving the performance of PSO significantly. The performance of MPSOPR is further validated by employing it for solving problems related to recommender system. [ABSTRACT FROM AUTHOR]

Weather describes the condition of our atmosphere during a specific period of time, and climate represents a composite of day to day weather over longer period of time. Climatology attempts to analyze and explain the impact of climate so that the society can plan accordingly. Climatology analysis is often done on radar images representing various climatic conditions. These images contain varying scale of severity for any specific climatic parameter of study. The climatologists often find it convenient to analyze climatic conditions if tools are available to segment the weather images based on the severity scale which is represented by different colors. Segmentation of the weather radar image is also used for automated analysis of weather conditions. Differential evolution (DE) approach instead is used for fast selection of optimal threshold. In present paper, we have applied DE with multilevel thresholding for weather image segmentation which results in minimum computational time and excellent image quality. A new mutation strategy for DE named reconstructed differential evolution (RDE) strategy is suggested for better performance over image segmentation. Using fuzzy entropy and RDE for multilevel thresholding provides better results in comparison with last suggested methods. [ABSTRACT FROM AUTHOR]

In the present study, the authors propose DE(A)NN, an integration of data envelopment analysis (DEA) and artificial neural networks (ANN) as a decision making tool. The performance of proposed DE(A)NN is validated on a case study for measuring the relative efficiency of 21 Indian state education boards. As expected, it is observed that DE(A)NN increases the discriminatory power of DEA for ranking of the decision making units. [ABSTRACT FROM AUTHOR]

The proposed study investigates the robust watermarking techniques and proposes an improved version of robust watermarking scheme. In order to ensure high value of robustness, capacity and imperceptibility of the proposed scheme, the watermark embedding is done in the IWT-transformed domain using the singular values of each band. Along with that, an improved signature insertion methodology is also proposed to make the scheme free from security errors. The third level of LL band is utilized for signature insertion as this minimizes the variation of extracted signature bits from the actual signature bits after attacks. Further, the embedding strengths of the embedding are also optimized with the help of artificial bee colony, which optimized the robustness and imperceptibility of proposed scheme. The comparative analysis of proposed scheme is also provided with the schemes proposed in recent past, and the comparison further supports the claim of better performance. [ABSTRACT FROM AUTHOR]

Protection relays in a power system makeup to pairs, one as main protection relay and the other as a backup protection relay. Directional over current relays (DOCR) have been using for backup protection mainly in distribution system. Coordination of these DOCRs is an optimization problem that is being solved using various types of optimizing algorithms by the researchers in the field. This paper attempts this problem in different direction which analyzes different inverse characteristics of DOCR and their sensitivities to different faults. Optimal values for time dial and plug settings for the DOCRs are obtained by using a recently developed algorithm: Opposition based chaotic differential evolution algorithms. IEEE 4-bus system has been taken as a test model and the algorithms are implemented in it for different types of characteristics based on the degree of inversion i.e., normally inverse, very inverse, extremely inverse and long-time inverse characteristics. [ABSTRACT FROM AUTHOR]

Color images are not ready to provide a desired value of information because of illumination or some other conditions like settings of the captured instrument. So for improving the quality of color images and making them a good source of information an improvement of quality is desired sometimes. To improve the quality of an existing image or extract some features from a degraded image; image enhancement techniques are used. Many conventional algorithms are available for color image enhancement; some of them are based on linear gain adjustments. These algorithms will provide a limited improvement in an image. For making an overall improvement in an image many algorithms are advised based on genetic algorithm and particle swarm optimization. It is very well known that differential evolution is a very robust and simple algorithm for optimization. 1D histogram technique of image enhancement takes information about the pixel value and manipulates it to a required output value according the problem nature. Some relevant information of the pixel is not considered in 1D histogram technique; 2D histogram will be design considering all the relevant information around the pixel and manipulate it to an output pixel value according this information. Each pixel will behave like a member of population for differential evolution and manipulated on the basis of best value. Results show a significant and considerable change in output image. In this paper a new algorithm with differential evolution is proposed. [ABSTRACT FROM AUTHOR]

An ensemble lossless watermarking scheme is proposed in the present study by integrating different concepts like redistributed invariant wavelet transform, discrete fractional Fourier transform, singular value decomposition (SVD) and visual cryptography within the framework of a single algorithm. The invariant wavelet transform helps to obtain the transform domain, which is invariant to flipping and rotation of image, this is followed by discrete fractional Fourier transform to obtain the translation invariant domain. Finally, embedding positions are selected based on a key and reliable features are extracted by performing SVD on a window centered at these positions. Based on these reliable features a binary map is generated through which a master share is created. The corresponding ownership share is produced from the master share and the watermark. In verification process the same operations of the embedding process are applied to the test image to obtain the master share and the watermark is recovered by stacking it over the ownership share. There are two main features of the proposed scheme (1) The quality of the image to be watermarked do not degrade during the process and (2) the extracted watermark can still be identified even from a seriously distorted image. These findings are also demonstrated with the help of a comparative study with several related schemes. [ABSTRACT FROM AUTHOR]

Watermarking is a process of secret information (watermark) insertion in the digital image, which later on helps in finding out the correct owner of that image. The insertion of watermark’s singular values into the host image provide a high value of capacity, robustness and imperceptibility but the scheme remain vulnerable towards false positive attacks. Alternatively, insertion of watermark’s principle components into the host image make the scheme secured towards security errors but it also reduces the robustness and imperceptibility of the scheme. The exact recovery of this information becomes even more difficult after attacks because attacks change the information even further. This eventually leads to low imperceptibility and robustness of scheme. In general, the optimization of embedding strengths for image watermarking focuses on the maximization of both imperceptibility and robustness equally. In doing so, often, imperceptibility suffers as it gets degraded beyond a certain limit (<35 dB). This degradation becomes unacceptable in many fields because important information of host image might get lost. In this study, the optimization is performed with a quality assurance so that imperceptibility of scheme does not fall below a user specific threshold. The proposed scheme shows a very decent performance after optimization. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) algorithm is come out as a leading tool for solving many real life optimization problems since last few years. Modified random localized DE (MRLDE) is an enhance variant of DE algorithm use strategically way for selecting vectors to generate mutation vector. In this paper MRLDE is applied to a real life application of recognizing the location of noisy sources in multi noise plants which is an essential and prerequisite for noise control work. A trail noise method is utilized to find the variation between exact sound pressure level SPL and trial SPL at monitoring points and then MRLDE is implemented in combination with the technique of minimizing variation square in searching for the best locations and sound power level (SWLs). The experimental results expose that the significant SWLs and locations of noisy sources can be accurately detected by MRLDE. [ABSTRACT FROM AUTHOR]

Evolutionary algorithm is a field of great interest to many researchers around the world. New algorithms are developed based on biological processes that exist in nature. In addition, different variants of the existing algorithms are also created with researchers working to find the most optimal method. This paper initially introduces Differential Evolution (DE) and Flower Pollination Algorithm (FPA). Subsequently, a description of the hybrid algorithm named ssFPA/DE that uses the search strategy of FPA and DE are explained along with their results. [ABSTRACT FROM AUTHOR]

The present study focuses on embedding the Supply chain management concepts on to the education sector i.e. educational supply chain management. It is an important area of research as it helps in the management of one of the most important sector of a country, viz. the education sector. The objective here is to measure efficiency of 19 departments of Indian Institute of Technology Roorkee (IIT Roorkee), a reputed higher education institute of India. [ABSTRACT FROM AUTHOR]

The process of medical diagnosis, like many other fields, has to pass through various stages of uncertainty, especially in cases where the data is mostly available in linguistic format. Under such circumstances of vague data, application of fuzzy logic concepts can play an important role in extracting approximate information which in turn may help in reaching to a particular diagnosis. The present study is devoted to the application of fuzzy logic rules for analyzing the psychology of adolescents with respect to Indian scenario. The objective here is to identify whether the subject requires counselling. Fuzzy logic approach is applied to Global Adjustment Scale, used by mental health clinicians to rate the general functioning of children under the age of 18. [ABSTRACT FROM AUTHOR]

In any factory or industry the high level noise can be very harmful to the employees. As investigated by Occupational Safety and Health Act of 1970, the high level noise not only causes physiological ailments in employees but also causes harmful environment in the neighborhood. Therefore it becomes essential to control the noise levels in any manufacturing plant or industry. This can be achieved by optimal allocation of noise equipment which is quite not easy to recognize the exact location. In this study a shuffled frog-leaping algorithm (SFLA) with modification is applied to identify optimal locations for equipment in order to reduce noise level in multi noise plant. Comparatively, SFLA is a recent addition to the family of nontraditional population based search methods that mimics the social and natural behavior of species (frogs). SFLA merges the advantages of particle swarm optimization and genetic algorithm (GA). Though SFLA has been successfully applied to solve many benchmark and real time problems but it limits in convergence speed. In order to improve its performance, the frog with best position in each memeplexes is allowed to slightly modify its position using random walk. This process improves the local search around the best position. The proposal is named as improved local search in SFLA. The simulated results defend the efficacy of the proposal when compared with the differential evolution, GA and SFL algorithms. [ABSTRACT FROM AUTHOR]

Due to the complexity of underlying data in a color image, retrieval of specific object features and relevant information becomes a complex task. Colour images have different color components and a variety of colour intensity which makes segmentation very challenging. In this paper we suggest a fitness function based on pixel-by-pixel values and optimize these values through evolutionary algorithms like differential evolution (DE), particle swarm optimization (PSO) and genetic algorithms (GA). The corresponding variants are termed GA-SA, PSO-SA and DE-SA; where SA stands for Segmentation Algorithm. Experimental results show that DE performed better in comparison of PSO and GA on the basis of computational time and quality of segmented image. [ABSTRACT FROM AUTHOR]

Past few decades have witnessed the growth and development of different optimization techniques that can be applied for solving complex problems that are otherwise difficult to solve by traditional methods. Differential evolution (DE) has attained the reputation of a powerful optimization technique that can be used for solving a wide range of problems. In DE, mutation is the most important operator as it helps in generating a new solution vector. In this paper we propose an additional mutation strategy for DE. The suggested strategy is named DE/rand-to-best-best/2. It makes use of an additional parameter called guiding force parameter K, which takes a value between (0,1) besides using the scaling factor F, which has a fixed value. DE/rand-to-best-best/2 makes use of two difference vectors, where the difference is taken from the best solution vector. One vector difference will be produced with a randomly generated mutation factor K (0,1). Advantage of this strategy is, it will add a different vector to the old one and search space will increase with a random factor. Result shows that this strategy performs well in comparison to other mutation strategies of DE. [ABSTRACT FROM AUTHOR]

In this study, we have introduced a hybrid version of artificial bee colony (ABC) and shuffled frog-leaping algorithm (SFLA). The hybrid version is a two-phase modification process. In the first phase to increase the global convergence, the initial population is produced using randomly generated and chaotic system, and then in the second phase to balance two antagonist factors, i.e., exploration and exploitation capabilities, population is portioned into two groups (superior and inferior) based on their fitness values. ABC is applied to the first group, whereas SFLA is applied to the second group of population. The proposed version is named as Shuffled-ABC. The proposal is implemented and tested on constrained benchmark consulted from CEC 2006 and five chemical engineering problems where constraints are handled using penalty function methods. The simulated results illustrate the efficacy of the proposal. [ABSTRACT FROM AUTHOR]

The ownership verification of digital images is possible by the help of image watermarking. Watermarking make the image secure towards unlawful use; but at the same time, it causes some information loss too. Medical and defense are few fields, where even a small change in data can be very problematic. So there is need of reliable and lossless watermarking schemes. The present study is focused on the development of lossless watermarking method that can fulfill five basic requirements (robustness, reversibility, invisibility, security and capacity) of ideal lossless watermarking scheme maximally. Arnold transformed watermark is embedded into the host to restrict any unauthorized access of watermark even after extraction. Slantlet transformed coefficients are known to be quite robust towards image processing attacks; so block wise Slantlet transform is employed to resist the maximum attacks and to ensure a decent capacity. Mean values of transformed coefficients are used for embedding to increase the robustness and imperceptibility. The spatial domain overflow/underflow (due to embedding) is taken care by a post processing to satisfy the reversibility requirements. The embedding strength of watermarking is controlled with the help of artificial bee colony (ABC) in order to get an optimal tradeoff between invisibility and robustness. The proposed scheme is applied to a range of images to show its applicability to different domains. [ABSTRACT FROM AUTHOR]

Nature inspired technique is moderately a new research paradigm that offers novel stochastic search techniques for solving many complex optimization problems. These techniques mimic the social and natural behavior of vertebrates. The basic idea behind modeling of such techniques is to achieve near optimum solutions to the large scale and complex optimization problems which can't be solved using traditional or gradient based mathematical techniques. In this study a recently introduced nature inspired technique called Artificial Bee Colony, which is modeled on the intelligent foraging behavior of honey bees is selected as a framework. ABC has some inherent limitations like it favors exploration in comparison to exploitation. This causes loss in domain knowledge during the successive iterations. The proposed variant is embedded with levy probability distribution and abandon factor taken from cuckoo search, to balance the tradeoff between exploration and exploitation to obtain quality food sources (solutions) as well as improves the acceleration rate. The proposed variant is named as ABC with changing factor (CF-ABC). CF-ABC is based on an assumption that the potential food sources may have different probability distributions. CF-ABC is tested and compared with state-of-art algorithms over thirteen constrained benchmark optimization problems consulted from CEC 2006 and further validated on the Software Project Scheduling problem. [ABSTRACT FROM AUTHOR]

This paper proposes a novel fragile watermarking approach for digital image tamper localization (TL) along with the self-recovery (SR) capability. The host image is first divided into blocks of size 4 × 4 and then, singular value decomposition is performed on each block. The trace of singular matrix is used to compute the TL bits for each block. These 4 × 4 blocks are further divided into blocks of size 2 × 2 to compute the SR information. This SR information is also used for 2nd level of tamper detection in order to enhance the resolution of TL. Proposed scheme implements a self-embedding of TL bits and mapped embedding of SR bits and for that, block mapping is performed. The encrypted combination of both the codes (TL of parent block + SR of mapped block) is inserted in the 1st and 2nd least significant bits of the parent block. Encryption of watermark bits (unique for each watermarked image) helps it to sustain the block based attacks (vector quantization, collage and four scanning attack). Whereas; two level of authentication helps it to sustain content only and constant average attack. The scheme provides an average detection rate of more than 99.5 % and average recovery of 28 dB for a 50 % tampering. The proposed scheme shows a superior performance, when it is compared with the earlier proposed schemes. [ABSTRACT FROM AUTHOR]

Genetic algorithms (GAs) are perhaps the oldest and most frequently used search techniques for dealing with complex and intricate real-life problems that are otherwise difficult to solve by the traditional methods. The present article provides an extensive literature review of the application of GA on supply chain management (SCM). SCM consists of several intricate processes and each process is equally important for maintaining a successful supply chain. In this paper, eight processes (where each process has a set of sub-processes) as given by Council of SCM Professionals (CSCMF) are considered. The idea is to review the application of GA on these aspects and to provide the readers a detailed study in this area. The authors have considered more than 220 papers covering a span of nearly two decades for this study. The analysis is shown in detail with the help of graphs and tables. It is expected that such an extensive study will encourage and motivate the fellow researchers working in related area; to identify the gaps and to come up with innovative ideas. [ABSTRACT FROM AUTHOR]

Determination of an optimal set of machining parameters is needed to produce an ordered product of considerable quality and minimal manufacturing cost. The nonlinear and highly constrained nature of machining models restricts the application of classical gradient based techniques for handling such problems. The present study focuses on obtaining the optimal machining conditions during multi-pass turning operations. Methodology used is, a chaotic PSO namely Totally Disturbed Particle Swarm Optimization (TDPSO), an enhanced variant of PSO is employed for obtaining the optimal machining conditions during multi-pass turning operations subject to various constraints. In TDPSO, the phenomenon of chaos is embedded at different stages of PSO in order to make the search process more efficient. Results obtained by TDPSO are compared with results available in literature and it is observed that TDPSO is quite efficient for dealing with such problems. [ABSTRACT FROM AUTHOR]