Artificial Intelligence Perspectives in Intelligent Systems

The Job Shop scheduling Problem (JSP) is one of the most known problems in the domain of the production task scheduling. The Job Shop scheduling Problem with Transportation resources (JSPT) is a generalization of the classical JSP consisting of two sub-problems: the job scheduling problem and the generic vehicle scheduling problem. In this paper, we make a state-of-the-art review of the different works proposed for the JSPT, where we present a new classification schema according to seven criteria such as the transportation resource number, the transportation resource type, the job complexity, the routing flexibility, the recirculation constraint, the optimization criteria and the implemented approaches.

Narration of Chinese ancient fiction images has been concerned by many researchers. In the today of the digital technology rapid development, it will affect research of the image narration for Chinese ancient fiction. Based on the existing digital technologies, in this paper, an image narration framework in digital environment for Chinese ancient fiction is proposed. In the proposed framework, we analyze the possibility of using variety digital techniques for achieving the narration of Chinese ancient fiction images, whose implementation can provide support for the digital narration of Chinese ancient fiction images.

Diagrams probably rank among the oldest forms of human communication. Traditional logic diagrams (e.g., Venn diagrams, Euler diagrams, Peirce existential diagrams) have been utilized as conceptual representations, and it is claimed that these diagrammatic representations, in general, have advantages over linguistic ones. Nevertheless, current representations are not satisfactory. Diagrams of logic problems incompletely depict their underlying semantics and fail to provide a clear, basic, static structure with elementary dynamic features, creating a conceptual gap that sometimes causes misinterpretation. This paper proposes a conceptual apparatus to represent mathematical structure, and, without loss of generality, it focuses on sets. Set theory is described as one of the greatest achievements of modern mathematics. Nevertheless, its metaphysical interpretations raise paradoxes, and the notion of a collection, in terms of which sets are defined, is inconsistent. Accordingly, exploring a new view, albeit tentative, attuned to basic notions such as the definition of set is justifiable. This paper aims at providing an alternative graphical representation of a set as a machine with five basic “operations”: releasing, transferring, receiving, processing, and creating of things. Here, a depiction of sets is presented, as in the case of Venn-like diagrams, and is not intended to be a set theory contribution. The paper employs schematization as an apparatus of descriptive specification, and the resultant high-level description seems a viable tool for enhancing the relationship between set theory and computer science.

The detection of grapes in real scene images is a serious task solved by researches dealing with precision viticulture. Our research has shown that in the case of white wine varieties, grape berry detectors based on a support vector machine classifier in combination with a HOG descriptor are very efficient. In this paper, simplified versions of our original solutions are introduced. Our research showed that skipping contrast normalization by image preprocessing accelerates the detection process; however, the performance of the detectors is not negatively influenced by this modification.

The nowadays trend in product design is the creation of an ontology containing all components of a manufacturer along with their features. It is expected that a huge amount of information will be available in the near future. The problem that arises is how all these ontologies may be explored in an automatic way. And moreover, if it is possible to automatically create new products in a bottom-up fashion using the available knowledge about existing components. We use a genetic algorithm which represents individuals as ontologies rather than fixed mathematical structures. This allows the creation, recombination and selection of dynamic products, with a variable number of components, which may interrelate in different ways. We prove that such an algorithm may provide to the product designer a series of innovative products which can be refined further for commercial purposes.

Nowadays the advanced radio system in mobile devices is utilized as part of wireless communication towards an upgrade of channel capacity. Top end applications have allowed high-speed networking interfaces to connect the mobile network with many wireless routers, which helps in data transmission in mobile systems. These network interfaces require huge power for high-speed data transmission. In this, diversity and spatial gains are the two principle points of interest of mobile devices with higher delivery of throughput that are utilized to concentrate on improving bit-rate by increasing the quantity of transceiver antenna systems. This paper introduces an energy conservation mechanism for mobile devices. The key idea in antenna management is to remove adaptively percentage of the antennas and additionally their RF chains to reduce energy dissipation due to circuit power. This mechanism will reduce the power consumption and improve power efficiency by disabling the subset antennas and its RF chains. The proposed system will decide the active antennas for power minimization while achieving its data rate requirements. Matlab simulation is used in the proposed study, and the results are validated using the performance parameters such as data rate, transmit power and data rate constraints.

Real Time Scheduling (RTS) optimization is a key step in Real Time Embedded Systems design flow. Since RTS is a hard problem especially on multiprocessors systems, researchers have adopted metaheuristics to find near optimal solutions. On the other hand, a new class of genetic algorithms inspired from quantum mechanics appeared and proved its efficiency with regard to conventional genetic algorithms. The objective of this work is to show how we can use quantum inspired genetic algorithm to resolve the RTS problem on embedded multicores architecture. Our proposed algorithm tries to minimize the tasks response times mean and the number of tasks missing their deadlines while balancing between processors cores usage ratios. Experimental results show a big improvement in research time with regard to conventional genetic algorithms.

In this paper, we present an energy aware fuzzy real time scheduling model for periodic independent tasks targeting mono-processor embedded architecture. Our proposed algorithm functions on two steps. The first step uses fuzzy system to generate fuzzy priorities. The second step uses the outputs of the first one to schedule tasks with minimum energy consumption basing on the EDF* algorithm. Energy consumption is reduced by processor use with minimum speed without tasks deadlines missing. In order to evaluate the performance of our algorithm, we have performed simulations in Matlab. These simulations in particular confirmed the very good performance of the proposed algorithm in terms of energy consumption.

We highlight in this paper the competitive performance of the Iterated Greedy algorithm (IG) for solving the flow shop problem under blocking. A new instance of IG is used to minimize the total tardiness criterion. Basically, due to the NP-hardness of this blocking problem, we employ another variant of the NEH heuristic to form primary solution. Subsequently, we apply recurrently constructive methods to some fixed solution and then we use an acceptance criterion to decide whether the new generated solution substitutes the old one. Indeed, the perturbation of an incumbent solution is done by means of the destruction and construction phases. Despite its simplicity, the IG algorithm under blocking has shown its effectiveness, based on Ronconi and Henriques benchmark, when compared to state-of-the-art meta-heuristics.

The Manufacturing Cell Design Problem (MCDP) consists in creating an optimal design of production plants, through the creation of cells grouping machines that process parts of a given product. The goal is to reduce costs and increase productivity by minimizing movements and exchange of material between these cells. In this paper, we present a Firefly Algorithm (FA) to tackle this problem. The FA is a recent bio-inspired metaheuristic based on the mating behavior of fireflies that employ its flashing capabilities to communicate with each other or attract potential prey. We incorporate efficient transfer and discretization methods in order to suitable handle the binary domains of the problem. Interesting experimental results are illustrated where several global optimums are reached for a set of 90 well-known MCDP instances.

Manufacturing plants are commonly organized in cells containing machines that process different parts of a given product. The Manufacturing Cell Design Problem (MCDP) aims at efficiently organizing the machines into cells in order to increase productivity by minimizing the inter-cell moves of parts. In this paper, we present a new approach based on Invasive Weed Optimization (IWO) for solving such a problem. The IWO algorithm is a recent metaheuristic inspired on the colonization behavior of the invasive weeds in agriculture. IWO represents the solutions as weeds that grow and produce seeds to be randomly dispersed over the search area. We additionally incorporate a binary neighbor operator in order to efficiently handle the binary nature of the problem. The experimental results demonstrate the efficiency of the proposed approach which is able to reach several global optimums for a set of 90 well-known MCDP instances.

The paper discusses a modified algorithm based on the ants’ behavior in nature. We suggest to apply this algorithm for solving the element placement problem—one of the most difficult problem in the VLSI design. This problem belongs to the NP-class problem that is there are no precise methods to solve this problem. Also we formulate the placement problem and choose an optimization criterion. The developed ant colony optimization (ACO) algorithm obtains optimal and quasi-optimal solutions during polynomial time. The distinguish feature of the algorithm is that alternative solution are represented as an undirected graph with weighted edges. Besides, at each generation the algorithm creates a taboo-list to eliminate the quantity of agent (ant) which is wrong from the point of view the using of Reverse Polish notation. To compare obtained results with known analogous algorithms we developed software which allows to carry out experiments on the basis of IBM benchmarks. Conducted experiments shown that the ACO algorithm is better than the other algorithms an average of 9 %.

In the article the actual problem of increasing the efficiency of solving the pattern recognition problem is considered. It is described a promising approach to solve this problem by the use of artificial neural networks. It is proposed the model of a neural network as the Boltzmann machine. As a neural network learning algorithm the authors propose to use a simulated annealing algorithm. The deep learning methods of neural networks are considered. The algorithm of neural network functioning based on the Boltzmann machine model is suggested. The authors describe possibilities of using multi-layer neural network models, such as the deep Boltzmann machines. Advantages and disadvantages of the proposed approaches were found out. To estimate the proposed method the authors carried out the comparison of the known test set of sample images (MNIST). The results confirm the effectiveness of the proposed approaches.

In this paper a method of joint solutions of placement and routing problems of digital equipment elements is offered. The authors suggested a new approach on the basis of evolutionary algorithm (EA) integration and a fuzzy control model of algorithm parameters. A fuzzy logical controller structure is described in the article. A model of parallel evolutionary algorithm is developed. To synchronize parallel computations, you proposed to use a modified migration operator. To confirm the method effectiveness a brief program description is reviewed.

Grid systems with centralized structure of the scheduling system and resource co-allocation are modeled by resource quadrant. A resource rectangle presents user’s task. Quality of scheduling with heuristic algorithms is estimated by a Non-Euclidean heuristic measure which takes into consideration both the area and the form of an occupied resource region. One of a study problem is resource rectangle sets, denoted as sets of precise form, which have the square resource enclosure with no hollow space. The question that is posed concerns level polynomial algorithms adaptivity for the sets of precise form that consist of tasks of the circular and hyperbolic type.

Sentiment analysis is the process of extracting subjective information in source materials. Sentiment analysis is a subfield of web and text mining. One major problem encountered in these areas is overwhelming amount of data available. Hence, instance selection and feature selection become two essential tasks for achieving scalability in machine learning based sentiment classification. Instance selection is a data reduction technique which aims to eliminate redundant, noisy data from the training dataset so that training time can be reduced, scalability and generalization ability can be enhanced. This paper examines the predictive performance of fifteen benchmark instance selection methods for text classification domain. The instance selection methods are evaluated by decision tree classifier (C4.5 algorithm) and radial basis function networks in terms of classification accuracy and data reduction rates. The experimental results indicate that the highest classification accuracies on C4.5 algorithm are generally obtained by model class selection method, while the highest classification accuracies on radial basis function networks are obtained by nearest centroid neighbor edition.

The article is connected with solving one of the main problems of automated engineering design stage of electronic computing equipment of placement of VLSI fragments in a limited area of a construction. Placement of VLSI fragments is NP-hard. The paper tells about the multilayered approach to solving this problem. Description of the placement problem is given in this work. Definition of the problem of placement of VLSI fragments in a grate is formulated. New search architecture based on the multilayered approach is proposed. The main difference of the suggested approach is division of the search process into two stages. At each stage different methods are used. This approach gives an opportunity to vectorize the solving process and to make optimal and quasioptimal solutions in a time similar to iteration algorithm realization time. A simulation experiment was conducted through the example of test cases (benchmarks). Quality of placement based on the suggested approach is averagely 2 % higher than quality of known algorithms such as Capo 8.6, Feng Shui 2.0, Dragon 2.23 what indicates the effectiveness of the combined search. A number of conducted test and experiments showed the prospects of using this approach. The time complexity of the suggested algorithms is ≈O(nlogn) at the best case and –O(n3) at the worst one.

Energy intake of individual have an important role to support daily activity and it must fulfill the energy requirement in appropriate amounts. Energy requirement is determined based on Basal Metabolic Rate (BMR)—which is affected by weights, heights, age and gender—and physical activity level (PAL). While energy intake is calculated based on calorie from each portion of food consumed. This food consists of five principal elements, namely main dish, vegetable side dish, meat, vegetable and fruit. In the daily life, the difference between energy requirement and energy intake must be set as minimum as possible in order to avoid overweight or underweight condition. However, an individual is still having difficulty in determining the ideal portion of every kind of food that will be consumed in everyday. Therefore it is important to develop a system which gives the information regarding an optimal portion of each kind of food for an individual consumption. Genetic Algorithm (GA) is used to find the best portion and composition of food so that it will provide a proportional energy intake according to individual requirement. In the analysis we compare the results from GA and linear programming approach, the experiment shows that GA is succeed in giving proportional portion and composition as well as providing the diversity of food based on individual requirement.

This paper proposes a method to transform of algorithm model presented in form of Kripke structure, as well as LTL-specification reflecting the algorithm requirements, into the knowledge base in language of first order predicate logic. This transformation makes it possible to use the studied algorithm of accelerated logical deduction inference methods in process of formal verification. Heuristic structure of such methods allows looking forward to the significant reduction of the overall time of verification with proper selection of the inference method and optimization of the formula specification syntactic tree. In addition, we propose a software system structure for verification of parallel algorithms based on technique of model checking and described methods. The system has a modular architecture that allows for flexible change of the inference method, depending on specificity of analyzed algorithm.

Automated music composition typically employs genetic algorithms and/or stochastic methods using randomness in lieu of creativity. When properly guided these approaches can yield listenable music yet they lack another aspect of the music composition process: planning. Without planning, there may be no coherent structure or themes in the composed music. Planning can be employed to provide such structure by overseeing or controlling the genetic algorithm and/or stochastic methods in a hybrid architecture. In this paper, the system MAGE is presented which combines stochastic processing, genetic algorithms and planning to compose music that contains both structure and elements of randomness.

The recognition of wine grapes in real-life images is a serious issue solved by researches dealing with precision viticulture. The detection of wine grapes of red varieties is a well mastered problem. On the other hand, the detection of white varieties is still a challenging task. In this contribution, detectors designed for recognition of white wine grapes in real-life images are introduced and evaluated. Two representations of object images are considered in this paper; namely, vector of normalized pixel intensities and histograms of oriented gradients. In both cases, classifiers are realized using feedforward multilayer neural networks. The detector based on the histograms of oriented gradients has proven to be very effective by cross-validation. The results obtained by its evaluation on independent testing data are slightly worse; however, still very good. On the other hand, the representation using the vector of normalized pixel intensities was stated as insufficient.

The paper presents a concept of implementation of augmented reality, interactive systems and an operator’s speech interface for controlling lifting devices. The aim of the experimental research is to design a prototype of an innovative system for controlling a mobile crane, equipped with a vision and sensorial system, interactive manipulators with force feedback, as well as a system for bi-directional voice communication through speech and natural language between an operator and the controlled lifting device. The system is equipped with several adaptive intelligent layers for human biometric identification, speech recognition, word recognition, analysis and recognition of commands and messages, sentence meaning analysis, command effect analysis and safety assessment, process supervision and human reaction assessment. The article presents the designed structure of an innovative system for interaction of lifting devices with their operators, which provides versatility in terms of application of the system when used for controlling and supervising modern machines and devices in conditions of difficulty or increased risk.

With tremendous growth of online content, sentiment analysis of customer reviews has become an active research topic for machine learning community. However, due to variety of products being reviewed online traditional methods do not give desirable results. As number of reviews expand, it is essential to develop robust sentiment analysis model capable of extracting product aspects and determine sentiments adhering to various accuracy measures. Here, hierarchical bidirectional recurrent neural network (HBRNN) is developed in order to characterize sentiment specific aspects in review data available at DBS Text Mining Challenge. HBRNN predicts aspect sentiments vector at review level. HBRNN is optimized by fine tuning different network parameters and compared with methods like long short term memory (LSTM) and bidirectional LSTM (BLSTM). The methods are evaluated with highly skewed data. All models are evaluated using precision, recall and F1 scores. The results on experimental dataset indicate superiority of HBRNN over other techniques.

In the paper the idea of multiple metrics fusion for binary image quality assessment is presented together with experimental results obtained using the images from Bilevel Image Similarity Ground Truth Archive. As the performance evaluation of any full-reference image quality assessment metric requires both the knowledge of reference images with perfect quality and the results of subjective evaluation of distorted images, several such datasets have been developed during recent years. Nevertheless, the specificity of binary images requires the use of some other metrics which should also be verified in view of their correlation with subjective perception. Such task can be done using a dedicated database of binary images followed by the combination of multiple metrics leading to even higher correlation with subjective scores presented in this paper.

Biogeography-Based Optimization Algorithm (BBOA) is a kind of new global optimization algorithm inspired by biogeography. It mimics the migration behavior of animals in nature to solve optimization and engineering problems. In this paper, BBOA for the Set Covering Problem (SCP) is proposed. SCP is a classic combinatorial problem from NP-hard list problems. It consist to find a set of solutions that cover a range of needs at the lowest possible cost following certain constraints. In addition, we provide a new feature for improve performance of BBOA, improving stagnation in local optimum. With this, the experiment results show that BBOA is very good at solving such problems.

The nesting problem arises in several manufacturing industries (e.g., furniture, garment, textile and wood). It is a representative cutting and packing problem in which a set of irregular polygons has to be placed within a rectangular container with a fixed width and a variable length to be minimized. We present a brief survey about the nesting problems in three different categories and its special approaches.

Analytical programming is a novel approach to symbolic regression independent on the used evolutionary algorithm. This research paper focuses on the usage of Lozi chaotic map based pseudo-random number generator for the generation of the initial population of the selected evolutionary algorithm. The researched benefit is the tendency to generate individuals which are mapped to more complex programs than that of individuals generated by classical pseudo-random number generator. The results show that there is a potential in replacing classical generator by the chaotic map based one in order to generate more complex programs.

The aim of this article is to compare a success rate of a chosen numerical weather prediction (NWP) models with a forecasting system of convective precipitation based on an analysis of ten historical weather events over the territory of the Zlin Region for the year 2015. This paper is based on a previous article “Evaluation of the accuracy of numerical weather prediction models”. The first chapter is a theoretical framework describing the current forecasting systems of convective precipitation, which are selected NWP models and forecasting system of convective precipitation. This chapter describes the principle of creating predictions and selection of individual NWP models. Furthermore, they are provided with basic information about the prediction of convective precipitation. The second chapter outlines the principles of the methods used for evaluating the success rate of forecast precipitation. In the discussion, results of these methods on selected historical weather situations are published. Finally, the work contains an overview of the most accurate NWP models in comparison with the forecasting system of convective precipitation. This refined predictive information of convective precipitation may be especially useful for the crisis management authorities for preventive measures against the occurrence of flash floods.

In this paper, we proposed a design methodology for high performance, efficient area, the lower power multiplier for signed-unsigned number. In the first phase, for generating partial products, we proposed the Novel Modified Booth Encoder (NMBE) scheme using 28 transistors, compared to the conventional Modified Booth Encoder (MBE) multiplier of 46 transistors. In the second phase, for reducing several partial products rows into two rows, we have designed the Vertical Column Adder (VCA) with a minimum number of transistors compared to the conventional Partial Product Reduction Tree (PPRT). In the final phase, to obtain the product of multiplication, we have proposed Carry Look-ahead and Carry Select Adder (CLCSA) technique, for high speed addition operation with minimum delay. Hence, the experimental results show that the proposed NMBE multiplier for signed-unsigned number can achieve improvement in speed, area and power dissipation by 38 %, 63 % and 39 % respectively.

This paper proposes models for predicting customer conversion from trial account to full paid account of web application. Two models are proposed with focus on content of the application and time. In order to make a customer’s behavior prediction, data is extracted from web application’s usage log in trial period and processed with data mining techniques. For both models, content and time based, the same selected classification algorithms are used: decision trees, Naïve Bayes, k-Nearest Neighbors and One Rule classification. Additionally, a cluster algorithm k-means is used to see if clustering by two clusters (for converted and not-converted users) can be formed and used for classification. Results showed high accuracy of classification algorithms in early stage of trial period which can serve as a basis for an identification of users that are likely to abandon the application and not convert.

Books being a valuable source of knowledge and learning, have always been searched for on the Web. Traditional Web Information Retrieval (IR) techniques of searching and ranking are applied for this purpose. These techniques, however, are basically designed for dealing with hyperlinked collections of rich text in the form of web pages. Books are inherently different from web pages and the traditional Web IR techniques do not account for their well-organized structure and the logically connected content. Book searching solutions currently available on the Web and in other digital environments, however, do not exploit these implicit semantics resulting in not satisfying the requirements of all stakeholders including readers, authors, publishers, and librarians. These semantics hidden in the well thought out structure and the logical connections in book contents are only visible to human beings. The position put forward here is that most of the available searching solutions treat books as plaintext collections leading to inaccurate and imprecise book search results. Ways and means must, therefore, be found to treat books differently from other web documents and to use their structural semantics and logical connections in the content for searching, ranking and recommendations. Development of comprehensive book structure ontology will help in harvesting these implicit semantics. Similarly, in order to fulfill information needs of the readers, different domain-level ontologies are required so that book contents can be conceptually connected and be made machine ‘understandable’. Moreover, tables in a book consist of structured data and are a rich source of semantics. Similarly, the context of images and figures may be exploited for relating contents within and across books. Discovery and the subsequent utilization of these semantics in book IR process will result in more precise and accurate systems and to the satisfaction of all stakeholders.

The need for special algebras is the common task for many research in mathematics and theoretical computer science. We present our research concerning automated generation of such algebras through evolutionary techniques. Our reserch concerning the usage genetic algorithms shows this task to be feasible and we demonstrate it on special algebras called EQ-algebras. We also present possible optimization of the process using an expert system.

The problem of symbolic regression is to find mathematical expressions in symbolic form, approximating the relationship between the finite set of values of the independent variables and the corresponding values of the dependent variables. The criterion of quality approach is a mean square error: the sum of the squares of the difference between the model and the values of the dependent variable for all values of the independent variable as an argument. The paper offers a hybrid algorithm for solving symbolic regression. The traditional idea of an algebraic formula in syntax tree form is used. Leaf nodes correspond to variables or numeric constants rather than leaf nodes contain the operation that is performed on the child nodes. A distinctive feature of the process tree representation as a linear recording is preclude loss plurality of terminal elements, but the model can be an arbitrary function of the superposition of a set.

Keyword generation and expansion are important problems in computational advertising. Keyword suggestion methods help advertisers to find more appropriate keywords. They involve discovering new words or phrases related to the existing keywords. Producing the proper hidden yet semantically relevant keywords is a hard problem. The problems real difficulty is in finding many such words. In this paper we propose an artificial keyword suggester for Albanian language by mimicking the human like systems. The possibility of a human to provide these keywords counts on the richness and deepness of its language and cultural qualifications. In order to provide additional keywords a human must accomplish multiple memory search tasks for meanings of huge number of concepts and their frame of references. Hence the memory of the proposed artificial keyword suggester is based on a large information repository formed by utilizing machine reading techniques for fact extraction from the web. As a memory we indirectly use the Albanian world-wide-web and the Gjirafa.com as a search engine. Complementary, the brain of the system is designed as a spreading activating network. The brain treats provided keywords and finds associations between them and concepts within its memory in order to incrementally compute and propose a new list of potential keywords. Experimental results show that our proposed method can successfully provide suggestion that meets the accuracy and coverage requirements.

In this work we present a beam-search approach applied to the Set Covering Problem. The goal of this problem is to choose a subset of columns of minimal cost covering every row. Beam Search constructs a search tree by using a breadth-first search strategy, however only a fixed number of nodes are kept and the rest are discarded. Even though original beam search has a deterministic nature, our proposal has some elements that makes it stochastic. This approach has been tested with a well-known set of 45 SCP benchmark instances from OR-Library showing promising results.

Diabetes disables body to regulate proper amount of glucose as insulin. It has impacted a vast global population. In this paper, we demonstrated a fuzzy c-means-neuro-fuzzy rule-based classifier to detect diabetic disease with an acceptable interpretability. We measured the accuracy of our implemented classifier by correctly recognizing diabetic records. Besides we measured the complexity of the classifiers by the number of selected fuzzy rules. To achieve good accuracy and interpretability, the implemented fuzzy classifier can be treated as an acceptable trade-off. At the end of the research, we compared our experiment results with the achieved results from certain medical institutions that worked on the same type of dataset which demonstrated the compactness, accuracy of the proposed approach.

This paper studies the problem of automatic categorization of provisions in Arabic normative texts. We propose a knowledge-based categorization approach coupling a taxonomy of Arabic normative provisions’ categories, an Arabic normative terminological base and a rule-based semantic annotator. The obtained model has been trained and tested over a collection of Arabic normative texts collected from the Official Gazette of the Republic of Tunisia. The performance of the approach was evaluated in terms of Precision, Recall and F-score in order to categorize instances over 14 normative categories. The obtained results over the test dataset are very promising. We have obtained 96.4 % for Precision, 96.06 % for Recall and 96.23 % for F-score.

Blind users face a number of challenges in performing common operations of text-entry, text selection, and text manipulation on smartphones. The existing keypad layouts make it difficult for the users to easily operate a touch screen device even for entry-level activities. This necessitates the need for customizing the current keypad and dialer to enable a blind user to perform common activities of making a call, sending and receiving SMS messages and e-mails and browsing internet without visual feedback. Based on our prior study on screen division layouts, this paper proposes and evaluates a dialer and keyboard for blind users of a smartphone. The proposed keypad was tested on selected groups of blind users from both the countries where the research was performed. They were initially trained on using the proposed keypad. Their experiences were then recorded using interviews and observation. The responses were then tested and analyzed using standard statistical tests. The results were then compared with the existing ordinary and QWERTY keypads. These results show that the proposed keypad and dialer has a gentle learning curve and results in minimum typing errors thus reducing cognitive load on the blind user.

The Set Covering Problem is a classic combinatorial problem which is looking for solutions to cover needs on a geographic area. In this paper, we applied new ideas to solve The Set Covering Problem. Intelligent Water Drop is a nature inspired algorithm based on water drops behavior on natural river systems and the events that change the nature of water drop and the river environment. It observes that a river can find an optimum path to its goal. The results of experiments seems to be promising with certain configurations for the instances given by OR-Library J.E. Beasley. In addition an innovation was introduced in the algorithm in order to obtain results. Also a heuristic undesirability chosen is presented in this paper.

This paper describes the Software Project Scheduling Problem (SPSP) as a combinatorial optimization problem. In this problem raises the need for a process to assign a set of resources to tasks for a project in a given time, trying to decrease the duration and cost. The workers are the main resource in the project. We present the design of the resolution model to solve the SPSP using an algorithm of fireflies (Firefly Algorithm, FA). We illustrate the experimental results in order to demonstrate the viability and soundness of our approach.

The Set Covering Problem (SCP) is a classic problem of combinatorial analytic. This problem consists in to find solutions what cover the needs to lower cost. Those can be services to cities, load balancing in production lines or databanks selections. In this paper, we study the resolution of SCP, through Invasive Weed Optimization (IWO), in its binary version; Binary Invasive Weed Optimization (BIWO). IWO, it is to imitate to Invasive Weed behavior (reproduction and selection natural), through mathematics formulations. Where the best weed has more chance of reproduction.

Fuzzy multiset finite automata represent fuzzy version of finite automata working over multisets. Description of these automata can be simplified to such a form where transition relation is bivalent and only the final states form a fuzzy set. In this paper it is proved that the simplified form preserves computational power of the automata and way of how to perform the corresponding transformation is described.

The Set Covering Problem is a formal model for many practical optimization problems. It consists in finding a subset of columns in a zero/one matrix such that they cover all the rows of the matrix at a minimum cost. To solve the Set Covering Problem we will use a metaheuristic called Fireworks Algorithm (FWA) inspired by the fireworks explosion. Through the observation of the way that fireworks explode is much similar to the way that an individual searches the optimal solution in swarm. Fireworks algorithm consists of four parts, i.e., the explosion operator, the mutation operator, the mapping rule and selection strategy.

In this paper, we study a classical problem in combinatorics and computer science, Set Covering Problem. It is one of Karp’s 21 NP-complete problems, using a new and original metaheuristic, Cat Swarm Optimization. This algorithm imitates the domestic cat through two states: seeking and tracing mode. The OR-Library of Beasley instances were used for the benchmark with additional fitness function, thus the problem was transformed from Mono-objective to Bi-objective. The Cat Swarm Optimization finds a set solution non-dominated based on Pareto concepts, and an external file for storing them. The results are promising for further continue in future work optimizing this problem.

Due to the competitiveness of the software industry a more stressful tasks for software project managers allocation of the human resources to the different tasks that perform the project. This is not an easy task and it is necessary that is computationally supported since every day projects are larger and these should be developed in the shortest time and possible costs. We propose to use a constructive metaheuristics called Intelligent Water Drops. In this paper the result are compared with another constructive metaheuristics obtaining promising performance.

In this paper, we present a Binary cat swarm optimization for solving the Set covering problem. The Set covering problem is a well-known NP-hard problem with many practical applications, including those involving scheduling, production planning and location problems. Binary cat swarm optimization is a recent swarm metaheuristic technique based on the behaviour of discrete cats. Domestic cats show the ability to hunt and are curious about moving objects. The cats have two modes of behavior: seeking mode and tracing mode. Moreover, eight different transfer functions and five discretization techniques are considered for solving the binary problem. We illustrate this approach with 65 instances of the problem and select the best transfer function and discretization technique to solve this problem.

This work deals with the hybridization of the complex networks framework and evolutionary algorithms. The population is visualized as an evolving complex network, which exhibits non-trivial features. This paper investigates briefly the time development of complex network within the run of selected metaheuristic algorithm, which is Differential Evolution (DE). This paper also briefly discuss possible utilization of the complex network attributes such as adjacency graph, centralities, clustering coefficient and others. Experiments were performed for one selected DE strategy and one simple test function.