Search Logic Research Articles

Logical Analysis of Complex Network Searching

This article describes the relationship between network search space, search strategies and search logic with logical language. The logicalproperty of complex network search algorithm is determined by the initial state (the search state) and the search strategy of complex network space, which can be defined logically. The article also presents the defi- nition of complex network search states set and its relationship with first-order modal predicate logic. All possible net- work search state sets in the search strategy can be described with first-order modal predicate logic. Analysis of net work search state properties lays a good foundation for the intelligentization of complex network search processes in the future.

A geometric algorithm for automatic riser determination and shrinkage identification in directionally solidifying castings

A geometric approach for analysis of castings is outlined and contrasted with physics-based simulations. The approach uses a linear-time algorithm to create an implicit geometry representation known as a distance field. Local maxima are extracted from the distance field using heuristic search logic to determine isolated heavy sections and hotspots. Unsound sections are determined by combining hotspot results with regions of constant distance-field value. Possible uses of the algorithm as an augmentation for application of physics-based solvers is explored. The algorithm is compared against a commercially available simulation software package.

Enhanced compact artificial bee colony

Challenges in many real-world optimization problems arise from limited hardware availability, particularly when the optimization must be performed on a device whose hardware is highly restricted due to cost or space. This paper proposes a new algorithm, namely Enhanced compact Artificial Bee Colony (EcABC) to address this class of optimization problems. The algorithm benefits from the search logic of the Artificial Bee Colony (ABC) algorithm, and similar to other compact algorithms, it does not store the actual population of tentative solutions. Instead, EcABC employs a novel probabilistic representation of the population that is introduced in this paper. The proposed algorithm has been tested on a set of benchmark functions from the CEC2013 benchmark suite, and compared against a number of algorithms including modern compact algorithms, recent population-based ABC variants and some advanced meta-heuristics. Numerical results demonstrate that EcABC significantly outperforms other state of the art compact algorithms. In addition, simulations also indicate that the proposed algorithm shows a comparative performance when compared against its population-based versions.

The effect of sustainability on the information search behaviour of Hungarian consumers through the practice of food purchasing

In the study we aim to model the information search behaviour of Hungarian consumers committed to sustainable consumption through the practice of food purchasing. To reach our goal we examined the logic of Grunert-Wills information search model.In our research the information search behaviour of “Trend followers” was investigated. To reach the mentioned group, data collection was carried out by the method of quota sampling, where quotas were defined according to characteristics of value system based lifestyle segments discovered in a 2011 national representative research by us. Through the analysis one-variable statistics and contingency table analysis were carried out.Our results prove the followings: (i) the logic of Grunert-Wills model is suitable for describing the information search behaviour of conscious consumers. (ii) Lifestyle has a highlighted effect on information search behaviour.

Usability Evaluation of Public Web Mapping Sites

Abstract. Web mapping sites are interactive maps that are accessed via Webpages. With the rapid development of Internet and Geographic Information System (GIS) field, public web mapping sites are not foreign to people. Nowadays, people use these web mapping sites for various reasons, in that increasing maps and related map services of web mapping sites are freely available for end users. Thus, increased users of web mapping sites led to more usability studies. Usability Engineering (UE), for instance, is an approach for analyzing and improving the usability of websites through examining and evaluating an interface. In this research, UE method was employed to explore usability problems of four public web mapping sites, analyze the problems quantitatively and provide guidelines for future design based on the test results. Firstly, the development progress for usability studies were described, and simultaneously several usability evaluation methods such as Usability Engineering (UE), User-Centered Design (UCD) and Human-Computer Interaction (HCI) were generally introduced. Then the method and procedure of experiments for the usability test were presented in detail. In this usability evaluation experiment, four public web mapping sites (Google Maps, Bing maps, Mapquest, Yahoo Maps) were chosen as the testing websites. And 42 people, who having different GIS skills (test users or experts), gender (male or female), age and nationality, participated in this test to complete the several test tasks in different teams. The test comprised three parts: a pretest background information questionnaire, several test tasks for quantitative statistics and progress analysis, and a posttest questionnaire. The pretest and posttest questionnaires focused on gaining the verbal explanation of their actions qualitatively. And the design for test tasks targeted at gathering quantitative data for the errors and problems of the websites. Then, the results mainly from the test part were analyzed. The success rate from different public web mapping sites was calculated and compared, and displayed by the means of diagram. And the answers from questionnaires were also classified and organized in this part. Moreover, based on the analysis, this paper expands the discussion about the layout, map visualization, map tools, search logic and etc. Finally, this paper closed with some valuable guidelines and suggestions for the design of public web mapping sites. Also, limitations for this research stated in the end.

Computational Intelligence in Cross Docking

Cross docking is a practice in logistics with the main operations of goods flow directly from receiving to the shipping docks without stopping or being put away into storage. It is a simple concept to talk about, but a challenging one to implement. So far, many different approaches have been followed in order to improve the efficiency of a cross docking system. However, as the complexity increases, the use of computational intelligence (CI) in those problems is becoming a unique tool of imperative value. In this paper, different CI methods, such as Tabu search, simulated annealing, genetic algorithm, and fuzzy logic. The key issues in implementing the proposed approaches are discussed, and finally the open questions are highlighted.

University Course Timetabling using Multi-population Genetic Algorithm Guided with Local Search and Fuzzy Logic

Problem of courses timetabling is a time consuming and demanding issues in any education environment that they are involved in every semester. The main aim of timetabling problem is the allocation of a number of courses to a limited set of resources such as classrooms, time slots, professors and students so that some predefined hard and soft constraints are satisfied. Furthermore, the available resources are used to the best.   In fact course timetabling is one of optimization problems. It has been proved computational complexity of this problem is NP, so there is no optimal solution for that. Therefore, approximation and heuristic techniques are used to find near optimal solutions. Genetic algorithm for its multidirectional feature has been one of the most widely used approaches in recent years. Hence, in this paper an improved genetics algorithm for timetabling problem has been proposed. In proposed algorithm, the fitness of solutions to satisfy soft constraints due to ambiguous nature of those has been specified using fuzzy logic. Also, local search methods have been applied to avoid the genetic algorithm to be trapped in a local optimum. As well as, the multi-population property is intended to reduce the time to reach the optimum solution.  Evaluation results show that the proposed solutions are able to produce promising results for the university courses timetabling.

A Genetic Algorithm‐Based Method for the Automatic Reduction of Reaction Mechanisms

ABSTRACTAn automatic method for the reduction of chemical kinetic mechanisms under specific physical or thermodynamic conditions is presented. The method relies on the genetic algorithm search logic to gradually reduce the number of reactions from the detailed mechanism while still preserving its ability to describe the overall chemistry at an acceptable error. Accuracy of the reduced mechanism is determined by comparing its solution to the solution obtained with the full mechanism under the same initial and/or physical conditions. However, not only the chemical accuracy and the size of the mechanism are considered but also the time for its solution which helps to avoid stiff and slow converging mechanisms, thus preferring the fast solutions. The reduction method is demonstrated for a detailed mechanism for methane combustion, GRI‐Mech 3.0, which was reduced from 325 reactions and 53 species to 58 reactions and 26 species, and for an iron oxide formation mechanism from iron pentacarbonyl doped flames by Wlokas et al. (Int J Chem Kinet 2013, 45(8), 487–498), originally consisting of 144 reactions and 34 species, which was reduced to 37 reactions and 24 species. The performance of the reduced mechanisms is shown for homogeneous constant pressure reactors and for burner‐stabilized flames. The results show a good agreement between reduced and full mechanisms for both the reactor and flame cases. The presented method is flexible and can be easily adjusted to either yield more accurate (but bigger) or smaller (but less accurate) reduced mechanisms, depending on the user's preference.

Nutrition Care Manual

Developed by the Academy of Nutrition and Dietetics, Nutrition Care Manual serves as an evidence-based, point-of-care tool for students, educators, and professionals. The content covers the most common nutrition issues, professional background information, treatment plans, and patient education information. However, the user interface is confusing with few paths for logical search or navigation and lacks many of the common features users expect from most electronic resources, such as permalinks or mobile viewing. While the content and flexibility meet a previously unfulfilled need in allied health resources, the interface problems hinder maximum utilization of the content quickly. Author’s Note: In September 2013, the Academy announced a series of updates to NCM including easier mobile navigation and content organization that may differ slightly from the body of this review.

Compact Particle Swarm Optimization

Some real-world optimization problems are plagued by a limited hardware availability. This situation can occur, for example, when the optimization must be performed on a device whose hardware is limited due to cost and space limitations. This paper addresses this class of optimization problems and proposes a novel algorithm, namely compact Particle Swarm Optimization (cPSO). The proposed algorithm employs the search logic typical of Particle Swarm Optimization (PSO) algorithms, but unlike classical PSO algorithms, does not use a swarm of particles and does not store neither the positions nor the velocities. On the contrary, cPSO employs a probabilistic representation of the swarm’s behaviour. This representation allows a modest memory usage for the entire algorithmic functioning, the amount of memory used is the same as what is needed for storing five solutions. A novel interpretation of compact optimization is also given in this paper. Numerical results show that cPSO appears to outperform other modern algorithms of the same category (i.e. which attempt to solve the optimization despite a modest memory usage). In addition, cPSO displays a very good performance with respect to its population-based version and a respectable performance also with respect to some more complex population-based algorithms. A real world application in the field of power engineering and energy generation is given. The presented case study shows how, on a model of an actual power plant, an advanced control system can be online and real-time optimized. In this application example the calculations are embedded directly on the real-time control system.

Computational Intelligence in Used Products Retrieval and Reproduction

Remanufacturing has become a superior option for product recovery management system. It mainly consists of three stages: retrieval, reproduction, and redistribution. So far, many different approaches have been followed in order to improve the efficiency of a remanufacturing process. However, as the complexity increases, the use of computational intelligence (CI) in those problems is becoming a unique tool of imperative value. In this paper, different CI methods, such as artificial neural network (ANN), ant colony optimization (ACO), biogeography-based optimization (BBO), cuckoo search (CS) and fuzzy logic (FL), are utilized to solve the problems involved in retrieval and reproduction stages for remanufacturing. The key issues in implementing the proposed approaches are discussed, and finally the applicability of the proposed methods are illustrated through different examples.

Parallel memetic structures

Memetic Computing (MC) structures are algorithms composed of heterogeneous operators (memes) for solving optimization problems. In order to address these problems, this study investigates and proposes a simple yet extremely efficient structure, namely Parallel Memetic Structure (PMS). PMS is a single solution optimization algorithm composed of tree operators, the first one being a stochastic global search which explores the entire decision space searching for promising regions. In analogy with electrical networks, downstream of the global search component there is a parallel of two alternative elements, i.e. two local search algorithms with different features in terms of search logic, whose purpose is to refine the search in the regions detected by the upstream element. The first local search explores the space along the axes, while the second performs diagonal movements in the direction of the estimated gradient.The PMS algorithm, despite its simplicity, displays a respectable performance compared to that of popular meta-heuristics and modern optimization algorithms representing the state-of-the-art in the field. Thanks to its simple structure, PMS appears to be a very flexible algorithm for various problem features and dimensionality values. Unlike modern complex algorithm that are specialized for some benchmarks and some dimensionality values, PMS achieves solutions with a high quality in various and diverse contexts, for example both on low dimensional and large scale problems. An application example in the field of magnetic sensors further proves the potentials of the proposed approach. This study confirms the validity of the Ockham’s Razor in MC: efficiently designed simple structures can perform as well as (if not better than) complex algorithms composed of many parts.

Noise analysis compact differential evolution

This article proposes a compact algorithm for optimisation in noisy environments. This algorithm has a compact structure and employs differential evolution search logic. Since it is a compact algorithm, it does not store a population of solutions but a probabilistic representation of the population. This kind of algorithmic structure can be implemented in those real-world problems characterized by memory limitations. The degree of randomization contained in the compact structure allows a robust behaviour in the presence of noise. In addition the proposed algorithm employs the noise analysis survivor selection scheme. This scheme performs an analysis of the noise and automatically performs a re-sampling of the solutions in order to ensure both reliable pairwise comparisons and a minimal cost in terms of fitness evaluations. The noise analysis component can be reliably used in noise environments affected by Gaussian noise which allow an a priori analysis of the noise features. This situation is typical of problems where the fitness is computed by means of measurement devices. An extensive comparative analysis including four different noise levels has been included. Numerical results show that the proposed algorithm displays a very good performance since it regularly succeeds at handling diverse fitness landscapes characterized by diverse noise amplitudes.

Debug and Development of ADAM 6022 Communication in Industry Automation Control

Automatic communication problem was always difficult for installation of industry supervision control system. ‘3Stages’ debug strategy was proposed and discussed for Adam 6022 in detail in this paper, which was composed by three steps which included address setup, port search and Graphical Condition Logic (GCL) test; and this strategy was proved effective after practical industry application.

Distributed model-checking and counterexample search for CTL logic

In this paper, we propose a distributed algorithm for CTL model-checking and a counterexample search whenever the CTL formula is not satisfied. The distributed approach is used in order to cope with the state space explosion problem. A cluster of workstations performs collaborative verification over a partitioned state space. Thus, every process involved in the distributed verification performs the labelling procedure on its own partial state space, and uses the parse tree of the CTL formula to evaluate sub-formulas and delay the synchronisations so as to minimise idle time. A counterexample search consists in a distributed construction of the tree-like corresponding to the failure executions. Some experiments have been carried out to evaluate the efficiency of this approach.

Education for sustainable development in Africa: The search for pedagogical logic

The United Nation's Decade of Education for Sustainable Development (DESD) is past its halfway milestone; yet, its overall impact on educational thinking and practice remains unclear in most places and regions of the world. While several efforts and programs are currently in place to promote and affirm the role of education in the global quest for sustainable development, most of these efforts remain largely unknown and invisible in most communities around the world. The Decade and ESD, arguably, are neither seen nor heard of in most of Africa. Most institutions, including schools, governments, businesses, civil society and individuals are yet to know and understand the role of education in the quest for sustainable development. The paper argues that in spite of the Decade and all the attention it is getting in some circles, the subject is muted in most educational policies and practices in Africa. In calling for more focused commitment to the roles of education in sustainable development in Africa, the paper also calls for a reconsideration of what sustainable development means, or might mean for Africans in their different places and cultures and to use that as a starting point for the exploration of more meaningful educational philosophies and pedagogies that responds to Africa's unique challenges.

Path Planning Strategies for UAVS in 3D Environments

The graph-search algorithms developed between 60s and 80s were widely used in many fields, from robotics to video games. The A* algorithm shall be mentioned between some of the most important solutions explicitly oriented to motion-robotics, improving the logic of graph search with heuristic principles inside the loop. Nevertheless, one of the most important drawbacks of the A* algorithm resides in the heading constraints connected with the grid characteristics. Different solutions were developed in the last years to cope with this problem, based on post-processing algorithms or on improvements of the graph-search algorithm itself. A very important one is Theta* that refines the graph search allowing to obtain paths with any heading. In the last two years, the Flight Mechanics Research Group of Politecnico di Torino studied and implemented different path planning algorithms. A Matlab based planning tool was developed, collecting four separate approaches: geometric predefined trajectories, manual waypoint definition, automatic waypoint distribution (i.e. optimizing camera payload capabilities) and a comprehensive A*-based algorithm used to generate paths, minimizing risk of collision with orographic obstacles. The tool named PCube exploits Digital Elevation Maps (DEMs) to assess the risk maps and it can be used to generate waypoint sequences for UAVs autopilots. In order to improve the A*-based algorithm, the solution is extended to tri-dimensional environments implementing a more effective graph search (based on Theta*). In this paper the application of basic Theta* to tri-dimensional path planning will be presented. Particularly, the algorithm is applied to orographic obstacles and in urban environments, to evaluate the solution for different kinds of obstacles. Finally, a comparison with the A* algorithm will be introduced as a metric of the algorithm performances.

Determination of geographic variance in stroke prevalence using Internet search engine analytics

Previous methods to determine stroke prevalence, such as nationwide surveys, are labor-intensive endeavors. Recent advances in search engine query analytics have led to a new metric for disease surveillance to evaluate symptomatic phenomenon, such as influenza. The authors hypothesized that the use of search engine query data can determine the prevalence of stroke. The Google Insights for Search database was accessed to analyze anonymized search engine query data. The authors' search strategy utilized common search queries used when attempting either to identify the signs and symptoms of a stroke or to perform stroke education. The search logic was as follows: (stroke signs + stroke symptoms + mini stroke--heat) from January 1, 2005, to December 31, 2010. The relative number of searches performed (the interest level) for this search logic was established for all 50 states and the District of Columbia. A Pearson product-moment correlation coefficient was calculated from the statespecific stroke prevalence data previously reported. Web search engine interest level was available for all 50 states and the District of Columbia over the time period for January 1, 2005-December 31, 2010. The interest level was highest in Alabama and Tennessee (100 and 96, respectively) and lowest in California and Virginia (58 and 53, respectively). The Pearson correlation coefficient (r) was calculated to be 0.47 (p = 0.0005, 2-tailed). Search engine query data analysis allows for the determination of relative stroke prevalence. Further investigation will reveal the reliability of this metric to determine temporal pattern analysis and prevalence in this and other symptomatic diseases.

Compact Differential Evolution

This paper proposes the compact differential evolution (cDE) algorithm. cDE, like other compact evolutionary algorithms, does not process a population of solutions but its statistic description which evolves similarly to all the evolutionary algorithms. In addition, cDE employs the mutation and crossover typical of differential evolution (DE) thus reproducing its search logic. Unlike other compact evolutionary algorithms, in cDE, the survivor selection scheme of DE can be straightforwardly encoded. One important feature of the proposed cDE algorithm is the capability of efficiently performing an optimization process despite a limited memory requirement. This fact makes the cDE algorithm suitable for hardware contexts characterized by small computational power such as micro-controllers and commercial robots. In addition, due to its nature cDE uses an implicit randomization of the offspring generation which corrects and improves the DE search logic. An extensive numerical setup has been implemented in order to prove the viability of cDE and test its performance with respect to other modern compact evolutionary algorithms and state-of-the-art population-based DE algorithms. Test results show that cDE outperforms on a regular basis its corresponding population-based DE variant. Experiments have been repeated for four different mutation schemes. In addition cDE outperforms other modern compact algorithms and displays a competitive performance with respect to state-of-the-art population-based algorithms employing a DE logic. Finally, the cDE is applied to a challenging experimental case study regarding the on-line training of a nonlinear neural-network-based controller for a precise positioning system subject to changes of payload. The main peculiarity of this control application is that the control software is not implemented into a computer connected to the control system but directly on the micro-controller. Both numerical results on the test functions and experimental results on the real-world problem are very promising and allow us to think that cDE and future developments can be an efficient option for optimization in hardware environments characterized by limited memory.

Shuffle or update parallel differential evolution for large-scale optimization

This paper proposes a novel algorithm for large-scale optimization problems. The proposed algorithm, namely shuffle or update parallel differential evolution (SOUPDE) is a structured population algorithm characterized by sub-populations employing a Differential evolution logic. The sub-populations quickly exploit some areas of the decision space, thus drastically and quickly reducing the fitness value in the highly multi-variate fitness landscape. New search logics are introduced into the sub-population functioning in order to avoid a diversity loss and thus premature convergence. Two simple mechanisms have been integrated in order to pursue this aim. The first, namely shuffling, consists of randomly rearranging the individuals over the sub-populations. The second consists of updating all the scale factors of the sub-populations. The proposed algorithm has been run on a set of various test problems for five levels of dimensionality and then compared with three popular meta-heuristics. Rigorous statistical and scalability analyses are reported in this article. Numerical results show that the proposed approach significantly outperforms the meta-heuristics considered in the benchmark and has a good performance despite the high dimensionality of the problems. The proposed algorithm balances well between exploitation and exploration and succeeds to have a good performance over the various dimensionality values and test problems present in the benchmark. It succeeds at outperforming the reference algorithms considered in this study. In addition, the scalability analysis proves that with respect to a standard Differential Evolution, the proposed SOUPDE algorithm enhances its performance while the dimensionality grows.