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Parallels between subsistence foraging and scholarly information seeking are described in the context of the World‐Wide Web. It is suggested that the prevailing information retrieval paradigm lacks requisite variety to capture the complex of behaviours and stimuli that drives scholars' quests for new ideas and insights. The authors outline a variety of research questions suggested by extended use of the optimal foraging metaphor in relation to distributed multimedia information resources.

Purpose of this paper are Real power loss reduction, voltage stability enhancement and minimization of Voltage deviation.

In HLG approach as per Henry gas law sum of gas dissolved in the liquid is directly proportional to the partial pressure on above the liquid. Gas dissolving in the liquid which based on Henry gas law is main concept to formulate the proposed algorithm. Populations are divided into groups and all the groups possess the similar Henry constant value. Exploration and exploitation has been balanced effectively. Ranking and position of the worst agents is done in order to avoid the local optima. Then in this work Mobula alfredi optimization (MAO) algorithm is projected to solve optimal reactive power problem. Foraging actions of Mobula alfredi has been imitated to design the algorithm. String foraging, twister foraging and backward roll foraging are mathematically formulated to solve the problem. In the entire exploration space the Mobula alfredi has been forced to discover new regions by assigning capricious position. Through this approach, exploration competence of the algorithm has been improved. In all iterations, the position of the Mobula alfredi has been updated and replaced with the most excellent solution found so far. Exploration and exploitation capabilities have been maintained sequentially. Then in this work balanced condition algorithm (BCA) is projected to solve optimal reactive power problem. Proposed BCA approach based on the conception in physics- on the subject of the mass; incoming, exit and producing in the control volume. Preliminary population has been created based on the dimensions and number of particles and it initialized capriciously in the exploration space with minimum and maximum concentration. Production control parameter and Production probability utilized to control the exploration and exploitation.

Proposed Henry's Law based -soluble gas optimization (HLG) algorithm, Mobula alfredi optimization (MAO) algorithm and BCA are evaluated in IEEE 30 bus system with L-index (Voltage stability) and also tested in standard IEEE 14, 30, 57, 118, 300 bus test systems without L- index. Real power loss minimization, voltage deviation minimization, and voltage stability index enhancement has been attained.

For the first time Henry's Law based -soluble gas optimization (HLG) algorithm, Mobula alfredi optimization (MAO) algorithm and BCA is projected to solve the power loss reduction problem.

Drawing on information foraging theory and the SERVQUAL model, this study built a research model to investigate the roles of middle-aged and elderly short-video creators' online attributes in attracting short-video viewers to be their followers.

Taking Douyin (a famous short-video platform in China) as an example, this study used a sequential triangulation mixed-methods approach (quantitative → qualitative) to examine the proposed model by investigating both creators and viewers.

Viewers who clicked the “like” button for the middle-aged and elderly creators' videos are more likely to follow the creators. Viewers will believe that middle-aged and elderly creators who received more likes are more popular. Thus, middle-aged and elderly creators with more likes usually have more followers. Viewers usually believe that middle-aged and elderly creators who more frequently publish professional and high-quality videos have invested more effort and who have official verification also have a high level of authority and are recognized by the platform. Thus, middle-aged and elderly creators with more professional videos and verification usually have more followers. Moreover, verification, the number of videos and the professionalism of videos can enhance the transformation of viewers who liked middle-aged and elderly creators' videos into their followers, and thus strengthen the positive relationship between the number of likes and the number of followers; however, the number of bio words will have an opposite effect.

These findings have implications for platform managers, middle-aged and elderly creators and the brands aiming to develop a “silver economy” by attracting more followers.

This study researches short-video platforms by using a mixed-methods approach to develop an understanding of viewers' decision-making when following middle-aged and elderly creators based on information foraging theory and the SERVQUAL model from the perspectives of both short-video creators and viewers.

The purpose of this paper is to present the application of an adaptive bacterial foraging (BF) algorithm for the design optimization of an energy efficient induction motor.

The induction motor design problem is formulated as a mixed integer nonlinear optimization problem. A set of nine independent variables is selected, and to make the machine feasible and practically acceptable, six constraints are imposed on the design. Two different objective functions are considered, namely, the annual active material cost, and the sum of the annual active material cost, annual cost of the active power loss of the motor and annual energy cost required to supply such power loss. A new adaptive BF algorithm is used for solving the optimization problem. A generic penalty function method, which does not require any penalty coefficient, is employed for constraint handling.

The adaptive BF algorithm is validated for two sample motors and benchmarked with the genetic algorithm, particle swarm optimization, simple BF algorithm, and conventional design methods. The results show that the proposed algorithm outperforms the other methods in both the solution quality and convergence rate. The annual cost of the induction motor is remarkably reduced when designed on the basis of minimizing its annual total cost, instead of minimizing its material cost only.

To the best of the knowledge, none of the existing work has applied the BF algorithms for electrical machine design problems. Therefore, the solution to this problem constitutes the main contribution of the paper. According to the huge number of induction motors operating all over the world, the BF techniques used in their design, on minimum annual cost basis, will lead to a tremendous saving in global energy consumption.

The innovation in big data is increasing day by day in such a way that the conventional software tools face several problems in managing the big data. Moreover, the occurrence of the imbalance data in the massive data sets is a major constraint to the research industry.

The purpose of the paper is to introduce a big data classification technique using the MapReduce framework based on an optimization algorithm. The big data classification is enabled using the MapReduce framework, which utilizes the proposed optimization algorithm, named chicken-based bacterial foraging (CBF) algorithm. The proposed algorithm is generated by integrating the bacterial foraging optimization (BFO) algorithm with the cat swarm optimization (CSO) algorithm. The proposed model executes the process in two stages, namely, training and testing phases. In the training phase, the big data that is produced from different distributed sources is subjected to parallel processing using the mappers in the mapper phase, which perform the preprocessing and feature selection based on the proposed CBF algorithm. The preprocessing step eliminates the redundant and inconsistent data, whereas the feature section step is done on the preprocessed data for extracting the significant features from the data, to provide improved classification accuracy. The selected features are fed into the reducer for data classification using the deep belief network (DBN) classifier, which is trained using the proposed CBF algorithm such that the data are classified into various classes, and finally, at the end of the training process, the individual reducers present the trained models. Thus, the incremental data are handled effectively based on the training model in the training phase. In the testing phase, the incremental data are taken and split into different subsets and fed into the different mappers for the classification. Each mapper contains a trained model which is obtained from the training phase. The trained model is utilized for classifying the incremental data. After classification, the output obtained from each mapper is fused and fed into the reducer for the classification.

The maximum accuracy and Jaccard coefficient are obtained using the epileptic seizure recognition database. The proposed CBF-DBN produces a maximal accuracy value of 91.129%, whereas the accuracy values of the existing neural network (NN), DBN, naive Bayes classifier-term frequency–inverse document frequency (NBC-TFIDF) are 82.894%, 86.184% and 86.512%, respectively. The Jaccard coefficient of the proposed CBF-DBN produces a maximal Jaccard coefficient value of 88.928%, whereas the Jaccard coefficient values of the existing NN, DBN, NBC-TFIDF are 75.891%, 79.850% and 81.103%, respectively.

In this paper, a big data classification method is proposed for categorizing massive data sets for meeting the constraints of huge data. The big data classification is performed on the MapReduce framework based on training and testing phases in such a way that the data are handled in parallel at the same time. In the training phase, the big data is obtained and partitioned into different subsets of data and fed into the mapper. In the mapper, the features extraction step is performed for extracting the significant features. The obtained features are subjected to the reducers for classifying the data using the obtained features. The DBN classifier is utilized for the classification wherein the DBN is trained using the proposed CBF algorithm. The trained model is obtained as an output after the classification. In the testing phase, the incremental data are considered for the classification. New data are first split into subsets and fed into the mapper for classification. The trained models obtained from the training phase are used for the classification. The classified results from each mapper are fused and fed into the reducer for the classification of big data.

Readability applications are the software products designed to make online text more readable. Using information foraging theory as a framework, the purpose of this paper is to study the extent, if at all, using a readability application improves skimming comprehension in a low-clutter online environment. It also seeks to identify the perceived benefits or effects of using a readability application for skimming comprehension.

Ten participants skimmed two articles each, one in a low-clutter online document presentation environment, the other using an online readability application, as a timed, information foraging exercise. After reading each article, respondents answered true/false comprehension questions and follow up questions.

There was little difference in the comprehension of respondents after skimming in the two online documentation presentation environments. The readability environment was the preferred environment.

This study suggests that since participants claimed to prefer the text presentation of the readability application interface, interface designers may wish to create library interfaces for information seeking that follow the readability application format. Because some of the participants found themselves reading rather than skimming when using the readability application, readability for tasks other than skimming may be enhanced.

This is a practical study investigating an existing online readability application and its effects on an existing online reading environment as they pertain to information seeking behavior in general and to information foraging in particular.

The purpose of this paper is to consider the logical specification, and automated verification, of high‐level robotic behaviours.

The paper uses temporal logic as a formal language for providing abstractions of foraging robot behaviour, and successively extends this to multiple robots, items of food for the robots to collect, and constraints on the real‐time behaviour of robots. For each of these scenarios, proofs of relevant properties are carried out in a fully automated way. In addition to automated deductive proofs in propositional temporal logic, the possibility of having arbitrary numbers of robots involved is considered, thus allowing representations of robot swarms. This leads towards the use of first‐order temporal logics (FOTLs).

The proofs of many properties are achieved using automatic deductive temporal provers for the propositional and FOTLs.

Many details of the problem, such as location of the robots, avoidance, etc. are abstracted away.

Large robot swarms are beyond the current capability of propositional temporal provers. Whilst representing and proving properties of arbitrarily large swarms using FOTLs is feasible, the representation of infinite numbers of pieces of food is outside of the decidable fragment of FOTL targeted, and practically, the provers struggle with even small numbers of pieces of food.

The work described in this paper is novel in that it applies automatic temporal theorem provers to proving properties of robotic behaviour.

The purpose of this paper is to build a unified model of human information behavior (HIB) for integrating classical constructs and reformulating the structure of HIB theory.

This paper employs equilibrium perspective from partial equilibrium theory to conceptualization and deduction, starting from four basic assumptions.

This paper develops two models to incorporate previous HIB research approaches into an equilibrium-analysis-oriented information supply-demand (ISD) framework: first, the immediate-task/problem-based and everyday life information-seeking (ELIS)-sense-making approaches are incorporated into the short-term ISD model; second, the knowledge-construction-oriented and ability-based HIB research approaches are elaborated by the long-term ISD model. Relations among HIB theories are illustrated via the method of graphical reasoning. Moreover, these two models jointly reveal the connection between information seeking in immediate problematic situations and long-term ability improvement.

The equilibrium framework enables future research to explore HIB from three perspectives: stages: group the classical concepts (e.g. anomalous state of knowledge, uncertainty) into different stages (i.e. start state, process, goal state) and see how they interact with each other within and across different stages; forces: explore information behaviors and information-related abilities as information supply and demand forces, and see how different forces influence each other and jointly motivate people to pursue the equilibriums between outside world and mental model; and short term and long term: study the connections between short-term information seeking and long-term ability improvement at both theoretical and empirical levels.

The purpose of this paper is to identify and analyze socio-cultural and political forces which have shaped anti-violent attitudes and strategies of the Batek and Batek Tanum of Peninsular Malaysia.

Data collection during the authors’ long-term, multi-sited ethnographic fieldwork among the Batek and Batek Tanum in Peninsular Malaysia. Methodology included participant observation, semi-structured interviews and a literature review of texts on the Orang Asli and anthropological theories on violence.

Traumatic experiences of past violence and atrocities greatly influence the Batek's and Batek Tanum's present attitudes toward direct and structural forms of violence. A variety of anti-violent strategies are adopted, including the choice to escape when physically threatened. Rather than demonstrating “weakness,” this course of action represents a smart survival strategy. External violence reinforces values of internal cooperation and mutual-aid that foraging societies, even sedentary groups, typically privilege. In recent years, the Batek's increasing political awareness has opened new forms of resistance against the structural violence embedded within Malaysian society.

The study proposes that societies cannot simply be labelled as violent or non-violent on the basis of socio-biological theories. Research into hunter-gatherer social organization and violence needs to be reframed within larger debates about structural violence. The “anti-violence” of certain foraging groups can be understood as a powerful form of resilience to outside pressures and foraging groups’ best possible strategy for survival.

The purpose of this paper is to develop a proportional-integral-derivative-based fuzzy neural network (PIDFNN) with elitist bacterial foraging optimization (EBFO)-based optimal membership functions (PIDFNN-EBFO) position controller to control the voice coil motor (VCM) for tracking reference trajectory accurately.

Because the control characteristics of the VCM are highly nonlinear and time varying, a PIDFNN, which integrates adaptive PID control with fuzzy rules, is proposed to control the mover position of the VCM. Moreover, an EBFO algorithm is further proposed to find the initial optimal fuzzy membership functions for the PIDFNN controller.

Due to the gradient descent method used in back propagation (BP) to derive the on-line learning algorithm for the PIDFNN, it may reach the local optimal solution due to the inappropriate initial values. Hence, a hybrid learning method, which includes BP and EBFO algorithms, is proposed to improve the learning performance of the PIDFNN controller.

Future work will consider reducing the computational burden of bacterial foraging optimization algorithm for on-line parameters optimization.

The real-time control system is implemented on a 32-bit floating-point digital signal processor (DSP). The experimental results demonstrate the favorable effectiveness of the proposed PIDFNN-EBFO controlled VCM system.

A new PIDFNN-EBFO control scheme is proposed and implemented via DSP for real-time VCM position control. The experimental results show the superior control performance of the proposed PIDFNN-EBFO compared with the other control systems.