Fad-like Technology Adoption as a Social Action

When technology adoption takes on fad-like characteristics it becomes critical to understand consumer behaviors due to the large swings in demand and expectations for the technology. Companies can see revenues skyrocket, only to fall just as fast without understanding the dynamics of the consumer adoption decision process. A model for fad-like technology adoption is described using the technology adoption lifecycle from Rogers adding the theory of information cascades and adopter thresholds. Adopter behavior in each stage of the lifecycle is described as individualistic or holistic utilizing the theories of Watkins and Durkheim. Adoption of the Apple iPhoneTM is shown to illustrate the application of the model and the individual and holistic social actions of fad-like technology adoption

Topological models of swarming

We study the collective behaviour of animal aggregations, swarming, using theoretical models of collective motion. Focusing on bird flocking, we aim to reproduce two main aspects of real world aggregations: cohesion and coalignment. Following the observation that interactions between birds in the flock does not have a characteristic length-scale, we concentrate on topological, metric-free models of collective motion. We propose and analyse three novel models of swarming: two based on topological interactions between particles, which define interacting neighbours based on Voronoi tessellation of the group of particles, and one which uses the visual field of the agent. We explore the problem of cohesion, bounding of topological flocks in free space, by introducing the mechanism of neighbour anticipation. This relies on going towards the inferred future position of an individuals neighbours and results in providing the bounding forces for the group. We also address the issue of unrealistic density distributions in existing metric-free models by introducing a homogeneous, tunable motional bias throughout the swarm. The proposed model produces swarms with density distributions corresponding to empirical data from flocks of Starlings. Furthermore, we show that for a group with a visual information input and individuals moving so as to seek marginal opacity that alignment and group cohesion can be induced without the need for explicit aligning interaction rules between group members. For each of the proposed models a comprehensive analysis of characteristics and behaviour under different parameter sets is performed

Refining self-propelled particle models for collective behaviour

Swarming, schooling, flocking and herding are all names given to the wide variety of collective behaviours exhibited by groups of animals, bacteria and even individual cells. More generally, the term swarming describes the behaviour of an aggregate of agents (not necessarily biological) of similar size and shape which exhibit some emergent property such as directed migration or group cohesion. In this paper we review various individual-based models of collective behaviour and discuss their merits and drawbacks. We further analyse some one-dimensional models in the context of locust swarming. In specific models, in both one and two dimensions, we demonstrate how varying the parameters relating to how much attention individuals pay to their neighbours can dramatically change the behaviour of the group. We also introduce leader individuals to these models with the ability to guide the swarm to a greater or lesser degree as we vary the parameters of the model. We consider evolutionary scenarios for models with leaders in which individuals are allowed to evolve the degree of influence neighbouring individuals have on their subsequent motion

A comparative performance analysis of intelligence-based algorithms for optimizing competitive facility location problems

Most companies operate to maximize profits and increase their market shares in competitive environments. Since the proper location of the facilities conditions their market shares and profits, the competitive facility location problem (CFLP) has been extensively applied in the literature. This problem generally falls within the class of NP-hard problems, which are difficult to solve. Therefore, choosing a proper solution method to optimize the problem is a key factor. Even though CFLPs have been consistently solved and investigated, an important question that keeps being neglected is how to choose an appropriate solution technique. Since there are no specific criteria for choosing a solution method, the reasons behind the selection approach are mostly unclear. These models are generally solved using several optimization techniques. As harder-to-solve problems are usually solved using meta-heuristics, we apply different meta-heuristic techniques to optimize a new version of the CFLP that incorporates reliability and congestion. We divide the algorithms into four categories based on the nature of the meta-heuristics: evolution-based, swarm intelligence-based, physics-based, and human-based. GAMS software is also applied to solve smaller-size CFLPs. The genetic algorithm and differential evolution of the first category, particle swarm optimization and artificial bee colony optimization of the second, Tabu search and harmony search of the third, and simulated annealing and vibration damping optimization of the fourth are applied to solve our CFLP model. Statistical analyses are implemented to evaluate and compare their relative performances. The results show the algorithms of the first and third categories perform better than the others

IKH-EFT: An improved method of workflow scheduling using the krill herd algorithm in the fog-cloud environment

peer reviewedGiven the increase diversity of smart devices and objectives of the application management such as energy consumption, makespan users expect their requests to be responded to in an appropriate computation environment as properly as possible. In this paper, a method of workflow scheduling based on the fog-cloud architecture has been designed given the high processing capability of the cloud and the close communication between the user and the fog computing node, which reduces delay in response. We also seek to minimize consumption and reduce energy use and monetary cost in order to maximize customer satisfaction with proper scheduling. Given the large number of variables that are used in workflow scheduling and the optimization of contradictory objectives, the problem is NP-hard, and the multi-objective metaheuristic krill herd algorithm is used to solve it. The initial population is generated in a smart fashion to allow fast convergence of the algorithm. For allocation of tasks to the available fog-cloud resources, the EFT (earliest finish time) technique is used, and resource voltage and frequency are assumed to be dynamic to reduce energy use. A comprehensive simulation has been made for assessment of the proposed method in different scenarios with various values of CCR. The simulation results indicate that makespan exhibits improvements by 9.9, 8.7% and 6.7% on average compared with respect to the methods of IHEFT, HEFT and IWO-CA, respectively. Moreover, the monetary cost of the method and energy use have simultaneously decreased in the fog-cloud environment

Bio-inspired Computing and Smart Mobility

Por último, se aborda la predicción de plazas libres de aparcamiento utilizando técnicas de aprendizaje automático, tales como series temporales, agrupamiento, etc., incluyendo un prototipo de aplicación web. La tercera parte de esta tesis doctoral se enfoca en el diseño y evaluación de un nuevo algoritmo inspirado en la epigénesis, el Algoritmo Epigenético. Luego de la descripción del modelo en el que se basa y de sus partes, se utiliza este nuevo algoritmo para la resolución del problema de la mochila multidimensional y se comparan sus resultados con los de otros algoritmos del estado de arte. Por último se emplea también el Algoritmo Epigenético para la optimización de la arquitectura Yellow Swarm, un problema de movilidad inteligente resuelto por un nuevo algoritmo bioinspirado. A lo largo de esta tesis doctoral se han descrito los problemas de movilidad inteligente y propuesto nuevas herramientas para su optimización. A partir de los experimentos realizados se concluye que estas herramientas, basadas en algoritmos bioinspirados, son eficientes para abordar estos problemas, obteniendo resultados competitivos comparados con los del estado del arte, los cuales han sido validados estadísticamente. Esto representa un aporte científico pero también una serie de mejoras para la sociedad toda, tanto en su salud como en el aprovechamiento de su tiempo libre. Fecha de lectura de Tesis: 01 octubre 2018.Esta tesis doctoral propone soluciones a problemas de movilidad inteligente, concretamente la reducción de los tiempos de viajes en las vías urbanas, las emisiones de gases de efecto invernadero y el consumo de combustible, mediante el diseño y uso de nuevos algoritmos bioinspirados. Estos algoritmos se utilizan para la optimización de escenarios realistas, cuyo trazado urbano se obtiene desde OpenStreetMap, y que son luego evaluados en el microsimulador SUMO. Primero se describen las bases científicas y tecnológicas, incluyendo la definición y estado del arte de los problemas a abordar, las metaheurísticas que se utilizarán durante el desarrollo de los experimentos, así como las correspondientes validaciones estadísticas. A continuación se describen los simuladores de movilidad como principal herramienta para construir y evaluar los escenarios urbanos. Por último se presenta una propuesta para generar tráfico vehicular realista a partir de datos de sensores que cuentan el número de vehículos en la ciudad, utilizando herramientas incluidas en SUMO combinadas con algoritmos evolutivos. En la segunda parte se modelan y resuelven problemas de movilidad inteligente utilizando las nuevas arquitecturas Red Swarm y Green Swarm para sugerir nuevas rutas a los vehículos utilizando nodos con conectividad Wi-Fi. Red Swarm se centra en la reducción de tiempos de viajes evitando la congestión de las calles, mientras que Green Swarm está enfocado en la reducción de emisiones y consumo de combustible. Luego se propone la arquitectura Yellow Swarm que utiliza una serie de paneles LED para indicar desvíos que los vehículos pueden seguir en lugar de nodos Wi-Fi haciendo esta propuesta más accesible. Además se propone un método para genera rutas alternativas para los navegadores GPS de modo que se aprovechen mejor las calles secundarias de las ciudades, reduciendo los atascos

Literature review on collective intelligence: a crowd science perspective

Purpose - Collective intelligence has drawn many scientists’ attention in many centuries. This paper shows the collective intelligence study process in a perspective of crowd science. Design/methodology/approach - After summarizing the time-order process of related researches, different points of views on collective intelligence’s measurement and their modeling methods were outlined. Findings - The authors show the recent research focusing on collective intelligence optimization. The studies on application of collective intelligence and its future potential are also discussed. Originality/value - This paper will help researchers in crowd science have a better picture of this highly related frontier interdiscipline

A Review of Research Methodologies Employed in Serendipity Studies in the Context of Information Research

Background: The concept of serendipity has become increasingly interesting for those undertaking serendipity research in recent years. However, serendipitous encounters are subjective and rare in a real-world context, making this an extremely challenging subject to study. Methods: Various methods have been proposed to enable researchers to understand and measure serendipity, but there is no broad consensus on which methods to use in different experimental settings. A comprehensive literature review was first conducted, which summarizes the research methods being employed to study serendipity. It was followed by a series of interviews with experts that specified the relative strengths and weaknesses of each method identified in the literature review, in addition to the challenges usually confronted in serendipity research. Results: The findings suggest using mixed research methods to produce a more complete picture of serendipity and contribute to the verification of any research findings. Several challenges and implications relating to empirical studies in the investigation of serendipity have been derived from this study. Conclusions: This paper investigated research methods employed to study serendipity by synthesizing finding from a literature review and the interviews with experts. It provides a methodological contribution to serendipity studies by systematically summarizing the methods employed in the studies of serendipity and identifying the strengths and weakness of each method. It also suggests the novel approach of using mixed research methods to study serendipity. This study has potential limitations related to a small number of experts involved in the expert interview. However, it should be noted that the nature of the topic is a relatively focused area, and it was observed after interviewing the experts that new data seems to not contribute to the findings owing to its repetition of comment