k-평균 개미 군집 최적화를 사용한 폐기물 수거를 위한 군집 차량 경로 문제

학위논문(석사) -- 서울대학교대학원 : 공과대학 산업공학과, 2022. 8. 문일경.Waste collection is one of the essential tasks in a smart city. The internet of things (IoT) is a promising technology that offers potential solutions for transforming traditional systems. An IoT-based smart bin is a modern technology that offers real-time fill level information to a cleaning authority. However, high uncertainty associated with the smart bin's fill levels and improper operation hinder efficient waste collection. In order to tackle the uncertainty in a smart bin and improve the waste collection operation, the IoT sensor's usage must be combined with optimization procedures. In this thesis, a neighboring bin is utilized to consider the sensor range of IoT. Furthermore, we introduced two operational management approaches to define dynamic optimal routes. We combined ant colony optimization (ACO) with a k-means clustering algorithm to solve the clustered vehicle routing problem for waste collection (CluVRP-WC) on a large scale. The first experiment was conducted in a case study, and comparison results of the operational management approaches were presented. Our hybrid metaheuristic is implemented in the second experiment. CluVRP-WC is a generalization of the capacitated vehicle routing problem (CVRP) in which smart bins are segmented into clusters by a k-means clustering algorithm. Our operational management approaches and proposed heuristic show the potential of building a smart waste collection system considering practical constraints.폐기물 수거는 스마트 시티의 필수 과제 중 하나이다. 사물 인터넷(IoT)은 기존 시스템을 변환하기 위한 잠재적인 해결책을 제공하는 유망한 기술이다. IoT 기반 스마트 폐기물통은 청소 기관에 실시간으로 폐기물 정보를 제공하는 최신 기술이다. 그러나 폐기물양의 불확실성과 부적절한 수거 방법은 효율적인 폐기물 수거를 방해한다. 스마트 폐기물통의 불확실성을 해결하고 폐기물 수거 방법을 개선하려면 IoT 센서의 사용이 적절한 운영 방법과 결합되어야 한다. 본 연구에서는 동적 최적 경로를 정의하기 위해 두 가지 운영 관리 접근 방식과 인접 폐기물통 개념을 도입했다. 첫 번째 접근 방식은 실제 사례 연구에 적용하고 그 한계를 제시한다. 두 번째 접근 방식에서는 스마트 폐기물통의 불확실성을 해결하기 위해 인접 폐기물통을 사용한다. 본 실험을 위해 개미 군집 최적화와 k-평균 알고리즘을 결합하여 폐기물 수거를 위한 군집 차량 경로 문제를 해결한다.Chapter 1 Introduction 1 Chapter 2 Literature Review 5 Chapter 3 Mathematical model 9 3.1 Motivation 9 3.2 Problem description 10 3.3 Operational management approaches 12 3.3.1 Estimation based collection approach 14 3.3.2 Neighborhood based collection approach 19 Chapter 4 Hybrid metaheuristic 22 4.1 Cluster computation and optimal path design 22 4.2 K-means-Ant colony optimization 24 4.2.1 Representation 24 4.2.2 Pheromone initialization 24 4.2.3 Path construction 24 4.2.4 Pheromone evaporation 25 4.2.5 Pheromone updating 26 Chapter 5 Computational experiments 27 Chapter 6 Conclusions 35 6.1 Managerial insights 35 6.2 Future study 36 Bibliography 37 국문초록 43석

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Opportunistic data collection and routing in segmented wireless sensor networks

La surveillance régulière des opérations dans les aires de manoeuvre (voies de circulation et pistes) et aires de stationnement d'un aéroport est une tâche cruciale pour son fonctionnement. Les stratégies utilisées à cette fin visent à permettre la mesure des variables environnementales, l'identification des débris (FOD) et l'enregistrement des statistiques d'utilisation de diverses sections de la surface. Selon un groupe de gestionnaires et contrôleurs d'aéroport interrogés, cette surveillance est un privilège des grands aéroports en raison des coûts élevés d'acquisition, d'installation et de maintenance des technologies existantes. Les moyens et petits aéroports se limitent généralement à la surveillance de quelques variables environnementales et des FOD effectuée visuellement par l'homme. Cette dernière activité impose l'arrêt du fonctionnement des pistes pendant l'inspection. Dans cette thèse, nous proposons une solution alternative basée sur les réseaux de capteurs sans fil (WSN) qui, contrairement aux autres méthodes, combinent les propriétés de faible coût d'installation et maintenance, de déploiement rapide, d'évolutivité tout en permettant d'effectuer des mesures sans interférer avec le fonctionnement de l'aéroport. En raison de la superficie d'un aéroport et de la difficulté de placer des capteurs sur des zones de transit, le WSN se composerait d'une collection de sous-réseaux isolés les uns des autres et du puits. Pour gérer cette segmentation, notre proposition s'appuie sur l'utilisation opportuniste des véhicules circulants dans l'aéroport considérés alors comme un type spécial de nœud appelé Mobile Ubiquitous LAN Extension (MULE) chargé de collecter les données des sous-réseaux le long de son trajet et de les transférer vers le puits. L'une des exigences pour le déploiement d'un nouveau système dans un aéroport est qu'il cause peu ou pas d'interruption des opérations régulières. C'est pourquoi l'utilisation d'une approche opportuniste basé sur des MULE est privilégiée dans cette thèse. Par opportuniste, nous nous référons au fait que le rôle de MULE est joué par certains des véhicules déjà existants dans un aéroport et effectuant leurs déplacements normaux. Et certains nœuds des sous- réseaux exploiteront tout moment de contact avec eux pour leur transmettre les données à transférer ensuite au puits. Une caractéristique des MULEs dans notre application est qu'elles ont des trajectoires structurées (suivant les voies de circulation dans l'aéroport), en ayant éventuellement un contact avec l'ensemble des nœuds situés le long de leur trajet (appelés sous-puits). Ceci implique la nécessité de définir une stratégie de routage dans chaque sous-réseau, capable d'acheminer les données collectées des nœuds vers les sous-puits et de répartir les paquets de données entre eux afin que le temps en contact avec la MULE soit utilisé le plus efficacement possible. Dans cette thèse, nous proposons un protocole de routage remplissant ces fonctions. Le protocole proposé est nommé ACME (ACO-based routing protocol for MULE-assisted WSNs). Il est basé sur la technique d'Optimisation par Colonies de Fourmis. ACME permet d'assigner des nœuds à des sous-puits puis de définir les chemins entre eux, en tenant compte de la minimisation de la somme des longueurs de ces chemins, de l'équilibrage de la quantité de paquets stockés par les sous-puits et du nombre total de retransmissions. Le problème est défini comme une tâche d'optimisation multi-objectif qui est résolue de manière distribuée sur la base des actions des nœuds dans un schéma collaboratif. Nous avons développé un environnement de simulation et effectué des campagnes de calculs dans OMNeT++ qui montrent les avantages de notre protocole en termes de performances et sa capacité à s'adapter à une grande variété de topologies de réseaux.The regular monitoring of operations in both movement areas (taxiways and runways) and non-movement areas (aprons and aircraft parking spots) of an airport, is a critical task for its functioning. The set of strategies used for this purpose include the measurement of environmental variables, the identification of foreign object debris (FOD), and the record of statistics of usage for diverse sections of the surface. According to a group of airport managers and controllers interviewed by us, the wide monitoring of most of these variables is a privilege of big airports due to the high acquisition, installation and maintenance costs of most common technologies. Due to this limitation, smaller airports often limit themselves to the monitoring of environmental variables at some few spatial points and the tracking of FOD performed by humans. This last activity requires stopping the functioning of the runways while the inspection is conducted. In this thesis, we propose an alternative solution based on Wireless Sensor Network (WSN) which, unlike the other methods/technologies, combines the desirable properties of low installation and maintenance cost, scalability and ability to perform measurements without interfering with the regular functioning of the airport. Due to the large extension of an airport and the difficulty of placing sensors over transit areas, the WSN might result segmented into a collection of subnetworks isolated from each other and from the sink. To overcome this problem, our proposal relies on a special type of node called Mobile Ubiquitous LAN Extension (MULE), able to move over the airport surface, gather data from the subnetworks along its way and eventually transfer it to the sink. One of the main demands for the deployment of any new system in an airport is that it must have little or no interference with the regular operations. This is why the use of an opportunistic approach for the transfer of data from the subnetworks to the MULE is favored in this thesis. By opportunistic we mean that the role of MULE will be played by some of the typical vehicles already existing in an airport doing their normal displacements, and the subnetworks will exploit any moment of contact with them to forward data to the sink. A particular characteristic of the MULEs in our application is that they move along predefined structured trajectories (given by the layout of the airport), having eventual contact with the set of nodes located by the side of the road (so-called subsinks). This implies the need for a data routing strategy to be used within each subnetwork, able to lead the collected data from the sensor nodes to the subsinks and distribute the data packets among them so that the time in contact with the MULE is used as efficiently as possible. In this thesis, we propose a routing protocol which undertakes this task. Our proposed protocol is named ACME, standing for ACO-based routing protocol for MULE-assisted WSNs. It is founded on the well known Ant Colony Optimization (ACO) technique. The main advantage of ACO is its natural fit to the decentralized nature of WSN, which allows it to perform distributed optimizations (based on local interactions) leading to remarkable overall network performance. ACME is able to assign sensor nodes to subsinks and generate the corresponding multi-hop paths while accounting for the minimization of the total path length, the total subsink imbalance and the total number of retransmissions. The problem is defined as a multi-objective optimization task which is resolved in a distributed manner based on actions of the sensor nodes acting in a collaborative scheme. We conduct a set of computational experiments in the discrete event simulator OMNeT++ which shows the advantages of our protocol in terms of performance and its ability to adapt to a variety of network topologie

Hybrid Swarm Intelligence Energy Efficient Clustered Routing Algorithm for Wireless Sensor Networks

Currently, wireless sensor networks (WSNs) are used in many applications, namely, environment monitoring, disaster management, industrial automation, and medical electronics. Sensor nodes carry many limitations like low battery life, small memory space, and limited computing capability. To create a wireless sensor network more energy efficient, swarm intelligence technique has been applied to resolve many optimization issues in WSNs. In many existing clustering techniques an artificial bee colony (ABC) algorithm is utilized to collect information from the field periodically. Nevertheless, in the event based applications, an ant colony optimization (ACO) is a good solution to enhance the network lifespan. In this paper, we combine both algorithms (i.e., ABC and ACO) and propose a new hybrid ABCACO algorithm to solve a Nondeterministic Polynomial (NP) hard and finite problem of WSNs. ABCACO algorithm is divided into three main parts: (i) selection of optimal number of subregions and further subregion parts, (ii) cluster head selection using ABC algorithm, and (iii) efficient data transmission using ACO algorithm. We use a hierarchical clustering technique for data transmission; the data is transmitted from member nodes to the subcluster heads and then from subcluster heads to the elected cluster heads based on some threshold value. Cluster heads use an ACO algorithm to discover the best route for data transmission to the base station (BS). The proposed approach is very useful in designing the framework for forest fire detection and monitoring. The simulation results show that the ABCACO algorithm enhances the stability period by 60% and also improves the goodput by 31% against LEACH and WSNCABC, respectively

Wind farm array cable layout optimisation for complex offshore sites - a decomposition based heuristic approach

As the number of turbines in offshore wind farms increases, so does the complexity of cable routing optimisation problems. Optimising the collector network is a crucial task for developers contributing between 15% and 30% of initial investment costs. For some algorithms, increasing the number of turbines leads to unfavourable scaling of the computational time and memory required to reach optimal solutions. Heuristics offer an alternative but are likely to incur increases in total costs relative to the optimal solution since heuristic searching cannot guarantee optimality. This study proposes a novel optimisation algorithm based on the ant-colony heuristic by introducing decomposition techniques informed by the problem formulation to improve the computational performance. The new algorithm can reach near-optimal solutions and requires little computational resource.Three algorithms are compared on a set of six case studies, including mixed-integer linear programming (MILP), classical ant-colony optimisation (ACO) algorithm, and the pro-posed ACO with decomposition, ACOsp. Optimal solutions were found using the MILP algorithm. ACO algorithm solutions cost 0.4–7.6% more than optimal solutions, whereas ACOsp solutions cost only 0.0–1.4% more than optimal solutions. The proposed ACOsp algorithm has shown to be a robust approach for large-scale cable layout optimisation problems (>100 turbines) without requiring high-performance computing facilities

Evolution of Control Programs for a Swarm of Autonomous Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) are rapidly becoming a critical military asset. In the future, advances in miniaturization are going to drive the development of insect size UAVs. New approaches to controlling these swarms are required. The goal of this research is to develop a controller to direct a swarm of UAVs in accomplishing a given mission. While previous efforts have largely been limited to a two-dimensional model, a three-dimensional model has been developed for this project. Models of UAV capabilities including sensors, actuators and communications are presented. Genetic programming uses the principles of Darwinian evolution to generate computer programs to solve problems. A genetic programming approach is used to evolve control programs for UAV swarms. Evolved controllers are compared with a hand-crafted solution using quantitative and qualitative methods. Visualization and statistical methods are used to analyze solutions. Results indicate that genetic programming is capable of producing effective solutions to multi-objective control problems

Cooperative Material Handling by Human and Robotic Agents:Module Development and System Synthesis

In this paper we present the results of a collaborative effort to design and implement a system for cooperative material handling by a small team of human and robotic agents in an unstructured indoor environment. Our approach makes fundamental use of human agents\u27 expertise for aspects of task planning, task monitoring, and error recovery. Our system is neither fully autonomous nor fully teleoperated. It is designed to make effective use of human abilities within the present state of the art of autonomous systems. It is designed to allow for and promote cooperative interaction between distributed agents with various capabilities and resources. Our robotic agents refer to systems which are each equipped with at least one sensing modality and which possess some capability for self-orientation and/or mobility. Our robotic agents are not required to be homogeneous with respect to either capabilities or function. Our research stresses both paradigms and testbed experimentation. Theory issues include the requisite coordination principles and techniques which are fundamental to the basic functioning of such a cooperative multi-agent system. We have constructed a testbed facility for experimenting with distributed multi-agent architectures. The required modular components of this testbed are currently operational and have been tested individually. Our current research focuses on the integration of agents in a scenario for cooperative material handling

Drone Base Station Trajectory Management for Optimal Scheduling in LTE-Based Sparse Delay-Sensitive M2M Networks

Providing connectivity in areas out of reach of the cellular infrastructure is a very active area of research. This connectivity is particularly needed in case of the deployment of machine type communication devices (MTCDs) for critical purposes such as homeland security. In such applications, MTCDs are deployed in areas that are hard to reach using regular communications infrastructure while the collected data is timely critical. Drone-supported communications constitute a new trend in complementing the reach of the terrestrial communication infrastructure. In this study, drones are used as base stations to provide real-time communication services to gather critical data out of a group of MTCDs that are sparsely deployed in a marine environment. Studying different communication technologies as LTE, WiFi, LPWAN and Free-Space Optical communication (FSOC) incorporated with the drone communications was important in the first phase of this research to identify the best candidate for addressing this need. We have determined the cellular technology, and particularly LTE, to be the most suitable candidate to support such applications. In this case, an LTE base station would be mounted on the drone which will help communicate with the different MTCDs to transmit their data to the network backhaul. We then formulate the problem model mathematically and devise the trajectory planning and scheduling algorithm that decides the drone path and the resulting scheduling. Based on this formulation, we decided to compare between an Ant Colony Optimization (ACO) based technique that optimizes the drone movement among the sparsely-deployed MTCDs and a Genetic Algorithm (GA) based solution that achieves the same purpose. This optimization is based on minimizing the energy cost of the drone movement while ensuring the data transmission deadline missing is minimized. We present the results of several simulation experiments that validate the different performance aspects of the technique

optimización da planificación de adquisición de datos LIDAR cara ó modelado 3D de interiores

The main objective of this doctoral thesis is the design, validation and implementation of methodologies that allow the geometric and topological modelling of navigable spaces, whether inside buildings or urban environments, to be integrated into three-dimensional geographic information systems (GIS-3D). The input data of this work will consist mainly of point clouds (which can be classified) acquired by LiDAR systems both indoors and outdoors. In addition, the use of BIM infrastructure models and cadastral maps is proposed depending on their availability. Point clouds provide a large amount of environmental information with high accuracy compared to data offered by other acquisition technologies. However, the lack of data structure and volume requires a great deal of processing effort. For this reason, the first step is to structure the data by dividing the input cloud into simpler entities that facilitate subsequent processes. For this first division, the physical elements present in the cloud will be considered, since they can be walls in the case of interior environments or kerbs in the case of exteriors. In order to generate navigation routes adapted to different mobile agents, the next objective will try to establish a semantic subdivision of space according to the functionalities of space. In the case of internal environments, it is possible to use BIM models to evaluate the results and the use of cadastral maps that support the division of the urban environment. Once the navigable space is divided, the design of topologically coherent navigation networks will be parameterized both geometrically and topologically. For this purpose, several spatial discretization techniques, such as 3D tessellations, will be studied to facilitate the establishment of topological relationships, adjacency, connectivity and inclusion between subspaces. Based on the geometric characterization and the topological relations established in the previous phase, the creation of three-dimensional navigation networks with multimodal support will be addressed and different levels of detail will be considered according to the mobility specifications of each agent and its purpose. Finally, the possibility of integrating the networks generated in a GIS-3D visualization system will be considered. For the correct visualization, the level of detail can be adjusted according to geometry and semantics. Aspects such as the type of user or transport, mobility, rights of access to spaces, etc. They must be considered at all times.El objetivo principal de esta tesis doctoral es el diseño, la validación y la implementación de metodologías que permitan el modelado geométrico y topológico de espacios navegables, ya sea de interiores de edificios o entornos urbanos, para integrarse en sistemas de información geográfica tridimensional (SIG). -3D). Los datos de partida de este trabajo consistirán principalmente en nubes de puntos (que pueden estar clasificados) adquiridas por sistemas LiDAR tanto en interiores como en exteriores. Además, se propone el uso de modelos BIM de infraestructuras y mapas catastrales en función de su disponibilidad. Las nubes de puntos proporcionan una gran cantidad de información del entorno con gran precisión con respecto a los datos ofrecidos por otras tecnologías de adquisición. Sin embargo, la falta de estructura de datos y su volumen requiere un gran esfuerzo de procesamiento. Por este motivo, el primer paso que se debe realizar consiste en estructurar los datos dividiendo la nube de entrada en entidades más simples que facilitan los procesos posteriores. Para esta primera división se considerarán los elementos físicos presentes en la nube, ya que pueden ser paredes en el caso de entornos interiores o bordillos en el caso de los exteriores. Con el propósito de generar rutas de navegación adaptadas a diferentes agentes móviles, el próximo objetivo intentará establecer una subdivisión semántica del espacio de acuerdo con las funcionalidades del espacio. En el caso de entornos internos, es posible utilizar modelos BIM para evaluar los resultados y el uso de mapas catastrales que sirven de apoyo en la división del entorno urbano. Una vez que se divide el espacio navegable, se parametrizará tanto geométrica como topológicamente al diseño de redes de navegación topológicamente coherentes. Para este propósito, se estudiarán varias técnicas de discretización espacial, como las teselaciones 3D, para facilitar el establecimiento de relaciones topológicas, la adyacencia, la conectividad y la inclusión entre subespacios. A partir de la caracterización geométrica y las relaciones topológicas establecidas en la fase anterior, se abordará la creación de redes de navegación tridimensionales con soporte multimodal y se considerarán diversos niveles de detalle según las especificaciones de movilidad de cada agente y su propósito. Finalmente, se contemplará la posibilidad de integrar las redes generadas en un sistema de visualización tridimensional 3D SIG 3D. Para la correcta visualización, el nivel de detalle se puede ajustar en función de la geometría y la semántica. Aspectos como el tipo de usuario o transporte, movilidad, derechos de acceso a espacios, etc. Deben ser considerados en todo momento.O obxectivo principal desta tese doutoral é o deseño, validación e implementación de metodoloxías que permitan o modelado xeométrico e topolóxico de espazos navegables, ben sexa de interiores de edificios ou de entornos urbanos, ca fin de seren integrados en Sistemas de Información Xeográfica tridimensionais (SIX-3D). Os datos de partida deste traballo constarán principalmente de nubes de puntos (que poden estar clasificadas) adquiridas por sistemas LiDAR tanto en interiores como en exteriores. Ademáis plantease o uso de modelos BIM de infraestruturas e mapas catastrais dependendo da súa dispoñibilidade. As nubes de puntos proporcionan unha gran cantidade de información do entorno cunha gran precisión respecto os datos que ofrecen outras tecnoloxías de adquisición. Sen embargo, a falta de estrutura dos datos e a seu volume esixe un amplo esforzo de procesado. Por este motivo o primeiro paso a levar a cabo consiste nunha estruturación dos datos mediante a división da nube de entrada en entidades máis sinxelas que faciliten os procesos posteriores. Para esta primeira división consideraranse elementos físicos presentes na nube como poden ser paredes no caso de entornos interiores ou bordillos no caso de exteriores. Coa finalidade de xerar rutas de navegación adaptadas a distintos axentes móbiles, o seguinte obxectivo tratará de establecer unha subdivisión semántica do espazo de acordo as funcionalidades do espazo. No caso de entornos interiores plantease a posibilidade de empregar modelos BIM para avaliar os resultados e o uso de mapas catastrais que sirvan de apoio na división do entorno urbano. Unha vez divido o espazo navigable parametrizarase tanto xeométricamente como topolóxicamene de cara ao deseño de redes de navegación topolóxicamente coherentes. Para este fin estudaranse varias técnicas de discretización de espazos como como son as teselacións 3D co obxectivo de facilitar establecer relacións topolóxicas, de adxacencia, conectividade e inclusión entre subespazos. A partir da caracterización xeométrica e das relación topolóxicas establecidas na fase previa abordarase a creación de redes de navegación tridimensionais con soporte multi-modal e considerando varios niveis de detalle de acordo as especificacións de mobilidade de cada axente e a súa finalidade. Finalmente comtemplarase a posibilidade de integrar as redes xeradas nun sistema SIX 3D visualización tridimensional. Para a correcta visualización o nivel de detalle poderá axustarse en base a xeometría e a semántica. Aspectos como o tipo de usuario ou transporte, mobilidade, dereitos de acceso a espazos, etc. deberán ser considerados en todo momento

Swarm intelligence for clustering dynamic data sets for web usage mining and personalization.

Swarm Intelligence (SI) techniques were inspired by bee swarms, ant colonies, and most recently, bird flocks. Flock-based Swarm Intelligence (FSI) has several unique features, namely decentralized control, collaborative learning, high exploration ability, and inspiration from dynamic social behavior. Thus FSI offers a natural choice for modeling dynamic social data and solving problems in such domains. One particular case of dynamic social data is online/web usage data which is rich in information about user activities, interests and choices. This natural analogy between SI and social behavior is the main motivation for the topic of investigation in this dissertation, with a focus on Flock based systems which have not been well investigated for this purpose. More specifically, we investigate the use of flock-based SI to solve two related and challenging problems by developing algorithms that form critical building blocks of intelligent personalized websites, namely, (i) providing a better understanding of the online users and their activities or interests, for example using clustering techniques that can discover the groups that are hidden within the data; and (ii) reducing information overload by providing guidance to the users on websites and services, typically by using web personalization techniques, such as recommender systems. Recommender systems aim to recommend items that will be potentially liked by a user. To support a better understanding of the online user activities, we developed clustering algorithms that address two challenges of mining online usage data: the need for scalability to large data and the need to adapt cluster sing to dynamic data sets. To address the scalability challenge, we developed new clustering algorithms using a hybridization of traditional Flock-based clustering with faster K-Means based partitional clustering algorithms. We tested our algorithms on synthetic data, real VCI Machine Learning repository benchmark data, and a data set consisting of real Web user sessions. Having linear complexity with respect to the number of data records, the resulting algorithms are considerably faster than traditional Flock-based clustering (which has quadratic complexity). Moreover, our experiments demonstrate that scalability was gained without sacrificing quality. To address the challenge of adapting to dynamic data, we developed a dynamic clustering algorithm that can handle the following dynamic properties of online usage data: (1) New data records can be added at any time (example: a new user is added on the site); (2) Existing data records can be removed at any time. For example, an existing user of the site, who no longer subscribes to a service, or who is terminated because of violating policies; (3) New parts of existing records can arrive at any time or old parts of the existing data record can change. The user\u27s record can change as a result of additional activity such as purchasing new products, returning a product, rating new products, or modifying the existing rating of a product. We tested our dynamic clustering algorithm on synthetic dynamic data, and on a data set consisting of real online user ratings for movies. Our algorithm was shown to handle the dynamic nature of data without sacrificing quality compared to a traditional Flock-based clustering algorithm that is re-run from scratch with each change in the data. To support reducing online information overload, we developed a Flock-based recommender system to predict the interests of users, in particular focusing on collaborative filtering or social recommender systems. Our Flock-based recommender algorithm (FlockRecom) iteratively adjusts the position and speed of dynamic flocks of agents, such that each agent represents a user, on a visualization panel. Then it generates the top-n recommendations for a user based on the ratings of the users that are represented by its neighboring agents. Our recommendation system was tested on a real data set consisting of online user ratings for a set of jokes, and compared to traditional user-based Collaborative Filtering (CF). Our results demonstrated that our recommender system starts performing at the same level of quality as traditional CF, and then, with more iterations for exploration, surpasses CF\u27s recommendation quality, in terms of precision and recall. Another unique advantage of our recommendation system compared to traditional CF is its ability to generate more variety or diversity in the set of recommended items. Our contributions advance the state of the art in Flock-based 81 for clustering and making predictions in dynamic Web usage data, and therefore have an impact on improving the quality of online services