251 research outputs found

    A Survey on Recent Trends of PIO and Its Variants Applied for Motion Planning of Dynamic Agents

    Get PDF
    Pigeon Inspired Optimization (PIO) algorithm is gaining popularity since its development due to faster convergence ability with great efficiencies when compared with other bio-inspired algorithms. The navigation capability of homing pigeons has been precisely used in Pigeon Inspired Optimization algorithm and continuous advancement in existing algorithms is making it more suitable for complex optimization problems in various fields. The main theme of this survey paper is to introduce the basics of PIO along with technical advancements of PIO for the motion planning techniques of dynamic agents. The survey also comprises of findings and limitations of proposed work since its development to help the research scholar around the world for particular algorithm selection especially for motion planning. This survey might be extended up to application based in order to understand the importance of algorithm in future studies

    Motion Planning

    Get PDF
    Motion planning is a fundamental function in robotics and numerous intelligent machines. The global concept of planning involves multiple capabilities, such as path generation, dynamic planning, optimization, tracking, and control. This book has organized different planning topics into three general perspectives that are classified by the type of robotic applications. The chapters are a selection of recent developments in a) planning and tracking methods for unmanned aerial vehicles, b) heuristically based methods for navigation planning and routes optimization, and c) control techniques developed for path planning of autonomous wheeled platforms

    Introductory Review of Swarm Intelligence Techniques

    Full text link
    With the rapid upliftment of technology, there has emerged a dire need to fine-tune or optimize certain processes, software, models or structures, with utmost accuracy and efficiency. Optimization algorithms are preferred over other methods of optimization through experimentation or simulation, for their generic problem-solving abilities and promising efficacy with the least human intervention. In recent times, the inducement of natural phenomena into algorithm design has immensely triggered the efficiency of optimization process for even complex multi-dimensional, non-continuous, non-differentiable and noisy problem search spaces. This chapter deals with the Swarm intelligence (SI) based algorithms or Swarm Optimization Algorithms, which are a subset of the greater Nature Inspired Optimization Algorithms (NIOAs). Swarm intelligence involves the collective study of individuals and their mutual interactions leading to intelligent behavior of the swarm. The chapter presents various population-based SI algorithms, their fundamental structures along with their mathematical models.Comment: Submitted to Springe

    Bio-inspired optimization in integrated river basin management

    Get PDF
    Water resources worldwide are facing severe challenges in terms of quality and quantity. It is essential to conserve, manage, and optimize water resources and their quality through integrated water resources management (IWRM). IWRM is an interdisciplinary field that works on multiple levels to maximize the socio-economic and ecological benefits of water resources. Since this is directly influenced by the river’s ecological health, the point of interest should start at the basin-level. The main objective of this study is to evaluate the application of bio-inspired optimization techniques in integrated river basin management (IRBM). This study demonstrates the application of versatile, flexible and yet simple metaheuristic bio-inspired algorithms in IRBM. In a novel approach, bio-inspired optimization algorithms Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are used to spatially distribute mitigation measures within a basin to reduce long-term annual mean total nitrogen (TN) concentration at the outlet of the basin. The Upper Fuhse river basin developed in the hydrological model, Hydrological Predictions for the Environment (HYPE), is used as a case study. ACO and PSO are coupled with the HYPE model to distribute a set of measures and compute the resulting TN reduction. The algorithms spatially distribute nine crop and subbasin-level mitigation measures under four categories. Both algorithms can successfully yield a discrete combination of measures to reduce long-term annual mean TN concentration. They achieved an 18.65% reduction, and their performance was on par with each other. This study has established the applicability of these bio-inspired optimization algorithms in successfully distributing the TN mitigation measures within the river basin. Stakeholder involvement is a crucial aspect of IRBM. It ensures that researchers and policymakers are aware of the ground reality through large amounts of information collected from the stakeholder. Including stakeholders in policy planning and decision-making legitimizes the decisions and eases their implementation. Therefore, a socio-hydrological framework is developed and tested in the Larqui river basin, Chile, based on a field survey to explore the conditions under which the farmers would implement or extend the width of vegetative filter strips (VFS) to prevent soil erosion. The framework consists of a behavioral, social model (extended Theory of Planned Behavior, TPB) and an agent-based model (developed in NetLogo) coupled with the results from the vegetative filter model (Vegetative Filter Strip Modeling System, VFSMOD-W). The results showed that the ABM corroborates with the survey results and the farmers are willing to extend the width of VFS as long as their utility stays positive. This framework can be used to develop tailor-made policies for river basins based on the conditions of the river basins and the stakeholders' requirements to motivate them to adopt sustainable practices. It is vital to assess whether the proposed management plans achieve the expected results for the river basin and if the stakeholders will accept and implement them. The assessment via simulation tools ensures effective implementation and realization of the target stipulated by the decision-makers. In this regard, this dissertation introduces the application of bio-inspired optimization techniques in the field of IRBM. The successful discrete combinatorial optimization in terms of the spatial distribution of mitigation measures by ACO and PSO and the novel socio-hydrological framework using ABM prove the forte and diverse applicability of bio-inspired optimization algorithms

    Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

    Get PDF
    The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

    Security analysis of NIST-LWC contest finalists

    Get PDF
    Dissertação de mestrado integrado em Informatics EngineeringTraditional cryptographic standards are designed with a desktop and server environment in mind, so, with the relatively recent proliferation of small, resource constrained devices in the Internet of Things, sensor networks, embedded systems, and more, there has been a call for lightweight cryptographic standards with security, performance and resource requirements tailored for the highly-constrained environments these devices find themselves in. In 2015 the National Institute of Standards and Technology began a Standardization Process in order to select one or more Lightweight Cryptographic algorithms. Out of the original 57 submissions ten finalists remain, with ASCON and Romulus being among the most scrutinized out of them. In this dissertation I will introduce some concepts required for easy understanding of the body of work, do an up-to-date revision on the current situation on the standardization process from a security and performance standpoint, a description of ASCON and Romulus, and new best known analysis, and a comparison of the two, with their advantages, drawbacks, and unique traits.Os padrões criptográficos tradicionais foram elaborados com um ambiente de computador e servidor em mente. Com a proliferação de dispositivos de pequenas dimensões tanto na Internet of Things, redes de sensores e sistemas embutidos, apareceu uma necessidade para se definir padrões para algoritmos de criptografia leve, com prioridades de segurança, performance e gasto de recursos equilibrados para os ambientes altamente limitados em que estes dispositivos operam. Em 2015 o National Institute of Standards and Technology lançou um processo de estandardização com o objectivo de escolher um ou mais algoritmos de criptografia leve. Das cinquenta e sete candidaturas originais sobram apenas dez finalistas, sendo ASCON e Romulus dois desses finalistas mais examinados. Nesta dissertação irei introduzir alguns conceitos necessários para uma fácil compreensão do corpo deste trabalho, assim como uma revisão atualizada da situação atual do processo de estandardização de um ponto de vista tanto de segurança como de performance, uma descrição do ASCON e do Romulus assim como as suas melhores análises recentes e uma comparação entre os dois, frisando as suas vantagens, desvantagens e aspectos únicos
    corecore