A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Solving Graph Coloring Problems Using Discrete Artificial Bee Colony

筑波大学 (University of Tsukuba)201

Accurate Localization with Ultra-Wideband Ranging for Multi-Robot Systems

RÉSUMÉ : Avec l’avancement de la technologie matérielle et logicielle, l’application de l’automatisation et de la robotique se développe rapidement. Les systèmes multi-robots sont particulièrement prometteurs en raison de leur grande efficacité et robustesse. De tels systèmes peuvent être utilisés pour aider les humains à effectuer efficacement des tâches dangereuses ou pénibles, telles que l’intervention en cas de catastrophe, l’exploration souterraine, etc. Pour déployer un système multi-robot dans un environnement sans GPS, la coordination des robots dans le système est un défi crucial. Chaque robot doit avoir une estimation précise de sa propre position pour permettre aux robots du système de collaborer pour la réalisation de leur tâche. Comme cette direction de recherche est relativement nouvelle, les approches existantes ne sont pas encore abouties. Elles consistent principalement en des systèmes centralisés qui reposent sur des signaux GPS. La dépendance sur un signal GPS limite l’application aux espaces extérieurs ouverts. De plus, les systèmes centralisés sont confrontés au risque d’un point de défaillance unique, qui limite la robustesse du système. Par ailleurs, un système centralisé n’est pas toujours approprié à une taille grandissante, comme lors d’ajout de nouveaux groupes de robots ou lors de la fusion de différents groupes. Par conséquent, une solution distribuée, décentralisée, et adaptée à de larges groupes de tailles variables pouvant produire une estimation et un suivi du positionnement des robots dans un environnement sans GPS est souhaitée. Dans ce travail, nous adoptons une stratégie descendante pour relever ces défis.----------ABSTRACT : With the advancement of hardware and software technology, the everyday applications of automation and robotics are developing rapidly. Multi-robot systems are particularly promising because of their high efficiency and robustness. Such systems can be used to assist humans in performing dangerous or strenuous tasks, such as disaster response, subterranean exploration, etc. To deploy a multi-robot system in an environment without a global positioning system (GPS), coordinating the robots in the system is a crucial challenge. Each robot needs to have the correct tracking of its own and its teammates positions to enable the robots to cooperate. Because this research direction is relatively new, there are not many mature methods: existing approaches are mainly centralized systems that rely on GPS signals. The dependence on GPS restricts the application to the outdoors or indoor spaces with expensive infrastructure. Centralized systems also face the risk of a single point of failure, which is not acceptable for critical systems. In addition, centralized systems can be hard to scale both statically and dynamically (e.g. adding new groups of robots or merging different groups). Therefore, a distributed and scalable solution with accurate positioning and tracking in a GPS-denied environment is desired. In this work, we follow a top-down strategy to address these challenges

Multi-Agent Systems

A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Surrogate-Assisted Unified Optimization Framework for Investigating Marine Structural Design Under Information Uncertainty.

Structural decisions made in the early stages of marine systems design can have a large impact on future acquisition, maintenance and life-cycle costs. However, owing to the unique nature of early stage marine system design, these critical structure decisions are often made on the basis of incomplete information or knowledge about the design. When coupled with design optimization analysis, the complex, uncertain early stage design environment makes it very difficult to deliver a quantified trade-off analysis for decision making. This work presents a novel decision support method that integrates design optimization, high-fidelity analysis, and modeling of information uncertainty for early stage design and analysis. To support this method this dissertation improves the design optimization methods for marine structures by proposing several novel surrogate modeling techniques and strategies. The proposed work treats the uncertainties that are sourced from limited information in a non-statistical interval uncertainty form. This interval uncertainty is treated as an objective function in an optimization framework in order to explore the impact of information uncertainty on structural design performance. In this examination, the potential structural weight penalty regarding information uncertainty can be quickly identified in early stage, avoiding costly redesign later in the design. This dissertation then continues to explore a balanced computational structure between fidelity and efficiency. A proposed novel variable fidelity approach can be applied to wisely allocate expensive high-fidelity computational simulations. In achieving the proposed capabilities for design optimization, several surrogate modeling methods are developed concerning worst-case estimation, clustered multiple meta-modeling, and mixed variable modeling techniques. These surrogate methods have been demonstrated to significantly improve the efficiency of optimizer in dealing with the challenges of early stage marine structure design.PhDNaval Architecture and Marine EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133365/1/yanliuch_1.pd

A Review of Resonant Converter Control Techniques and The Performances

paper first discusses each control technique and then gives experimental results and/or performance to highlights their merits. The resonant converter used as a case study is not specified to just single topology instead it used few topologies such as series-parallel resonant converter (SPRC), LCC resonant converter and parallel resonant converter (PRC). On the other hand, the control techniques presented in this paper are self-sustained phase shift modulation (SSPSM) control, self-oscillating power factor control, magnetic control and the H-∞ robust control technique

A Review of Resonant Converter Control Techniques and The Performances

paper first discusses each control technique and then gives experimental results and/or performance to highlights their merits. The resonant converter used as a case study is not specified to just single topology instead it used few topologies such as series-parallel resonant converter (SPRC), LCC resonant converter and parallel resonant converter (PRC). On the other hand, the control techniques presented in this paper are self-sustained phase shift modulation (SSPSM) control, self-oscillating power factor control, magnetic control and the H-∞ robust control technique

OBSERVER-BASED-CONTROLLER FOR INVERTED PENDULUM MODEL

This paper presents a state space control technique for inverted pendulum system. The system is a common classical control problem that has been widely used to test multiple control algorithms because of its nonlinear and unstable behavior. Full state feedback based on pole placement and optimal control is applied to the inverted pendulum system to achieve desired design specification which are 4 seconds settling time and 5% overshoot. The simulation and optimization of the full state feedback controller based on pole placement and optimal control techniques as well as the performance comparison between these techniques is described comprehensively. The comparison is made to choose the most suitable technique for the system that have the best trade-off between settling time and overshoot. Besides that, the observer design is analyzed to see the effect of pole location and noise present in the system