Novel Framework for Navigation using Enhanced Fuzzy Approach with Sliding Mode Controller

The reliability of any embedded navigator in advanced vehicular system depends upon correct and precise information of navigational data captured and processed to offer trustworthy path. After reviewing the existing system, a significant trade-off is explored between the existing navigational system and present state of controller design on various case studies and applications. The existing design of controller system for navigation using error-prone GPS/INS data doesn‟t emphasize on sliding mode controller. Although, there has been good number of studies in sliding mode controller, it is less attempted to optimize the navigational performance of a vehicle. Therefore, this paper presents a novel optimized design of a sliding mode controller that can be effectively deployed on advanced navigational system. The study outcome was found to offer higher speed, optimal control signal, and lower error occurances to prove that proposed system offers reliable and optimized navigational services in contrast to existing system

Structural <i>k</i>-means (S <i>k</i>-means) and clustering uncertainty evaluation framework (CUEF) for mining climate data

Dramatic increases in climate data underlie a gradual paradigm shift in knowledge acquisition methods from physically based models to data-based mining approaches. One of the most popular data clustering/mining techniques is k-means, and it has been used to detect hidden patterns in climate systems; k-means is established based on distance metrics for pattern recognition, which is relatively ineffective when dealing with “structured” data, that is, data in time and space domains, which are dominant in climate science. Here, we propose (i) a novel structural-similarity-recognition-based k-means algorithm called structural k-means or S k-means for climate data mining and (ii) a new clustering uncertainty representation/evaluation framework based on the information entropy concept. We demonstrate that the novel S k-means could provide higher-quality clustering outcomes in terms of general silhouette analysis, although it requires higher computational resources compared with conventional algorithms. The results are consistent with different demonstration problem settings using different types of input data, including two-dimensional weather patterns, historical climate change in terms of time series, and tropical cyclone paths. Additionally, by quantifying the uncertainty underlying the clustering outcomes we, for the first time, evaluated the “meaningfulness” of applying a given clustering algorithm for a given dataset. We expect that this study will constitute a new standard of k-means clustering with “structural” input data, as well as a new framework for uncertainty representation/evaluation of clustering algorithms for (but not limited to) climate science.</p

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

Applying genetic architectural synthesis in software development and run-time maintenance

Software systems are becoming complex entities with an increasing diffusion into many new domains. A complex software system requires more resources to develop and maintain. Some domains demand continuous operation like security or control systems, web services and communication systems etc. The trend will lead software industry to a situation where it will be difficult to develop software systems through traditional manual software engineering practices in a feasible budget. Any level of automation can relieve the pressure on the cost. This thesis work explores the potential of genetic architectural synthesis to introduce automation in software development and maintenance. The genetic algorithm operates at the architectural level. The fitness functions envelop the expert knowledge needed to gauge the quality (modifiability, efficiency and complexity) of architectures. The algorithm uses solutions which can be design patterns, architectural styles, best practices or application specific solutions to maintain the quality attributes. Each solution has a positive or negative impact on one or more quality attributes. Once calibrated, the genetic algorithm has been able to suggest good quality architectures. An empirical study has also been performed that suggests that the genetic algorithm’s proposals are comparatively better than the under-graduate level students’ designs. Tool support has been provided in the form of the Darwin environment. It facilitates a human architect to initiate, modify, monitor and analyze the results of a genetic architectural synthesis. Moreover, the genetic algorithm has been used to evolve software architectures to be easily distributable among the teams involved in its development. The algorithm takes into account the organizational information and proposes an initial work distribution plan along with the improved architecture. The SAGA (Self-Architecting using Genetic Algorithms) infrastructure has been developed to enable self-adaptive and manual run-time maintenance in Java-based applications. SAGA allows Java-based distributed systems to self-maintain reliability and efficiency. Furthermore, non-self-maintainable properties of a system can be maintained manually at run-time. The decision making engine is the genetic algorithm. The unit of run-time modification is an architectural solution which in its entirety enters of leaves the running instance of a system therefore affecting the system’s run-time quality. A solution is composed of roles which are bound to real artifacts in the system. Multiple attributes concerning reliability and efficiency of the running system are monitored by SAGA. In the case of poor system quality in a changed environment, SAGA triggers the genetic algorithm to propose improvements in the architecture taking into account the monitoring data. The proposal is then reflected to the run-time and the cycle continues. In the experiments, an example distributed system used in changing environment has been implemented with self-maintaining capability. A significant improvement in both reliability and efficiency of the running system has been observed

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

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

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

Applied (Meta)-Heuristic in Intelligent Systems

Engineering and business problems are becoming increasingly difficult to solve due to the new economics triggered by big data, artificial intelligence, and the internet of things. Exact algorithms and heuristics are insufficient for solving such large and unstructured problems; instead, metaheuristic algorithms have emerged as the prevailing methods. A generic metaheuristic framework guides the course of search trajectories beyond local optimality, thus overcoming the limitations of traditional computation methods. The application of modern metaheuristics ranges from unmanned aerial and ground surface vehicles, unmanned factories, resource-constrained production, and humanoids to green logistics, renewable energy, circular economy, agricultural technology, environmental protection, finance technology, and the entertainment industry. This Special Issue presents high-quality papers proposing modern metaheuristics in intelligent systems

Adaptive Heterogeneous Multi-Population Cultural Algorithm

Optimization problems is a class of problems where the goal is to make a system as effective as possible. The goal of this research area is to design an algorithm to solve optimization problems effectively and efficiently. Being effective means that the algorithm should be able to find the optimal solution (or near optimal solutions), while efficiency refers to the computational effort required by the algorithm to find an optimal solution. In other words, an optimization algorithm should be able to find the optimal solution in an acceptable time. Therefore, the aim of this dissertation is to come up with a new algorithm which presents an effective as well as efficient performance. There are various kinds of algorithms proposed to deal with optimization problems. Evolutionary Algorithms (EAs) is a subset of population-based methods which are successfully applied to solve optimization problems. In this dissertation the area of evolutionary methods and specially Cultural Algorithms (CAs) are investigated. The results of this investigation reveal that there are some room for improving the existing EAs. Consequently, a number of EAs are proposed to deal with different optimization problems. The proposed EAs offer better performance compared to the state-of-the-art methods. The main contribution of this dissertation is to introduce a new architecture for optimization algorithms which is called Heterogeneous Multi-Population Cultural Algorithm (HMP-CA). The new architecture first incorporates a decomposition technique to divide the given problem into a number of sub-problems, and then it assigns the sub-problems to different local CAs to be optimized separately in parallel. In order to evaluate the proposed architecture, it is applied on numerical optimization problems. The evaluation results reveal that HMP-CA is fully effective such that it can find the optimal solution for every single run. Furthermore, HMP-CA outperforms the state-of-the-art methods by offering a more efficient performance. The proposed HMP-CA is further improved by incorporating an adaptive decomposition technique. The improved version which is called Adaptive HMP-CA (A-HMP-CA) is evaluated over large scale global optimization problems. The results of this evaluation show that HMP-CA significantly outperforms the state-of-the-art methods in terms of both effectiveness and efficiency

Studies in particle swarm optimization technique for global optimization.

Ph. D. University of KwaZulu-Natal, Durban 2013.Abstract available in the digital copy.Articles found within the main body of the thesis in the print version is found at the end of the thesis in the digital version

Algorithms for Image Analysis in Traffic Surveillance Systems

Import 23/07/2015The presence of various surveillance systems in many areas of the modern society is indisputable and the most perceptible are the video surveillance systems. This thesis mainly describes novel algorithm for vision-based estimation of the parking lot occupancy and the closely related topics of pre-processing of images captured under harsh conditions. The developed algorithms have their practical application in the parking guidance systems which are still more popular. One part of this work also tries to contribute to the specific area of computer graphics denoted as direct volume rendering (DVR).Přítomnost nejrůznějších dohledových systémů v mnoha oblastech soudobé společnosti je nesporná a systémy pro monitorování dopravy jsou těmi nejviditelnějšími. Hlavní část této práce se věnuje popisu nového algoritmu pro detekci obsazenosti parkovacích míst pomocí analýzy obrazu získaného z kamerového systému. Práce se také zabývá tématy úzce souvisejícími s předzpracováním obrazu získaného za ztížených podmínek. Vyvinuté algoritmy mají své praktické uplatnění zejména v oblasti pomocných parkovacích systémů, které se stávají čím dál tím více populárními. Jedna část této práce se snaží přispět do oblasti počítačové grafiky označované jako přímá vizualizace objemových dat.Prezenční460 - Katedra informatikyvyhově