34 research outputs found
A Novel Fast Path Planning Approach for Mobile Devices using Hybrid Quantum Ant Colony Optimization Algorithm
With IoT systems' increasing scale and complexity, maintenance of a large
number of nodes using stationary devices is becoming increasingly difficult.
Hence, mobile devices are being employed that can traverse through a set of
target locations and provide the necessary services. In order to reduce energy
consumption and time requirements, the devices are required to traverse
following a Hamiltonian path. This problem can be formulated as a Travelling
Salesman Problem (TSP), an NP-hard problem. Moreover, in emergency services,
the devices must traverse in real-time, demanding speedy path planning from the
TSP instance. Among the well-known optimization techniques for solving the TSP
problem, Ant Colony Optimization has a good stronghold in providing good
approximate solutions. Moreover, ACO not only provides near-optimal solutions
for TSP instances but can also output optimal or near-optimal solutions for
many other demanding hard optimization problems. However, to have a fast
solution, the next node selection, which needs to consider all the neighbors
for each selection, becomes a bottleneck in the path formation step. Moreover,
classical computers are constrained to generate only pseudorandom numbers. Both
these problems can be solved using quantum computing techniques, i.e., the next
node can be selected with proper randomization, respecting the provided set of
probabilities in just a single execution and single measurement of a quantum
circuit. Simulation results of the proposed Hybrid Quantum Ant Colony
Optimization algorithm on several TSP instances have shown promising results,
thus expecting the proposed work to be important in implementing real-time path
planning in quantum-enabled mobile devices
Quantum Optimization Algorithms in Operations Research: Methods, Applications, and Implications
Quantum optimization algorithms (QOAs) have the potential to fundamentally
transform the application of optimization methods in decision making. For
certain classes of optimization problems, it is widely believed that QOA
enables significant run-time performance benefits over current state-of-the-art
solutions. With the latest progress on building quantum computers entering the
industrialization stage, quantum-based optimization algorithms have become more
relevant. The recent extreme increase in the number of publications in the
field of QOA demonstrates the growing importance of the topic in both the
academia and the industry. The objectives of this paper are as follows: (1)
First, we provide insight into the main techniques of quantum-based
optimization algorithms for decision making. (2) We describe and compare the
two basic classes of adiabatic and gate-based optimization algorithms and argue
their potentials and limitations. (3) Herein, we also investigate the key
operations research application areas that are expected to be considerably
impacted by the use of QOA in decision making in the future. (4) Finally,
current implications arising from the future use of QOA from an operations
research perspective are discussed.Comment: 16 pages, 6 figure
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
Telecommunications Networks
This book guides readers through the basics of rapidly emerging networks to more advanced concepts and future expectations of Telecommunications Networks. It identifies and examines the most pressing research issues in Telecommunications and it contains chapters written by leading researchers, academics and industry professionals. Telecommunications Networks - Current Status and Future Trends covers surveys of recent publications that investigate key areas of interest such as: IMS, eTOM, 3G/4G, optimization problems, modeling, simulation, quality of service, etc. This book, that is suitable for both PhD and master students, is organized into six sections: New Generation Networks, Quality of Services, Sensor Networks, Telecommunications, Traffic Engineering and Routing
Evolutionary Computation
This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field
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
Applied Metaheuristic Computing
For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC
Smart Monitoring and Control in the Future Internet of Things
The Internet of Things (IoT) and related technologies have the promise of realizing pervasive and smart applications which, in turn, have the potential of improving the quality of life of people living in a connected world. According to the IoT vision, all things can cooperate amongst themselves and be managed from anywhere via the Internet, allowing tight integration between the physical and cyber worlds and thus improving efficiency, promoting usability, and opening up new application opportunities. Nowadays, IoT technologies have successfully been exploited in several domains, providing both social and economic benefits. The realization of the full potential of the next generation of the Internet of Things still needs further research efforts concerning, for instance, the identification of new architectures, methodologies, and infrastructures dealing with distributed and decentralized IoT systems; the integration of IoT with cognitive and social capabilities; the enhancement of the sensing–analysis–control cycle; the integration of consciousness and awareness in IoT environments; and the design of new algorithms and techniques for managing IoT big data. This Special Issue is devoted to advancements in technologies, methodologies, and applications for IoT, together with emerging standards and research topics which would lead to realization of the future Internet of Things
Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting
More accurate and precise energy demand forecasts are required when energy decisions are made in a competitive environment. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated. Examples include seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. These forecasting models have resulted in an over-reliance on the use of informal judgment and higher expenses when lacking the ability to determine data characteristics and patterns. The hybridization of optimization methods and superior evolutionary algorithms can provide important improvements via good parameter determinations in the optimization process, which is of great assistance to actions taken by energy decision-makers. This book aimed to attract researchers with an interest in the research areas described above. Specifically, it sought contributions to the development of any hybrid optimization methods (e.g., quadratic programming techniques, chaotic mapping, fuzzy inference theory, quantum computing, etc.) with advanced algorithms (e.g., genetic algorithms, ant colony optimization, particle swarm optimization algorithm, etc.) that have superior capabilities over the traditional optimization approaches to overcome some embedded drawbacks, and the application of these advanced hybrid approaches to significantly improve forecasting accuracy