225 research outputs found
A Review of the Family of Artificial Fish Swarm Algorithms: Recent Advances and Applications
The Artificial Fish Swarm Algorithm (AFSA) is inspired by the ecological
behaviors of fish schooling in nature, viz., the preying, swarming, following
and random behaviors. Owing to a number of salient properties, which include
flexibility, fast convergence, and insensitivity to the initial parameter
settings, the family of AFSA has emerged as an effective Swarm Intelligence
(SI) methodology that has been widely applied to solve real-world optimization
problems. Since its introduction in 2002, many improved and hybrid AFSA models
have been developed to tackle continuous, binary, and combinatorial
optimization problems. This paper aims to present a concise review of the
family of AFSA, encompassing the original ASFA and its improvements,
continuous, binary, discrete, and hybrid models, as well as the associated
applications. A comprehensive survey on the AFSA from its introduction to 2012
can be found in [1]. As such, we focus on a total of {\color{blue}123} articles
published in high-quality journals since 2013. We also discuss possible AFSA
enhancements and highlight future research directions for the family of
AFSA-based models.Comment: 37 pages, 3 figure
Index to 1984 NASA Tech Briefs, volume 9, numbers 1-4
Short announcements of new technology derived from the R&D activities of NASA are presented. These briefs emphasize information considered likely to be transferrable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. This index for 1984 Tech B Briefs contains abstracts and four indexes: subject, personal author, originating center, and Tech Brief Number. The following areas are covered: electronic components and circuits, electronic systems, physical sciences, materials, life sciences, mechanics, machinery, fabrication technology, and mathematics and information sciences
A multiple system level modeling approach to coupled energy markets: Incentives for combined heat and power generation at the plant, city and regional energy system levels
The energy system can be subdivided into interconnected structural levels with differing boundary conditions and objectives. For heat and power generation, these levels may be the: electricity price area (regional); heat price area (city); and production site (power plant). This work presents a multi-system modeling approach for the analysis of investments and operation of combined heat and power (CHP) plants, as optimized on a regional, city, or production site energy system level. The modeling framework, comprising three energy system optimization models at the respective levels, is applied to a case study of Sweden, electricity price area SE3. The modeling levels are optimized separately but linked through electricity and heat prices. The results show that optimized CHP plant investments and operation on the three levels can both align and differ, depending on conditions. With a low biomass price and moderate congestion in transmission capacity into the city, the results from the three levels generally align. Differences arise if the biomass price is increased, which impacts the competitiveness of CHP plants in the region, while city-level CHP investments are mainly determined by the local heat demand and less-sensitive to external changes. The differences indicate a risk for diverging expectations between system levels
Green communication approach for the smart city using renewable energy systems
A smart city is an evolving Internet of Things (IoT) technique that links different digital gadgets
via a network, offering several new services to the manufacturing and medical field to commerce.
A smart city is an omnipresent and fundamental change that has altered the whole environment
using Information Communication Technology (ICT) and sensor-enabled IoT gadgets. Renewable energy
storage, the solar, wind, and distributed resources can be better integrated into the grid. The leading
theory in the digital domain for improved and broad use of all the situations with high digital media
accessibility (i.e., video, sound, words, and pictures), nevertheless it is challenging to talk freely about
such small appliances because of resource constraints (starving power and battery capacity), and
large quantities of the information. The green communication approach for the smart city (GCA-SC) is
proposed in this article. Thus, using saved video streams to solve these difficulties is recommended by
Hybrid Adaptation and Power Algorithms and Delay-tolerant Streamed Algorithms. A new architecture
is similarly proposed for the smart city network. Empirical findings such as power drainage, battery
capacity, latency, and bandwidth are acquired and evaluated. It was reached that, with less effort than
Baseline, GCA-SC optimises energy drainage, the battery capacity, variance, power delivery ratio of the
IoT compatible gadgets in the smart city environment. The simulation analysis of the proposed GCA-SC
method enhances the packet delivery ratio of 39% and throughput of 99 kbps. It reduces the delay by 2.5 s and the standard deviation by −0.9 s.publishedVersio
Power-Aware Job Dispatching in High Performance Computing Systems
This works deals with the power-aware job dispatching problem in supercomputers; broadly speaking the dispatching consists of assigning finite capacity resources to a set of activities, with a special concern toward power and energy efficient solutions. We introduce novel optimization approaches to address its multiple aspects.
The proposed techniques have a broad application range but are aimed at applications in the field of High Performance Computing (HPC) systems.
Devising a power-aware HPC job dispatcher is a complex, where contrasting goals must be satisfied. Furthermore, the online nature of the problem request that solutions must be computed in real time respecting stringent limits. This aspect historically discouraged the usage of exact methods and favouring instead the adoption of heuristic techniques. The application of optimization approaches to the dispatching task is still an unexplored area of research and can drastically improve the performance of HPC systems.
In this work we tackle the job dispatching problem on a real HPC machine, the Eurora supercomputer hosted at the Cineca research center, Bologna. We propose a Constraint Programming (CP) model that outperforms the dispatching software currently in use. An essential element to take power-aware decisions during the job dispatching phase is the possibility to estimate jobs power consumptions before their execution. To this end, we applied Machine Learning techniques to create a prediction model that was trained and tested on the Euora supercomputer, showing a great prediction accuracy. Then we finally develop a power-aware solution, considering the same target machine, and we devise different approaches to solve the dispatching problem while curtailing the power consumption of the whole system under a given threshold. We proposed a heuristic technique and a CP/heuristic hybrid method, both able to solve practical size instances and outperform the current state-of-the-art techniques
Recent trends of the most used metaheuristic techniques for distribution network reconfiguration
Distribution network reconfiguration (DNR) continues to be a good option to reduce technical losses in a distribution
power grid. However, this non-linear combinatorial problem is not easy to assess by exact methods when solving for
large distribution networks, which requires large computational times. For solving this type of problem, some researchers
prefer to use metaheuristic techniques due to convergence speed, near-optimal solutions, and simple programming. Some
literature reviews specialize in topics concerning the optimization of power network reconfiguration and try to cover
most techniques. Nevertheless, this does not allow detailing properly the use of each technique, which is important to
identify the trend. The contributions of this paper are three-fold. First, it presents the objective functions and constraints
used in DNR with the most used metaheuristics. Second, it reviews the most important techniques such as particle swarm
optimization (PSO), genetic algorithm (GA), simulated annealing (SA), ant colony optimization (ACO), immune
algorithms (IA), and tabu search (TS). Finally, this paper presents the trend of each technique from 2011 to 2016. This
paper will be useful for researchers interested in knowing the advances of recent approaches in these metaheuristics
applied to DNR in order to continue developing new best algorithms and improving solutions for the topi
Green Technologies for Production Processes
This book focuses on original research works about Green Technologies for Production Processes, including discrete production processes and process production processes, from various aspects that tackle product, process, and system issues in production. The aim is to report the state-of-the-art on relevant research topics and highlight the barriers, challenges, and opportunities we are facing. This book includes 22 research papers and involves energy-saving and waste reduction in production processes, design and manufacturing of green products, low carbon manufacturing and remanufacturing, management and policy for sustainable production, technologies of mitigating CO2 emissions, and other green technologies
Wind-solar-hydrothermal dispatch using convex optimization
In this research a convex optimization methodology is proposed for the Shortterm hydrothermal scheduling (STHS). In addition, wind and solar generation are also considered under a robust approach by modeling the equilibrium of power flow constraint as chance box constraints, which allows determining the amount of renewable source available with a specific probability value. The proposed methodology guarantees global optimum of the convexified model andfast convergences..
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