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International Conference on Intelligent Computing and Control Systems (ICICCS)

作者： Reddy, P. Krishna Mohan Prakash, M. Natl Inst Technol Nagaland Dept Elect & Elect Engn Chumukedima 797103 Nagaland India

ISBN: (纸本)9781728148762

With the demise of conventional fossil fuels and increased pollution rates, the intermittent renewables are considered suitable alternates. The erratic nature of loads in the distribution grid further affects the reliability of power systems. Micro-grid with interconnected loads and distributed generation will be beneficial for certain areas, where the extension of grid connectivity is not feasible. Further, the advancement of the battery storage system enables the distribution grid to consider it as the source, whenever the power generation is not available. In the present study, a micro-grid with distributed generation and interconnected loads up to 82kW has been considered. The main objective of the present study is to minimize the overall fuel cost by achieving the optimal scheduling of energy sources. Extensive analysis has been carried to achieve the optimal dispatch condition at all the instances while solving the demand criteria. To reduce the fuel cost, several optimization strategies including particle swarm optimization (PSO), whale optimization algorithm (WOA), grey wolf optimization (GWO), and modified grey wolf optimization (MGWO) have been adopted. For the present test study, the MGWO algorithm has yielded better results when compared with other traditional optimization algorithms. The total cost of microgrid optimized through WOA, GWO, PSO, MGWO is 1455.50 ((sic)/day), 1451.70 ((sic)/day), 1449.87 ((sic)/day), and 1433.60 ((sic)/day) respectively.

关键词： grid Economic Generation Dispatch Battery Energy Storage System (BESS) optimization algorithms and Fuel Cost Reduction

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Dynamic User Preference Parameters Selection and Energy Consumption optimization for Smart Homes Using Deep Extreme Learning Machine and Bat Algorithm

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IEEE ACCESS 2020年 8卷 204744-204762页

作者： Shah, Abdul Salam Nasir, Haidawati Fayaz, Muhammad Lajis, Adidah Ullah, Israr Shah, Asadullah Univ Kuala Lumpur UniKl MIIT Dept Comp Engn Kuala Lumpur 50250 Malaysia Univ Cent Asia Naryn Dept Comp Sci Naryn 722918 Kyrgyzstan Virtual Univ Pakistan Dept Comp Sci Lahore 54792 Pakistan Int Islamic Univ Malaysia IIUM Dept Informat Syst Kulliyyah ICT Kuala Lumpur 50728 Malaysia

The advancements in electronic devices have increased the demand for the internet of things (IoT) based smart homes, where the connecting devices are growing at a rapid pace. Connected electronic devices are more common in smart buildings, smart cities, smart grids, and smart homes. The advancements in smart grid technologies have enabled to monitor every moment of energy consumption in smart buildings. The issue with smart devices is more energy consumption as compared to ordinary buildings. Due to smart cities and smart homes' growth rates, the demand for efficient resource management is also growing day by day. Energy is a vital resource, and its production cost is very high. Due to that, scientists and researchers are working on optimizing energy usage, especially in smart cities, besides providing a comfortable environment. The central focus of this paper is on energy consumption optimization in smart buildings or smart homes. For the comfort index (thermal, visual, and air quality), we have used three parameters, i.e., Temperature (degrees F), illumination (lx), and CO2 (ppm). The major problem with the previous methods in the literature is the static user parameters (Temperature, illumination, and CO2);when they (parameters) are assigned at the beginning, they cannot be changed. In this paper, the Alpha Beta filter has been used to predict the indoor Temperature, illumination, and air quality and remove noise from the data. We applied a deep extreme learning machine approach to predict the user parameters. We have used the Bat algorithm and fuzzy logic to optimize energy consumption and comfort index management. The predicted user parameters have improved the system's overall performance in terms of ease of use of smart systems, energy consumption, and comfort index management. The comfort index after optimization remained near to 1, which proves the significance of the system. After optimization, the power consumption also reduced and stayed around the maxim

关键词： Artificial neural network Bat algorithm deep extreme learning machine energy optimization energy prediction optimization algorithms smart building smart home

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Broad Learning System with Proportional-Integral-Differential Gradient Descent 20th

Broad Learning System with Proportional-Integral-Differentia...

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20th International Conference on algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Zou, Weidong Xia, Yuanqing Cao, Weipeng Ming, Zhong Beijing Inst Technol Sch Automat Beijing Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China

ISBN: (纸本)9783030602451;9783030602444

Broad learning system (BLS) has attracted much attention in recent years due to its fast training speed and good generalization ability. Most of the existing BLS-based algorithms use the least square method to calculate its output weights. As the size of the training data set increases, this approach will cause the training efficiency of the model to be seriously reduced, and the solution of the model will also be unstable. To solve this problem, we have designed a new gradient descent method (GD) based on the proportional-integral-differential technique (PID) to replace the least square operation in the existing BLS algorithms, which is called PID-GD-BLS. Extensive experimental results on four benchmark data sets show that PID-GD can achieve faster convergence rate than traditional optimization algorithms such as Adam and AdaMod, and the generalization performance and stability of the PID-GD-BLS are much better than that of BLS and its variants. This study provides a new direction for BLS optimization and a better solution for BLS-based data mining.

关键词： Broad learning system Neural networks with random weights Proportional-integral-differential Randomized algorithms optimization algorithms

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Optimal Beamforming Using Clustered Evolutionary Teaching and Learning 14

Optimal Beamforming Using Clustered Evolutionary Teaching an...

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14th European Conference on Antennas and Propagation (EuCAP)

作者： Darvish, Amirashkan Kishk, Ahmed Ebrahimzadeh, Ataollah Golazari, Samineh Sarbazi Concordia Univ Elect & Comp Engn Montreal PQ Canada Babol Noshirvani Univ Technol Elect & Comp Engn Babol Iran

ISBN: (纸本)9788831299008

An improved Teaching and Learning Based optimization algorithm, C-TLBO, is employed to obtain user-defined shaped radiation patterns. An example of 70-element linear array with a specific asymmetric mask is considered. A novel redistribution mechanism is adopted in order to increase the convergence rate of the original optimizer. The mechanism uses the concept of multiple teachers to cluster the learner groups. It triggers an extra exploration with the same exploitation of the cost function to alter the distribution of the proposed solutions. The performance is validated using a set of standard objective functions. Comparisons to Gradient Descent (GD), Particle Swarm optimization (PSO) and Genetic Algorithm (GA) illustrate a superior performance for designing such large dimensional beam-shaping problem.

关键词： antennas Beam forming Beam Shaping optimization algorithms

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Numerical Simulations for Fitting Parameters of Linear and Logistic-Type Fractional-, Variable-Order Equations - Comparision of Methods 10th

Numerical Simulations for Fitting Parameters of Linear and L...

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10th International Conference on Non-Integer Order Calculus and Its Applications

作者： Oziablo, Piotr Bialystok Tech Univ Fac Comp Sci Wiejska 45A PL-15351 Bialystok Poland

ISBN: (纸本)9783030173449;9783030173432

In the work variable-, fractional-order backward difference of the Grunwald-Letnikov type is presented. The backward difference is used to generate simulated experimental data to which additional noise signal is added. Using prepared data four different algorithms of finding the parameter of the order function (assuming that the general family of the function is known) and constant lambda coefficient are compared. The algorithms are: trust region algorithm, particle swarm algorithm, simulated annealing algorithm and genetic algorithm.

关键词： Difference equations Eigenfunction Fractional variable-order optimization algorithms

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A Dynamic Heuristic Algorithm for Management of Public Health Emergencies in Unreliable Settings 8

A Dynamic Heuristic Algorithm for Management of Public Healt...

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8th IEEE International Conference on Healthcare Informatics (ICHI)

作者： Akwafuo, Sampson E. Ihinegbu, Christopher Urbanovsky, Joshua Mikler, Armin R. Univ North Texas Ctr Computat Epidemiol & Response Anal CeCERA Denton TX USA Univ Nigeria Dept Geog Nsukka Nigeria

ISBN: (纸本)9781728153827

Prompt and efficient intervention is vital in reducing the impacts of sudden disease outbreaks, natural disasters, or terrorist attacks. This can only be achieved if there is a fit-for-purpose logistics plan in place, incorporating geographical, time, location demand, and vehicular capacity constraints. This response plan will be immediately activated at the onset of such events. Our algorithm involves three stages: Selection of depot and clustering;route construction;and real-time route improvement. In the initial phase, locations to be visited are strategically clustered near a depot. The second phase iteratively constructs routes through all locations clustered in a depot, subject to time, demand, and vehicle capacity constraints. Pruning is heuristically implemented until a feasible set of solutions is obtained. The third phase accepts real-time changes, dynamically re-constructs, and improves routing. Computational application of our algorithm to test instances in a most-at-risk region, clearly indicate its efficiency. It yields an optimal set of routes, incorporating real-time demands and capable of implementing either single or multi-depot scenarios.

关键词： optimization algorithms Africa Emergency Response Planning Vehicle Routing Problem (VRP) Disasters Dynamic Heuristics

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Efficient algorithms towards Network Intervention 20

Efficient Algorithms towards Network Intervention

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29th World Wide Web Conference (WWW)

作者： Hung, Hui-Ju Lee, Wang-Chien Yang, De-Nian Shen, Chih-Ya Lei, Zhen Chow, Sy-Miin Penn State Univ University Pk PA 16802 USA Acad Sinica Taipei Taiwan Natl Tsing Hua Univ Hsinchu Taiwan

ISBN: (纸本)9781450370233

Research suggests that social relationships have substantial impacts on individuals' health outcomes. Network intervention, through careful planning, can assist a network of users to build healthy relationships. However, most previous work is not designed to assist such planning by carefully examining and improving multiple network characteristics. In this paper, we propose and evaluate algorithms that facilitate network intervention planning through simultaneous optimization of network degree, closeness, betweenness, and local clustering coefficient, under scenarios involving Network Intervention with Limited Degradation - for Single target (NILD-S) and Network Intervention with Limited Degradation - for Multiple targets (NILD-M). We prove that NILD-S and NILD-M are NP-hard and cannot be approximated within any ratio in polynomial time unless P=NP. We propose the Candidate Re-selection with Preserved Dependency (CRPD) algorithm for NILD-S, and the Objective-aware Intervention edge Selection and Adjustment (OISA) algorithm for NILD-M. Various pruning strategies are designed to boost the efficiency of the proposed algorithms. Extensive experiments on various real social networks collected from public schools and Web and an empirical study are conducted to show that CRPD and OISA outperform the baselines in both efficiency and effectiveness.

关键词： Network intervention optimization algorithms social networks

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NUMERICAL PERFORMANCE PREDICTIONS OF ARTIFICIAL INTELLIGENCE-DRIVEN CENTRIFUGAL COMPRESSOR DESIGNS 18

NUMERICAL PERFORMANCE PREDICTIONS OF ARTIFICIAL INTELLIGENCE...

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ASME-Fluids-Engineering-Division Summer Meeting (FEDSM)

作者： Manuel, Fritsche Philipp, Epple Boris, Kubrak Stefan, Gast Antonio, Delgado Valentyn, Barannik Coburg Univ Appl Sci & Arts Coburg Bavaria Germany SoftInWay Inc New York NY USA Friedrich Alexander Univ Inst Fluid Mech Erlangen Bavaria Germany

ISBN: (纸本)9780791883716

This paper demonstrates the application of artificial intelligence-driven turbomachinery design, its numerical performance predictions and their numerical validation. A common problem in the industrial application of turbomachinery is that readily available turbomachines are not necessarily matching the desired performance targets (performance characteristics) required for a specific application. Many machines operate under off-design conditions and hence are not operating at maximum efficiency. Traditional numerical analysis and response-driven optimization methods are ineffective and still too time-consuming and are particularly sensitive to changing performance targets. Most commercially available optimization algorithms are based on maximizing or minimizing a response function, for instance the standard error from a desired target performance characteristic of a turbomachine, by changing design variables. This work uses a newly developed artificial intelligence-based approach that is not dependent on the specific design target using the turbomachinery design software AxSTREAM from SoftInWay. Here a neural network was trained within a constraint design space by many samples of design variables and their respective numerical performance predictions. For the numerical verification of the designs the solver Simcenter STAR-CCM+ from Siemens was used. Subsequently the trained neural network was applied to generate a set of design parameters that satisfied the physically feasible desired target performance characteristics very fast. This trained neural network enabled an effective reversal of the traditional iterative design process where now the desired target performance characteristics became the input and the geometry became the output, turning it into a generative inverse design process. This method was applied to generate a centrifugal compressor design within a given geometrically and physically constraint design space. A specific desired target performance char

关键词： Compressors Design Turbomachinery Performance characterization Artificial neural networks Computer software Errors Geometry optimization algorithms Numerical analysis optimization Machinery

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Dynamic differential annealed optimization: New metaheuristic optimization algorithm for engineering applications

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APPLIED SOFT COMPUTING 2020年 93卷 106392-106392页

作者： Ghafil, Hazim Nasir Jarmai, Karoly Univ Miskolc H-3515 Miskolc Egyetemvaros Hungary Univ Kufa Fac Engn Najaf Iraq

This work proposes a novel optimization algorithm which can be used to solve a wide range of mathematical optimization problems where the global minimum or maximum is required. The new algorithm is based on random search and classical simulated annealing algorithm (it mimics the modern process of producing high-quality steel) and is designated dynamic differential annealed optimization (DDAO). The proposed algorithm was benchmarked for 51 test functions. The dynamic differential annealed optimization algorithm has been compared to a large number of highly cited optimization algorithms. Over numerical tests, DDAO has outperformed some of these algorithms in many cases and shown high performance. Constrained path planning and spring design problem were selected as a practical engineering optimization problem. DDAO converged to the global minimum of problems efficiently, and for spring design problem DDAO has found the best feasible solution than what is found by many algorithms. (C) 2020 The Author(s). Published by Elsevier B.V.

关键词： Dynamic differential annealed optimization optimization algorithms Computational intelligence Path planning Engineering design optimization

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A fitness dependent salp swarm algorithm

A fitness dependent salp swarm algorithm

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IEEE Congress on Evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Pelusi, Danilo Mascella, Raffaele Tallini, Luca Univ Teramo Fac Commun Sci Teramo Italy

ISBN: (纸本)9781728169293

Salp Swarm Algorithm (SSA) is a novel swarm technique using to optimize design problems. SSA is inspired by the swarming behavior of salp observed in the deep area of sea. In spite of its application versatility, SSA suffers from mediocre convergence rate and limited exploratory capabilities. In this paper, a Fitness Dependent Salp Swarm Algorithm (FDSSA) is proposed. The novelties of the proposed approach are the definition of a fitness coefficient able to enhance the exploration, the introduction of a novel mathematical model to describe the trajectory of salps and a mutation mechanism to increase the convergence speed. The designed algorithm is tested on unimodal and multimodal benchmark functions and then compared with well-known heuristic algorithms. The results show the superiority of FDSSA with respect to the comparison algorithms in terms of optimization and convergence performances according to their computational complexity.

关键词： Evolutionary Computation Swarm Intelligence Salp Swarm Algorithm optimization algorithms Mutation

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