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Nonlinear multiobjective and dynamic real-time predictive optimization for optimal operation of baseload power plants under variable renewable energy

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OPTIMAL CONTROL APPLICATIONS & METHODS 2023年第2期44卷 798-829页

作者： Kim, Rebecca Lima, Fernando, V West Virginia Univ Dept Chem & Biomed Engn POB 6102 Morgantown WV 26506 USA

Considering the increase of disruptive variable renewable energy penetration into the power grid, this article focuses on the investigation of a multiobjective and dynamic real-time optimization framework to address the cycling of large-scale power plants under renewable penetration. In this framework, a parallelized particle swarm optimization step is first performed to generate feasible initial points. Then, a multiobjective and dynamic real-time optimization formulation generates optimal trajectories. The benefit of predictive capability is investigated for the dynamic component, which introduces the novel nonlinear multiobjective and dynamic real-time predictive optimization approach. Two multiobjective formulations to obtain Pareto front optimal in real time are explored: the modified Tchebycheff-based weighted metric and epsilon-constraint methods. Economic and environmental objectives are considered in this study. A novel topical discussion on the intersection of dynamic real-time optimization with model predictive control is also presented. The developed framework is successfully applied to a baseload coal-fired power plant with postcombustion CO2 capture. Results indicate that the approach can be deployed for a large-scale system if automatic differentiation, model reduction, and parallelization are adopted to improve computational tractability, with computational improvement up to 120-folds after performing these steps. Finally, market and carbon policies showed an impact on the optimal compromise between the objectives with an additional 63 ton of CO2 captured under favorable market conditions.

关键词： dynamic real-time optimization energy systems multi-objective optimization nonlinear systems optimization algorithms variable renewable energy

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A novel heuristic algorithm for solving engineering optimization and real-world problems: People identity attributes-based information-learning search optimization

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2023年 416卷

作者： Wang, Kaiguang Guo, Min Dai, Cai Li, Zhiqiang Shaanxi Normal Univ Sch Comp Sci Key Lab Modern Teaching Technol Minist Educ Xian 710119 Peoples R China

With the scale and dimension of engineering optimization and real-world problems increasing, it will be difficult to find the optimum solutions. This paper proposes a novel people identity attributes-based heuristic technology, named the People Identity Attributes-based Information-learning Search optimization (ISO), inspired by people's psychological assessment and learning behaviors for information resources based on the social status-based identity attribute and the self-survival demands-based identity attribute. For the former, a four-level information delivery mechanism is constructed through leader, manager, executor, and freelancer, emphasizing global optimization. For the latter, the staged transforming model based on random accumulation and directed induction behaviors are constructed according to self-survival demands, emphasizing global optimization and exploration. The dual identity attributes-based search strategy emphasizes psychological assessment and selection for the information loss, including the learning behavior for two kinds of information resources with different identity attributes, and the recovering behavior for the information loss by constructing the information resilience equation, which emphasizes local optimization and exploitation. This paper qualitatively analyzes the swarm behavior, search history, and the exploration and exploitation capabilities of ISO. The optimization performances are quantitatively analyzed for ISO and 9 competitive algorithms on 39 benchmark tests, including the convergence, solution accuracy, robustness, sensitivity, significance, statistical investigation-based Wilcoxon test and Friedman test. The scalability of ISO is investigated on CEC2017 (30Dim, 50Dim, 100Dim) and the latest CEC2022 (10Dim, 20Dim) suites. The results reveal that compared to other competitive algorithms, ISO possesses best computing performance with ranking the first in all competitors. In addition, the proposed ISO and 12 competitors co

关键词： optimization algorithms Metaheuristics Constraint optimization Benchmarks Engineering problems Path planning

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Psychoacoustic optimization of the Spacing of Propellers, Helicopter Rotors, and Axial Fans

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JOURNAL OF PROPULSION AND POWER 2016年第6期32卷 1422-1432页

作者： Anghinolfi, Davide Canepa, Edward Cattanei, Andrea Paolucci, Massimo Univ Genoa DIBRIS Via AllOpera Pia 13 I-16145 Genoa Italy Univ Genoa DIME Via Montallegro 1 I-16145 Genoa Italy

The paper deals with the minimization of the subjective annoyance (that is, of the quality of the received sound rather than of its acoustic power) due to tonal noise generated by subsonic low-solidity axial-flow rotors. The employed method is based on the uneven spacing of the blades, is typically applied to open or nearly open rotors, and is based on acoustic interference. Thus, linear propagation of the acoustic waves is a prerequisite for its application. An optimization problem is formulated for which the objective function is derived from a psychoacoustic metric, the decision variables are the blades angular positions, the sound pressure level spectrum generated by a single blade is a parameter, and the constraints are the rotor balancing and the minimum angular distance between adjacent blades. A further intuitive criterion, based on lowering the highest peaks, is also considered. The solution of the problem is critical due to the presence of a very large number of minima in the objective function, which prevents employment of the most common optimization algorithms. Thus, a method based on the simulated annealing is modified and adapted to the present problem. The application to a number of test cases and the analysis of the results show that the method is effective in finding the optimal solution and is computationally affordable.

关键词： sound power axial flow fans PROPELLER Objective function Blades PROBLEM SOLUTION CASING INSPECTION rotor balancing Helicopter rotors Psychoacoustics optimal solution Spacing Acoustic Wave optimization algorithms Rotors

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EAOA: An Enhanced Archimedes optimization Algorithm for Feature Selection in Classification

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IEEE ACCESS 2021年 9卷 120795-120814页

作者： Desuky, Abeer S. Hussain, Sadiq Kausar, Samina Islam, Md Akhtarul El Bakrawy, Lamiaa M. Al Azhar Univ Fac Sci Cairo 11754 Egypt Dibrugarh Univ Examinat Branch Dibrugarh 786004 Assam India Univ Kotli Azad Jammu & Kashmir Dept Comp Sci & IT Kotli Azad Kashmir 11100 Pakistan Khulna Univ Stat Discipline Sci Engn & Technol Sch Khulna 9208 Bangladesh

Feature selection plays a crucial role in order to mitigate the high dimensional feature space in different classification problems. The computational cost is reduced, and the accuracy of the classification is improved by reducing the dimension of feature space. Hence, in the classification task, finding the optimal subset of features is of utmost importance. Metaheuristic techniques have proved their efficacy in solving many real-world optimization issues. One of the recently introduced physics-inspired optimization methods is Archimedes optimization Algorithm (AOA). This paper proposes an Enhanced Archimedes optimization Algorithm (EAOA) by adding a new parameter that depends on the step length of each individual while revising the individual location. The EAOA algorithm is proposed to improve the AOA exploration and exploitation balance and enhance the classification performance for the feature selection issue in real-world data sets. Experiments were performed on twenty-three standard benchmark functions and sixteen real-world data sets to investigate the performance of the proposed EAOA algorithm. The experimental results based on the standard benchmark functions show that the EAOA algorithm provides very competitive results compared to the basic AOA algorithm and five well-known optimization algorithms in terms of improved exploitation, exploration, local optima avoidance, and convergence rate. In addition, the results based on sixteen real-world data sets ascertain that reduced feature subset yields higher classification performance when compared with the other feature selection methods.

关键词： Archimedes optimization algorithm classification feature selection machine learning metaheuristic algorithms optimization algorithms

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IoT Intrusion Detection System Using Deep Learning and Enhanced Transient Search optimization

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IEEE ACCESS 2021年 9卷 123448-123464页

作者： Fatani, Abdulaziz Abd Elaziz, Mohamed Dahou, Abdelghani Al-Qaness, Mohammed A. A. Lu, Songfeng Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China Umm Al Qura Univ Comp Sci Dept Mecca 24381 Saudi Arabia Zagazig Univ Fac Sci Dept Math Zagazig 44519 Egypt Univ Ahmed Draia Fac Sci & Technol LDDI Lab Adrar 01000 Algeria Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China Huazhong Univ Sci & Technol Hubei Engn Res Ctr Big Data Secur Sch Cyber Sci & Engn Wuhan 430074 Peoples R China Shenzhen Huazhong Univ Sci & Technol Res Inst Shenzhen 518057 Peoples R China

The great advancements in communication, cloud computing, and the internet of things (IoT) have opened critical challenges in security. With these developments, cyberattacks are also rapidly growing since the current security mechanisms do not provide efficient solutions. Recently, various artificial intelligence (AI) based solutions have been proposed for different security applications, including intrusion detection. In this paper, we propose an efficient AI-based mechanism for intrusion detection systems (IDS) in IoT systems. We leverage the advancements of deep learnings and metaheuristics (MH) algorithms that approved their efficiency in solving complex engineering problems. We propose a feature extraction method using the convolutional neural networks (CNNs) to extract relevant features. Also, we develop a new feature selection method using a new variant of the transient search optimization (TSO) algorithm, called TSODE, using the operators of differential evolution (DE) algorithm. The proposed TSODE uses the DE to improve the process of balancing between exploitation and exploration phases. Furthermore, we use three public datasets, KDDCup-99, NSL-KDD, BoT-IoT, and CICIDS-2017 to assess the performance of the developed method, which achieved higher accuracy compared to several existing approaches.

关键词： Feature extraction Intrusion detection Transient analysis Deep learning Convolutional neural networks Cloud computing Internet of Things Internet of Things (IoT) security cyberattack intrusion detection system feature selection optimization algorithms

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Precise modeling of PEM fuel cell using improved chaotic MayFly optimization algorithm

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2021年第13期45卷 18754-18769页

作者： Shaheen, Mohamed A. M. Hasanien, Hany M. El Moursi, M. S. El-Fergany, Attia A. Future Univ Egypt Elect Engn Dept Cairo 11835 Egypt Ain Shams Univ Elect Power & Machines Dept Fac Engn Cairo Egypt Khalifa Univ Elect Engn & Comp Sci Dept Abu Dhabi U Arab Emirates Zagazig Univ Elect Power & Machines Dept Fac Engn Zagazig Egypt

The accuracy of the modeling of the fuel cell is important for achieving precise simulation results. This article presents a newly developed optimization method "Chaotic MayFly optimization algorithm" (CMOA) for obtaining the proton exchange membrane fuel cell (PEMFC) parameters. This research mainly targets an accurate modeling of the PEMFC that provides good match between the simulation results and those measured practically. In this regard, the I-V characteristics of the PEMFC's are non-linear, and there are seven design variables are considered because of the manufacturer's shortage in providing such information. The optimization problem formulated in this study is a non-linear problem. The objective function is mathematically expressed as the total squared error between the PEMFC terminal voltage measured in the laboratory vs the estimated terminal voltage from the simulation of the model. Since the metaheuristic optimization techniques are significantly influenced by the problem initialization, a new hybridization between the chaotic mapping and the MOA is employed to tackle the problem of the PEMFC design variables estimation and achieving better results. The CMOA is applied to find the best solution of the objective function that satisfies the preset conditions. The accurateness of the PEMFC approximated model is verified numerically using the optimal design variables. The simulation results are verified under various conditions of temperature and pressure. The estimated numerical results are compared with the measured data in case of many standard PEMFCs, such as Ballard, Mark V 5 kW, 500 W BCS, and 250 W stacks. The robustness of the proposed CMOA applied to the PEMFC model is also tested. The findings of the simulations of the proposed CMOA are compared with other findings obtained by other optimization methods. Applying the CMOA results in an accurate development of the PEMFC model.

关键词： fuel cell modeling optimization algorithms parameter determination polarization curves

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Optimal Water Distribution Network Design with Honey-Bee Mating optimization

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JOURNAL OF COMPUTING IN CIVIL ENGINEERING 2010年第1期24卷 117-126页

作者： Mohan, S. Babu, K. S. Jinesh Indian Inst Technol Dept Civil Engn Madras 600036 Tamil Nadu India

Water distribution network is a costly infrastructure and plays a crucial role in supplying water for the consumers especially for those who are living in the urban areas. The importance and huge capital cost of the system leads to considerable attention on seeking the optimal cost design. The necessity for such a sound research attention arises from the complexity associated with the problem. In the recent years, stochastic optimization algorithms like genetic algorithm, simulated annealing, ant colony optimization etc. are found to be successful in exploring the optimal combination of pipe diameters that can satisfy the hydraulic-head requirements with least cost. In this paper, the details on the optimal water distribution network design with a novel technique called honey-bee mating optimization and its validation with two benchmark water distribution networks are presented. From the results, it is observed that the proposed algorithm identifies the optimal solution with relatively less number of evaluations than the other well-established stochastic optimization algorithms.

关键词： optimization algorithms Stochastic models Water distribution systems Water supply

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Automated Multitrajectory Method for Reaction optimization in a Microfluidic System using Online IR Analysis

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ORGANIC PROCESS RESEARCH & DEVELOPMENT 2012年第8期16卷 1409-1415页

作者： Moore, Jason S. Jensen, Klavs F. Dept Chem Engn Novartis MIT Ctr Continuous Mfg Ma Cambridge MA 02139 USA

An automated multitrajectory optimization platform with continuous online infrared (IR) monitoring is presented. The production rate of a Paal-Knorr reaction is maximized within a constrained temperature and residence time design space. The automated platform utilizes a microreactor system to carry out optimizations with low material requirements and implements a micro IR flow cell for continuous online monitoring of reaction conversion. The approach to steady state at each set of reaction conditions is assessed continuously before the objective function is evaluated and reactor conditions move to the next set point. Several optimization algorithms are tested for their performance on a complex objective terrain. Each function comes to agreement on the optimal conditions but requires a significantly different number of experiments to reach the final conditions. Additionally, multiple objective functions are compared to analyze the trade-off between production rate and conversion.

关键词： on line detection Automation Objective function iridium reaction optimization Natural Increase Microfluidics Conversion Disorder optimization algorithms Set of points

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Gannet optimization algorithm : A new metaheuristic algorithm for solving engineering optimization problems

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MATHEMATICS AND COMPUTERS IN SIMULATION 2022年 202卷 343-373页

作者： Pan, Jeng-Shyang Zhang, Li-Gang Wang, Ruo-Bin Snasel, Vaclav Chu, Shu-Chuan Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao Peoples R China Chaoyang Univ Technol Dept Informat Management Taichung Taiwan North China Univ Technol Sch Informat Sci & Technol Beijing Peoples R China VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci Ostrava Czech Republic

Engineering design problems are usually large-scale constrained optimization problems, and metaheuristic algorithms are vital for solving such complex problems. Therefore, this paper introduces a new nature-inspired metaheuristic algorithm: the gannet optimization algorithm (GOA). The GOA mathematizes the various unique behaviors of gannets during foraging and is used to enable exploration and exploitation. GOA's U-shaped and V-shaped diving patterns are responsible for exploring the optimal region within the search space, with sudden turns and random walks ensuring better solutions are found in this region. In order to verify the ability of the GOA to find the optimal solution, we compared it with other comparison algorithms in multiple dimensions of 28 benchmark functions. We found that the GOA has a shorter running time in high dimensions and can provide a better solution. Finally, we apply the GOA to five engineering optimization problems. The experimental results show that the GOA is suitable for many constrained engineering design problems and can provide better solutions in most cases.(c) 2022 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： Gannet optimization algorithm optimization algorithms Swarm intelligence Evolutionary computation

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3D Mesh Animation optimization Algorithm Based on Integer Programming and Nonlinear Constraints

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IEEE ACCESS 2024年 12卷 86649-86661页

作者： Jiang, Jing Wang, Xiaojun Anhui Xinhua Univ Coll Arts Hefei 230088 Peoples R China

With the rapid development of digital media and entertainment industry, the importance of 3D mesh animation has become increasingly prominent. However, the traditional production methods are faced with problems such as high computational complexity and unnatural effects when dealing with complex motion. To improve the production efficiency and quality of 3D mesh animation, this study innovatively integrates integer programming, nonlinear constraint optimization, and machine learning algorithms to construct a new 3D mesh animation optimization algorithm. Comparative analysis of the improved machine learning algorithm shows that the mean absolute error of the algorithm is 0.00048 and the fit degree is 98.8%, which is better than the comparison algorithm. Then, the performance of the proposed 3D mesh animation optimization algorithm is analyzed. The results show that the rendering speed and average frame rate of the proposed algorithm are 29.5 FPS and 28.7 FPS, respectively, which is superior to the comparison algorithm. The algorithm can effectively improve the efficiency and quality of animation production, and inject new vitality into the development of digital media and entertainment industry. This study not only provides new ideas and methods for optimizing 3D mesh animation, but also provides useful references for research and applications in related fields.

关键词： Animation Three-dimensional displays Machine learning algorithms optimization Integer programming Production Mathematical models Digital communication Mesh networks 3D mesh animation integer programming nonlinear constraints machine learning optimization algorithms

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