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A particle swarm optimization algorithm with novelty search for combustion systems with ultra-low emissions and minimum fuel consumption

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APPLIED SOFT COMPUTING 2023年 143卷

作者： Martinez-Rodriguez, David Novella, Ricardo Bracho, Gabriela Gomez-Soriano, Josep Fernandes, Cassio Lucchini, Tommaso Della Torre, Augusto Villanueva, Rafael-J. Hidalgo, J. Ignacio Univ Politecn Valencia Inst Univ Matemat Multidisciplinar Algiros Spain Univ Politecn Valencia Ctr Motores Term Algiros Spain Politecn Milan Milan Italy Univ Complutense Madrid Dept Comp Arquitecture Madrid Spain Univ Complutense Madrid Inst Tecnol Conocimiento Madrid Spain Univ Politecn Valencia CMT Motores Term E-46022 Valencia Spain

The particle swarm optimization algorithm is primarily inspired by the natural behaviour of swarms and achieves important results in different applications. However, it is not exempt from stagnation in local optima and has a tendency to prematurely converge to them. Novelty Search is a concept that appeared recently in different fields of computational intelligence. It aims at exploring non-visited areas of the search space through solutions that bring novelty to already discovered solutions. The novelty of this work can be divided into two steps: on one side, this article proposes a variant of the particle swarm optimization algorithm which uses Novelty Search concepts to improve the algorithm's performance. Our proposal is first checked and compared using the CEC 2005 benchmark suite and then, we apply it to solve a real-world optimization problem: the design of a combustion system targeting the reduction of pollutant emissions and fuel consumption. The combustion chamber design phase usually is a complex and time-consuming process even with advanced supercomputers, since it depends on several input variables which are highly non-linear and with crossed interaction. Then, the second contribution of this work is to develop a methodology that couples a computational fluid dynamics (CFD) simulation tool with the new optimization algorithm for minimizing the specific fuel consumption of a compression-ignited engine, while constraining the NOx and soot emissions. A 3D-CFD model of the combustion system was built to predict and analyse the performance of the combustion system and hence, select the parameters with a higher impact on the system. The method reduces the computational time and includes tools for the automatic preparation of the input parameters and geometry of the system. The input parameters correspond to geometrical variables that control the bowl shape, the number of holes in the injector, the injection pressure, the swirl number and the exhaust gas recir

关键词： PSO NS optimization algorithm CI engine Combustion Emissions Efficiency

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Enhanced design parameters of a cylindrical helical compression spring using an optimization algorithm and a non-linear behavior model

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MECHANICS BASED DESIGN OF STRUCTURES AND MACHINES 2024年第12期52卷 9609-9620页

作者： Cadet, Guillaume Paredes, Manuel Univ Toulouse ICA CNRS UMR 5312 INSA ISAE SUPAEROMINES-ALBIUPS Toulouse France Univ Toulouse ICA UPS INSAISAE SUPAEROMINES ALBICNRS 3 rue Caroline Aigle F-31400 Toulouse France

An optimization algorithm based on SLSQP solver was created to design helical compression springs. The goal is to reach the required behavior even when the standards formula is inaccurate. Indeed, it was already shown in previous works that the classic formula determining the global load-length behavior of the spring is not always accurate enough because it does not consider the effects of the spring's ends, in particular for springs with low index and low number of coils. Based on a new tri-linear model, this algorithm finds optimized spring design which respects the required operative points. In order to test the algorithm, the first tests were carried out to optimize the design of a PLA 3D-printed spring. It was shown that the initial design stiffness is overestimated by over 28% by the formula extracted from the standards. Then, the design of the spring was implemented in the algorithm to optimize the pitch and the number of active coils. The experimental curve of the optimized design spring goes through the target behavior, with a non-linearity at the beginning of the deflection. The experimental error about the final stiffness is successfully reduced to 1.1%. It is hoped that this work will provide a valuable progress to assist engineers and manufacturers in helical spring design.

关键词： optimization algorithm 3D printing manufacturing helical spring

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An online optimization algorithm for the real-time quantum state tomography

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QUANTUM INFORMATION PROCESSING 2020年第10期19卷 361-361页

作者： Zhang, Kun Cong, Shuang Li, Kezhi Wang, Tao Univ Sci & Technol China Dept Automat Hefei 230027 Peoples R China UCL 222 Euston Rd London NW1 2DA England

Considering the presence of measurement noise in the continuous weak measurement process, the optimization problem of online quantum state tomography (QST) with corresponding constraints is formulated. Based on the online alternating direction multiplier method (OADM) and the continuous weak measurement (CWM), an online QST algorithm (QST-OADM) is designed and derived. Specifically, the online QST problem is divided into two subproblems about the quantum state and the measurement noise. The proposed algorithm adopts adaptive learning rate and reduces the computational complexity to O(d(3)), which provides a more efficient mechanism for real-time quantum state tomography. Compared with most existing algorithms of online QST based on CWM which require time-consuming iterations in each estimation, the proposed QST-OADM can exactly solve two subproblems at each sampling. The merits of the proposed algorithm are demonstrated in the numerical experiments of online QST for 1-, 2-, 3-, and 4-qubit systems.

关键词： Online quantum state tomography optimization algorithm Online alternating direction multiplier method Continuous weak measurement

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Energy Aware Resource optimization using Unified Metaheuristic optimization algorithm Allocation for Cloud Computing Environment

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2022年 35卷

作者： Al-Wesabi, Fahd N. Obayya, Marwa Hamza, Manar Ahmed Alzahrani, Jaber S. Gupta, Deepak Kumar, Sachin King Khalid Univ Coll Sci & Arts Muhayel Dept Comp Sci Muhayel Aseer Saudi Arabia Sanaa Univ Fac Comp & Informat Technol Sanaa Yemen Princess Nourah bint Abdulrahman Univ Dept Biomed Engn Coll Engn POB 84428 Riyadh 11671 Saudi Arabia Prince Sattam bin Abdulaziz Univ Dept Comp & Self Dev Alkharj Saudi Arabia Umm Al Qura Univ Coll Engn Alqunfudah Dept Ind Engn Mecca Saudi Arabia Maharaja Agrasen Inst Technol Dept Comp Sci & Engn Delhi India South Ural State Univ Dept Comp Sci Chelyabinsk Russia

In recent times, cloud computing (CC) has rapidly emerged as an effective framework for offering IT infrastructure, resources, and services on a pay-per-use basis. An extensive utilization of CC and virtualization technologies has resulted in the development of large-scale data centers which spend massive quantity of energy and have significant carbon footprints. Since 3% of global electricity is being consumed by the data centers in the present world, energy efficiency becomes a major issue in data centres and cloud computing. At the same time, resource allocation finds useful in CC to effectively utilize the available computing resources in the network for facilitating the processing of complex task which necessitate large-scale processing. In this view, this paper presents new hybrid metaheuristics for energy efficiency resource allocation (HMEERA) for the CCC environment. The proposed model initially performs the feature extraction process based on the task demands from many clients and feature reduction process takes place using principal component analysis (PCA). Then, the integrated features are used by the HMEERA technique for optimal resource allocation. The HMEERA model involves the hybridization of the Group Teaching optimization algorithm (GTOA) with rat swarm optimizer (RSO) algorithm, called GTOA-RSO for optimal resource allocation. The integration of GTOA and RSO algorithms assist to improve the allocation of resources among VMs in cloud datacenter. For experimental validation, a comprehensive set of simulations were performed using CloudSim tool. The experimental results showcased the superior performance of the HMEERA model interms of different aspects.

关键词： Cloud Computing Resource allocation Metaheuristics Energy efficiency GTOA Feature extraction optimization algorithm

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Non-invasive method of melanoma detection on the skin surface through extraction of image features using modified CAT optimization algorithm

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CURRENT SCIENCE 2023年第5期124卷 562-569页

作者： Prabhakaran, N. Aalim Muhammed Salegh Coll Engn Dept Elect & Commun IAF Chennai 600055 India

In this study, melanoma was detected at an early stage using modified CAT optimization algorithm (MCOA) based on non-convex boundary edge extraction, pixel size, shape and intensity variations on the skin. MCOA can detect skin cancer at an early stage by extracting the non-convex border of the affected region prevent cancer spread. Thus melanoma is curable when detected at an early stage. MCOA extracts image features and obtains non-convex boundaries of melanoma in the skin image. The non-convex boundary region leads to visua-lization of discriminative features of melanoma based on the region of interest and scaling. The proposed MCOA delineates the affected region through non-convex border extraction and edge detection. An accuracy of 85% was obtained in the detection of melanoma using MCOA, when compared to traditional algorithms.

关键词： Contour refinement edge detection mela-noma non-convex boundary optimization algorithm

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Hybrid whale optimization algorithm with gathering strategies for high-dimensional problems

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 179卷 115032-115032页

作者： Zhang, Xinming Wen, Shaochen Henan Normal Univ Coll Comp & Informat Engn Xinxiang Henan Peoples R China Engn Lab Intelligence Business & Internet Things Xinxiang Henan Peoples R China

In order to solve the problems, such as insufficient search ability and low search efficiency, of Whale optimization algorithm (WOA) in solving high-dimensional problems, a novel Hybrid WOA with Gathering strategies (HWOAG) is proposed in this paper. Firstly, an individual-based updating way is used in HWOAG instead of the dimension-based updating one of WOA to reduce the computational complexity and to be more suitable for highdimensional problems. Secondly, a random opposition learning strategy is embedded into the individual-based WOA to form an opposition learning WOA (OWOA), and Grey Wolf Optimizer (GWO) is integrated into OWOA to form an OWOA with GWO (OWOAG) so as to improve the global search ability of WOA. Finally, two standalone OWOAGs are formulated to balance exploration and exploitation better. The two OWOAGs adopt strategies such as switching parameter tuning, random differential disturbance and global-best spiral operator to get stronger search ability. A lot of experimental results on high-dimensional (i.e. 1000-, 2000-, 4000- and 8000dimensional) benchmark functions and clustering datasets for Fuzzy C-Means (FCM) optimization show that HWOAG has stronger search ability and higher search efficiency than WOA and quite a few state-of-the-art algorithms and that all the strategies gathered to WOA are effective. The source codes of the proposed algorithm HWOAG are available at https://***/kangzhai/HWOAG.

关键词： optimization algorithm Whale optimization algorithm Grey Wolf optimizer High-dimensional problems Fuzzy C-means

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Application Research of CFD-MOEA/D optimization algorithm in Large-Scale Reservoir Flood Control Scheduling

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PROCESSES 2022年第11期10卷 2318-2318页

作者： Jiao, Hongbo Wei, Huaibin Yang, Qi Li, Min North China Univ Water Resources & Elect Power Sch Water Resources Zhengzhou 450046 Peoples R China

Reservoir flood control has an important impact on flood protection and plays an important role in reducing the loss of people's lives and property. In order to play an important role in flood control operation of large-scale reservoirs, a control flood dispatching multi-objective evolutionary algorithm based on decomposition (CFD-MOEA/D) is proposed. The same type of multi-objective optimization algorithm (non-dominated sorting genetic algorithm II (NSGA-II)) is introduced, and CFD-MOEA/D, NSGA-II, and traditional MOEA/D algorithms are compared. The research results show that the CFD-MOEA/D algorithm can obtain the non-dominated solution of the higher water level in the upstream, and the solution obtained by the CFD-MOEA/D algorithm is more sufficient than the NSGA-II algorithm and the MOEA/D algorithm. When analyzing the HV value curve, the uniformity and convergence of the optimal solution obtained by the CFD-MOEA/D algorithm are better than those of the other two algorithms. The optimal dispatching scheme of the CFD-MOEA/D algorithm is compared with the actual dispatching scheme of the reservoir, and it is found that the maximum upstream water level and the final water level obtained by the CFD-MOEA/D algorithm are both kept at approximately 325 m, which is consistent with the actual dispatching scheme. The new feature of the algorithm is that it uses a decomposition method from coarse to fine and improves the hourly scheduling scheme to obtain higher scheduling efficiency.

关键词： CFD-MOEA D optimization algorithm pareto optimal solution reservoir flood control scheduling

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Multi-objective proportional-integral-derivative optimization algorithm for parameters optimization of double-fed induction generator-based wind turbines

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APPLIED SOFT COMPUTING 2021年 110卷 107673-107673页

作者： Yin, Linfei Gao, Qi Guangxi Univ Coll Elect Engn Nanning 530004 Guangxi Peoples R China Guangxi Univ Inst Artificial Intelligence Nanning 530004 Guangxi Peoples R China

The meta-heuristic algorithm inspired by natural may reduce the optimization performance due to excessive imitation. This paper proposes a novel multi-objective proportional-integral-derivative optimization algorithm inspired by mathematical thought to provide a better non-dominated solution for multi-objective problems. The idea of the proportional-integral-derivative control algorithm is introduced to cooperate with multi-objective optimization problems for the first time. The proposed algorithm is employed to store and maintain non-dominated solutions. Two groups of controllers of the proposed algorithm are designed for the multi-objective optimization problems, i.e., exploitative controllers aim to obtain the local optimal solution;explorative controllers aim to obtain the global optimal solution. To verify the effectiveness of the multi-objective proportional-integral-derivative optimization algorithm, eight comparison algorithms are compared with eight benchmark functions;five comparison algorithms are compared under the multi-objective parameters optimization problem of double-fed induction generator-based wind turbines. The results of benchmark functions show that the multi-objective proportional-integral-derivative optimization algorithm has superior convergence performances and outperforms other comparison algorithms. The proposed algorithm has excellent optimization performance to obtain the minimum deviation of rotor speed and reactive power for the wind power system controller. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Multi-objective optimization problems Multi-objective proportional-integral-derivative optimization algorithm Meta-heuristic algorithm Double-fed induction generator

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Predictive optimization algorithm for beam combination systems based on adaptive fiber optics collimators

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OPTICS AND LASERS IN ENGINEERING 2022年 148卷 106753-106753页

作者： Huang, Guan Lv, Guoyun Fan, Yangyu Geng, Chao Li, Xinyang Northwestern Polytech Univ Sch Elect & Informat Xian 710129 Peoples R China Chinese Acad Sci Key Lab Adapt Opt Chengdu 610209 Peoples R China Chinese Acad Sci Inst Opt & Elect Chengdu 610209 Peoples R China

Beam combining of fiber lasers based on adaptive fiber optics collimator (AFOC) plays an important role in the laser propagation applications, whereas the system performance is severely affected by the target occlusion during the beam's long-range atmosphere transmission. In this paper, the sources, characteristics, and potential impacts of the target occlusion were analyzed for the first time, and the predictive stochastic parallel gradient descent (P-SPGD) algorithm was proposed to solve this problem. The basic principle of P-SPGD was to monitor the target occlusion in real-time, and then dynamically choose whether to use the observed gradient or the predicted gradient to update the control voltages of AFOCs. Beam combining experiments based on three AFOCs were conducted in the free space with a distance of 40 m. The results showed that when the moving target was obscured for 3 s, the AFOCs based on the traditional optimization algorithm had lost track of the target, whereas the P-SPGD algorithm can detect this emergency with a probability of 98.8%, and accurately model the motion state of the target. As a result, the AFOCs can still achieve stable target tracking during the period of target occlusion, and keep the combined beams from divergence.

关键词： Laser beam combining optimization algorithm Model predictive control Target occlusion Adaptive fiber optics collimator

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On further enhancing the bandwidth of wideband RCS reduction checkerboard metasurfaces using an optimization algorithm

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INTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING 2021年第7期31卷 e22686-e22686页

作者： Murugesan, Akila Selvan, Krishnasamy T. Sri Sivasubramaniya Nadar SSN Coll Engn Dept Elect & Commun Engn Kalavakkam Tamil Nadu India

This article reports an optimization procedure for further enhancing the wideband radar cross section (RCS) reduction bandwidth using a checkerboard metasurface (CMS). The improved fruitfly optimization algorithm is used to determine the unit cells dimensions that offer a further enhanced bandwidth. Two previously reported CMSs are chosen: (a) A square-circle pair designed on Rogers 5880 with a thickness of 6.35 mm and (b) a Jerusalem cross pair designed on Rogers 3010 with a thickness of 1.27 mm. First, unit cell (a) is optimized, resulting in an improvement of 7% from the originally reported bandwidth. Similarly, unit cell (b) shows an enhancement of 5%. Because of cost considerations and in the interest of exploring alternative methods, the substrates in both cases are replaced by Taconic TLY-3 with a thickness of 1.57 mm over an air gap of 5 mm. After optimization, this configuration enhances the 10 dB RCS reduction bandwidth substantially to 81% for (a) and 102% for (b). It is also observed that the - 15 dB bandwidth of the Jerusalem cross-based CMS is 48%, which is the widest reported so far. The alternative configurations of (a) and (b) are fabricated and measured. The measurements are in good agreement with the simulation results.

关键词： bandwidth checkerboard metasurface optimization algorithm wideband RCS reduction

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