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A new hybrid optimization ensemble learning approach for carbon price forecasting

APPLIED MATHEMATICAL MODELLING 2021年 97卷 182-205页

作者： Sun, Shaolong Jin, Feng Li, Hongtao Li, Yongwu Xi An Jiao Tong Univ Sch Management Xian 710049 Peoples R China Lanzhou Jiaotong Univ Sch Traff & Transportat Lanzhou 730070 Peoples R China Beijing Univ Technol Coll Econ & Management Beijing 100124 Peoples R China

Accurate carbon price forecast plays a vital role in energy conservation, emission reduction and environmental protection. In previous studies, more attention was focused on the prediction accuracy and stability, while the problem of disharmony between the prediction model and the data pattern is usually ignored. Considering the matching utility with deeper understanding of data and model, this paper proposes a novel approach to forecast carbon price, which combines the data preprocessing mechanism, decomposition technology, forecast module with selection and matching strategy and ensemble model based on an original hybrid optimization algorithm. According to a comprehensive evaluation index in consideration of several evaluation perspectives, the optimal parameter structures of the three forecast models are selected in this framework. Then, the data components decomposed by variational mode decomposition are reconstructed into three novel range entropy series with different levels of complexity by range entropy. As a result, the matching relation between the three forecasting models and the three range entropy series is correspondingly established. Additionally, a feedback neural network optimized by hybrid optimization algorithm, which persists more superiorities of reasonable weight assignment than the usual ensemble method, is initially used to synthesize three forecasting results of range entropy series. The carbon price data from four different trading markets in China is used to test the novel approach and the experimental results indicate that it does enhance the performance of carbon price forecasting, and provide a convincing tool for the operation and investment of the carbon markets. (c) 2021 Elsevier Inc. All rights reserved.

关键词： Carbon price forecasting Comprehensive evaluation index Model matching strategy hybrid optimization algorithm

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Double-Loop-optimization-Based Joint Parameter Tuning for Dynamic Matrix Control With Validation in Intelligent Cigarette Drying Process

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第5期20卷 7303-7315页

作者： Zhang, Yang Wang, Peng Li, Ning Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

Dynamic matrix control (DMC) is widely used in intelligent manufacturing. Its performance is heavily affected by coupled parameters, e.g., horizons and weighting matrices. However, existing methods only focus on tuning of single type of parameter and cannot jointly tune them to improve the performance of DMC comprehensively. To bridge the gap, we propose a framework to tune DMC parameters based on double-loop-optimization jointly. The horizons of prediction and control are tuned in the outer loop, while the weighting matrices of error and control are tuned in the inner loop. An improved genetic algorithm fused with the particle swarm optimization is developed to minimize the cost function considering the constraints on horizons and weighting matrices. In the developed algorithm, population evolution and individual updating are deeply integrated to improve global optimality. The efficacy of the proposed framework is verified with real data of moisture control in cigarette drying process, which improves the quality of the moisture control by stabilizing the production equipment and reducing energy consumption.

关键词： Monte Carlo methods Tuning Moisture control Statistics Sociology Predictive models Heuristic algorithms Double-loop-optimization (DLO) framework dynamic matrix control (DMC) hybrid optimization algorithm intelligent moisture control joint tuning

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TWT magnetic focusing structure optimization by parallel evolutionary algorithm

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COMPEL-THE INTERNATIONAL JOURNAL FOR COMPUTATION AND MATHEMATICS IN ELECTRICAL AND ELECTRONIC ENGINEERING 2012年第5期31卷 1338-1346页

作者： Coco, Salvatore Laudani, Antonino Pollicino, Giuseppe Pulcini, Giuseppe Fulginei, Francesco Riganti Salvini, Alessandro Univ RomaTre Dipartimento Elettron Applicata Rome Italy Univ Catania Dipartimento Ingn Elettr Elettron & Informat Catania Italy Univ RomaTre Res Unit Electrotech Rome Italy

Purpose - The purpose of this paper is to present the application of a novel hybrid algorithm, called MeTEO (Metric-Topological-Evolutionary-optimization), based on the combination of three heuristics inspired by artificial life to the solution of optimization problems of a real electronic vacuum device. Design/methodology/approach - The Particle Swarm optimization (PSO), the Flock-of-Starlings optimization (FSO) and the Bacterial Chemotaxis algorithm (BCA) were adapted to implement a novel meta-heuristic MeTEO the FSO has been powerfully employed for exploring the whole space of solutions, whereas the PSO is used to explore local regions where FSO had found solutions, and BCA to refine the solutions found by PSO, thanks its better performances in local search. Findings - The optimization of the focusing magnetic field of a Travelling Wave Tubes (TWT) collector is presented in order to show the effectiveness of MeTEO, in combination with COLLGUN FE simulator and equivalent source representation. The optimization of the focusing magnetic structure is obtained by using a maximum of 100 steps for each heuristic. Practical implications - The paper describes the development of a novel efficient parallel method for the solution of electromagnetic device optimization problems. Originality/value - The paper shows the capabilities of a novel combination of optimization methods inspired by "artificial life" which allows us to achieve effective solutions of multimodal optimization problems, typical of the electromagnetic device optimization, with an acceptable computational cost, thanks also to its natural parallel implementation.

关键词： Programming and algorithm theory Artificial intelligence Magnetic fields Swarm intelligence Artificial life hybrid optimization algorithm Traveling wave tube Focusing magnetic field Particle-in-cell method Finite element method

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Research on optimization method for flyby observation mission adapted to satellite on-board computation

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2024年第6期238卷 629-644页

作者： Wang, Jiyuan Xiao, Yan Ye, Dong Wang, Zijun Sun, Zhaowei Harbin Inst Technol Res Ctr Satellite Technol Mailbox 30132 Yi Kuang St Harbin 150001 Peoples R China Shanghai Satellite Engn Inst Dept Remote Sensing Satellite Shanghai Peoples R China

In this paper, the optimization problem of orbital transfer strategy for orbital flyby observation missions is studied. A hybrid optimization method is proposed, which is improved to make it more suitable for satellite on-board computing. This new algorithm is designed to solve the initial value sensitivity problem of the sequential quadratic programming algorithm (SQP). It is consisted of the depth-first search algorithm (DFS) and the SQP algorithm and thus has the characteristics of fast convergence, high reliability, and good robustness. With this method, the DFS with a large step size is calculated first, and then the optimal value in the calculation result is used as the initial value of the SQP algorithm for further optimization. This method can obtain the approximate optimal solution available in engineering. The numerical simulation of an orbital transfer optimization problem is set to verify the effectiveness of the new hybrid algorithm. The simulation results compared with the genetic algorithm (GA) show that the proposed hybrid algorithm can effectively reduce the on-board resource occupation when getting similar results and thus can meet the needs of satellite on-board computing.

关键词： Orbital observation optimal orbital transfer hybrid optimization algorithm sequential quadratic programming depth-first search

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Design of band-notched UWB antenna using a hybrid optimization based on ABC and DE algorithms

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AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2018年 87卷 10-21页

作者： Ustun, Deniz Akdagli, Ali Karamanoglu Mehmetbey Univ Dept Comp Engn Fac Engn TR-70100 Karaman Turkey Mersin Univ Dept Elect Elect Engn Fac Engn TR-33343 Yenisehir Mersin Turkey

In this study, a novel UWB antenna with dual band-rejection is designed by using a new hybrid optimization algorithm based artificial bee colony algorithm (ABC) and differential evolution (DE). The hybrid performance of ABC and DE (H-ABCDE) is tested on well-known benchmark functions. To show its performance on a design problem, an optimization interface (OI) which simultaneously communicates with H-ABCDE and an electromagnetic simulation tool is developed. Hence OI integrating H-ABCDE and HyperLynxa (R) 3D EM is used to design and optimize an antenna. A low profile UWB monopole antenna operating over the frequency range of 2.9-13.0 GHz is then designed through OI. In order to achieve band-rejection operation, various slits and strips have been loaded on the antenna. It thus rejects the dual-band of 3.3-3.6 GHz and 5.15-5.825 GHz corresponds to the operation bands of WLAN and WiMAX. Furthermore, the optimized antenna is prototyped to investigate the measurement performance and it is compared with several designs in the literature. Therefore, H-ABCDE can successfully adapt to the extended electromagnetic problem as well as engineering optimization tasks.

关键词： hybrid optimization algorithm Artificial bee colony Differential evolution Ultra-wide band antenna Dual-band rejection

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Immune particle swarm optimization of SUV rollover crashworthiness and weight

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STRUCTURAL AND MULTIDISCIPLINARY optimization 2021年第3期64卷 1161-1174页

作者： Jin, Zhilin Zhang, Senhao Weng, Jiansheng Nanjing Univ Aeronaut & Astronaut Dept Vehicle Engn Nanjing 210016 Peoples R China

When designing a sport utility vehicle (SUV), designers strive to improve the vehicle's rollover crashworthiness while avoiding a significant increase in its weight. To aid in optimizing such a trade-off, this paper proposes a multi-disciplinary and multi-objective hybrid optimization algorithm that combines particle swarm optimization and the artificial immune method. First, the SUV structure's influence on body mass and rollover crashworthiness is studied using contribution analysis, and structural improvements are discussed according to Federal Motor Vehicle Safety Standard 216. Building on the analysis results, the SUV's rollover crashworthiness and weight optimization model are proposed. Radial basis function neural network and a genetic algorithm are used to build and optimize surrogate models of total weight, maximum contact force, and torsion frequency. The proposed algorithm then utilizes particle swarm and artificial immune to seek Pareto solutions that optimize SUV structure. Finally, the technique for order preference by similarity to ideal solution method determines a final solution from Pareto-optimal solutions. Compared to previous studies, the results show that the proposed hybrid optimization algorithm improves the Pareto solution sets' diversity and distribution uniformity, enhances SUV rollover crashworthiness, and reduces SUV structure components' weight.

关键词： SUV rollover crashworthiness Weight reduction Material structure integrated design Multi-disciplinary and multi-objective optimization hybrid optimization algorithm

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Velocity based artificial bee colony algorithm for high dimensional continuous optimization problems

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2014年第0期36卷 148-163页

作者： Imanian, Nafiseh Shiri, Mohammad Ebrahim Moradi, Parham Amirkabir Univ Technol Dept Math & Comp Sci Tehran Iran Univ Kurdistan Dept Comp Engn Sanandaj Iran

Artificial bee colony (ABC) is a swarm optimization algorithm which has been shown to be more effective than the other population based algorithms such as genetic algorithm (GA), particle swarm optimization (PSO) and ant colony optimization (ACO). Since it was invented, it has received significant interest from researchers studying in different fields because of having fewer control parameters, high global search ability and ease of implementation. Although ABC is good at exploration, the main drawback is its poor exploitation which results in an issue on convergence speed in some cases. Inspired by particle swarm optimization, we propose a modified ABC algorithm called VABC, to overcome this insufficiency by applying a new search equation in the onlooker phase, which uses the PSO search strategy to guide the search for candidate solutions. The experimental results tested on numerical benchmark functions show that the VABC has good performance compared with PSO and ABC. Moreover, the performance of the proposed algorithm is also compared with those of state-of-the-art hybrid methods and the results demonstrate that the proposed method has a higher convergence speed and better search ability for almost all functions. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Particle swarm optimization Artificial bee colony Continuous optimization hybrid optimization algorithm

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A novel approach of intensified barnacles mating optimization for the mitigation of power system oscillations

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2021年第17期33卷 e6303-e6303页

作者： Devarapalli, Ramesh Bhattacharyya, Biplab Kumari, Archana Indian Inst Technol ISM Dept Elect Engn Dhanbad Jharkhand India BIT Sindri Dept Elect Engn Dhanbad Jharkhand India

optimization is the process of attaining the best solution from the available set of prioritized constraints to maximize or minimize the desired function involved in it. It is imperative in engineering to plan and design to find the best out of confined resource and time. A recently recommended barnacles matting optimization (BMO) has been recognized in computing the optimal parameters of the electric power system with excellent performance characteristics. In this paper, the intensified version of BMO has been proposed with the aid of cuckoo search (CS) and traditional particle swarm optimization (PSO). An in-depth analysis was made on the BMO technique and proposed suitable modifications to deal with the electrical power system stability enhancement issue efficiently. The proposed technique in the power system stabilizer (PSS) parameter computation is validated on the 23 benchmark functions to examine for its suitability. The robustness of the proposed method is presented via statistical analysis and boxplot of the 23 benchmark functions. The PSS parameters are computed in a benchmark two area four machine system using intensified BMO under self-clearing fault conditions. A multi-objective function is designed to improve the damping nature offered under system uncertainties, and the comparative analysis is presented among conventional PSS, BMO, intensified BMO with CS and PSO (BMO-CS and BMO-PSO), and harris hawks optimizer.

关键词： barnacles matting optimization cuckoo search Harris hawks optimizer hybrid optimization algorithm power system oscillation damping

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Redesign of casing treatment for a transonic centrifugal compressor based on a hybrid global optimization method

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2022年第7期236卷 3398-3417页

作者： Li, Xiaojian Liu, Zhengxian Zhao, Yijia Tianjin Univ Dept Mech Tianjin 300350 Peoples R China Tianjin Univ Commerce Sch Mech Engn Tianjin Peoples R China

As a typical black-box problem, recirculating casing treatment (RCT) optimization of compressor stages is computationally intensive and time consuming, even though surrogate models are usually employed. In order to improve efficiency and robustness of the optimization, an expected-improvement (EI) based hybrid global optimization (EHGO) algorithm is developed by coupling an EI-based surrogate model with a hybrid optimization algorithm. Highly nonlinear and multiple modality mathematical tests show that the EHGO algorithm is able to create a high-fidelity surrogate model near targeted regions with less evaluated samples, and to obtain the global optimal solution simultaneously. The RCTof a compressor stage is optimized based on this algorithm. The number of CFD simulations required for obtaining an optimum solution is greatly reduced, as compared to similar studies using conventional methods. The optimization results show that the aerodynamic performance is improved over the whole speed line and the flow range is also extended. The dominant factors for the performance improvements and the enhanced stall margin are addressed by analyzing the local flow characteristics before and after optimization. It is found that those factors include: removing a larger amount of low-momentum fluid, achieving a more uniform flow of impeller passage in circumferential direction, and reducing the radial distortion of impeller inlet flow. The proposed algorithm has the potential to considerably speed up the optimization process and make the optimization much more accessible. It can be generalized to deal with other computationally intensive black-box problems, for example, turbomachinery optimization.

关键词： Centrifugal compressor recirculating casing treatment aerodynamic performance hybrid optimization algorithm adaptive surrogate model

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Fuel-Optimal Multi-Impulse Orbit Transfer Using a hybrid optimization Method

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2020年第4期21卷 1359-1368页

作者： Zhou, Hongyu Wang, Xiaogang Cui, Naigang Harbin Inst Technol Sch Astronaut Harbin 150001 Peoples R China

For the N-impulse transfer between two earth orbits, this paper introduces N - 1 intermediate orbits to describe the orbit transfer scheme. The eccentricity vector of the intermediate orbits and the true anomalies corresponding to the impulsive points are chosen as the optimization variables. Based on the patched conic theory, candidate solution can be analytically derived, constraints are removed from the optimization model, and the original problem is converted to a parameter optimization problem. The only difficulty lies in the initialization because the number of optimization variables increases linearly with N, which can be very large. This is settled by a hybrid optimization algorithm that comprises two searching methods. The problem is solved first by an improved particle swarm optimization method and, then, by an adaptive conjugate gradient method. The proposed method is adaptive to problems with any finite N and can calculate the optimal N in any transfer scenarios. The simulation validates the proposed method with some well-known cases and demonstrates its adaptation to N.

关键词： Multi impulse orbit transfer space transportation hybrid optimization algorithm

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