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A multi-stage ensemble model for power load forecasting based on decomposition, error factors, and multi-objective optimization algorithm

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2024年 155卷

作者： Fan, Chaodong Nie, Shanghao Xiao, Leyi Yi, Lingzhi Wu, Yuetang Li, Gongrong Xiangtan Univ Coll Automat & Elect Informat Xiangtan 411105 Peoples R China Hainan Univ Sch Comp Sci & Technol Haikou 570228 Peoples R China Hunan Software Vocat & Tech Univ Sch Software & Informat Engn Xiangtan 411100 Peoples R China Hunan Univ Finance & Econ Sch Informat Technol & Management Changsha 410205 Peoples R China

Short-term power load forecasting plays an important role in ensuring the stable operation of power systems and improving economic benefits. However, most of the previous studies ignored the limitations of a single pre-diction model and the useful information in the error factors, resulting in low prediction accuracy. Therefore, this paper proposes a multi-stage integrated model based on decomposition, error factors, and a multi-objective evolutionary algorithm based on decomposition (MOEA/D). The proposed model consists of three stages: in the first stage, the gated recurrent unit (GRU) is used to predict the components of complete ensemble empirical modal decomposition with adaptive noise, and new data sets are obtained by combining them with the original data sets to fully mine the data characteristics. In the second stage, the MOEA/D based on angle and distance selection strategy and adaptive population generation strategy is used to optimize GRU network parameters with accuracy and diversity as the objective functions, obtaining several load forecasting models and error forecasting models that consider accuracy and diversity. In the third stage, a new nonlinear integration method based on GRU optimized by MOEA/D is used to integrate load forecasting values and error forecasting values, considering error factors to further improve forecasting accuracy. Experimental results on the Australian wholesale electricity market and energy market datasets show that the proposed model outperforms the comparative model in terms of accuracy and generalization and can be widely applied in load forecasting.

关键词： Short-term load forecasting GRU network Ensemble learning multi-objective evolutionary algorithm

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multi-objective flexible flow-shop rescheduling with rigid-flexible hybrid constraints and preventive maintenance

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COMPUTERS & INDUSTRIAL ENGINEERING 2025年 200卷

作者： Zhao, Ziye Chen, Xiaohui An, Youjun Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400030 Peoples R China China Three Gorges Univ Coll Mech & Power Engn Yichang 443002 Peoples R China

The synergy between production rescheduling and machine maintenance is critical, particularly incases where unforeseen equipment failures, not fully prevented by maintenance, might threaten the viability of the original plan. In this context, this paper explores a novel integrated optimization problem of production rescheduling and preventive maintenance in a capacity-limited flexible flow-shop (CLFFS), in which random equipment failures, hybrid rigid-flexible constraints of buffer capacity and due window are considered. Specifically, (1) an integrated optimization model is established to minimize the makespan, average flow time, earliness/tardiness penalty, machine workload extreme deviation and system instability;(2) an adaptive hybrid rescheduling strategy (AHRS) that amalgamates three classical rescheduling approaches is designed to effectively respond to random equipment failures;and (3) an improved bi-population cooperative evolutionary algorithm with an adaptive environment selection mechanism (AES-IBCEA) is developed to deal with the integrated problem. In the numerical experiments, Taguchi method is first employed to set the parameters of the proposed algorithm. Second, the effectiveness and superiority of designed operators and proposed AES-IBCEA are validated through algorithm comparison. Next, the competitiveness of the proposed AHRS is demonstrated by contrasting it with other rescheduling strategies, and the average improvement rate is up to 22.12%. Finally, a sensitivity analysis on the fault impact threshold (delta 0) and the individual selection threshold (beta) is performed, and the results reveal that beta has a significant impact on the algorithm's performance.

关键词： Flexible flow-shop rescheduling Preventive maintenance Rigid-flexible hybrid constraints Adaptive hybrid rescheduling strategy multi-objective evolutionary algorithm

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Modeling the Optimal Transition of an Urban Neighborhood towards an Energy Community and a Positive Energy District

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ENERGIES 2024年第16期17卷

作者： Viesi, Diego Borelli, Gregorio Ricciuti, Silvia Pernigotto, Giovanni Mahbub, Md Shahriar Fdn Bruno Kessler FBK Ctr Sustainable Energy Via Sommar 18 I-38123 Trento Italy Free Univ Bozen Bolzano UNIBZ Fac Engn Piazza Univ 5 I-39100 Bolzano Italy Ahsanullah Univ Sci & Technol AUST Dept Comp Sci & Engn Love Rd 141&142Tejgaon Ind Area Dhaka 1208 Bangladesh

Building renovation is a key initiative to promote energy efficiency, the integration of renewable energy sources (RESs), and a reduction in CO2 emissions. Supporting these goals, emerging research is dedicated to energy communities and positive energy districts. In this work, an urban neighborhood of six buildings in Trento (Italy) is considered. Firstly, the six buildings are modeled with the Urban Modeling Interface tool to evaluate the energy performances in 2024 and 2050, also accounting for the different climatic conditions for these two time periods. Energy demands for space heating, domestic hot water, space cooling, electricity, and transport are computed. Then, EnergyPLAN coupled with a multi-objective evolutionary algorithm is used to investigate 12 different energy decarbonization scenarios in 2024 and 2050 based on different boundaries for RESs, energy storage, hydrogen, energy system integration, and energy community incentives. Two conflicting objectives are considered: cost and CO2 emission reductions. The results show, on the one hand, the key role of sector coupling technologies such as heat pumps and electric vehicles in exploiting local renewables and, on the other hand, the higher costs in introducing both electricity storage to approach complete decarbonization and hydrogen as an alternative strategy in the electricity, thermal, and transport sectors. As an example of the quantitative valuable finding of this work, in scenario S1 "all sectors and EC incentive" for the year 2024, a large reduction of 55% of CO2 emissions with a modest increase of 11% of the total annual cost is identified along the Pareto front.

关键词： urban modeling interface EnergyPLAN multi-objective evolutionary algorithm energy community positive energy district

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Solving multi-objective weapon-target assignment considering reliability by improved MOEA/D-AM2M

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NEUROCOMPUTING 2024年 563卷

作者： Yi, Xiaojian Yu, Huiyang Xu, Tao Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China Beijing Inst Technol Yangtze Delta Reg Acad Jiaxing 314019 Zhejiang Peoples R China Beijing Inst Technol Tangshan Res Inst Tangshan 063099 Hebei Peoples R China

The weapon-target assignment problem is a challenging optimization issue, but reliability is seldom considered in the majority of existing literature. To address the high-reliability weapon-target assignment problem, this paper integrates weapon reliability and mission reliability into a multi-objective optimization model (MOD) and presents an improved algorithm termed MOEA/D-iAM2M to the problem. This algorithm effectively combines the strengths of adaptive search space decomposition-based MOEA (MOEA/D-AM2M) and two-stage hybrid learning-based MOEA (HLMEA), resulting in a faster convergence rate and a more extensive distribution of the Pareto solution set. Furthermore, a reference point is incorporated into MOEA/D-iAM2M to facilitate the adaptive weight adjustment. Numerical experiments are carried out to confirm the effectiveness of the proposed MOEA/D-iAM2M. This research is significant in the field of optimization and has practical value in the defense industry.

关键词： multi-objective optimization Weapon-target assignment Reliability multi-objective evolutionary algorithm

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multi-objective boxing match algorithm for multi-objective optimization problems

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 239卷

作者： Tavakkoli-Moghaddam, Reza Akbari, Amir Hosein Tanhaeean, Mehrab Moghdani, Reza Gholian-Jouybari, Fatemeh Hajiaghaei-Keshteli, Mostafa Univ Tehran Sch Ind Engn Coll Engn Tehran Iran Iran Univ Sci & Technol Sch Ind Engn Tehran Iran Univ Huddersfield Sch Business & Educ Dept Accountancy Finance & Econ Queensgate Rd Huddersfield HD1 3DH W Yorkshire England Tecnol Monterrey Sch Engn & Sci Puebla Mexico

In the last two decades, due to having fast computation after inventing computers and also considering realworld optimization problems, research on developing new algorithms for problem having more than one objective have been one of the appealing and attractive topics both for academia and industrial practitioners. By this motivation, we introduce a multi-objective Boxing Match algorithm (MOBMA) in this paper. The proposed algorithm studies the multi-objective version of the Boxing Match algorithm (BMA) by incorporating a unique search strategy and new solutions-producing mechanism, enhancing the algorithm's capability for exploration and exploitation phases. Besides, its performance is analyzed with famous and capable multi-objective metaheuristics. We consider ten multi-objective benchmarks and three classical engineering problems. Statistical analyses are also conducted on the benchmark test functions from three engineering design problems. This study shows the superior performance of the proposed algorithm, considering both quantitative and qualitative analyses.

关键词： Meta-heuristics multi-objective optimization Boxing match algorithm multi-objective evolutionary algorithm

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Evolving filter criteria for randomly initialized network pruning in image classification

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NEUROCOMPUTING 2024年 594卷

作者： Chen, Xiangru Liu, Chenjing Hu, Peng Lin, Jie Gong, Yunhong Chen, Yingke Peng, Dezhong Geng, Xue Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China Agcy Sci Technol & Res Inst Infocomm Res Singapore 138632 Singapore Sichuan Newstrong UHD Video Technol Co Ltd Chengdu 610095 Peoples R China Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne NE1 8ST England

Network pruning aims to enhance the performance of deep neural networks by eliminating redundant components from the model. However, existing pruning methods typically require a well -trained model and employ fixed, single pruning criteria throughout the pruning cycles. To address these limitations, we propose a novel method called evolutionary Filter Criteria (EvoFC). This method enables the automated search for the network pruning ratio and criterion during a population -based heuristic search process. We introduce a unique encoding space that represents the chosen pruning criterion and ratio for each layer, facilitating the acquisition of optimal architecture configurations for candidate networks during iterations. Additionally, we devise a novel weight inheritance mechanism to mitigate the computational burden associated with the population -based nature of the method, resulting in a significant reduction in overall training time. We validate our method by applying it to randomly initialized networks and conducting empirical experiments on CIFAR-10/100, ILSVRC2012 and Places365 datasets. The results demonstrate that our method effectively reduces the number of FLOPs while striking a fine balance between accuracy and computational efficiency. This underscores the practical value of our method in optimizing performance while efficiently utilizing computational resources, particularly when pruning networks starting from random initialization.

关键词： Genetic algorithm Evolving deep learning Network pruning multi-objective evolutionary algorithm

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A clustering-based archive handling method and multi-objective optimization of the optimal power flow problem

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APPLIED INTELLIGENCE 2024年第22期54卷 11603-11648页

作者： Akbel, Mustafa Kahraman, Hamdi Tolga Duman, Serhat Temel, Seyithan Pusula Iletisim Bilisim Internet Sanayi Ve Ticaret Istanbul Turkiye Karadeniz Tech Univ Software Engn Technol Fac Trabzon Turkiye Bandirma Onyedi Eylul Univ Elect Engn Engn & Nat Sci Fac Bandirma Turkiye TEKNOPAR Ankara Turkiye

The main challenge in finding the optimal Pareto Front (PF) and Pareto Set (PS) sets for multimodal multi-objective optimization problems (MMOPs) with conflicting objective functions is to exhibit a balanced and sustainable diversity and exploitation capability in both the objective and decision spaces. This paper introduces dynamic reference spaces based clustering (DRSC) as a new archive handling method to overcome this challenge. DRSC incorporates a niche method called dynamically switched reference spaces and adapts the K-means-based method for clustering non-dominant vectors and handling the archive. The performance of the proposed DRSC is tested on twenty-four benchmark problems. According to the results of non-parametric statistical analysis using data from four different performance metrics, DRSC-MOAGDE designed using the proposed archiving mechanism managed to achieve the best Friedman rank among thirty different competitors. According to the stability analysis results, the average success rates and average computation times of the three best performing algorithms DRSC-MOAGDE, MMODE-ICD and SSMOPSO are (88.69%, 3.01 s), (66.87%, 9.47 s) and (64.29%, 1372.49 s), respectively. It is also observed that the proposed DRSC-MOAGDE outperforms the best cost optimization values in the literature with a minimum of 0.1838 $/h and a maximum of 30.9157 $/h for the multi-objective OPF real-world problem.

关键词： Archive handling Dynamic reference spaces based clustering multi-objective evolutionary algorithm multimodal multi- objective optimization Real-world constrained optimal power flow problem

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A data-driven optimisation method for a class of problems with redundant variables and indefinite objective functions

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INFORMATION SCIENCES 2024年 656卷

作者： Zhou, Jin Zhou, Kang Zhang, Gexiang Neri, Ferrante Shen, Wangyang Jin, Weiping Wuhan Polytech Univ Sch Math & Comp Sci Wuhan 430023 Peoples R China Chengdu Univ Informat Technol Sch Automat Chengdu 610225 Peoples R China Univ Surrey Sch Comp Sci & Elect Engn NICE Grp Guildford GU27XH England Nanjing Univ Informat Sci & Technol Sch Software Nanjing 210000 Peoples R China Wuhan Polytech Univ Sch Food Sci & Engn Wuhan 430023 Peoples R China

In the realm of practical problem-solving, multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) are becoming increasingly prevalent. MOPRVIF involve determining the optimal decision variables that optimise multiple objectives, leveraging the relational data of a set of variables and multiple objectives. For these problems, this paper focuses on the following two issues: one is the demand for a unified computational model to solve this problem;the other is how to improve the algorithm's deep intelligent search capability. In this regard, this paper designs a dual data-driven multi-objective optimisation method. The method used consisted of four parts: elimination of redundant variables (ERV), objective function construction (OFC), selection evolution operator (SEO), and multi-objective evolutionary algorithm (MOEA). MOEA was the main focus of the method. ERV is data preparation and variable selection according to multiple objectives. OFC involves constructing the relationship model between variables and objectives, and a high-accuracy model is important for guaranteeing reliable results. Furthermore, SEO can adjust the evolution operator during a deep search. This is an important guarantee for deep, intelligent search. MOEA combined OFC and SEO to form the final solution algorithm-Dual Data Driven multi-objective evolutionary algorithm (DDMOEA). DDMOEA was explored using two different disciplinary problems of drug compound optimisation and wild blueberry cultivation and benchmarks were selected. The first two problem domains are distinct. The first problem is more complex than the second;however, both encompass redundant variables and indefinite objective functions. Benchmarks are utilised independently to gauge the profound intelligent search capability. The experiments affirm that the dual data -driven optimization approach proposed in this paper is effective, practical, and scalable.

关键词： Data-driven optimisation method multi-objective evolutionary algorithm Adaptive optimisation

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Design and Investigation of a New Torque-Enhanced Flux Modulated Permanent Magnet Arc Motor With Flux-Concentrated Stator PMs

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IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY 2024年第8期34卷

作者： Chen, Nan Fang, Shuhua Southeast Univ Sch Elect Engn Nanjing 210096 Jiangsu Peoples R China

This article proposes an innovative flux modulated permanent magnet (PM) arc motor enhanced with flux-concentrated stator PMs (FCS-FMPMAM). With the primary goal of increasing the average torque output, the FCS-FMPMAM incorporates a set of flux-focused stator PMs. This article initially introduces the configuration and performance of the FCS-FMPMAM. The combination of the flux-switching PMs, consequent-pole PMs and PMs in the stator yoke is beneficial to generate abundant harmonics and bolster the triple flux-modulation effect. Then, the optimal rotor pole number of the proposed machine is analyzed and selected. The sensitivity analysis is performed on the design parameters of FCS-FMPMAM for the consequent optimization of the torque performance of FCS-FMPMAM. This facilitates the subsequent torque performance optimization of FSC-FMPMAM. Based on the multi-objective evolutionary algorithm, the torque capabilities of FCS-FMPMAM are optimized to obtain the maximized average torque output and the minimized torque ripple. The optimized machine is analyzed under different PM excitation conditions to reveal the superiority of the proposed PM arrangement. Finally, the proposed machine is compared with an existing flux-modulated stator-PM arc motor in electromagnetic performances with finite element analysis method to exhibit the superiority of FCS-FMPMAM.

关键词： Arc motor flux-concentrated stator PMs multi-objective evolutionary algorithm sensitivity analysis triple flux-modulation effect torque

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Cultural transmission based multi-objective evolution strategy for evolutionary multitasking

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INFORMATION SCIENCES 2022年 582卷 215-242页

作者： Xu, Zhiwei Liu, Xiaoming Zhang, Kai He, Juanjuan Wuhan Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430065 Peoples R China Hubei Prov Key Lab Intelligent Informat Proc & Re Wuhan 430065 Peoples R China

In recent years, many efficient evolutionary multitasking (EMT) algorithms have been pro-posed to solve multi-objective multi-task optimization problems. However, EMT algo-rithms often face negative transfer problems. In this paper, a novel multi-objective evolution strategy, called CT-EMT-MOES, is proposed based on a cultural transmission the-ory for solving multi-objective multitask optimization problems. First, two evolutionary operators inspired by cultural transmission theory are proposed. The elite-guided variation strategy can transfer the information from the current Pareto front to all individuals and guide the population to quickly converge. The horizontal cultural transmission strategy can efficiently transfer information from the source task to the target task. Second, to solve the negative transfer problem, an adaptive information transfer strategy is proposed to adaptively adjust the probability of an information transfer. Third, the proposed algorithm can gain a Pareto front with good convergence and diversity by utilizing a smaller popula-tion size and fewer computing resources. As a result, the proposed algorithm can effec-tively utilize the implicit similarity and complementarity between simultaneous optimized tasks to improve the overall convergence efficiency and reduce a negative trans-fer. Finally, comprehensive experimental results show that the proposed algorithm can achieve a better performance compared with previous state-of-the-art multi-objective EMT algorithms. (c) 2021 Elsevier Inc. All rights reserved.

关键词： multi-objective evolutionary algorithm evolutionary multitasking multi-task optimization Cultural transmission Evolution strategy

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