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Applying the new multi-objective algorithms for the operation of a multi-reservoir system in hydropower plants

SCIENTIFIC REPORTS 2024年第1期14卷 1-27页

作者： Hashemi, Syed Mohsen Samare Robati, Amir Kazerooni, Mohammad Ali Islamic Azad Univ Dept Civil Engn Kerman Branch Kerman Iran

The optimal operation of the multi-purpose reservoir system is a difficult, and, sometimes, non-linear problem in multi-objective optimization. By simulating biological behavior, meta-heuristic algorithms scan the decision space and can offer a set of points as a group of solutions to a problem. Because it is essential to simultaneously optimize several competing objectives and consider relevant constraints as the main problem in many optimization problems, researchers have improved their ability to solve multi-objective problems by developing complementary multi-objective algorithms. Because the AHA algorithm is new, its multi-objective version, MOAHA (multi-objective artificial hummingbird algorithm), was used in this study and compared with two novel multi-objective algorithms, MOMSA and MOMGA. Schaffer and MMF1 were used as two standard multi-objective benchmark functions to gauge the effectiveness of the proposed method. Then, for 180 months, the best way to operate the reservoir system of the Karun River basin, which includes Karun 4, Karun 3, Karun 1, Masjed-e-Soleyman, and Gotvand Olia dams to generate hydropower energy, supply downstream demands (drinking, agriculture, industry, environmental), and control flooding was examined from September 2000 to August 2015. Four performance appraisal criteria (GD, S, Delta, and MS) and four evaluation indices (reliability, resiliency, vulnerability, and sustainability) were used in Karun's multi-objective multi-reservoir problem to evaluate the performance of the multi-objective algorithm. All three algorithms demonstrated strong capability in criterion problems by using multi-objective algorithms' criteria and performance indicators. The large-scale (1800 dimensions) of the multi-objective operation of the Karun Basin reservoir system was another problem. With a minimum of 1441.71 objectives and an average annual hydropower energy manufacturing of 17,166.47 GW, the MOAHA algorithm demonstrated considerable ability co

关键词： multi-objective evolutionary algorithm multi-reservoir systems Karun basin

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A fast nondominated sorting-based MOEA with convergence and diversity adjusted adaptively

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JOURNAL OF SUPERCOMPUTING 2024年第2期80卷 1426-1463页

作者： Gao, Xiaoxin He, Fazhi Zhang, Songwei Luo, Jinkun Fan, Bo Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China Zhengzhou Inst Elect & Mech Engn Zhengzhou 450015 Peoples R China Wuhan Univ Inst Sci & Technol Dev Wuhan 430072 Peoples R China

In the past few decades, to solve the multi-objective optimization problems, many multi-objective evolutionary algorithms (MOEAs) have been proposed. However, MOEAs have a common difficulty: because the diversity and convergence of solutions are often two conflicting conditions, the balance between the diversity and convergence directly determines the quality of the solutions obtained by the algorithms. Meanwhile, the nondominated sorting method is a costly operation in part Pareto-based MOEAs and needs to be optimized. In this article, we propose a multi-objective evolutionary algorithm framework with convergence and diversity adjusted adaptively. Our contribution is mainly reflected in the following aspects: firstly, we propose a nondominated sorting-based MOEA framework with convergence and diversity adjusted adaptively;secondly, we propose a novel fast nondominated sorting algorithm;thirdly, we propose a convergence improvement strategy and a diversity improvement strategy. In the experiments, we compare our method with several popular MOEAs based on two widely used performance indicators in several multi-objective problem test instances, and the empirical results manifest the proposed method performs the best on most test instances, which further demonstrates that it outperforms all the comparison algorithms.

关键词： Nondominated sorting multi-objective evolutionary algorithm Convergence Diversity Adjusted adaptively

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Optimum Charge Plan of Steelmaking Continuous Casting Based on the Modified multi-objective evolutionary algorithm

Optimum Charge Plan of Steelmaking Continuous Casting Based ...

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4th International Conference on Manufacturing Science and Engineering (ICMSE 2013)

作者： Song, Hong Xi Zhang, Jian Xin Gao, Kun Meng, Wei Cheng, Li Inner Mongolia Univ Technol Sch Elect Power Hohhot 010080 Inner Mongolia Peoples R China

ISBN: (纸本)9783037856932

The charge plan is a basic problem of the metallurgical industry Through a comprehensive consideration of the process constraints and production costs, charge plan is a typical multi-objective optimization problem. A multi-objective optimization model of charge plan was built in this paper. As direct encoding is hard to be used in the many constraints problem case, a new method of generate feasible solutions based on constraint satisfaction was proposed. A novel adaptive grid algorithm was also proposed to improve the current multi-objective evolutionary algorithm's population maintain strategy. Finally, the proposed multi-objective charge plan model was solved, and more meaningful solutions for this problem were obtained.

关键词： Steelmaking and continuous casting Charge plan Adaptive grid Maintain strategy constraint satisfaction multi-objective evolutionary algorithm

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Ensemble summarization of bio-medical articles integrating clustering and multi-objective evolutionary algorithms

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APPLIED SOFT COMPUTING 2021年 106卷 107347-107347页

作者： Mallick, Chirantana Das, Asit Kumar Ding, Weiping Nayak, Janmenjoy Indian Inst Engn Sci & Technol Sibpur 711103 Howrah India Nantong Univ Sch Informat Sci & Technol Nantong 226019 Peoples R China Aditya Inst Technol & Management AITAM Tekkali 532201 Andhra Pradesh India

The rapid exponential growth of scientific literature of bio-medicine, genomics, and other bio-sciences increases difficulties to access useful information timely and efficiently. The medical researchers, physicians and clinicians need to extract the gist of the novel articles effectively to gather knowledge and apply it properly for better treatment of the patients. In the paper, the bio-medical articles available in an open access source, PubMed MEDLINE, are considered for generating their extractive summaries using clustering and multi-objective evolutionary algorithm based ensemble summarization technique. Initially, we have extracted only the medical terms of an article and determined the concepts of all medical terms using Unified Medical Language System (UMLS) to represent the article in terms of a set of concepts. Next, extractive summaries of the article have been generated using various popularly used clustering algorithms and different centrality measures. The generated summaries are referred to as the base summaries of the article. Finally, multi-objective evolutionary algorithm is applied on these base summaries for generating an ensemble summary of the given article. The evolutionary algorithm uses two competent objective functions to measure the fitness value of each chromosome in the population. One objective function is the linear combination of jaccard similarity and Word2Vec similarity between the original article and the ensemble summary generated by the chromosome. The other objective function is defined as the information gain of the chromosome about the article by generating the ensemble summary. The best pareto optimal solution of the final population of the evolutionary algorithm provides the ensemble summary of the given article. The proposed ensemble method is compared with some state-of-art methods to demonstrate that it is both effective and statistically significant. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Extractive ensemble summarization Natural language processing multi-objective evolutionary algorithm Centrality measures Clustering Bio-medical informatics

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multi-objective dynamic distribution adaptation with instance reweighting for transfer feature learning

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KNOWLEDGE-BASED SYSTEMS 2023年第1期263卷

作者： Li, Haoran He, Fazhi Pan, Yiteng Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China Wuhan Univ Sch Comp Sci Wuhan Peoples R China

In knowledge adaptation, the source and target knowledge are transferred into the same mapping space by simultaneously reducing the difference between the marginal and conditional distributions;however, it is difficult to precisely balance the two distributions at each transformation. To address this problem, a novel multi-objective dynamic distribution adaptation (MODDA) with instance reweighting is proposed to reduce discrepancies between the two distributions. In addition, a customised non-dominated sorting genetic algorithm-II (NSGA2) optimisation method is presented for searching the optimal cumulative weight path, and four genetic operator combinations are compared to determine which one is optimal for MODDA. Moreover, kernel mean matching is proposed for the first time for dynamic compensation based on an individual's relevance in instance reweighting. The experimental results confirm that MODDA outperforms other state-of-the-art algorithms in terms of the classification accuracy for 16 well-known cross-domain tasks.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Domain adaptation multi-objective evolutionary algorithm Instance reweighting

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High-dimensional interactive adaptive RVEA for multi-objective optimization of polyester polymerization process

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INFORMATION SCIENCES 2023年 650卷

作者： Zhu, Xiuli Jiang, Chunli Hao, Kuangrong Wang, Rui Univ Shanghai Sci & Technol Coll Opt Elect & Comp Engn Shanghai 200093 Peoples R China Chongqing Univ Posts & Telecommun Coll Software Engn Chongqing 400065 Peoples R China Donghua Univ Coll Informat Sci & Technol Shanghai Peoples R China

The optimization of operating conditions in the polyester polymerization process is crucial for enhancing the quality of the resulting polyester. A novel multi-objective optimization algorithm, named the Interaction Adaptive RVEA (IARVEA), is presented and designed specifically to address the four-objective optimization problem in the polymerization esterification process in this paper. Three strategies are employed to guide the search for optimal solutions and approach the unknown Pareto fronts associated with this problem. Firstly, the maximum mean discrepancy distance is utilized to compare the distribution of the upper and lower solutions, subsequently determining whether an update to the reference vector is necessary. Secondly, the interaction adaptive strategy generates a reference vector based on interactive information from the current and the preceding iteration. Lastly, the projector distance is incorporated into the environment selection operator of IARVEA, which aids in achieving superior convergence properties and greater solution diversity when compared to the conventional RVEA method. Additionally, a comparative analysis is conducted between IARVEA and four other algorithms using seven benchmark test problems and the four-objective optimization problem in the polyester polymerization esterification process. The experimental results demonstrate the superiority of IARVEA over other algorithms in terms of both IGD and HV metrics. Meanwhile, the research findings have important implications for the polyester industry and related fields, offering valuable insights and guidelines for optimizing the operating conditions of the polymerization esterification process.

关键词： multi-objective evolutionary algorithm Polyester Polymerization esterification process RVEA

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Integrated optimization of real-time order acceptance and flexible job-shop rescheduling with multi-level imperfect maintenance constraints

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SWARM AND evolutionary COMPUTATION 2023年 77卷

作者： An, Youjun Chen, Xiaohui Gao, Kaizhou Zhang, Lin Li, Yinghe Zhao, Ziye Chongqing Univ State Key Lab Mech Transmiss Chongqing 400030 Peoples R China Macau Univ Sci & Technol Macau Inst Syst Engn Collaborat Lab Intelligent Sci & Syst Macau 999078 Peoples R China

Nowadays, the make-to-order (MTO) production has gradually become a new manufacturing trend, and the real-time arrival of orders has brought great challenges to order acceptance, production scheduling and maintenance planning in the actual production. Therefore, in this study, we focus on the integrated optimization of real-time order acceptance and flexible job-shop rescheduling with multi-level imperfect maintenance constraints. More precisely, (1) a multi-level imperfect maintenance model with minimal repair (MR), preventive maintenance (PM), overhaul maintenance (OM) and replacement is designed for each production machine, and the optimality is derived;(2) an integrated multi-objective optimization model is developed to characterize the concerned problem;and (3) an improved non-dominated sorting genetic algorithm III with adaptive reference vector (INSGA-III/ARV) is proposed to deal with the problem. In the numerical simulation, the effect of different order sorting rules is first analyzed. Second, the effectiveness of improved operators is demonstrated by internal analysis of the proposed algorithm. Third, the superiority and competitiveness of the proposed algorithm is verified by comparing with the variants of five state-of-the-art algorithms. Fourth, the benefits of both the designed multi-level imperfect maintenance model and real-time order acceptance and scheduling (ROAS) strategy are proved by contrasting with other two traditional maintenance models and all order acceptance and scheduling (AOAS) strategy, respectively. Finally, a comprehensive sensitivity analysis is performed to illustrate the impact of several key parameters on the integrated optimization model.

关键词： Real-time order acceptance Flexible job-shop rescheduling multi-level imperfect maintenance multi-objective evolutionary algorithm (MOEA)

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Self-adaptive polynomial mutation in NSGA-II

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SOFT COMPUTING 2023年第23期27卷 17711-17727页

作者： Carles-Bou, Jose L. Galan, Severino F. Univ Nacl Educ Distancia UNED Escuela Int Doctorado EIDUNED Madrid Spain UNED Dept Artificial Intelligence Madrid Spain

evolutionary multi-objective optimization is a field that has experienced a rapid growth in the last two decades. Although an important number of new multi-objective evolutionary algorithms have been designed and implemented by the scientific community, the popular Non-Dominated Sorting Genetic algorithm (NSGA-II) remains as a widely used baseline for algorithm performance comparison purposes and applied to different engineering problems. Since every evolutionary algorithm needs several parameters to be set up in order to operate, parameter control constitutes a crucial task for obtaining an effective and efficient performance in its execution. However, despite the advancements in parameter control for evolutionary algorithms, NSGA-II has been mainly used in the literature with fine-tuned static parameters. This paper introduces a novel and computationally lightweight self-adaptation mechanism for controlling the distribution index parameter of the polynomial mutation operator usually employed by NSGA-II in particular and by multi-objective evolutionary algorithms in general. Additionally, the classical NSGA-II using polynomial mutation with a static distribution index is compared with this new version utilizing a self-adapted parameter. The experiments carried out over twenty-five benchmark problems show that the proposed modified NSGA-II with a self-adaptive mutator outperforms its static counterpart in more than 75% of the problems using three quality metrics (hypervolume, generalized spread, and modified inverted generational distance).

关键词： multi-objective evolutionary algorithm NSGA-II Polynomial mutation Distribution index self-adaptation

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Optimal capacity allocation of wind-light-water multi-energy complementary capacity based on improved multi-objective optimization algorithm

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FRONTIERS IN ENERGY RESEARCH 2023年 10卷

作者： Wang, Ying Liu, Jiajun Xian Univ Technol Sch Elect Engn Xian Peoples R China Yinchuan Univ Energy Sch Elect Power Yinchuan Peoples R China

As a basic industry for national economic development, the power industry is closely related to the overall economic and environmental development of China. At present, China is still dominated by thermal power generation. In order to reduce carbon emissions, promote the realization of the "double carbon " goal, and improve the level of clean energy utilization and the operating efficiency of the power system, a wind-light-water storage complementary power generation system is built, and a mathematical model of multi energy complementation is established. The minimum economic cost and the minimum battery capacity are proposed as the objective functions of system capacity configuration. Then a multi-objective evolutionary algorithm based on Pareto optimal space of the NDWA-GA and the PCA is proposed for optimal capacity allocation of multi energy complementary systems in this paper. Compared with the traditional multi-objective optimization algorithm, the correctness and effectiveness of the proposed method are verified. In addition, according to the actual research object, the optimal capacity configuration of the multi energy complementary system is given, which can guide the production and has an important promotion significance for energy saving and emission reduction.

关键词： multi energy complementary model multi-objective evolutionary algorithm capacity configuration conserve energy reduce emissions production guidance

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Modeling and Control of a Peltier Thermoelectric System Applying a multi-objective Optimization Approach 12th

Modeling and Control of a Peltier Thermoelectric System Appl...

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12th Ecuadorian Conference on Information and Communication Technologies

作者： Huilcapi, Victor Garcia, Geovanny Ghia, Elias Soto, Brian Univ Politecn Salesiana Guayaquil Ecuador

ISBN: (纸本)9783031754302;9783031754319

This paper proposes a design procedure based on multi-objective optimization for modeling a Peltier thermoelectric system using experimental data. A multi-objective evolutionary algorithm was used to identify a set of optimal parameters that satisfactorily characterize the system's dynamics. The proposed methodology offers a designer valuable information about the dynamics of the temperatures of the Peltier's hot and cold surfaces and the trade-offs between their design objectives (visualized in the Pareto fronts). In this way, a control engineer can be sufficiently informed to choose, according to their preferences, a model for the Peltier cell with the best performance (for different system operation scenarios). A Peltier cold-side temperature model was selected to tune a proportional-integral-derivative (PID) controller and evaluate the robustness of the model. The tuned PID controller works directly on the nonlinear model of the Peltier cell, allowing it to effectively control the temperature of the cold surface of the thermoelectric module. The methodology uses the Integral Absolute Error (IAE) as a performance index to evaluate the quality of system modeling. The results show that the methodological approach applied to model and control the system performs very satisfactorily.

关键词： Peltier cell multi-objective optimization Pareto front multi-objective evolutionary algorithm Nonlinear model PID control

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