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Result diversification by multi-objective evolutionary algorithms with theoretical guarantees

ARTIFICIAL INTELLIGENCE 2022年 309卷

作者： Qian, Chao Liu, Dan-Xuan Zhou, Zhi-Hua Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Peoples R China

Given a ground set of items, the result diversification problem aims to select a subset with high "quality " and "diversity " while satisfying some constraints. It arises in various real world artificial intelligence applications, such as web-based search, document summarization and feature selection, and also has applications in other areas, e.g., computational geometry, databases, finance and operations research. Previous algorithms are mainly based on greedy or local search. In this paper, we propose to reformulate the result diversification problem as a bi-objective maximization problem, and solve it by a multi objective evolutionary algorithm (EA), i.e., the GSEMO. We theoretically prove that the GSEMO can achieve the (asymptotically) optimal theoretical guarantees under both static and dynamic environments. For cardinality constraints, the GSEMO can achieve the optimal polynomial-time approximation ratio, 1/2. For more general matroid constraints, the GSEMO can achieve an asymptotically optimal polynomial-time approximation ratio, 1/2 - epsilon/(4n), where epsilon > 0 and n is the size of the ground set of items. Furthermore, when the objective function (i.e., a linear combination of quality and diversity) changes dynamically, the GSEMO can maintain this approximation ratio in polynomial running time, addressing the open question proposed by Borodin et al. [7]. This also theoretically shows the superiority of EAs over local search for solving dynamic optimization problems for the first time, and discloses the robustness of the mutation operator of EAs against dynamic changes. Experiments on the applications of web-based search, multi-label feature selection and document summarization show the superior performance of the GSEMO over the stateof-the-art algorithms (i.e., the greedy algorithm and local search) under both static and dynamic environments. (C) 2022 Published by Elsevier B.V.

关键词： Result diversification Monotone submodular functions Diversities Cardinality constraints Matroid constraints Dynamic environments multi-objective evolutionary algorithms Running time analysis Experimental studies

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Performance comparison of multi-objective evolutionary algorithms for exergetic and exergoenvironomic optimization of a benchmark combined heat and power system

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ENERGY 2021年 233卷 121135-121135页

作者： Nondy, J. Gogoi, T. K. Tezpur Univ Dept Mech Engn Tezpur 784028 Assam India

This paper compares the Non-dominated sorting genetic algorithm-II, Pareto envelope-based selection algorithm-II, and Strength Pareto evolutionary algorithm-II, while optimizing a benchmark combined heat and power system with two conflicting objectives. The most effective algorithm is determined based on the statistical parameters evaluated from 30 runs of execution, considering the hypervolume indicator and the average computational time as the performance criteria. A comparative assessment shows that the Pareto envelope-based selection algorithm-II is superior to the other two algorithms. Further, in this study, a multi-criteria decision analysis is performed on the Pareto set obtained from the Pareto envelope-based selection algorithm-II, using the technique for order preference by similarity to an ideal solution, combined with the Entropy method. To show the advantages of multi-objective optimization, the optimal solutions are also compared with the base case, and previously published results of the benchmark problem corresponding to single-objective optimization. From the Pareto envelope-based selection algorithm-II derived optimal solutions, 15.82% increase in the exergy efficiency and 12.22% reduction in the system cost rate are achieved over the base case results. The present optimal exergy efficiency is 11.89% higher and the system cost rate is 1.9% lower than the single-objective based optimal results. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Exergoenvironomic optimization multi-objective evolutionary algorithms NSGA-II PESA-II SPEA-II multi-criteria decision analysis

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Maximizing Submodular or Monotone Functions Under Partition Matroid Constraints by multi-objective evolutionary algorithms 16th

Maximizing Submodular or Monotone Functions Under Partition ...

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16th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Anh Viet Do Neumann, Frank Univ Adelaide Adelaide SA Australia

ISBN: (纸本)9783030581145;9783030581152

Many important problems can be regarded as maximizing submodular functions under some constraints. A simple multi-objective evolutionary algorithm called GSEMO has been shown to achieve good approximation for submodular functions efficiently. While there have been many studies on the subject, most of existing run-time analyses for GSEMO assume a single cardinality constraint. In this work, we extend the theoretical results to partition matroid constraints which generalize cardinality constraints, and show that GSEMO can generally guarantee good approximation performance within polynomial expected run time. Furthermore, we conducted experimental comparison against a baseline GREEDY algorithm in maximizing undirected graph cuts on random graphs, under various partition matroid constraints. The results show GSEMO tends to outperform GREEDY in quadratic run time.

关键词： evolutionary algorithms multi-objective evolutionary algorithms Run-time analysis

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Supplier Selection and Order Allocation Under Disruption: multi-objective evolutionary algorithms

Supplier Selection and Order Allocation Under Disruption: Mu...

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IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

作者： Gargari, F. Javadi Pourjavad, E. Alzahra Univ Tech & Engn Dept Tehran Iran Polytech Montreal Dept Math & Ind Engn Montreal PQ Canada

ISBN: (纸本)9781538672204

Disruption is one of the critical issues that affect the performance and costs of supply chain management. The appropriate adjusting of supply chain disruptions is considered as a competitive privilege for companies. Hence, this paper aims to improve an optimization approach to select suppliers and allocate the proper quota of order to each one considering supplier disruption. A multi-objective Mixed Integer Linear Programming (MOMILP) is proposed model with five objective functions, minimize costs of the transaction and supplying, the percentage of delayed products, and the percentage of returned products, as well as maximize capabilities of orders tracking by customers. Strength Pareto evolutionary Algorithm-II (SPEA-II) and Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) are developed to settle this problem. The efficiency of the solution algorithms is investigated based on four criteria for eight computational experiments. The results indicate the SPEA-II algorithm provides better solutions in comparison with the NSGA-II algorithm.

关键词： Disruption multi-objective evolutionary algorithms Operation research Quota allocation Supply chain management Supplier selection

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multi-objective evolutionary algorithms applied to non-intrusive load monitoring

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ELECTRIC POWER SYSTEMS RESEARCH 2019年 177卷 105961-000页

作者： Li, Ling Yang, Liyu Chen, Hao Li, Ming Zhang, Congxuan Nanchang Hongkong Univ Minist Educ Key Lab Nondestruct Test Nanchang 330063 Jiangxi Peoples R China Chinese Acad Sci Adv Technol Inst Suzhou Ultimage Lab Suzhou 215123 Peoples R China Shandong Coinnovat Ctr Future Intelligent Comp Yantai 264005 Peoples R China

The task of load disaggregation is inherently an optimization problem. Owing to the existence of noise level and electrical interference from neighboring systems, the real operating state of appliances is not the optimal solution for a single-objective function. However, most recent works weigh objective functions into a single one to construct an aggregate objective function to solve, and the weighted parameters for the different objective functions are sensitive to different datasets and are difficult to tune. Only using load data of appliances running individually to model, proposed method can identify several appliances with multiple operating modes operating simultaneously. A multi-objective load disaggregation model integrates more features including macroscopic features and microscopic features which help model to describe appliances from multiple perspectives. Five objective functions using active power, apparent power, reactive power, current waveform, and harmonics as load signatures are established to identify several electrical appliances. Proposed framework using multi-objective evolutionary algorithms for load disaggregation not only avoid adjusting weighted parameters, but also consider conflict among objectives. A problem-specific method during initialization is presented to deal with the problem that one type of appliance only works on one of these operating modes for a moment. To deal with the constraint on the number of appliances operating simultaneously, objective-rank assignment is applied. The load disaggregation is finally solved as a multi-objective problem by multi-objective evolutionary algorithms. Experimental results demonstrate the effectiveness of the proposed method for load disaggregation. The use of multi-feature methods significantly outperforms the methods using any single or two load signatures.

关键词： Non-intrusive load monitoring multi-objective optimization multi-objective evolutionary algorithms multi-feature Steady-state signatures

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Simultaneous topology, shape, and size optimization of trusses, taking account of uncertainties using multi-objective evolutionary algorithms

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ENGINEERING WITH COMPUTERS 2019年第2期35卷 721-740页

作者： Techasen, Teerapol Wansasueb, Kittinan Panagant, Natee Pholdee, Nantiwat Bureerat, Sujin Khon Kaen Univ Dept Mech Engn Sustainable & Infrastruct Res & Dev Ctr Fac Engn Khon Kaen 40002 Thailand

This paper proposes the design of trusses using simultaneous topology, shape, and size design variables and reliability optimization. objective functions consist of structural mass and reliability, while the probability of failure is set as a design constraint. Design variables are treated to simultaneously determine structural topology, shape, and sizes. Six test problems are posed and solved by a number of multi-objective evolutionary algorithms, and it is found that Hybridized Real-Code Population-Based Incremental Learning and Differential Evolution is the best performer. This work is considered an initial study for the combination of reliability optimization and simultaneous topology, shape, and sizing optimization of trusses.

关键词： Truss optimization multi-objective evolutionary algorithms Pareto dominance Reliability index

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A Co-evolutionary Scheme for multi-objective evolutionary algorithms Based on ε-Dominance

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IEEE ACCESS 2019年 7卷 18267-18283页

作者： Menchaca-Mendez, Adriana Montero, Elizabeth Miguel Antonio, Luis Zapotecas-Martinez, Saul Coello Coello, Carlos A. Riff, Maria-Cristina Univ Nacl Autonoma Mexico ENES Tecnol Informac Ciencias Campus Morelia Morelia 58190 Michoacan Mexico Univ Andres Bello Fac Ingn Vina Del Mar 2531015 Chile Inst Politecn Nacl Ctr Invest & Estudios Avanzados Dept Computac Ciudad De Mexico 07360 Mexico Univ Autonoma Metropolitana Unidad Cuajimalpa Dept Matemat Aplicadas & Sistemas Ciudad De Mexico 05348 Mexico Univ Tecn Federico Santa Maria Dept Informat Valparaiso 2390123 Chile

Convergence and diversity of solutions play an essential role in the design of multi-objective evolutionary algorithms (MOEAs). Among the available diversity mechanisms, the epsilon-dominance has shown a proper balance between convergence and diversity. When using epsilon-dominance, diversity is ensured by partitioning the objective space into boxes of size epsilon and, typically, a single solution is allowed at each of these boxes. However, there is no easy way to determine the precise value of epsilon. In this paper, we investigate how this goal can be achieved by using a co-evolutionary scheme that looks for the proper values of epsilon along the search without any need of a previous user's knowledge. We include the proposed co-evolutionary scheme into an MOEA based on epsilon-dominance giving rise to a new MOEA. We evaluate the proposed MOEA solving standard benchmark test problems. According to our results, it is a promising alternative for solving multi-objective optimization problems because three main reasons: 1) it is competitive concerning stateof-the-art MOEAs, 2) it does not need extra information about the problem, and 3) it is computationally efficient.

关键词： multi-objective evolutionary algorithms epsilon-dominance co-evolutionary schemes parameter setting

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Natural laminar flow airfoil shape design at transonic regimes with multi-objective evolutionary algorithms

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2019年第3期233卷 991-999页

作者： Chen, Yongbin Tang, Zhili Nanjing Univ Aeronaut & Astronaut Coll Aerosp Engn 29 Yudao St Nanjing 210016 Jiangsu Peoples R China

The natural laminar flow airfoil shape design at transonic regime is solved using multi-objective evolutionary algorithms in this paper. A shock wave control bump is used to reduce wave drag of natural laminar flow airfoil in transonic flow, and the e(N) transition prediction method based on the linear stability theory is used to predict the transition location. The multi-island parallel multi-objective evolutionary algorithms is implemented to optimize the airfoil shape equipped with shock wave control bump for obtaining a larger laminar flow region and a weaker wave drag simultaneously. Optimization experiment shows that it is easy to capture the Pareto front of wave drag minimization and laminar flow region maximization. Results demonstrate that both wave drag and friction drag performance of several chosen Pareto members are significantly improved via the optimal airfoil shape and shock wave control bump device compared to that of the baseline shape.

关键词： Natural laminar flow airfoil transition prediction linear stability theory active shock control bump multi-objective evolutionary algorithms

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Maximizing submodular or monotone approximately submodular functions by multi-objective evolutionary algorithms

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ARTIFICIAL INTELLIGENCE 2019年 275卷 279-294页

作者： Qian, Chao Yu, Yang Tang, Ke Yao, Xin Zhou, Zhi-Hua Nanjing Univ Natl Key Lab Novel Software Technol Nanjing 210023 Jiangsu Peoples R China Southern Univ Sci & Technol Shenzhen Key Lab Computat Intelligence Dept Comp Sci & Engn Shenzhen 518055 Peoples R China

evolutionary algorithms (EAs) are a kind of nature-inspired general-purpose optimization algorithm, and have shown empirically good performance in solving various real-word optimization problems. During the past two decades, promising results on the running time analysis (one essential theoretical aspect) of EAs have been obtained, while most of them focused on isolated combinatorial optimization problems, which do not reflect the general-purpose nature of EAs. To provide a general theoretical explanation of the behavior of EAs, it is desirable to study their performance on general classes of combinatorial optimization problems. To the best of our knowledge, the only result towards this direction is the provably good approximation guarantees of EAs for the problem class of maximizing monotone submodular functions with matroid constraints. The aim of this work is to contribute to this line of research. Considering that many combinatorial optimization problems involve non-monotone or non-submodular objective functions, we study the general problem classes, maximizing submodular functions with/without a size constraint and maximizing monotone approximately submodular functions with a size constraint. We prove that a simple multi-objective EA called GSEMO-C can generally achieve good approximation guarantees in polynomial expected running time. (C) 2019 Elsevier B.V. All rights reserved.

关键词： evolutionary algorithms Submodular optimization multi-objective evolutionary algorithms Running time analysis Computational complexity

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Comparison of multi-objective evolutionary algorithms in hybrid Kansei engineering system for product form design

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ADVANCED ENGINEERING INFORMATICS 2018年 36卷 31-42页

作者： Shieh, Meng-Dar Li, Yongfeng Yang, Chih-Chieh Natl Cheng Kung Univ Dept Ind Design 1 Univ Rd Tainan 70101 Taiwan Southern Taiwan Univ Sci & Technol Dept Multimedia & Entertainment Sci 1 Nantai St Tainan 71005 Taiwan

Understanding the affective needs of customers is crucial to the success of product design. Hybrid Kansei engineering system (HKES) is an expert system capable of generating products in accordance with the affective responses. HKES consists of two subsystems: forward Kansei engineering system (FKES) and backward Kansei engineering system (BKES). In previous studies, HKES was based primarily on single-objective optimization, such that only one optimal design was obtained in a given simulation run. The use of multi-objective evolutionary algorithm (MOEA) in HKES was only attempted using the non-dominated sorting genetic algorithm-II (NSGA-II), such that very little work has been conducted to compare different MOEAs. In this paper, we propose an approach to HKES combining the methodologies of support vector regression (SVR) and MOEAs. In BKES, we constructed predictive models using SVR. In FKES, optimal design alternatives were generated using MOEAs. Representative designs were obtained using fuzzy c-means algorithm for clustering the Pareto front into groups. To enable comparison, we employed three typical MOEAs: NSGA-II, the Pareto envelope-based selection algorithm-II (PESA-II), and the strength Pareto evolutionary algorithm-2 (SPEA2). A case study of vase form design was provided to demonstrate the proposed approach. Our results suggest that NSGA-II has good convergence performance and hybrid performance;in contrast, SPEA2 provides the strong diversity required by designers. The proposed HKES is applicable to a wide variety of product design problems, while providing creative design ideas through the exploration of numerous Pareto optimal solutions.

关键词： Product form design Kansei engineering multi-objective evolutionary algorithms Support vector regression

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