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An improved multi-objective evolutionary algorithm based on decomposition for energy-efficient permutation flow shop scheduling problem with sequence-dependent setup time

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2019年第6期57卷 1756-1771页

作者： Jiang, En-da Wang, Ling Tsinghua Univ Dept Automat Tsinghua Natl Lab Informat Sci & Technol TNList Beijing 100084 Peoples R China

With the increasing attention on environment issues, green scheduling in manufacturing industry has been a hot research topic. As a typical scheduling problem, permutation flow shop scheduling has gained deep research, but the practical case that considers both setup and transportation times still has rare research. This paper addresses the energy-efficient permutation flow shop scheduling problem with sequence-dependent setup time to minimise both makespan as economic objective and energy consumption as green objective. The mathematical model of the problem is formulated. To solve such a bi-objective problem effectively, an improved multi-objective evolutionary algorithm based on decomposition is proposed. With decomposition strategy, the problem is decomposed into several sub-problems. In each generation, a dynamic strategy is designed to mate the solutions corresponding to the sub-problems. After analysing the properties of the problem, two heuristics to generate new solutions with smaller total setup times are proposed for designing local intensification to improve exploitation ability. Computational tests are carried out by using the instances both from a real-world manufacturing enterprise and generated randomly with larger sizes. The comparisons show that dynamic mating strategy and local intensification are effective in improving performances and the proposed algorithm is more effective than the existing algorithms.

关键词： energy-efficient scheduling sequence-dependent setup time multi-objective evolutionary algorithm decomposition dynamic mating strategy local intensification

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A multi-objective evolutionary algorithm based on decomposition and constraint programming for the multi-objective team orienteering problem with time windows

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APPLIED SOFT COMPUTING 2018年 73卷 383-393页

作者： Hu, Wanzhe Fathi, Mahdi Pardalos, Panos M. Chongqing Univ Coll Mat Sci & Engn Chongqing Peoples R China Univ Florida Dept Ind & Syst Engn Ctr Appl Optimizat Gainesville FL 32611 USA

The team orienteering problem with time windows (TOPTW) is a well-known variant of the orienteering problem (OP) originated from the sports game of orienteering. Since the TOPTW has many applications in the real world such as disaster relief routing and home fuel delivery, it has been studied extensively. In the classical TOPTW, only one profit is associated with each checkpoint while in many practical applications each checkpoint can be evaluated from different aspects, which results in multiple profits. In this study, the multi-objective team orienteering problem with time windows (MOTOPTW), where checkpoints with multiple profits are considered, is introduced to find the set of Pareto optimal solutions to support decision making. Moreover, a multi-objective evolutionary algorithm based on decomposition and constraint programming (CPMOEA/D) is developed to solve the MOTOPTW. The advantages of decomposition approaches to handle multi-objective optimization problems and those of the constraint programming to deal with combinatorial optimization problems have been integrated in CPMOEA/D. Finally, the proposed algorithm is applied to solve public benchmark instances. The results are compared with the best-known solutions from the literature and show more improvement. (C) 2018 Elsevier B.V. All rights reserved.

关键词： multi-objective combinatorial optimization Team orienteering problem multi-objective evolutionary algorithm Decomposition approach Constraint programming

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A multi-level knee point based multi-objective evolutionary algorithm for AUC maximization

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MEMETIC COMPUTING 2019年第3期11卷 285-296页

作者： Qiu, Jianfeng Liu, Minghui Zhang, Lei Li, Wei Cheng, Fan Anhui Univ Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230039 Anhui Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230039 Anhui Peoples R China

The area under receiver operating characteristic curve (AUC) is one of the widely used metrics for measuring imbalanced data classification results. Designing multi-objective evolutionary algorithms for AUC maximization problem has attracted much attention of researchers recently. However, most of these methods either search the Pareto front directly, or perform tailored convex hull search for AUC maximization. None of them take the advantage of multi-level knee points found in the process of evolution for AUC maximization. To this end, this paper proposes a multi-level knee point based multi-objective evolutionary algorithm (named MKnEA-AUC) for AUC maximization on the basis of a recently developed knee point driven evolutionary algorithm for multi/many-objective optimization. In MKnEA-AUC, an adaptive clustering strategy is proposed for automatically determining the knee points on the current population. By utilizing the preference of found knee points, the evolution of the population can converge quickly. We verify the effectiveness of the proposed algorithm MKnEA-AUC on 13 widely used benchmark data sets and the experimental results demonstrate that MKnEA-AUC is superior over the state-of-the-art algorithms for AUC maximization.

关键词： AUC maximization multi-objective evolutionary algorithm Knee points Adaptive clustering strategy

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A multi-objective evolutionary algorithm for multi-period dynamic emergency resource scheduling problems

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2017年 99卷 77-95页

作者： Zhou, Yawen Liu, Jing D Zhang, Yutong Gan, Xiaohui Xidian Univ Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China

The resource distribution in post-disaster is an important part of emergency resource scheduling. In this paper, we first design a multi-objective optimization model for multi period dynamic emergency resource scheduling (ERS) problems. Then, using the framework of multi-objective evolutionary algorithm based on decomposition (MOEA/D), an MOEA is proposed to solve this model. In the proposed algorithm, new evolutionary operators are designed with the intrinsic properties of multi-period dynamic ERS problems in mind. The experimental results show that the proposed algorithm can get a set of better candidate solutions than the non-dominated sorting genetic algorithm II (NSGA-II). (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Emergency resource scheduling problems multi-period multi-objective optimization multi-objective evolutionary algorithm

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Automatic calibration of hydrologic models with multi-objective evolutionary algorithm and Pareto optimization

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JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION 2007年第4期43卷 981-989页

作者： Confesor, Remegio B., Jr. Whittaker, Gerald W. Oregon State Univ Dept Agr & Resource Econ Corvallis OR 97331 USA USDA ARS Natl Forage Seed Prod Res Ctr Corvallis OR 97331 USA

In optimization problems with at least two conflicting objectives, a set of solutions rather than a unique one exists because of the trade-offs between these objectives. A Pareto optimal solution set is achieved when a solution cannot be improved upon without degrading at least one of its objective criteria. This study investigated the application of multi-objective evolutionary algorithm (MOEA) and Pareto ordering optimization in the automatic calibration of the Soil and Water Assessment Tool (SWAT), a process-based, semi-distributed, and continuous hydrologic model. The nondominated sorting genetic algorithm II (NSGA-II), a fast and recent MOEA, and SWAT were called in FORTRAN from a parallel genetic algorithm library (PGAPACK) to determine the Pareto optimal set. A total of 139 parameter values were simultaneously and explicitly optimized in the calibration. The calibrated SWAT model simulated well the daily streamflow of the Calapooia watershed for a 3-year period. The daily Nash-Sutcliffe coefficients were 0.86 at calibration and 0.81 at validation. Automatic multi-objective calibration of a complex watershed model was successfully implemented using Pareto ordering and MOEA. Future studies include simultaneous automatic calibration of water quality and quantity parameters and the application of Pareto optimization in decision and policy-making problems related to conflicting objectives of economics and environmental quality.

关键词： simulation optimization nonpoint source pollution Pareto multi-objective evolutionary algorithm automatic calibration

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A multi-objective evolutionary algorithm With Hierarchical Clustering-Based Selection

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IEEE ACCESS 2023年 11卷 2557-2569页

作者： Zhou, Shenghao Chen, Ze Li, Qi Gu, Mengjun Bao, Zhoucheng He, Wenda Sheng, Weiguo Hangzhou Normal Univ Sch Informat Sci & Technol Hangzhou 311121 Peoples R China Hangzhou Normal Univ Engn Res Ctr Mobile Hlth Management Syst Hangzhou 311121 Peoples R China China Telecom Corp Ltd Zhejiang Branch Hangzhou 310022 Peoples R China

This paper proposes an evolutionary algorithm with hierarchical clustering based selection for multi-objective optimization. In the proposed algorithm, a hierarchical clustering is employed to design the environmental and mating selections, named local coverage selection and local area selection, respectively, for multi-objective evolutionary algorithm. The local coverage selection strategy aims to preserve well-distributed individuals with good convergence. While, the local area selection strategy is devised to deliver a balanced evolutionary search. This is achieved by encouraging individuals for exploration or exploitation according to the I-epsilon+ indicator. In both strategies, a hierarchical clustering method is employed to discover the population structure. Based on the clustering results, in local coverage selection, the individuals of different clusters will be retained according to their coverage areas and crowding distances, such that distributing as evenly as possible in the Pareto front. In local area selection, the individual(s) with the best value of I-epsilon+ indicator in each cluster will be selected to perform mating, with the purpose of achieving a balanced exploration and exploitation. The proposed algorithm has been evaluated on 26 bench-mark problems and compared with related methods. The results clearly show the significance of the proposed method.

关键词： multi-objective evolutionary algorithm multi-objective optimization problem hierarchical clustering

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QAR-CIP-NSGA-II: A new multi-objective evolutionary algorithm to mine quantitative association rules

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INFORMATION SCIENCES 2014年 258卷 1-28页

作者： Martin, D. Rosete, A. Alcala-Fdez, J. Herrera, F. Higher Polytech Inst Jose Antonio Echeverria Dept Artificial Intelligence & Infrastruct Inform Havana 19390 Cuba Univ Granada CITIC UGR Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain

Some researchers have framed the extraction of association rules as a multi-objective problem, jointly optimizing several measures to obtain a set with more interesting and accurate rules. In this paper, we propose a new multi-objective evolutionary model which maximizes the comprehensibility, interestingness and performance of the objectives in order to mine a set of quantitative association rules with a good trade-off between interpretability and accuracy. To accomplish this, the model extends the well-known multi-objective evolutionary algorithm Non-dominated Sorting Genetic algorithm II to perform an evolutionary learning of the intervals of the attributes and a condition selection for each rule. Moreover, this proposal introduces an external population and a restarting process to the evolutionary model in order to store all the nondominated rules found and improve the diversity of the rule set obtained. The results obtained over real-world datasets demonstrate the effectiveness of the proposed approach. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Data mining Quantitative association rule multi-objective evolutionary algorithm NSGA-II

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Low emittance lattice optimization using a multi-objective evolutionary algorithm

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Chinese Physics C 2011年第9期35卷 859-864页

作者：高巍巍王琳李为民何多慧 National Synchrotron Radiation Lab University of Science and Technology of China

A low emittance lattice design and optimization procedure are systematically studied with a non-dominated sorting-based multi-objective evolutionary algorithm which not only globally searches the low emittance lattice, but also optimizes some beam quantities such as betatron tunes, momentum compaction factor and dispersion function simultaneously. In this paper the detailed algorithm and lattice design procedure are presented. The Hefei light source upgrade project storage ring lattice, with fixed magnet layout, is designed to illustrate this optimization procedure.

关键词： emittance multi-objective evolutionary algorithm lattice

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Decentralized Load-Frequency Control of Interconnected Power Systems with SMES Units and Governor Dead Band using multi-objective evolutionary algorithm

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2009年第4期4卷 443-450页

作者： Ganapathy, S. Velusami, S. Annamalai Univ Dept Elect Engn Annamalainagar 608002 Tamil Nadu India

This paper deals with the design of decentralized controller for load-frequency control of interconnected power systems With Superconducting magnetic energy storage units and Governor Dead Band Nonlinearity using multi-objective evolutionary algorithm. The superconducting magnetic energy storage unit exhibits favourable damping effects by Suppressing the frequency oscillations as well as stabilizing the inter-area oscillations effectively. The proposed control strategy is mainly based on a compromise between Integral Squared Error and Maximum Stability Margin criteria. Analysis on a two-area interconnected thermal power system reveals that the proposed controller improves the dynamic performance of the system and guarantees good closed-loop stability even in the presence of nonlinearities and with parameter changes.

关键词： Governor Dead Band Nonlinearity Load-Frequency Control multi-objective evolutionary algorithm Power Systems Superconducting Magnetic Energy Storage

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Optimization of cutting parameters using multi-objective evolutionary algorithm based on decomposition

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JOURNAL OF VIBROENGINEERING 2013年第2期15卷 833-844页

作者： Fu Tao Liu Weijun Zhao Jibin Chinese Acad Sci Shenyang Inst Automat Beijing 100864 Peoples R China

High-strength die steel is widely used for cutting, stamping and molding because of its advantages of high hardness, high toughness and abrasion resistance. However, its efficiency in cutting has always been difficult. In a complex machining process, the optimization of cutting parameters has a significant influence on the stability and quality of products. In this study, an orthogonal experiment of NAK80 for milling is carried out with cutting speed, feed rate and depth of cut, all of which are considered as relevant variables. According to the experimental results, statistical models for cutting force and surface roughness are built using a regression method as objective functions. Superadded the theoretical formula of material removal rate, a multi-objective evolutionary algorithm based on decomposition is used to optimize the cutting parameters and a Pareto solution set is obtained finally.

关键词： NAK80 cutting parameters multi-objective evolutionary algorithm optimization

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