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multi-objective evolutionary algorithm in tables for placement of SCADA and PMU considering the concept of Pareto Frontier

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2019年 106卷 373-382页

作者： Vigliassi, Marcos Paulo Massignan, Julio A. D. Delbem, Alexandre Claudio B. London, Joao Bosco A., Jr. Univ Sao Paulo Sao Carlos Sch Engn Av Trabalhador Sao Carlense 400 Sao Carlos SP Brazil Univ Sao Paulo Inst Math & Comp Sci Av Trabalhador Sao Carlense 400 Sao Carlos SP Brazil

The main concern of Metering Systems Planning (MSP) for state estimation is the determination of the number, type and location to install metering devices to attend performance requirements related to observability and measurement redundancy. Due to financial constraints, the MSP is a multi-objective, combinatorial optimization problem, which involves conflicting objectives of investment cost and performance requirements. This paper proposes a multi-objective formulation for the MSP problem and a method for its solution. The method combines a multi-objective evolutionary algorithm based on subpopulation tables with the properties of the so-called H-Delta matrix. The method can solve the MSP problem considering the existence of only measurements from supervisory control and data acquisition system, only from Phasor Measurement Units (PMUs) or both types of measurements. Using the concept of Pareto Frontier the method enables the project of reliable metering systems (those guaranteeing system observability during normal operating conditions and in the presence of contingency situations that cause the loss of one or two measurements or one remote terminal unit or one PMU) and other three types of metering systems with different performance requirements and costs in only one execution. Simulation results with IEEE test systems and a Brazilian system validate the paper propositions.

关键词： Power system state estimation Measurement placement Synchronized phasor measurements Combinatorial optimization multi-objective evolutionary algorithm

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A multi-objective evolutionary algorithm Approach for Optimizing Part Quality Aware Assembly Job Shop Scheduling Problems 24th

A Multi-objective Evolutionary Algorithm Approach for Optimi...

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24th International Conference on the Applications of evolutionary Computation (EvoApplications) Held as Part of EvoStar Conference

作者： Prince, Michael H. DeHaan, Kristian Tauritz, Daniel R. Auburn Univ BONSAI Lab Auburn AL 36830 USA

ISBN: (纸本)9783030726980;9783030726997

Motivated by a real-world application, we consider an Assembly Job Shop Scheduling Problem (AJSSP), with three objectives: product quality, product quantity, and first product lead time. Using real-world inspection data, we demonstrate the ability to model product quality transformations during assembly jobs via genetic programming by considering the quality attributes of subparts. We investigate integrating quality transformation models into an AJSSP. Through the use of the de facto standard multi-objective evolutionary algorithm, NSGA-II, and a novel genotype to handle the constraints, we describe an evolutionary approach to optimizing all stated objectives. This approach is empirically shown to outperform random search and hill climbing in both performance and usability metrics expected to be valuable to administrators involved in plant scheduling and operations.

关键词： Assembly Job Shop Scheduling evolutionary algorithm Genetic programming Manufacturing multi-objective evolutionary algorithm

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New search strategy for multi-objective evolutionary algorithm

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Heliyon 2024年第24期10卷 e40917页

作者： Yuejun, Liu Software School of Anyang Normal University Henan Anyang 455002 China Key Laboratory of Oracle Bone Inscriptions Information Processing Ministry of Education Henan Anyang 455002 China

To address the problem of low search efficiency of multi-objective evolutionary algorithm during iterations, we proposed a new idea which considering a single individual to generate better solutions in a single iteration as a starting point to improve the search performance of multi-objective evolutionary algorithm and designedthe neighbor strategy and guidance strategy based on this improved approach in this paper. We used our proposed new search strategy to improve NSGA-III algorithm(named as NSGA-III/NG) and MOEA/D algorithm(named as MOEA/D-NG). On ZDT, DTLZ and WFG public test sets, the NSGA-III/NG algorithm using the new search strategy was compared with NSGA-II algorithm, NSGA-III algorithm, ANSGA-III algorithm and NSGA-II/ARSBX algorithm. The MOEA/D-NG algorithm using the new search strategy was compared with MOEA/D algorithm, MOEA/D-CMA algorithm, MOEA/D-DE algorithm and CMOEA/D algorithm. Experimental results indicate that the performance of NSGA-III/NG algorithm using our search strategy is superior to NSGA-II, NSGA-III,ANSGA-III and NSGA-II/ARSBX algorithm and the performance of MOEA/D-NG algorithm using our search strategy is superior toMOEA/D, MOEA/D-CMA,MOEA/D-DE and CMOEA/D algorithm. Our proposed search strategy can improve the convergence speed of NSGA-III algorithm and MOEA/D algorithm by 12.54 %,the accuracy of the non dominated solution set by 3.67 %. This situation indicates that our search strategy could significantly improve the search capability of the multi-objective evolutionary algorithm. In addition, this strategy has excellent applicability and could be combined with mainstream multi-objective evolutionary algorithms. © 2024

关键词： Guidance strategy multi-objective evolutionary algorithm Neighbor strategy Search efficiency Search strategy

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A Bi-Population Based multi-objective evolutionary algorithm Using Hybrid Identification Method for Finding Knee Points

A Bi-Population Based Multi-Objective Evolutionary Algorithm...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Tang, Junfeng Wang, Handing Xidian Univ Sch Artificial Intelligence Xian Peoples R China

ISBN: (纸本)9781728190488

In the preference-based multi-objective optimization, decision makers may be interested in only a part of the representative solutions and hardly specify their preferences. In this case, knee points are considered as the naturally preferred trade-off solutions. Most research utilizes the trade-off information or certain properties to find knee points. However, little attention has been paid to combine them to further enhance the knee identification. This paper proposes a multi-objective evolutionary algorithm using a hybrid identification method and a bi-population structure to find knee points. The hybrid identification method is based on the localized alpha-dominance and the distance to the hyperplane. Firstly, two populations are partitioned by a set of predefined reference vectors and apply the localized alpha-dominance to guide the search towards potential knee regions. Then knee solutions are detected based on the distance to hyperplane constructed by the extreme points. Finally in the environmental selection, a niche-preserving operation is applied to take the knee solutions of all sub-populations into account. The first population is the main part of the search, and affects the offspring generation and environmental selection of the second population. The experiments demonstrate that the proposed method is effective and competitive in identifying knee solutions.

关键词： multi-objective evolutionary algorithm knee points bi-population structure hybrid identification method

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Neural architecture search via reference point based multi-objective evolutionary algorithm

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PATTERN RECOGNITION 2022年 132卷

作者： Tong, Lyuyang Du, Bo Wuhan Univ Sch Comp Sci Natl Engn Res Ctr Multimedia Software Inst Artificial Intelligence Wuhan Peoples R China Wuhan Univ Hubei Key Lab Multimedia & Network Commun Engn Wuhan Peoples R China

For neural architecture search, NSGA-Net has searched a representative neural architecture set of Pareto-optimal solutions to consider both accuracy and computation complexity simultaneously. However, some decision-makers only concentrate on such neural architectures in the subpart regions of Pareto-optimal Frontier that they have interests in. Under the above circumstances, certain uninterested neural architectures may cost many computing resources. In order to consider the preference of decision-makers, we propose the reference point based NSGA-Net (RNSGA-Net) for neural architecture search. The core of RNSGA-Net adopts the reference point approach to guarantee the Pareto-optimal region close to the reference points and also combines the advantage of NSGAII with the fast nondominated sorting approach to split the Pareto front. Moreover, we augment an extra bit value of the original encoding to represent two types of residual block and one type of dense block for residual connection and dense connection in the RNSGA-Net. In order to satisfy the decision-maker preference, the multi-objective is measured to search competitive neural architecture by minimizing an error metric and FLOPs of computational complexity. Experiment results on the CIFAR-10 dataset demonstrate that RNSGA-Net can improve NSGA-Net in terms of the more structured representation space and the preference of decision-makers. (C) 2022 Elsevier Ltd. All rights reserved.

关键词： Neural architecture search multi-objective evolutionary algorithm The image classification

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A new hierarchy framework for feature engineering through multi-objective evolutionary algorithm in text classification

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第3期34卷

作者： Asgarnezhad, Razieh Monadjemi, S. Amirhassan Aghaei, Mohammadreza Soltan Islamic Azad Univ Dept Comp Engn Isfahan Khorasgan Branch Esfahan Iran Natl Univ Singapore Sch Continuing & Lifelong Educ Singapore 119077 Singapore Univ Isfahan Fac Comp Engn Esfahan Iran

Sentiment classification is a field of sentiment analysis concerned with analyzing opinions, emotions, evaluations, and attitudes regarding a special topic like a product, an organization, a person, or an incident. With the growth of user-generated content on the Web, this field gained great importance in online reviews. With a wide range of reviews, customers cannot read all reviews. Considering the increasing rate of electronic documents and the urgent need manually mine for keywords that are hard and time-consuming, doing the same automatically is of high demand. A new framework proposed here to mine and classify users' comments based on mining keywords by applying the sequence pattern mining through the Separation-Power concept, a multi-objective evolutionary algorithm based on decomposition with four objectives, and a neural network as the final classifier. Some modifications are made on multi-objective evolutionary algorithm based on decomposition and Apriori algorithms to improve the text classification efficiency. To evaluate the proposed framework, three datasets applied;which compared with the two methods to measure accuracy, precision, recall, and error-index. The results indicate that this framework provides a better outcome than its counterparts with 99.45 precision, 99.34 accuracy, 99.48 recall, and 99.28% f-measure.

关键词： Apriori algorithm data mining feature selection multi-objective evolutionary algorithm sequence pattern mining text classification

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A novel multi-objective evolutionary algorithm for hybrid renewable energy system design

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SWARM AND evolutionary COMPUTATION 2022年 75卷

作者： Jiang, Bo Lei, Hongtao Li, Wenhua Wang, Rui Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Key Lab Multienergy Syst Intelligent Interconnect Changsha 410073 Peoples R China

As a result of the fossil fuel energy crisis and the focus on environmental protection, renewable energy is favoured throughout the world. Due to the unstable and unpredictable nature of renewable energy, a hybrid renewable energy system (HRES) that integrates traditional fossil fuel and renewable energy is a promising solution to overcome this challenge. In this paper, a novel multi-objective evolutionary algorithm with a diversity-maintained mechanism (MOEA-DM) is applied to the design of a multi-objective HRES. We propose a special environmental selection strategy to enhance the diversity of solutions, considering the discrete optimization of the HRES design. In the experiments, a stand-alone hybrid system including photovoltaic (PV) panels, wind power generators, batteries and diesel generators is applied to find the optimal combination of components with a set of nondominated solutions. The effectiveness, superiority and generalizability of the proposed algorithm are validated through a comparison with state-of-the-art algorithms.

关键词： Hybrid renewable energy systems multi-objective evolutionary algorithm Optimization design

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Chiller-pump system optimisation method for minimum energy operation based on multi-objective evolutionary algorithm

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APPLIED THERMAL ENGINEERING 2022年 208卷 118150-118150页

作者： Xing, Shanshuo Zhang, Jili Dalian Univ Technol Fac Infrastruct Engn Dalian 116024 Peoples R China

The operation optimisation of chilled water systems, comprising chillers and pumps, should consider variations in the cooling demand. Although a number of approaches have been proposed, achieving an overall optimum in actual operation is complicated. Herein, we propose a hybrid optimisation method for a chiller-pump system in parallel-arranged systems with non-identical chillers and pumps, which are commonly applied in engineering practice but are rarely considered in the literature. A holistic optimisation based on a multi-objective evolutionary algorithm was conducted for the overall minimum energy by solving the trade-off problem between chillers and pumps. The overall energy consumption and system flow violation were simultaneously minimised by optimising practical parameters, including binary (alpha(chiller,i), alpha(pump,j)) and continuous ((Q) over dot(chiller,i), omega(j), (m) over dot(chw,j)) variables. The proposed method was evaluated via a simulation. The results suggest that the optimisation is rational, and energy saving ratios of 7.3%, 5.3%, and 34.0% for overall, chillers, and pumps, respectively, can be achieved relative to the baseline.

关键词： HVAC Chiller -pump system Optimisation multi-objective evolutionary algorithm Energy saving

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An automatic multi-objective evolutionary algorithm for the hybrid flowshop scheduling problem with consistent sublots

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KNOWLEDGE-BASED SYSTEMS 2022年 238卷 1页

作者： Zhang, Biao Pan, Quan-ke Meng, Lei-lei Lu, Chao Mou, Jian-hui Li, Jun-qing Liaocheng Univ Sch Comp Sci Liaocheng 25200 Shandong Peoples R China Shanghai Univ Sch Mech Engn & Automat Shanghai 200072 Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Yantai Univ Sch Mech Elect & Automobile Engn Yantai 264005 Peoples R China

Lot streaming is the most widely used technique to facilitate overlapping of successive operations. Inspired by real-world scenarios, this paper studies a multi-objective hybrid flowshop scheduling problem with consistent sublots, aiming to simultaneously optimize two conflicting objectives: the makespan and total number of sublots. Considering the setup and transportation operations, a multi-objective mixed integer programming model is developed and the trade-off between the two objectives is evaluated. Because of the NP-hard property of the addressed problem, metaheuristics are suggested. It is well known that the performance of metaheuristics is highly dependent on the setting of algorithmic parameters, referred to as numerical and categorical parameters. However, the traditional design process might be biased by previous experience. To eliminate these issues, an automated algorithm design (AAD) methodology is introduced to conceive a multi-objective evolutionary algorithm (MOEA) in a promising framework. The AAD enables designing the algorithm by automatically determining parameters and their combinations with minimal user intervention. With regard to the problem-specific characteristics and the employed algorithm framework, for the categorical parameters, including decomposition, solution encoding and decoding, solution initialization and neighborhood structures, several operators are designed specifically. Along with the numerical parameters, these categorical parameters are determined and combined using the designed iterated racing procedure. Comprehensive computational results demonstrate that the automated MOEA outperforms other state-of-the-art MOEAs for the addressed problem. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Hybrid flowshop scheduling Lot streaming Consistent sublots multi-objective evolutionary algorithm Automatic algorithm design

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multi-objective evolutionary algorithm on simplified bi-objective minimum weight minimum label spanning tree problems

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INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING 2019年第3期20卷 354-361页

作者： Lai, Xinsheng Xia, Xiaoyun ShangRao Normal Univ Sch Math & Comp Sci Shangrao 334001 Peoples R China Jiaxing Univ Coll Math Phys & Informat Engn Jiaxing 314001 Peoples R China

As general purpose optimisation methods, evolutionary algorithms have been efficiently used to solve multi-objective combinatorial optimisation problems. However, few theoretical investigations have been conducted to understand the efficiency of evolutionary algorithms on such problems, and even fewer theoretical investigations have been conducted on multi-objective combinatorial optimisation problems coming from real world. In this paper, we analyse the performance of a simple multi-objective evolutionary algorithm on two simplified instances of the bi-objective minimum weight minimum label spanning tree problem, which comes from real world. Though these two instances are similar, the analysis results show that the simple multi-objective evolutionary algorithm is efficient for one instance, but it may be inefficient for the other. According to the analysis on the second instance, we think that the restart strategy may be useful in making the multi-objective evolutionary algorithm more efficient for the bi-objective problem.

关键词： multi-objective evolutionary algorithm bi-objective spanning tree problem minimum weight minimum label

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