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Improved hybrid Strength Pareto Evolutionary Algorithms for multi-objective optimization

International Journal of Intelligent Computing and Cybernetics 2018年第1期11卷 20-46页

作者： K.Shankar Akshay S.Baviskar Department of mechanical engineering indian institute of technology madraschennaiindia

Purpose–The purpose of this paper is to design an improved multi-objective algorithm with better spread and convergence than some current *** proposed application is for engineering design ***/methodology/approach–This study proposes two novel approaches which focus on faster convergence to the Pareto front(PF)while adopting the advantages of Strength Pareto Evolutionary Algorithm-2(SPEA2)for better *** first method,decision variables corresponding to the optima of individual objective functions(Utopia Point)are strategically used to guide the search toward *** second method,boundary points of the PF are calculated and their decision variables are seeded to the initial ***–The proposed methods are tested with a wide range of constrained and unconstrained multi-objective test functions using standard performance *** evaluation demonstrates the superiority of proposed algorithms over well-known existing algorithms(such as NSGA-II and SPEA2)and recent ones such as NSLS and E-NSGA-II in most of the benchmark *** is also tested on an engineering design problem and compared with a currently used *** implications–The algorithms are intended to be used for practical engineering design problems which have many variables and conflicting objectives.A complex example of Welded Beam has been shown at the end of the *** implications–The algorithm would be useful for many design problems and social/industrial problems with conflicting ***/value–This paper presents two novel hybrid algorithms involving SPEA2 based on:local search;and Utopia point directed search *** concept has not been investigated before.

关键词： Evolutionary algorithms Boundary points multi-objective optimization problems Strength Pareto Evolutionary Algorithm 2(SPEA2)

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Comparing multi-objective optimization techniques to calibrate a conceptual hydrological model using in situ runoff and daily GRACE data

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COMPUTATIONAL GEOSCIENCES 2018年第3期22卷 789-814页

作者： Mostafaie, A. Forootan, E. Safari, A. Schumacher, M. Univ Tehran Sch Surveying & Geospatial Engn Coll Engn Tehran Iran Cardiff Univ Sch Earth & Ocean Sci Cardiff S Glam Wales Univ Bristol Sch Geog Sci Bristol Avon England

Hydrological models are necessary tools for simulating the water cycle and for understanding changes in water resources. To achieve realistic model simulation results, real-world observations are used to determine model parameters within a "calibration" procedure. optimization techniques are usually applied in the model calibration step, which assures a maximum similarity between model outputs and observations. practical experiences of hydrological model calibration have shown that single-objective approaches might not be adequate to tune different aspects of model simulations. These limitations can be as a result of (i) using observations that do not sufficiently represent the dynamics of the water cycle, and/or (ii) due to restricted efficiency of the applied calibration techniques. To address (i), we assess how adding daily Total Water Storage (dTWS) changes derived from the Gravity Recovery And Climate Experiment (GRACE) as an extra observations, besides the traditionally used runoff data, improves calibration of a simple 4-parameter conceptual hydrological model (GR4J, in French: modSle du G,nie Rural A 4 paramStres Journalier) within the Danube River Basin. As selecting a proper calibration approach (in ii) is a challenging task and might have significant influence on the quality of model simulations, for the first time, four evolutionary optimization techniques, including the Non-dominated Sorting Genetic Algorithm II (NSGA-II), the multi-objective Particle Swarm optimization (MPSO), the Pareto Envelope-Based Selection Algorithm II (PESA-II), and the Strength Pareto Evolutionary Algorithm II (SPEA-II) along with the Combined objective function and Genetic Algorithm (CGA) are tested to calibrate the model in (i). A number of quality measures are applied to assess cardinality, accuracy, and diversity of solutions, which include the Number of Pareto Solutions (NPS), Generation Distance (GD), Spacing (SP), and Maximum Spread (MS). Our results indicate that accord

关键词： multi-objective calibration GRACE GR4J Conceptual hydrological model Evolutionary optimization techniques Daily total water storage

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A Synergistic Approach to Robust Output Feedback Control: Tube-based multi-stage NMPC

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IFAC-PapersOnLine 2018年第18期51卷 500-505页

作者： Subramanian, Sankaranarayanan Lucia, Sergio Engell, Sebastian Process Dynamics and Operations Group DortmundTU Germany Laboratory of Internet of Things for Smart Buildings BerlinTU United States Einstein Center Digital Future Wilhelmstr. 67 Berlin10117 Germany

Plant-model mismatch and estimation errors are critical issues in the practical implementation of Nonlinear Model Predictive Control (NMPC). To address these challenges, we formulate a robust output feedback NMPC scheme that is real-time implementable and provides robust constraint satisfaction in the presence of parametric and additive uncertainties, and estimation errors. The robustness is achieved by combining the tube-based and the multi-stage NMPC approaches. Two controllers are used in the proposed framework: a primary controller with tightened constraints that optimizes a given objective and an ancillary controller that tracks the trajectories provided by the primary controller. Unlike standard tube-based NMPC, the primary controller predicts different state trajectories for different realizations of the most important uncertainties using the multi-stage NMPC framework. The synergy between the two approaches leads to a better trade-off between optimality and complexity. The advantages of the proposed approach are demonstrated for an industrial-scale fed-batch polymerization reactor example. © 2016

关键词： Feedback Controllers Economic and social effects Model predictive control optimization Predictive control systems Process control Robust control Constraint Satisfaction Estimation errors Industrial scale Nonlinear model predictive control Output feedback Plant model mismatches Robust output feedbacks State trajectory

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An efficient multi-objective optimization method for water quality sensor placement within water distribution systems considering contamination probability variations

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WATER RESEARCH 2018年 143卷 165-175页

作者： He, Guilin Zhang, Tuqiao Zheng, Feifei Zhang, Qingzhou Zhejiang Univ Coll Civil Engn & Architecture 866 Yuhangtang Rd Hangzhou 310058 Zhejiang Peoples R China

Water quality security within water distribution systems (WDSs) has been an important issue due to their inherent vulnerability associated with contamination intrusion. This motivates intensive studies to identify optimal water quality sensor placement (WQSP) strategies, aimed to timely/effectively detect (un)intentional intrusion events. However, these available WQSP optimization methods have consistently presumed that each WDS node has an equal contamination probability. While being simple in implementation, this assumption may do not conform to the fact that the nodal contamination probability may be significantly regionally varied owing to variations in population density and user properties. Furthermore, the low computational efficiency is another important factor that has seriously hampered the practical applications of the currently available WQSP optimization approaches. To address these two issues, this paper proposes an efficient multi-objective WQSP optimization method to explicitly account for contamination probability variations. Four different contamination probability functions (CPFs) are proposed to represent the potential variations of nodal contamination probabilities within the WDS. Two real-world WDSs are used to demonstrate the utility of the proposed method. Results show that WQSP strategies can be significantly affected by the choice of the CPF. For example, when the proposed method is applied to the large case study with the CPF accounting for user properties, the event detection probabilities of the resultant solutions are approximately 65%, while these values are around 25% for the traditional approach, and such design solutions are achieved approximately 10,000 times faster than the traditional method. This paper provides an alternative method to identify optimal WQSP solutions for the WDS, and also builds knowledge regarding the impacts of different CPFs on sensor deployments. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Contamination probability function (CPF) Contamination intrusion Data-archive method Water distribution system (WDS) Water quality sensor placement (WQSP)

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Recent Advances in Evolutionary multi-objective optimization

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2018年

作者： Slim Bechikh Rituparna Datta Abhishek Gupta

ISBN: (纸本)9783319827094

This book covers the most recent advances in the field of evolutionary multiobjective optimization. With the aim of drawing the attention of up-and coming scientists towards exciting prospects at the forefront of computational intelligence, the authors have made an effort to ensure that the ideas conveyed herein are accessible to the widest audience. The book begins with a summary of the basic concepts in multi-objective optimization. This is followed by brief discussions on various algorithms that have been proposed over the years for solving such problems, ranging from classical (mathematical) approaches to sophisticated evolutionary ones that are capable of seamlessly tackling practical challenges such as non-convexity, multi-modality, the presence of multiple constraints, etc. Thereafter, some of the key emerging aspects that are likely to shape future research directions in the field are presented. These include: optimization in dynamic environments, multi-objective bilevel programming, handling high dimensionality under many objectives, and evolutionary multitasking. In addition to theory and methodology, this book describes several real-world applications from various domains, which will expose the readers to the versatility of evolutionary multi-objective optimization.

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Evaluation of solution approaches for a stochastic lot-sizing and sequencing problem

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2018年 199卷 179-192页

作者： Schemeleva, Kseniya Delorme, Xavier Dolgui, Alexandre Univ Lyon 2 Univ Lyon LAET F-69007 Lyon France Mines St Etienne LIMOS UMR CNRS 6158 F-42023 St Etienne France IMT Atlantique LS2N CNRS F-44307 Nantes France

A stochastic multi product lot sizing and sequencing problem is studied. Two kinds of uncertainties are integrated into the model: defective items due to the process imperfections and random lead times because of randomly arising machine breakdowns and uncertain repair times. There are also sequence-dependent set-up times between two items of different types. The objective is to find a sequence of lots and lot sizes maximizing the probability of demand satisfaction for all products. A decomposition approach has been proposed in the literature to reduce this problem to a sequence of known optimization problems with different algorithms available for each of them. However, a proper evaluation of the practical performance of the whole method has never been presented. The goal of this paper is to analyze and compare the behavior of different solution frameworks (with and without decomposition) and techniques for the considered problem.

关键词： Stochastic production lines Lot-sizing Sequencing Decomposition Dynamic programming Genetic algorithms

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Learning and multi-objective optimization for Automatic Identity Linkage

Learning and Multi-Objective Optimization for Automatic Iden...

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IEEE International Conference on Big Data (Big Data)

作者： Jalali, Leila Khan, Misbah Biswas, Rahul Adobe Inc San Jose CA 95110 USA

ISBN: (纸本)9781538650356

There has been exponential growth on the Internet connectable and connected devices in recent years. With the world becoming more connected than ever, each individual user accesses services from a range of devices, including personal desktops and laptop computers, tablets, mobile devices, vehicles, and entertainment systems. One fundamental problem is to identify the user using the fragmentation of identity and consumer profiles across these connected devices. Adobe Identity Graph solves this fundamental problem for the digital marketing domain. An Identity graph is built by discovering identity relationships using both online data traffic and offline data logs to establish links between different identities allowing for richer insights into the consumer. In this paper, we explore using a machine learning based approach for Identity Graph. In particular, we focus on linking identities using structured learning and multi objective optimization. Our proposed solution can help in improving current approaches in to give a complete holistic view of the consumer. Additionally, it can help learning about the consumer over time. Towards automatic identity linkage of the same natural person, we can study to construct statistical learning method based on massive user access logs. Our evaluation results show that our proposed approach can be very effective in linking identities and achieving a practical solution on a real data set.

关键词： Automatic Identity Linkage Activity Logs Learning multi-objective optimization

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Extension of zigzag search algorithms for power system multi-objective optimization

Extension of zigzag search algorithms for power system multi...

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作者： Qiwei Zhang The University of Tennessee Knoxville

学位级别：硕士

The work presented in this thesis focuses on the application and extension the zigzag search algorithms in power systems. The zigzag search method is a multi-objective algorithm which has recently been applied in multiple engineering fields, such as oil well replacement, with fast computational time and accurate ***-objective optimization algorithms in power systems have been investigated for years. Most of the literatures focus on evolutionary algorithms (EA) such as a non-dominated sorting genetic algorithm (NSGA) or multi-objective particle swarm optimization (MOPSO) for their simplicity and ease of implementation. However, there have been several issues regarding the evolutionary algorithm (EA). For example, the computational time of EA is significant and the parameter configurations are complicated. Other approaches mainly reply on the weight sum method by lumping together different objective functions to form a new single objective function; however, the priority is hard to determine and the characteristic between different objectives may be *** order to improve the performance of power system multi-objective optimization problems, this thesis will first introduce the zigzag search algorithm. Second, by modifying the classic zigzag search algorithm, the zigzag interior point method and zigzag genetic algorithm method will both be proposed to broaden the applications of the classic zigzag search method. Also, in order to provide a systematic method for step-size configuration, a zigzag search method with adaptive step-size will be proposed. Thirdly, all algorithms will be applied to several practical power system multi-objective problems to demonstrate their practicability and *** case study will be carried out on a modified IEEE 30-bus system and the IEEE 118-bus system. A comparison will be made with classic multiobjective algorithms which have been widely applied in power systems to demonstrate the effectiveness and efficiency of

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The optimization ordering model for intuitionistic fuzzy preference relations with utility functions

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KNOWLEDGE-BASED SYSTEMS 2018年 162卷 174-184页

作者： Gong, Zaiwu Zhang, Ning Chiclana, Francisco Nanjing Univ Informat Sci & Technol Sch Management Sci & Engn Nanjing 210044 Jiangsu Peoples R China Linyi Univ Sch Business Linyi 276000 Peoples R China Nanjing Univ Informat Sci & Technol Sch Management Sci & Engn Nanjing 210044 Jiangsu Peoples R China De Montfort Univ Inst Artificial Intelligence Sch Comp Sci & Informat Leicester LE1 9BH Leics England Univ Granada Dept Comp Sci & Artificial Intelligence Granada Spain

Intuitionistic fuzzy sets describe information from the three aspects of membership degree, non-membership degree and hesitation degree, which has more practical significance when uncertainty pervades qualitative decision problems. In this paper, we investigate the problem of ranking intuitionistic fuzzy preference relations (IFPRs) based on various non-linear utility functions. First, we transform IFPRs into their isomorphic interval value fuzzy preference relations (IVFPRs), and utilise non-linear utility functions, such as parabolic, S-shaped, and hyperbolic absolute risk aversion, to fit the true value of a decision-maker's judgement. Ultimately, the optimization ordering models for the membership and non-membership of IVFPRs based on utility function are constructed, with objective function aiming at minimizing the distance deviation between the multiplicative consistency ideal judgment and the actual judgment, represented by utility function, subject to the decision maker's utility constraints. The proposed models ensure that more factual and optimal ranking of alternative is acquired, avoiding information distortion caused by the operations of intervals. Second, by introducing a non-Archimedean infinitesimal, we establish the optimization ordering model for IFPRs with the priority of utility or deviation, which realises the goal of prioritising solutions under multi-objective programming. Subsequently, we verify that a close connection exists between the ranking for membership and non-membership degree IVFPRs. Comparison analyses with existing approaches are summarized to demonstrate that the proposed models have advantage in dealing with group decision making problems with IFPRs.

关键词： Intuitionistic fuzzy preference relation Utility function Ranking multiplicative consistency Non-archimedean infinitesimal

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Interval Markov decision processes with multiple objectives: From Robust strategies to Pareto curves

Interval Markov decision processes with multiple objectives:...

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作者： Hahn, Ernst Moritz Hashemi, Vahid Hermanns, Holger Lahijanian, Morteza Turrini, Andrea School of Electronics Electrical Engineering and Computer Science Queen's University Belfast Belfast United Kingdom State Key Laboratory of Computer Science Institute of Software CAS Beijing China Department of Information Technology Audi AG Ingolstadt Germany Saarland University Saarland Informatics Campus Saarbrücken Germany Institute of Intelligent Software Guangzhou Guangzhou China Department of Smead Aerospace Engineering and Sciences University of Colorado BoulderCO United States

Accurate Modelling of a real-world system with probabilistic behaviour is a difficult task. Sensor noise and statistical estimations, among other imprecisions, make the exact probability values impossible to obtain. In this article, we consider Interval Markov decision processes (IMDPs), which generalise classical MDPs by having interval-valued transition probabilities. They provide a powerful modelling tool for probabilistic systems with an additional variation or uncertainty that prevents the knowledge of the exact transition probabilities. We investigate the problem of robust multi-objective synthesis for IMDPs and Pareto curve analysis of multi-objective queries on IMDPs. We study how to find a robust (randomised) strategy that satisfies multiple objectives involving rewards, reachability, and more general ω-regular properties against all possible resolutions of the transition probability uncertainties, as well as to generate an approximate Pareto curve providing an explicit view of the trade-offs between multiple objectives. We show that the multi-objective synthesis problem is PSPACE-hard and provide a value iteration-based decision algorithm to approximate the Pareto set of achievable points. We finally demonstrate the practical effectiveness of our proposed approaches by applying them on several case studies using a prototype tool. © 2019 Copyright held by the owner/author(s).

关键词： multiobjective optimization

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